Rochelle Q2 Report 2009 (1)

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CO2 Capture by Aqueous Absorption Summary of 2nd Quarterly Progress Reports 2009

Supported by the Luminant Carbon Management Program

and the

Industrial Associates Program for CO2 Capture by Aqueous Absorption

by Gary T. Rochelle

Department of Chemical Engineering

The University of Texas at Austin

August 1, 2009

Introduction This research program is focused on the technical obstacles to the deployment of CO2 capture and sequestration from flue gas by alkanolamine absorption/stripping and on integrating the design of the capture process with the aquifer storage/enhanced oil recovery process. The objective is to develop and demonstrate evolutionary improvements to monoethanolamine (MEA) absorption/stripping for CO2 capture from coal-fired flue gas. The Luminant Carbon Management Program and the Industrial Associates Program for CO2 Capture by Aqueous Absorption support 15 graduate students. These students have prepared detailed quarterly progress reports for the period April 1, 2009 to June 30, 2009. We have also attached Powerpoint presentations by Freeman, and Nguyen and posters by Chen and Plaza presented at the Trondheim conference in June 2009. The presentation made at Trondheim by Gary Rochelle will be sent out separately.

Conclusions CO2 solubility and absorption rate for three amine solvents, AEP, EDA, and MDEA/PZ, were measured with WWC in this quarter and compared to those amines tested previously. 6 m AEP has a capacity close to 7.7 m HEP but less than 8 m PZ. 12 m EDA and 7 m MDEA/2 m PZ both have a capacity similar to 8 m PZ. In terms of absorption rate at 5 kPa, MDEA/PZ performs even a little better than PZ, while EDA is not an attractive solvent. 6m AEP absorbs CO2 at a rate less than half of the rate of 8 m PZ. Heat of CO2 absorption of 12 m EDA is about 80 kJ/mol at average operational CO2 loading, close to that of 7 MEA. AEP has a similar heat of absorption as seen for 7.7 HEP.

We have successfully modified the PZ model developed by Hilliard in Aspen Plus® to give a better fit of recent VLE, heat capacity, and heat of absorption data by adjusting the PZ dielectric constant along with other parameters.

The calculation of the theoretical difference between the heats of absorption at absorber and stripper temperatures verifies that the heat of absorption at 40 °C should only be approximately 3.2 kJ/mol CO2 lower than the value at 120 °C. This calculation raises concerns about the accuracy of the available data for heat of absorption.

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Thermal degradation rates for 7 m MDEA are similar to those for 7 m MDEA/2 m PZ, suggesting that PZ plays a secondary role in the overall degradation process in 7 m MDEA/2 m PZ.

Oxidation of PZ with low gas flow provides poor water balance and repeatability. This experimental method needs enhancement to provide adequate quantification of a system that oxidizes slowly, producing few degradation products.

Mass spectrometry has given the masses of numerous products from numerous masses the thermal degradation of PZ. These have not been positively identified, but possible candidates include N-formyl PZ, N-(hydroxymethyl) PZ, 1-methyl PZ, and N,N’-diformyl EDA.

Initial results on PZ solutions show that thermally degraded PZ corrodes 316 stainless steel less than 7 m MEA. After 18 weeks at 150 °C, the iron and nickel concentration in an 8 m PZ solution were 0.9 and 0.7 mM, respectively. For a 7 m MEA experiment at 135 °C, the final concentrations of iron and nickel were 13.7 and 4.2 mM, respectively, after four weeks.

Concentrated, aqueous PZ solutions oxidized 3 to 5 times slower than 7 m MEA in systems with iron, copper, or stainless steel metals (chromium, nickel, and iron). The thermal degradation rate in concentrated PZ systems is 23 to 70 times less than 7 m MEA systems.

Thermal loss of 8 m EDA (0.4 loading) is about 35% at 135 oC after 4 weeks, but only 10% at 100 oC after 16 weeks. EDA urea is the most concentrated product, representing only 20% of the lost EDA.

Oxidation of EDA at 55 oC with 2%/98% CO2/O2 consumed 10% after 168 hours with 1mM Fe2+. However EDA loss was negligible after 168 hours with 100 mM inhibitor A.

Foaminess of 8 m EDA (0.4 loading) is comparable to 7 m MEA (0.4 loading) with or without formaldehyde.

With an equilibrium CO2 partial pressures range of 500 to 5000 P at 40 oC, the lean loading of 8 or 12 m EDA is greater than 0.4 and the rich loading is less than 0.5, giving a capacity with 12 m EDA of 0.78 moles CO2/kg EDA + H2O.

Amine partial pressure of 12 m EDA is comparable with 7 m MEA and 8 m PZ at operating conditions.

CO2 absorption rate in 12 m EDA is about 50% of that of 7 m MEA at rich conditions.

The viscosity of 8 m EDA is a little less than 8 m PZ solution and comparable with other amines.

The diffusion coefficient of total dissolved CO2 in 7 to 13 m MEA and 2 to 8 m PZ varies with the 0.72 power of the viscosity.

The CO2 absorption rate in MEA showed no significant effect of temperature or amine concentration on kg’.

Most PZ experiments did not show an effect of temperature or amine concentration on kg’. Some data points have reduced kg’ values but the reduction seems to correlate with the possibility of these conditions being limited by diffusion of reactants and products near the gas-liquid interface.

PZ absorbs CO2 2 to 3 times faster than MEA over the applicable CO2 capture range for coal-fired power plants.

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The pseudo first order expression for kg’ was modified to raise the amine activity to the 2nd power and include activity coefficients of MEA and CO2 in loaded MEA solutions. The rigorous expression was shown to completely account for the negligible deviations in kg’ with changing temperature and amine concentration.

The Kg calculated from the pilot plant results concentrated piperazine are consistent with the values of kg

’ obtain by Dugas in the wetted wall column. The pilot plants rates for 8 m Pz are 4 times faster than 7 m MEA and 2 times faster than 9 m MEA. Total pressure was measured in CO2-loaded solutions at 100 to 150 oC with the following results: 7.4 to 25.8 bar (147 oC), 8 m PZ, 0.465 loading 1.1 to 7.2 bar, 5 m PZ, 0.293 loading 1.1 to 6.7 bar, 7 m MEA, 0.316 loading 3.3 to 15.9 bar, 7 m MEA, 0.479 loading The data for PZ at 40 to 190 ºC are represented well by the empirical relationships:

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21ln 38.4 ( 102,000 / ) 20.6 13,200 3.23COP J molRT T

αα α= + − ⋅ − + +

ln ( 102,000 13,200 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

The data for MEA at 40 to 160 oC are represented well by the empirical relationships:

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21ln 44.2 ( 116,000 / ) 29.7 11,600 17.3COP J molRT T

αα α= + − ⋅ − + +

ln ( 116,000 11,600 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

Dynamic absorber modeling gives steady state response time of 4 to 5 minutes for alternative strategies of load reduction.

In 7 m MEA with 0.1 Fe++/50 mM A, the NH3 production rate increased from 1 to 3 mM/hr as T increased from 54 to 64 oC.

EDA oxidizes at 1 mM/hr ammonia with 0.1 mM Fe. 5 mM Cu produces roughly 4 times the degradation rate as 0.1–1 mM Fe.

11 m MEA oxidizes to ammonia at a 2 times higher rate than 7 m MEA.

DGA, AMP, and PZ do not oxidize to significant quantities of ammonia or heat stable salts.

Formate reacts with MEA at 135 oC to form n-formyl-ethanolamine in less than 24 hours.

1. Wetted Wall Column Rate Measurements p. 13 by Xi Chen

The CO2 solubility and adsorption/desorption rate were measured in the wetted wall column for 6 m 1-(2-Aminoethyl)piperazine (AEP), 12 m Ethylenediamine (EDA), and 7 m MDEA/2 m PZ blend with varied CO2 loading (mol CO2/mol alkalinity). VLE models of CO2 were regressed from experimental data to calculate CO2 capacity and enthalpy of CO2 absorption (∆Habs). The

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liquid film mass transfer coefficients (kg’) and CO2 partial pressures (P*) obtained are compared to those of 8 m piperazine (PZ) and 7 m monoethanolamine (MEA) as well as other amines studied in the previous quarter.

2. Influence of Liquid Properties on Effective Mass Transfer Area of Structured Packing p. 27 by Robert Tsai (also supported by the Separations Research Program)

High viscosity tests (approximately 10 cP) were completed with Sulzer Mellapak 250X (M250X). In addition, two new Sulzer structured packings (Mellapak 125Y (M125Y) and MellapakPlus (MP252Y)) were evaluated and compared with Mellapak 250Y (M250Y).

Dry and pre-loading pressure drops for the packings were ordered: M250X (0.4) < M125Y (0.5) < MP252Y (0.7) < M250Y. Pre-loading liquid hold-ups were ranked as follows: M125Y (0.6) < M250X (0.9) ~ MP252Y (0.9) < M250Y. (The numbers in parentheses represent the values relative to M250Y.) Increasing the solution viscosity had little effect on pre-loading pressure drops but significantly reduced the capacities of the packings. Higher hold-ups were also associated with the more viscous solutions.

The mass transfer areas of M250Y, M250X, and MP252Y were equivalent, ranging from roughly 0.65–1.1 on a fractional basis (ae/ap) over liquid loads from 1–30 gpm/ft2. M125Y exhibited higher fractional areas (0.7–1.2) than M250Y. For all of the packings, a reduction in surface tension (30 dynes/cm) increased the mass transfer area by 10%. A ten-fold viscosity enhancement had no appreciable impact on the area. Both findings were consistent with previous results.

The mass transfer area database was updated, and the current global (ae/ap) correlation, able to represent the entire database within limits of ±15%, is displayed below:

( )( )[ ] 116.03

1LL

p

e 334.1 −= FrWeaa

A global pre-loading liquid hold-up model was developed for structured packing. The correlation (shown below) is accurate within approximately ±25% with respect to the experimental data.

( )( )[ ] 72.03

2PLL 84.21 −= GaReh

A basic Excel model was created to evaluate the economics of an amine scrubber (absorber) as a function of gas throughput and column configuration. The minimum cost was calculated to be $5–7/tonne CO2 for absorber capacities in the 100–500 MW range.

3. Modeling Stripper Performance for CO2 Removal p. 49 by David Van Wagener

Since Hilliard developed thermodynamic models for various amine solvents, additional experimental data have been collected at new conditions. The data primarily of interest have been for concentrated piperazine (PZ). The Hilliard model performed well for low concentrations, 0.9 m–5 m, but 8 m PZ will be used in future simulations. VLE data collected by Dugas as well as heat capacity data collected by Nguyen for concentrated piperazine were incorporated into previous parameter regression files. Additionally, heat of absorption data were

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collected by Freeman. The parameters to be regressed were reconsidered, and more focus was put on the heat capacity parameters of the dominant species at relevant loadings. This quarter the dielectric constant of PZ was also included. The original value used by Hilliard was for piperidine, a molecule similar to PZ with one amine group instead of two. Including the dielectric constant in the regression greatly improved the fit, and the new value of the dielectric constant is between that of piperidine and MEA. The theoretical difference between the heat of absorption for 8 m MEA at 40 °C and 120 °C was calculated using an energy balance, and the difference was found to be approximately 3.2 kJ/mol CO2. This difference is much smaller than what is observed in the experimental data, so the data collection for heat of absorption should be reevaluated.

4. Solvent Management of MDEA/Piperazine p. 61 by Fred Closmann

(also supported by the Process Science & Technology Center)

A thermal degradation experiment was conducted on 7 m MDEA in the second quarter. The compounds dimethylaminoethanol (DMAE), diethanolamine (DEA), N,N-dimethyl ethanamine (DMEA), dimethyl piperazine, 1-(2-hydroxyethyl)-4-methylpiperazine (HMP), and triethanolamine (TEA) were tentatively identified in degraded solvent samples through ion chromatography (IC) and IC-mass spectrometry (IC-MS) methods. We calculate an activation energy for the degradation of MDEA of approximately 47 kJ/gmol, and rates of degradation of MDEA of 66 and 112 mmolal/day were calculated at 150 °C and loadings of 0.1 and 0.2 moles CO2/mole alkalinity, respectively. Proposed degradation mechanisms for the MDEA loss include the protonation of MDEA, the formation of an MDEA-carbamate, and the disproportionation of MDEA. All three mechanisms are followed by subsequent reactions and could involve PZ when present in a solvent blend. However, because the MDEA loss rate is similar to rates calculated for MDEA in 7 m MDEA/2 m PZ, we believe the role of PZ in the degradation of the solvent is of lesser importance.

The construction of the integrated solvent cycling/degradation apparatus is near completion, and basic temperature metrics for amine cycling have been met. Modifications to the system to achieve accurate temperature measurement are ongoing. Amine cycling degradation experiments will be completed in 3rd quarter 2009.

5. Solvent Management of Concentrated Piperazine p. 71 by Stephanie Freeman

The Cation IC-Mass Spectrometer was used to identify numerous masses of unidentified degradation products in 8 m PZ thermal degradation experiments. Although not yet positively identified, possible candidates include N-formyl PZ, N-(hydroxymethyl) PZ, 1-methyl PZ, and others. Further work is needed to provide positive identification.

Quantification of heavy metals in solution has been used to analyze the corrosion of 316 stainless steel. Initial results show that thermally degraded PZ solutions contain less metal than 7 m MEA solutions. After 18 weeks at 150 °C, the iron and nickel concentration in an 8 m PZ solution were 0.9 and 0.7 mM, respectively. For a 7 m MEA experiment at 135 °C, the final concentrations of iron and nickel were 13.7 and 4.2 mM, respectively, after four weeks. Further work is needed to understand if the amines themselves or their degradation products are responsible for this corrosion.

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Concentrated, aqueous PZ solutions oxidized 3 to 5 times slower than 7 m MEA in systems with iron, copper, or stainless steel metals (chromium, nickel, and iron). The thermal degradation rate in concentrated PZ systems is 23 to 70 times less than 7 m MEA systems.

6. Ethylenediamine as a solvent for CO2 capture p. 97 by Shan Zhou

Thermal and oxidative degradation products of 8 m EDA (ethylenediamine) solution were measured by cation IC, anion IC, HPLC, and TIC. Foaming was measured in samples from oxidative degradation. Vapor liquid equilibrium and amine volatility of 8 m and 12 m EDA solution were measured using hot gas FTIR. CO2 solubility and absorption/desorption rate were measured in the wetted wall column. Viscosity of 8 m and 12 m EDA was measured at different temperatures.

About 35% of the EDA was lost after 4 weeks at 135 oC. Only about 10% of EDA degraded after 16 weeks at 100 oC. EDA urea was the most concentrated product from thermal degradation, representing about 20% of the degraded EDA.

10% of the EDA was oxidized after 168 hours with 1 mM Fe2+ at 55 oC. With 100 mM inhibitor A the oxidation of EDA was insignificant. DETA (diethylenetriamine) and formate were the most concentrated in the quantified oxidative degradation products of EDA.

The foaminess and foam break time of EDA solution are close to that of 7 m MEA solution, much better than that of 8 m PZ.

Although EDA is volatile at low CO2 loading, 8 m and 12 m EDA solutions have amine partial pressure comparable to 7 m MEA and 8 m PZ in expected lean loading. However, the CO2 flux normalized by partial pressure driving force of 12 m EDA solution was lower than that of 7 m MEA at the same conditions. VLE models were regressed with experiment data from the wetted wall column. CO2 capacity and enthalpy of CO2 absorption were calculated. The Habsorption of EDA is similar to MEA at operating conditions, higher than most of the other amine systems.

7. Rate Measurements for MEA and PZ p. 124 by Ross E. Dugas

This draft chapter on experimental results from the dissertation includes diaphragm cell experiments to evaluate diffusion coefficient behavior in 7–13 m MEA and 2–8 m PZ. Measured diffusion coefficients varied to the 0.72 power of the viscosity.

This chapter also includes equilibrium CO2 partial pressure and rate data for 7–13 m MEA, 2–12 m PZ and 7 m MEA/2 m PZ experiments in the wetted wall column at 40, 60, 80, and 100 ˚C. All of the CO2 partial pressure measurements matched literature sources very well with the exception of a few data points near 0.5 loading in MEA solutions. Rate data for MEA and PZ generally showed no significant effect of temperature or amine concentration on kg’. Some PZ data points did show reduced kg’ values but the reduction seems to correlate with the possibility of these conditions being limited by diffusion of reactants and products near the gas-liquid interface. Over the applicable CO2 capture range for coal-fired power plants, PZ was shown to absorb CO2 2–3 times faster than MEA.

The pseudo first order expression for kg’ was modified to a more rigorous form including activity coefficients. Activity coefficients of MEA and CO2 in loaded MEA solutions were correlated

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from the literature. The wetted wall column data suggested the amine activity to have a 2nd order dependency. Incorporating these changes into the pseudo first order expression completely explains the negligible deviations in kg’ with changing temperature and amine concentration.

8. CO2 Absorption Modeling Using Aqueous Amines p. 152 by Jorge M. Plaza

A CO2 absorber model for the 8 m PZ solvent is under development. It uses a modified version of the Hilliard (2008) thermodynamic representation by Van Wagener, version 02/06/09 (Rochelle et al., 2009)). It includes a reduced reaction set based on the more relevant species present at the expected operating loading (0.3–0.4 mol CO2/mol alkalinity). Kinetics will be based on Cullinane (2005) and regressed to include data generated by Dugas (Rochelle et al., 2008a). Initial work accesses proper representation of solvent physical properties. Density and viscosity were regressed to match experimental data generated by Freeman (Rochelle et al., 2009; Rochelle et al., 2008b). The activity coefficient of CO2 was also examined and compared to values found in Cullinane (2005) as a function of amine concentration and loading.

Density and viscosity match the experimental data with a deviation maximum of 10%. The model represents correctly the expected change in the activity coefficient of CO2 with amine concentration and its values are consistent with the available low concentration experimental data. However, the effect of loading is not represented correctly. This issue needs to be addressed. Kinetics will be implemented once this issue is resolved.

The overall gas-side mass transfer coefficient (Kg) was calculated for the November 2008 PZ pilot plant data and compared to kg

’ data by Dugas (Rochelle et al., 2008a) and previous pilot plant campaigns. Results show a higher Kg for 5 m PZ.

9. Total Pressure Measurement of CO2 Loaded Aqueous Amines at High T and P p. 165 by Qing Xu, Martin Metzner

In this quarter a series of total pressure measurements were conducted with CO2 loaded monoethanolamine (MEA) or piperazine (PZ) at 100 to 160 oC (for MEA) or 190 ºC (for PZ). A 500 mL 316 SS autoclave was used as the equilibrium cell. The total pressure of 8 m PZ with 0.465 CO2 loading varied from 7.4 to 25.8 bar at 100 to 147 ºC. At 100 to 150 ºC, for 5 m PZ with 0.293 CO2 loading Pt varied from 1.1 to 7.2 bar; for 7 m MEA with 0.316 CO2 loading Pt is from 1.1 to 6.7 bar; for 7 m MEA with 0.479 CO2 loading Pt is from 3.3 to 15.9 bar.

The partial pressure of CO2 at each experimental condition was estimated by subtracting partial pressures of water and amines. The calculated results for 7 m MEA match well with the work by Jou et al. (1995). The regression based on data from 40 to 190 ºC gives empirical models for CO2 partial pressure over loaded aqueous PZ and MEA respectively and the models predict the data well. Heat of absorption for CO2 loaded aqueous PZ and MEA was calculated from these empirical models.

For PZ:

2

21ln 38.4 ( 102,000 / ) 20.6 13,200 3.23COP J molRT T

αα α= + − ⋅ − + + (1)

7

8

ln ( 102,000 13,200 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂ (2)

For MEA:

2

21ln 44.2 ( 116,000 / ) 29.7 11,600 17.3COP J molRT T

αα α= + − ⋅ − + + (3)

ln ( 116,000 11,600 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂ (4)

10. Volatility and heat capacity of amine alternatives p. 187 by Bich-Thu Nguyen

Amine volatility is a crucial screening criterion which affects fugitive emission and necessitates appropriate water wash design. At a lean CO2 partial pressure of 500 Pa at 40 ºC, the ranking of amine volatility is: 7 m MDEA/2 m PZ (7/6 ppm) < 12 m EDA (9 ppm) < 8 m PZ (14 ppm) < 7 m MEA (28 ppm) < 5 m AMP (112 ppm). The less volatile amines appear to have higher heat of amine solution than the more volatile amines. There is no apparent correlation between volatility and the amine heat of desorption.

11. Oxidative Degradation of MEA p. 203 by Alex Voice

Oxidative degradation experiments in the high gas flow (HGF) apparatus continued this quarter. An experiment was conducted to determine the sensitivity of the oxidative degradation rate of 7 m MEA under kinetically limited conditions in the high gas flow apparatus on various operating conditions. Temperature was by far the most important parameter, showing a 100–500% change in ammonia production for a 10 ºC temperature change.

Amine screening for oxidative degradation rate was also conducted in the HGF. Ethylene diamine (EDA, 8 m) degraded at a rate of 1 mM/hr for 0.1–1 mM Fe as evidenced by NH3 production. Addition of 1 mM Fe produced a burst of 0.15 mM of NH3, although the steady state rate remained unchanged. 4.8 m AMP and 17.7 m DGA did not degrade to produce NH3 in significant quantities. AMP did not produce significant quantities of heat stable salts. The presence of other degradation products, including NO2 and formaldehyde, had a low signal to noise ratio in the FTIR spectrum, and will require verification. 8 m piperazine did not produce NH3 or heat stable salts in the HGF. 11 m MEA produced ammonia at a rate of 4.1 mM/hr, as compared with 1.8 mM/hr for previous 7 m experiments conducted by Sexton.

Formic acid and 7 m MEA at 0.4 loading reacted rapidly to produce hydroxyl-ethyl-formamide at 135 ºC. Formate and formamide concentrations were stable after 24 hours (the first sample taken). The formate to formamide ratio was 1:1 in a solution which initially contained only formic acid. In the solution loaded with formic acid and formaldehyde, the ratio was 2:1. Formate was also detected in the solution containing only formaldehyde at a concentration of 22 mM, well above the ~1 mM formate detected in the neat solution.

The metal dissolution rate (metal concentration divided by time) in thermal degradation experiments was found to be a clean Arrhenius function of T.

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12. Dynamic Operation of CO2 Capture p. 216 by Sepideh Ziaii

The dynamic model of the absorber created in ACM® has been run at the design and operating conditions of a pilot plant run with 9 m MEA. The steady state results give 58.8% CO2 removal, which is 1.7% less than a reconciled value (59.9%). The temperature profile is not completely matched with experimental data because of inaccurate calculation of heat of absorption for 9 m MEA.

In addition, the dynamic model of the absorber has been run for two stripper ratio control strategies in flexible CO2 capture. The CO2 removal, rich loading, and temperature profiles were calculated for each strategy and overall dynamic and steady state behavior were compared.

13. Electric Grid Level Implications of Flexible CO2 Capture Operation p. 223 by Stuart Cohen

A flexible carbon dioxide (CO2) capture system with large-scale solvent storage allows continuous high CO2 removal from flue gas while power output is increased by turning stripping and CO2 compression systems to partial- or zero-load. In such a system, the basic tradeoff is that longer solvent storage times provide more opportunity to increase plant output when electricity prices are high, but at the expense of additional energy cost to regenerate stored solvent when electricity prices are low as well as additional capital costs of solvent inventory, storage tanks, and larger stripping, compression, and auxiliary equipment.

For storage times of 1–12 hours using monoethanolamine (MEA) solvent, 10 million kg MEA, 10–100 m3 storage capacity, and $10-$100 million additional capital costs are required. Storage tanks are a relatively insignificant capital cost relative to that of solvent inventory and larger stripping/compression equipment. A preliminary optimization study finds that solvent storage of a few hours or more is attractive when the electricity market experiences large differences between high and low electricity prices, even if only in the form of infrequent price spikes. Lower capital costs can improve the economics of a solvent storage system, but capital cost reductions are insufficient to promote installation of a solvent storage system without a favorable electricity price distribution. However, 2008 annual average electricity prices in the Electric Reliability Council of Texas (ERCOT) along with base case parameters and the preliminary modeling methodology do not provide the conditions required for solvent storage to be desirable.

14. Modeling Absorber/Stripper Performance with MDEA/PZ p. 237 by Peter Frailie

The goal of this study is to evaluate the performance of an absorber/stripper operation that utilizes the MDEA/PZ blended amine system. Due to the complexity of this system the model will be developed in several smaller, more manageable parts that can later be combined to form the final model. The first section that will be developed is an MDEA/PZ model based on thermodynamic data, which must initially be developed as separate MDEA and PZ models. Once the MDEA/PZ model has been completed it must be incorporated into separate absorber and stripper models similar to those developed by Van Wagener and Plaza. Those models can then be combined to form the final MDEA/PZ absorber/stripper model. This study is currently in the process of developing the MDEA/PZ model based on thermodynamic data. Over the next

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three months the thermodynamic model should be completed and work should have begun on the absorber and stripper models.

15. Measurement of Packing Liquid Phase Film Mass Transfer Coefficient p. 244 by Chao Wang

Packings are widely used in distillation, stripping and scrubbing processes because of their relatively low pressure drop, good mass transfer efficiency, and ease of installation. Packings are also are also being investigated for the post combustion carbon capture process for these reasons. Research continues to focus on development of high performance packing, especially on minimizing pressure drop, maximizing mass transfer efficiency, and minimizing costs. The design of packed absorbers for carbon dioxide capture will require the reliable measurement and accurate prediction of the effective area, gas and liquid film mass transfer coefficient. A variety of experimental methods for measuring effective area, gas and liquid film mass transfer coefficient kLa have been reported. Consistent measurements of these important design parameters will begin this summer.

Absorption of CO2 with NaOH is applied to measure the effective area of packings. Atmospheric carbon dioxide in air is used as gas phase and 0.1 M NaOH is used as liquid phase. This is a liquid phase controlled mass transfer system so the liquid phase mass transfer coefficient kl or often referred to as kg

’ can be assumed as the overall mass transfer coefficient KG. In the proposed summer work, the gas flow rate set points are 180, 300, 450 ACFM and liquid flow rate set points are 1, 2.5, 5, 7.5, 10, 15 and 25 gpm/ft2 (same operating conditions explored by Robert Tsai). The effective area can then be calculated by the equationl:

RTZkyyu

RTZKyyu

ag

outCO

inCOG

G

outCO

inCOG

e '2

2

2

2 )ln()ln(≈= .

Absorption of SO2 with NaOH is applied to measure the gas phase mass transfer coefficient. Sulfur dioxide (SO2), blended with ambient air at a composition of approximately 80 ppm, will be absorbed by 1 M NaOH solution. The reaction is instantaneous and the mass transfer process is controlled by the gas phase. Thus the overall mass transfer coefficient KG can be replaced by gas phase mass transfer coefficient kG. This experiment can be combined with the effective area experiment as long as the gas and liquid flow rates are set at the same level. The gas phase mass transfer coefficient can be calculated by the equation:

e

outSO

inSOG

G ZRTayyu

k)ln(

2

2

=

Desorption of toluene in water with air is applied to measure the liquid phase mass transfer coefficient. Ambient air is used to strip toluene from water. As a result of the high Henry’s constant, the mass transfer resistance is controlled by the liquid phase. The gas flow rates and liquid flow rates are set at the same value with the effective area measurement to make the 3 experiments consistent. The liquid phase mass transfer coefficient can be calculated by the equation:

)/ln( 21 LALAe

LL cc

Zauk =

10

11

16. Pilot Plant Testing of Advanced Process Concepts using Concentrated Piperazine p. 254 by Eric Chen

Pilot plant testing of 8 m piperazine in a two-stage heated flash is planned for Fall 2009. Substantial modifications to the existing pilot plant at SRP will be needed. Process flow diagrams (PFD) and piping and instrument diagrams (P&ID) have been developed for the new process. Preliminary specifications for the high pressure pump, cross-exchanger, and steam heaters have been developed and are in the process of being formally designed and quoted by various vendors.

Attachments 1. Degradation of Concentrated PZ in CO2 capture p. 262 by Stephanie A. Freeman, et al. 2. Amine Volatility p. 281 by Thu Nguyen, et al. 3. Foaming Behavior of Piperazine Aqueous Solutions for CO2 Capture (poster) p. 298 by Xi Chen, et al. 4. Absorber Intercooling in CO2 Absorption (poster) p. 299 by Jorge M. Plaza, et al.

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Wetted Wall Column Rate Measurements

Quarterly Report for April 1 – June 30, 2009

by Xi Chen


and the




July 1, 2009

Abstract The CO2 solubility and adsorption/desorption rate were measured in the wetted wall column for 6 m 1-(2-Aminoethyl)piperazine (AEP), 12 m Ethylenediamine (EDA) and 7 m MDEA/2 m PZ blend with varied CO2 loading (mol CO2/mol alkalinity). VLE models of CO2 were regressed from experimental data to calculate CO2 capacity and enthalpy of CO2 absorption (∆Habs). The liquid film mass transfer coefficients (kg’) and CO2 partial pressures (P*) obtained are compared to those of 8 m piperazine (PZ) and 7 m monoethanolamine (MEA) as well as other amines studied in the previous quarter. The capacity of AEP is about 15% less than that of PZ. EDA and MDEA/PZ both have a similar capacity as PZ. The rate of CO2 absorption for AEP is less than half of that for PZ. kg’ for EDA is only about one third of kg’ for PZ. MDEA/PZ absorbs CO2 at a slightly higher rate than PZ at rich loading, about twice as fast as MEA. EDA has a much higher ∆Habs (80 kJ/mol) than AEP (72 kJ/mol), PZ (70 kJ/mol), or MDEA/PZ (67 kJ/mol)

Introduction More amine solvents were tested with the wetted wall column (WWC) in this quarter. The names and chemical structures of amines that have been studied so far are shown in Table 1. AEP is a derivative of piperazine. With the additional amino group, AEP is expected to have a greater CO2 capacity than PZ while still having a high absorption rate. EDA is known for its fast kinetic rate of reaction with CO2 at zero or low loading. MDEA/PZ has been reported to have a good CO2 capacity and reasonably high reaction rate (Bishnoi, 2000). More measurements have been done to complete the study on MDEA/PZ.

Table 1: Name and chemical structure of the amines screened in this work Name Chemical structure

N-(2-hydroxyethyl)piperazine (HEP)

N

NH

OH

1-(2-Aminoethyl)piperazine (AEP)

N

NH

NH2

13

2

2-amino-2-methyl-1-propanol (AMP)

OH

NH2

CH3

CH3

2-piperidineethanol (2-PE)

NH

OH

Ethylenediamine (EDA) NH2

NH2

Methyldiethanolamine (MDEA) /Piperazine (PZ) OH

NOH

CH3

NHNH

Experimental Methods Experimental apparatus, procedure, and analytical methods have been described in previous reports and will not be repeated here. AEP (99%, Acros), EDA (Labgrade, Fisher), MDEA (95%–99%, Huntsman), and PZ (99%, Alfa Aesar) were used without further purification.

Viscosity measurements were also done for all the amine solutions. The method has been described by Stephanie Freeman (Rochelle, 2008a). Viscosity under shear rate from 100 s-1 to 1000 s-1 was measured and the average value will be reported.

Results and Discussion

CO2 partial pressure Equilibrium CO2 partial pressure is plotted against CO2 loading for each amine, as shown in Figures 1, 2, and 3. The data is also tabulated in Table 2. A semi-empirical model was regressed from solubility data and plotted in solid lines in each figure:

2//ln ααα ⋅+⋅+⋅++= eTdcTbaP (1)

14

3

0

20

40

60

80

100

0.001

0.01

0.1

1

10

100

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Heat o

f absorption (kJ/mol)

P* (kPa)

CO2 Loading (mol/mol alkalinity)

40 °C

60 °C

80 °C

100 °C

Figure 1: CO2 solubility and heat absorption as a function of CO2 loading for 6 m AEP.

The CO2 solubility data for 6 m AEP are shown in Figure 1 and Table 2. The gap between lines for different temperatures indicates the value of heat of absorption. As CO2 loading increases, the lines get closer to each other, in line with a decrease in heat of absorption (the orange line in the figure).

15

4

0.001

0.01

0.1

1

10

100

0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55

P* (kPa)


40 °C60 °C

80 °C

100 °C

MEA@100°C (Hilliard & Dugas)

MEA@40°C

Figure 2: CO2 solubility for 12 m EDA, compared with data for MEA obtained by Hilliard

and Dugas (Rochelle, 2008b). The partial pressure of CO2 in 12 m EDA is compared to that of MEA in Figure 2. As CO2 loading is less than 0.45, CO2 has a higher solubility in EDA than in MEA. At loadings higher than 0.45, the trend is reversed.

16

5

0.001

0.01

0.1

1

10

100

0 0.05 0.1 0.15 0.2 0.25

P* (kPa)


40 °C

60 °C

80 °C

100 °CPZ@100 °C

PZ@40 °C (Hilliard & Dugas)

Figure 3: CO2 solubility for 7 m MDEA/2 m PZ, compared with data for PZ (dashed line)

obtained by Hilliard and Dugas (Rochelle, 2008b). As shown in Figure 3, MDEA/PZ has a lower CO2 solubility than PZ. The highest loading for MDEA/PZ has not been established. More experiments will be done to verify the solubility data.

Table 2: CO2 solubility and absorption/desorption rates for AEP, EDA, and MDEA/PZ.

Amine/ Conc Temp CO2 Loading P*CO2 kg’

(m) (°C) (mol/mol alk) (kPa) (×107mol/s·Pa·m2)

AEP/6 40 0.10 0.01 472.0 AEP/6 40 0.20 0.06 30.3 AEP/6 40 0.29 1.79 5.7 AEP/6 40 0.36 24.95 0.8 AEP/6 60 0.10 0.06 55.7 AEP/6 60 0.20 0.54 21.9 AEP/6 60 0.30 8.62 4.8 AEP/6 80 0.10 0.36 65.2 AEP/6 80 0.20 3.23 29.4 AEP/6 80 0.30 40.50 3.5 AEP/6 100 0.10 2.00 56.2 AEP/6 100 0.20 12.87 20.5

EDA/12 40 0.36 0.03 26.0 EDA/12 40 0.43 0.19 10.3

17

6

EDA/12 40 0.49 4.03 1.7 EDA/12 60 0.22 0.01 N/A EDA/12 60 0.29 0.03 112.0 EDA/12 60 0.37 0.20 20.4 EDA/12 60 0.43 1.82 7.6 EDA/12 60 0.49 23.76 1.4 EDA/12 80 0.22 0.05 N/A EDA/12 80 0.29 0.24 56.1 EDA/12 80 0.35 1.52 16.7 EDA/12 80 0.43 9.62 7.7 EDA/12 100 0.22 0.22 N/A EDA/12 100 0.29 1.64 50.0 EDA/12 100 0.35 7.13 19.9 EDA/12 100 0.43 41.62 5.2

MDEA/7+PZ/2 40 0.09 0.19 16.5 MDEA/7+PZ/2 40 0.14 0.95 10.3 MDEA/7+PZ/2 40 0.19 3.55 6.2 MDEA/7+PZ/2 60 0.09 1.25 16.8 MDEA/7+PZ/2 60 0.14 4.41 9.8 MDEA/7+PZ/2 60 0.19 15.60 6.1 MDEA/7+PZ/2 80 0.03 1.27 27.6 MDEA/7+PZ/2 80 0.09 5.62 12.3 MDEA/7+PZ/2 80 0.14 17.64 6.8 MDEA/7+PZ/2 100 0.03 5.21 16.3 MDEA/7+PZ/2 100 0.09 19.78 7.6

CO2 capacity The solubility model enables calculation of CO2 capacity for 5 kPa rich solution of each amine, as shown in Figure 4. Since there is some uncertainty about CO2 loading for MDEA/PZ solubility data, no calculation will be done for MDEA/PZ at this time. At the same lean CO2 partial pressure of 0.5 kPa, the sequence for CO2 capacity from high to low is: 8 m 2-PE > 4.8 m AMP > 8 m PZ > 7.7 m HEP ≅ 6 m AEP ≅ 12 m EDA > 7 m MEA. As the lean CO2 partial pressure increases or decreases, this trend holds since the curves remains relatively parallel with each other.

18

7

0.1

1.0

0.005 0.05 0.5 5

CO2capa

city (m

ol/kg

(water+amine))

Lean Partial Pressure of CO2 (kPa)

7m MEA

6m AEP 8m PZ

8m 2‐PE

4.8m AMP

12m EDA

7.7m HEP

Figure 4: CO2 capacity as a function of lean CO2 partial pressure at 40 °C.

Heat of CO2 absorption Heat of absorption was calculated from the model by applying the Gibbs-Duhem equation:

)/1()(ln

TdPdRHabs −=Δ

(2) Heat of absorption obtained for all the amines was compared in Figure 5. For each amine, ∆Habs was only plotted within the CO2 loading range corresponding to 0.5 kPa to 5 kPa CO2 partial pressure. Apparently ∆Habs of AEP is only slightly higher than that of PZ. EDA has a comparable ∆Habs to MEA.

19

8

65

69

73

77

81

85

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Enthalpy

of C

O2ab

sorption

(kJ/mol)


7.7m HEP

8m 2‐PE4.8m AMP

8m PZ

7m MEA

6m AEP

12m EDA

Figure 5: Enthalpy of CO2 absorption vs. CO2 loading for different amines.

Absorption/Desorption rates In Figure 6, absorption/desorption rates for 6 m AEP at 40, 60, 80, and 100 °C are compared with those of 7 m MEA and 8 m PZ at 40 °C. The rate for AEP is less than for PZ but higher than MEA as CO2 partial pressure is less than 5 kPa. However, it drops below MEA at P*CO2 higher than 5 kPa. Temperature does not have a significant effect on the absorption rate of AEP.

20

9

5E-08

5E-07

5E-06

0.005 0.05 0.5 5

k g' (

mol

/s. P

a.m

2 )

P*CO2 @ 40C (kPa)

7 m MEA@40°C

8 m PZ@40°C

100°C

80°C60°C

40°C

Figure 6: CO2 mass transfer rate for 6 m AEP, compared with 7 m MEA and 8 m PZ.

21

10

1E-07

1E-06

0.01 0.1 1 10

k g' (

mol

/s. P

a.m

2 )

P*CO2 @ 40C (kPa)

7 m MEA@40°C

8 m PZ@40°C

100°C

80°C

60°C40°C

Figure 7: CO2 mass transfer rate for 12 m EDA, compared with 7 m MEA and 8 m PZ.

As shown in Figure 7, kg’ for EDA is close to that for MEA at 40 °C and low P*CO2. Absorption with EDA becomes slower than MEA as P*CO2 is above 0.2 kPa.

22

11

1E-07

1E-06

0.01 0.1 1 10

k g' (

mol

/s. P

a.m

2 )

P*CO2 @ 40C (kPa)

7 m MEA@40°C

8 m PZ@40°C

100°C

80°C60°C40°C

Figure 8: CO2 mass transfer rate for (7 m MDEA/2 m PZ), compared with 7 m MEA and

8 m PZ. The 7 m MDEA /2 m PZ blend performed pretty well at the CO2 loading we studied. As shown in Figure 8, at 40 °C, kg’ for the blend has almost the same value as PZ at rich loading with P*CO2 around 3.5 kPa, which is desirable. At leaner loading, MDEA/PZ has a slower absorption rate than PZ, but still about twice as fast as MEA. Also the rates of the blend at 40 °C and 60 °C are identical.

23

12

5E-08

5E-07

5E-06

0.05 0.5 5 50

k g' (

mol

/s. P

a. m2 )

P*CO2 @ 40C (kPa)

8m PZ

7m MEA

7.7m HEP

6m AEP

8m 2‐PE

12m EDA

4.8m AMP

7m MDEA/2 PZ

Figure 9: Comparison of CO2 mass transfer at 40 °C for all the amines studied so far.

The curves of kg’ vs. CO2 partial pressure at 40 °C for all the amines are put together in Figure 9. By interpolation or extrapolation from the current kg’ data, kg’ at 5 kPa CO2 partial pressure was obtained for each amine solvent and compared. (The values of kg’ at 5 kPa are also given in Table 3.) The sequence in absorption rate from high to low is: 7 m MDEA/2 m PZ > 8 m PZ > 7 m MEA > 7.7 m HEP > 6 m AEP > 8 m 2-PE > 4.8 AMP > 12 m EDA. Apparently in terms of absorption rate, EDA has the worst performance at rich loading. In contrast, MDEA/PZ is an efficient solvent, even better than PZ.

24

13

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Viscosity@40C (cP)

CO2 loading (mol/mol alka)

7.7 m HEP

8m 2‐PE

4.8 m AMP

6m AEP

12 m EDA

Figure 10: Viscosity of loaded amine solutions vs. CO2 loading at 40 °C.

As shown in Figure 10, viscosity of amine solutions increases with loading. 8 m 2-PE and 6 m AEP have a viscosity higher than 20 cP at rich loading. A relatively high viscosity was also found for 7.7 m HEP at high CO2 loading. Reduction in concentration of these amines might be considered to lower operation cost.

Table 3: Summary table for all the tested amines

CO2 Capacity kg’ @PCO2 =5kPa ∆Habs@PCO2 =1.5kPa

Amine Conc. (m)

(mol/kg (water+amine)) (×107mol/s·Pa·m2) (kJ/mol)

MDEA/PZ 7/2 0.71* 5.7 67

PZ 8 0.79 5.3 70

MEA 7 0.47 3.1 82

HEP 7.7 0.68 2.9 69

AEP 6 0.66 2.3 72

25

14

2-PE 8 1.23 2 73

AMP 4.8 0.96 1.7 73

EDA 12 0.78 1.6 80

*: Capacity for MDEA/PZ is obtained from Bishnoi’s data (Bishnoi, 2000).

Conclusions CO2 solubility and absorption rates for three amine solvents, AEP, EDA, and MDEA/PZ, were measured with WWC in this quarter and compared to those amines tested previously. 6 m AEP has a capacity close to 7.7 m HEP but less than 8 m PZ. 12 m EDA and 7 m MDEA/2 m PZ both have a capacity similar to 8 m PZ. In terms of absorption rate, at 5 kPa, MDEA/PZ performs even a little better than PZ, while EDA is not an attractive solvent. 6 m AEP absorbs CO2 at a rate less than half of the rate of 8 m PZ. Heat of CO2 absorption of 12 m EDA is about 80 kJ/mol at average operational CO2 loading, close to that of 7 m MEA. AEP has a heat of absorption similar to 7.7 HEP.

Future Work Diglycolamine (DGA®), N-Methyl-1,3-Propanediamine, and piperazine derivatives will be tested with the WWC in the following quarter.

References Bishnoi S. Carbon dioxide absorption and solution equilibrium in piperazine activated

methyldiethanolamine Department of Chemical Engineering. The University of Texas at Austin. Ph.D. Dissertation. 2000.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2008." Luminant Carbon Management Program. The University of Texas at Austin. 2008a.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report 2008." Luminant Carbon Management Program. The University of Texas at Austin. 2008b.

26

1

Influence of Viscosity and Surface Tension on the Effective Mass Transfer Area of Structured Packing


by Robert Tsai


and the




July 14, 2009

Abstract

High viscosity tests (approximately 10 cP) were completed with Sulzer Mellapak 250X (M250X). In addition, two new Sulzer structured packings (Mellapak 125Y (M125Y) and MellapakPlus (MP252Y)) were evaluated and compared with Mellapak 250Y (M250Y).

Dry and pre-loading pressure drops for the packings were ordered: M250X (0.4) < M125Y (0.5) < MP252Y (0.7) < M250Y. Pre-loading liquid hold-ups were ranked as follows: M125Y (0.6) < M250X (0.9) ~ MP252Y (0.9) < M250Y. (The numbers in parentheses represent the values relative to M250Y.) Increasing the solution viscosity had little effect on pre-loading pressure drops but significantly reduced the capacities of the packings. Higher hold-ups were also associated with the more viscous solutions.

The mass transfer areas of M250Y, M250X, and MP252Y were equivalent, ranging from roughly 0.65–1.1 on a fractional basis (ae/ap) over liquid loads from 1–30 gpm/ft

2. M125Y exhibited higher fractional areas (0.7–1.2) than M250Y. For all of the packings, a reduction in surface tension (30 dynes/cm) increased the mass transfer area by 10%. A ten-fold viscosity enhancement had no appreciable impact on the area. Both findings were consistent with previous results.

The mass transfer area database was updated, and the current global (ae/ap) correlation, able to represent the entire database within limits of ±15%, is displayed below:

( )( )[ ] 116.031

LL

p

e 334.1−= FrWe

a

a

A global pre-loading liquid hold-up model was developed for structured packing. The correlation (shown below) is accurate within approximately ±25% with respect to the experimental data.

( )( )[ ] 72.032

PLL 84.21−= GaReh

27

2

A basic Excel model was created to evaluate the economics of an amine scrubber (absorber) as a function of gas throughput and column configuration. The minimum cost was calculated to be $5–7/tonne CO2 for absorber capacities in the 100–500 MW range.

Introduction

Packing is commonly used in industrial processes to provide efficient gas-liquid contacting. One important application for which packed columns are being considered is treating flue gas for CO2 capture. The conventional method consists of an aqueous amine solvent such as monoethanolamine (MEA) contacting the gas, resulting in the absorption of CO2 (Kohl and Nielsen, 1997). The enriched solvent is sent to a stripper for regeneration and is then recycled back to the absorber. Gas-liquid contact in both the absorber and stripper is enhanced through the use of packing.

Reliable mass transfer models are important for design and analysis of these systems. A critical factor involved in modeling is the prediction of the effective area of packing (ae), which can be considered as the total gas-liquid interfacial area that is actively available for mass transfer. The current research effort is focused on this parameter. The effective area is especially critical for CO2 capture by amine absorption, because the CO2 absorption rate typically becomes independent of conventional mass transfer coefficients (kG or kL

0) but remains directly proportional to the area. Thus, it is highly desirable to have an accurate area model.

Numerous packing area correlations have been presented in the literature, but none has been shown to be predictive over a wide range of conditions. The Rocha-Bravo-Fair (Rocha et al., 1996) and Billet-Schultes (Billet and Schultes, 1993) models, two of the more widely used correlations for structured packing, seem to be notably poor in their predictions involving aqueous systems (Tsai et al., 2008b). Wang et al. (2005) performed a comprehensive review of the available models. The various correlations predict different and sometimes even contradictory effects of liquid viscosity and surface tension, properties that would be expected to fundamentally influence the wetted area of packing. It is evident that their role is not well understood, and there is a definite need for work in this subject matter.

Limited understanding of the fluid mechanics and mass transfer phenomena in packed columns has been noted, and the need for experiments over a broader range of conditions has been identified (Wang et al., 2005). The Separations Research Program (SRP) at the University of Texas at Austin has the capability of measuring packing mass transfer areas. Measurements are performed by absorbing CO2 from air with 0.1 M NaOH in a 427 mm (16.8 in) ID column. However, it is potentially inaccurate to extend these results to other fluids of interest, such as amine solvents, due to variations in viscosity and surface tension.

The goal of this research is to ultimately develop an improved effective area model for structured packing. The general objectives are to:

• Develop a fundamental understanding of the fluid mechanics associated with structured packing operation;

• Determine suitable chemical reagents to modify the surface tension and viscosity of the aqueous caustic solutions employed to make packing area measurements, and characterize potential impacts of such additives on the CO2-NaOH reaction kinetics;

28

3

• Expand the SRP database by measuring the hydraulic performance and mass transfer areas of various structured packings over a range of liquid viscosities and surface tensions;

• Combine the data and theory into a semi-empirical model that captures the features of the tested systems and adequately represents effective area as a function of viscosity, surface tension, and liquid load.

Experimental Methods

Packed Column

The packed column had an outside diameter of 460 mm (18 in), inside diameter of 427 mm (16.8 in), and a 3 m (10 ft) packed height. Other sources may be consulted for details regarding the apparatus and experimental protocol (Tsai et al., 2008a; Rochelle et al., 2008b).

Goniometer

The goniometer (ramé-hart Inc., Model #100-00) included an adjustable stage, a syringe support arm, a computer-linked camera for live image display, and a light source (Rochelle et al., 2006). This system was used in conjunction with FTA32 Video 2.0 software (developed by First Ten Angstroms, Inc.) to make surface tension measurements via the pendant drop method.

Rheometer

The Physica MCR 300 rheometer (Anton Paar, USA) employed for viscosity measurements was first described in the Q4 2006 report (Rochelle et al., 2007). The apparatus was equipped with a cone-plate spindle (CP 50-1). Temperature was regulated (±0.1 °C) with a Peltier unit (TEK 150P-C) and a Julabo F25 water bath unit (for counter-cooling). Measurement profiles consisted of a logarithmically increased or decreased shear rate (100–500 s-1), with 10 data points recorded at 15-second intervals. Viscosity was determined from a plot of shear stress (measured) vs. shear rate.

Materials

A nonionic surfactant, TergitolTM NP-7 (Dow), was used to reduce the surface tension of solutions. POLYOX WSR N750 (Dow) – essentially, poly(ethylene oxide) with a molecular weight of 300,000 – was employed as a viscosity enhancer. Dow Corning® Q2-3183A antifoam was used for foam suppression, in quantities typically ranging from 50–100 ppmw/v.

Results and Discussion

Mellapak 250X (M250X) – Mass Transfer

Baseline (0.1 M NaOH) and low surface tension (30 dynes/cm) mass transfer area tests with M250X were presented in the previous quarterly report (Rochelle et al., 2009a). In this quarter, a high viscosity (11 cP, 40 dynes/cm) experiment was conducted to complete the M250X characterization. Figure 1 displays the M250X results, together with M250Y data under similar circumstances. The data points at a given liquid load have been averaged for clarity.

Viscosity (up to approximately 15 cP) has previously been concluded to have no appreciable impact on the mass transfer area (Tsai et al., 2008b). The new results with M250X affirmed this

29

4

assessment. (The data are close enough that it does not matter if the viscous data may have been influenced by a small boost (< 10%) from the reduced surface tension (40 dynes/cm); the conclusion should remain the same.) It appears that on the basis of mass transfer area, M250Y and M250X are essentially indistinguishable, regardless of physical properties.

0.6

0.7

0.8

0.9

1

1.1

1.2

0 5 10 15 20 25 30

Fractional area, ae/a

p

Liquid load (gpm/ft2)

M250Y - Baseline

14 cP, 40 dynes/cm

M250X - Baseline

11 cP, 40 dynes/cm

Figure 1: M250Y and M250X (ap = 250 m2/m3) mass transfer area data.

Mellapak 250X (M250X) – Hydraulics

Pressure drop data for M250Y and M250X at a liquid load of 10 gpm/ft2 are shown in Figure 2. The results have been normalized by equation 1, a simple power law expression obtained from a regression of all of the M250Y dry pressure drop data.

856.1M250Ydry,309.0

ZF

P=

∆ (1)

Analogous results to M250Y were obtained. That is, while the ten-fold viscosity increase did noticeably reduce capacity, its impact on pre-loading pressure drops was rather marginal relative to the effect of irrigation. Thus, it seems that the inherent interaction of gas and liquid is similar for the two packings, despite their different inclination angles (45° vs. 60°).

Hold-up data for M250Y and M250X are presented in Figure 3. The results are displayed on a relative basis, where each measured fractional hold-up (hL) has been normalized by a baseline value (calculated from an average of the M250Y hold-up(s) at the corresponding liquid load). The data were somewhat scattered at the low-end liquid loads (< 5 gpm/ft2), likely attributable to accuracy constraints on our volumetric hold-up measurement method. The overall trend, though, was indicative of lower hold-up in M250X by about 10–20% under both baseline (water) and high viscosity conditions.

30

5

0.1

1

0.5 1 1.5 2 2.5 3 3.5 4 4.5

∆P / ∆Pdry, M250Y

F-factor (Pa)0.5

M250Y - Baseline

14 cP, 45 dynes/cm

M250X - Baseline

14 cP, 45 dynes/cm

Figure 2: M250Y and M250X pressure drop data at liquid load of 10 gpm/ft2.

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0 5 10 15 20 25 30 35

Relative h

L


M250Y - Baseline

14 cP, 45 dynes/cm

M250X - Baseline

14 cP, 45 dynes/cm

Figure 3: M250Y and M250X hold-up data. F-factor was low (0.7 Pa0.5) to ensure data

were within the pre-loading region.

31

6

Mellapak 125Y (M125Y) – Mass Transfer

The primary rationale for testing M125Y (ap = 125 m2/m3) was to expand the lower boundary of

the packing database (previously Mellapak 2Y (M2Y), ap = 205 m2/m3) and observe the behavior

of our global model (Tsai et al., 2008b) at this limit. The effective area data (again, averaged at each liquid load) for M250Y and M125Y are compared in Figure 4.

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

0 5 10 15 20 25 30


p


M250Y - Baseline

30 dynes/cm

M125Y - Baseline

30 dynes/cm

Figure 4: M250Y (ap = 250 m2/m

3) and M125Y (ap = 125 m

2/m

3) mass transfer area data.

M250Y and M2Y were previously found to exhibit approximately the same fractional areas, which led to speculation that a maximum efficiency had been attained that could not be surpassed, regardless of packing coarseness (Rochelle et al., 2009b). However, the measured M125Y fractional areas consistently exceeded the M250Y areas by 10% for both baseline and low surface tension (30 dynes/cm) experiments, approaching a value of 1.3 at the high-end loads. It was considered that with such a low specific area packing, the wall mass transfer area could be becoming significant; for M125Y, a fully wetted, constantly renewing column wall would account for 7.5% of the packing area, versus only 3.7% for M250Y. Even if this were assumed, though, there would still be a noticeable difference between M125Y and M250Y.

It would seem that as structured packings become coarser, they behave more like random packings in the sense that the mass transfer area starts to exceed the specific packing area. Henriques de Brito et al. (1994) speculated that there should be a greater tendency for liquid flow instabilities such as rippling or formation of satellite droplets in low ap packings, due to longer film running lengths. While either of these mechanisms could explain our results, it is believed that the former is more plausible, given that both M250Y and M125Y were equivalently impacted by a reduction in surface tension. (The droplet theory would suggest more of an amplifying effect for M125Y.)

32

7

Mellapak 125Y (M125Y) – Hydraulics

Dry pressure drop data for M250Y and M125Y are shown in Figure 5, and irrigated pressure drops (10 gpm/ft2) are presented in Figure 6. The results have been normalized by equation 1.

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

∆P / ∆Pdry, M

250Y

F-factor (Pa)0.5

M250Y

M125Y

Figure 5: M250Y and M125Y dry pressure drop data.

0.1

1

0.5 1 1.5 2 2.5 3 3.5 4 4.5


F-factor (Pa)0.5

M250Y

M125Y

Figure 6: M250Y and M125Y pressure drop data at liquid load of 10 gpm/ft2.

33

8

Pressure drop generally scales with specific area, so it was no surprise that the M125Y values (dry and pre-loading) were lower than the M250Y values by a factor of two. The capacity of M125Y (defined in terms of the F-factor at loading) was 15% higher as well. Interestingly, pressure drops for M250X, as shown in Figure 2 and in Rochelle et al. (2009a), were on par with (actually slightly lower than) the M125Y measurements, meaning that making the flow channels steeper (45° to 60°) has about the same effect as halving the density of packing in the column.

Hold-up data (water) for M250Y and M125Y are displayed in Figure 7. As in Figure 3, the results have been interpreted on a relative basis.

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

0 5 10 15 20 25 30 35

Relative h

L


M250Y

M125Y

Figure 7: M250Y and M125Y hold-up data. F-factor was low (0.7 Pa0.5) to ensure data


The hold-ups for M125Y were around 60% those of M250Y on average, which, assuming the experimental accuracy (or lack thereof) is not being over-interpreted, would indicate that hold-up/specific area do not scale as neatly as pressure drop and specific area. A similar observation was made with M250Y and M2Y, where the two packings were measured to have basically equivalent hold-ups despite the latter having 20% less area (Rochelle et al., 2009b).

MellapakPlus 252Y (MP252Y) – Mass Transfer

MellapakPlus 252Y (MP252Y) is essentially M250Y with a minor modification; the interface between packing elements, often referred to as the joint and cited as a problem-spot for liquid accumulation (Green et al., 2007), has been smoothed in order to increase capacity. This is accomplished by bending the sheets at the top and bottom of each element from the standard 45° inclination to a vertical (90°) orientation. With our test packing, this modification occurred at the top/bottom 0.5-in of each element (8.25 in). The wetted perimeter (Lp), as defined in Tsai et al. (2008b) and Rochelle et al. (2009b), was measured to be the same as for M250Y, and the specific area was assumed to be equivalent as well (250 m2/m3). (It should be noted that Alix

34

9

and Raynal (2008) listed slightly different channel dimensions for MP252Y than our values, but even if their numbers were used, the calculated Lp of MP252Y still would very closely match (within 3%) that of M250Y.)

The mass transfer area results (averaged at each liquid load) for M250Y and MP252Y are shown in Figure 8. The two packings were indistinguishable under comparable conditions. This result would suggest that the joint does not tangibly contribute to the mass transfer area – a somewhat surprising conclusion. It is worth noting that the majority of data in these experiments are collected at fairly low pressure drops, far from the loading region, where one would not expect there to be a great deal of gas-liquid turbulence or mixing at the joints. This could explain the similarities in performance for M250Y and MP252Y. It is possible that the two packings only deviate (in terms of mass transfer area) near flooding, where M250Y might be anticipated to exhibit greater mass transfer (at the expense of pressure drop) on account of its more abrupt element-to-element transition.

0.6

0.7

0.8

0.9

1

1.1

1.2

0 5 10 15 20 25 30

Fractional area, ae/ap


M250Y - Baseline

30 dynes/cm

14 cP, 40 dynes/cm

MP252Y - Baseline

30 dynes/cm

9 cP, 40 dynes/cm

Figure 8: M250Y and MP252Y (ap = 250 m2/m

3) mass transfer area data.

MellapakPlus 252Y (MP252Y) – Hydraulics

Dry pressure drop data for M250Y and MP252Y are shown in Figure 9, and irrigated pressure drops (10 gpm/ft2) are presented in Figure 10. The results have been normalized by equation 1.

35

10

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5


F-factor (Pa)0.5

M250Y

MP252Y

Figure 9: M250Y and MP252Y dry pressure drop data.

0.1

1

0.5 1 1.5 2 2.5 3 3.5 4 4.5


F-factor (Pa)0.5

M250Y - Baseline

14 cP, 45 dynes/cm

MP252Y - Baseline

12 cP, 45 dynes/cm

Figure 10: M250Y and MP252Y pressure drop data at liquid load of 10 gpm/ft2.

The MP252Y pressure drops (dry and pre-loading) were around 70% those of M250Y – not quite as low as M250X but still striking, considering the relatively small modification to the overall packing geometry. Given that all three 250-series packings (M250Y, M250X, and MP252Y) had

36

11

identical mass transfer areas and yet displayed very different “absolute” pressure drop behavior, it would appear that the channel configuration has a drastically larger effect on the vapor flow than on the liquid. Table 1 summarizes some of the hydraulic data at 5, 10, and 15 gpm/ft2.

Table 1: Comparison of capacities for M250Y, M250X, and MP252Y.

Fload (water) (Pa)0.5

Fload (10x cP) (Pa)0.5

∆Pload / ∆Pdry, M250Y

5 gpm/ft2 M250Y

3.2

2.8

1.4

M250X 3.9 3.2 0.6 MP252Y 4.3 3.9 1.05

10 gpm/ft2 M250Y M250X MP252Y

2.8 3.3 3.7

1.8 3.1 2.9

1.8 0.75 1.15

15 gpm/ft2 M250Y M250X MP252Y

2.7 3.1 3.2

1.2 2.4 1.8

2 0.9 1.4

First, it should be noted that the loading point F-factors (Fload) provided in Table 1 are only approximate values, since an exact loading point was not necessarily easy to define in the data. With that said, several interesting trends are evident. The baseline tests (water) appeared to show a capacity trend in the order M250Y < M250X < MP252Y. Thus, it would seem there is an interesting trade-off between M250X and MP252Y, with the former yielding lower absolute pressure drops but the latter offering slightly more resistance to flooding. The high viscosity results confounded this conclusion somewhat, since M250X actually appeared to have the most capacity at 10 and 15 gpm/ft2. However, it is suspected that this anomaly may have been attributable to foaming – an erratic phenomenon, especially near flooding – and therefore should not be overly scrutinized. The difference in capacities between the packings also decreased with increasing liquid load. This occurred presumably because, as liquid began to fill the void spaces in the packings, they became equally prone to liquid shearing and entrainment. Finally, as has been mentioned before, the effect of an enhanced liquid viscosity was a decrease in capacity, with the impact generally becoming more significant at higher liquid loads.

Hold-up data for M250Y and MP252Y are displayed in Figure 11. Similar to the M250X results, the MP252Y hold-ups were clearly smaller in the moderate liquid load range (5–15 gpm/ft2) but more closely resembled the M250Y values at higher loads. The x-ray images of Green et al. (2007) revealed local maxima at the joints for M250Y, with hold-ups being 2–5 times greater than in the packing bulk. These spikes would be expected to be less significant for MP252Y due to the smoother transition between elements, but this does not mean the overall hold-up would have to be much different. After all, the vast majority of MP252Y is still identical to M250Y. Consequently, it seemed quite logical for the MP252Y hold-ups to only be slightly lower (10%) on average. It is not known why the high viscosity tests exaggerated this disparity so much more than the baseline experiments. It is possible that because the absolute hold-ups were larger, they were essentially “cleaner” (i.e. more representative of the M250Y/MP252Y difference).

37

12

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0 5 10 15 20 25 30 35

Relative h

L


M250Y - Baseline

14 cP, 45 dynes/cm

MP252Y - Baseline

12 cP, 45 dynes/cm

Figure 11: M250Y and MP252Y hold-up data. F-factor was low (0.7 Pa0.5) to ensure data


Mass Transfer Area Database

Figure 12 shows the entire structured packing mass transfer area database and the global model (equation 2). The relevant geometric dimensions for each packing are listed in Table 2.

( )( )[ ]116.0

34

p

31

L116.0

31

LL

p

e 334.1334.1

== −

L

QgFrWe

a

a

σρ

(2)

The new 250-series data sets (M250X and MP252Y) align with the M250Y data, which makes sense, since these packings are identical from the viewpoint of equation 2; the model has no angle dependence or joint-related factor. The M125Y results are the most striking feature of the updated plot. Whereas one perhaps would have expected the fractional areas to taper off, the data instead follow the trend of the preceding points. This, of course, raises questions about an upper fractional limit for structured packings. The model would predict fractional area to continue increasing for even coarser packings, but it seems natural to believe that there would have to be some kind of threshold – if not for Mellapak 64Y, then for M32Y or M16Y, etc.

This uncertainty aside, it is important to emphasize what we did discover. The lower database limit was extended all the way down to 125 m2/m3, and the data were still found to adhere to the (WeL)(FrL)

-1/3 dependence – a fact that gives the model even greater credibility. Overall, we have a correlation capable of representing the mass transfer areas of a broad range of structured packings – not only in terms of specific area (125–500 m2/m3) but also accounting for factors like texture, angle, etc. – with very acceptable accuracy (± 15%).

38

13

While the model in its current form is satisfactory, further improvements can obviously be made. The two issues that need to be addressed both relate to the high ap packings (e.g. M500Y), which are more strongly affected by surface tension and also have a more distinct liquid load asymptote compared to other packings. One manner of capturing these effects could be to introduce a third dimensionless group to the model. It has always been hypothesized that capillary phenomena, such as liquid pooling and bridging between packing sheets, become significant for the high ap packings. As such, it does not necessarily make sense to use the Nusselt film thickness as the characteristic dimension, as was done for the Weber and Froude numbers in equation 2 (Tsai et al., 2008b), since the film is essentially unbounded. To capture the effects induced by narrow, constricted packing sheets, it would seem to be more logical to use a geometric parameter – for instance, the sheet spacing – as the characteristic dimension. Some work has been done in this regard, but so far there is nothing noteworthy to present.

Table 2: Structured packing parameters.

Packing Specific area,

ap (m2/m3)

Channel side,

S (mm)

Channel base,

B (mm)

Crimp height,

h (mm)

Source(s)

Mellapak 250Y

(M250Y)

250 17 24.1 11.9 Petre et al. (2003)

Mellapak 500Y

(M500Y)

500 8.1 9.6 6.53 Aroonwilas (2001)

Mellapak 250X

(M250X)

250 17 24.1 11.9 Direct measurement

MellapakPlus 252Y

(MP252Y)

250 17 24.1 11.9 Direct measurement

Mellapak 250Y (smooth)

(M250YS)

250 (?) 17 24.1 11.9 Direct measurement

Mellapak 125Y

(M125Y)

125 37 55 24.8 Direct measurement

Mellapak 2Y

(M2Y)

205 21.5 33 13.8 Sulzer contact

Direct measurement

Flexipac 1Y

(F1Y)

410 9 12.7 6.4 Koch contact

Petre et al. (2003)

Prototype 500

(P500)

500

8.1

9.6

6.53

Assumed same as

M500Y

39

14

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

0.0001 0.001 0.01 0.1

Fractional area, ae/ap

(WeL)(FrL)-1/3

M250Y - Baseline30 dynes/cm4 cP, 60 dynes/cm14 cP, 45 dynes/cm


M250X - Baseline30 dynes/cm11 cP, 40 dynes/cm

MP252Y - Baseline30 dynes/cm9 cP, 40 dynes/cm

M250YS - Baseline30 dynes/cm

M125Y - Baseline30 dynes/cm

M2Y - BaselineF1Y - Baseline

6 cP, 65 dynes/cmP500 - Baseline

30 dynes/cm

-15%

+15%

Equation 2

Figure 12: Structured packing mass transfer area database, compared with global model

(equation 2).

Tsai et al. (2008b) demonstrated the models of Rocha-Bravo-Fair (Rocha et al., 1996) and Billet-Schultes (Billet and Schultes, 1993) to be especially poor in their handling of aqueous systems and speculated that this could be due to their heavy reliance on distillation data, which generally consist of very low surface tension systems. An important objective of this entire research project was to address this shortcoming and establish a model suitable for aqueous solvents such as 7 m MEA. Ideally, though, this model would be more universal and be capable of bridging the apparent gap between hydrocarbon and aqueous systems. To test this, equation 2 was evaluated under distillation conditions, as reported by Olujic et al. (2000). The SRP investigation (cyclohexane/n-heptane) consisted of four test pressures and liquid loads ranging from 1–20 gpm/ft2. The relevant physical parameters are shown in Table 3.

Table 3: Physical properties of the cyclohexane/n-heptane system (avg. at column bottom).

Pressure (bar)

Liquid density, ρL (kg/m3)

Liquid viscosity, µL (cP)

Surface tension, σ (dynes/cm)

Temperature (°C)

0.33 657 0.43 17 61 1.03 625 0.30 14 97 1.66 609 0.23 12 114 4.14 561 0.16 8 154

Figure 13 compares the predictions from equation 2 for the four conditions in Table 3 and for water (ρL = 1000 kg/m

3, µL = 1 cP, σ = 72 dynes/cm). At the most extreme condition (P = 4.14 bar, σ = 8 dynes/cm), the model predicts 20% greater mass transfer areas than water for M250Y.

40

15

This is something of an extrapolation – data for M250Y at 30 dynes/cm have shown 10% higher areas at most – but at the very least the predicted fractional areas are still reasonable, ranging from 0.73 to 1.17. In other words, the model does not go berserk, like Rocha-Bravo-Fair does for water, and therefore it indeed appears to be capable of handling distillation-type systems.

Figure 14 compares equation 2 with the Rocha-Bravo-Fair and Billet-Schultes correlations at the distillation condition of 4.14 bar. It is interesting to note that the two literature models converge at this limit, which is perhaps indicative of their development with common data sources. Also worth pointing out is the fact that the models are actually not too far off from equation 2 in this case, particularly at moderate liquid loads (10–20 gpm/ft2). Thus, while we do not endorse the use of Rocha-Bravo-Fair or Billet-Schultes for the analysis of aqueous systems, they may actually be acceptable when applied toward the distillation-type systems from which they were developed.

Figure 13: Predicted M250Y mass transfer areas from equation 2 for cyclohexane/n-

heptane and water.

0.6

0.7

0.8

0.9

1

1.1

1.2

0 5 10 15 20 25


p


Cyclohexane/n-Heptane: P = 0.33 bar

P = 1.03 bar

P = 1.66 bar

P = 4.14 bar

Water

41

16

Figure 14: Predicted M250Y mass transfer areas from various models for cyclohexane/n-

heptane system at 4.14 bar.

Hold-up Database

While the liquid hold-up model of Suess and Spiegel (1992) has been found to be fairly reasonable when compared against our data (Rochelle et al., 2008a), it is certainly not without questionable aspects. For example, the correlation is strictly empirical but is based on a limited databank (M250Y, M250X, and M500Y). Furthermore, it is systematically overpredictive at low liquid loads and underpredictive at high ones – a feature confirmed by Brunazzi et al. (1995). Finally, Suess and Spiegel reported no effect of corrugation angle (45° or 60°) on hold-up, whereas our results seemed to show a slight decrease for the steeper configuration. For these reasons, an attempt was made to develop a new global hold-up model based on our own hydraulic measurements.

As a first pass, experimental hold-ups were plotted as a direct function of the Nusselt film thickness (equation 3), since the two parameters should be relatable in some respect. This approach was unsuccessful in collapsing the entire database.

3

pL

L

L

LLNusselt

sin

3

sin

3

L

Q

gg

u

αρµ

αρµ

δ == (3)

Next, a dimensional analysis approach was taken. Numerous dimensionless group combinations were examined, but these efforts all failed. Shetty and Cerro (1997) proposed an expression with a dependence on the Reynolds and Galileo numbers (equation 4), but this was not found to be effective either.

31

Ga31

ReL 096.6−

= NNh (4)

0.3

0.5

0.7

0.9

1.1

1.3

0 5 10 15 20 25


p


Equation 2

Rocha-Bravo-Fair

Billet-Schultes

42

17

In the “Mass Transfer Area Database” section, it was postulated that an additional characteristic dimension aside from the Nusselt film thickness might be required to fully capture the fluid mechanics within structured packing. A phenomenon like liquid accumulation within channel recesses, for instance, might be better suited by a geometric parameter basis. It was decided to test this idea on equation 4. Surprisingly, when the characteristic length of the Galileo number (ratio of gravitational-to-viscous forces) was defined by the packing channel side dimension (S), the hold-up results became somewhat unified. (This modified number was henceforth denoted as Gap). An Excel power law regression was subsequently performed to determine if a better fit than the 1/3 and -1/3 exponents could be obtained. Equation 5 was the result of this analysis.

( )( )[ ] 72.032

PLL 84.21−= GaReh (5)

The experimental hold-up data are plotted alongside equation 5 in Figure 15. The global model is able to represent the majority of points within ±25% and has less than half the mean squared error of the Suess and Spiegel model. An even better fit could be obtained by omitting some of the low-end liquid load data in future analyses, since they exhibit an unreasonably dramatic (and therefore suspect) drop-off in hold-ups.

The theoretical significance of equation 5 is not entirely clear, given the somewhat arbitrary manner in which it was derived. Nevertheless, at worst, it can be considered as an empirical hold-up model that may be a better (or at least more universal) option than the correlation of Suess and Spiegel.

0.1

1

10

0.00001 0.0001 0.001

Fractional hold-up (%)

(ReL)(GaP)-2/3



M250X - Baseline14 cP, 45 dynes/cm

MP252Y - Baseline12 cP, 45 dynes/cm

M250YS - BaselineM125Y - BaselineM2Y - BaselineP500 - Baseline

+25%

-25%

Equation 5

Figure 15: Structured packing hold-up database, compared with global model (equation 5).

Absorber Economic Model

There are many factors that deserve consideration in the design of the large absorbers planned for post-combustion CO2 capture. The selection of packing and the treatment of liquid

43

18

distribution, as well as how to design the column itself (i.e. short/fat vs. tall/skinny), are all important issues. Much research is devoted to the maximization of certain variables (e.g. effective area) or minimization of others (e.g. pressure drop), but in the end, the final decision will rely on economics. Consequently, work was begun on a basic economic model to investigate this optimization problem.

An Excel program was set up to analyze the interaction of gas rate and column configuration. Basically, the SOLVER function was utilized to vary superficial gas velocity and calculate a minimum cost for a given throughput (0.15–3 MMCFM or roughly 50–1000 MW). Five major cost components were identified: the column body (including the shell and auxiliaries such as manholes, ladders, etc.), packing, pressure drop, blower, and pump. Estimations were derived from various sources (Peters and Timmerhaus, 1991; Stichlmair et al., 1989; Rochelle et al., 2005). Several key assumptions were: factors of 4 and 0.4 for the installed and annualized costs (necessary for conversion to an annualized basis), a conversion factor of 0.6 between 1990 and present dollars, a pressure drop ceiling of 80% approach to flood (defined at 1025 Pa/m), and a blower operation cost of $50/MWh. The basis of the model was a 7 m MEA system (unloaded) with 90% CO2 removal. A required area of 0.1 m

2/mol CO2-hr was used based on several Aspen Plus® simulation scenarios run by Plaza. The column was designed as square, and no constraints were placed on its size.

Table 4 shows the results from a few cases run with Mellapak 250Y. The total minimum cost was calculated to be around $5–7/tonne CO2, which seemed reasonable, and was found to decrease with greater throughput. The packing and column dominated the total cost (90%) in every instance. Interestingly, the column benefitted from economies of scale whereas the packing did not; it always accounted for roughly $3.30/tonne CO2. The cost associated with pressure drop was smaller than expected, although it did become more significant as the gas load increased. Over the 100 to 500 MW range, the gas velocity and packing height remained relatively constant, and the only parameter that really changed was the column side, increasing from 10 to 20 m.

Table 4: Economic model results for M250Y.

Gas rate (MW)

Minimum cost ($/tonne CO2)

Gas velocity (m/s)

Column side (m)

Packing height (m)

100 $7.35 Packing: 45% Column: 45%

∆P: 2.6%

1.53 9.6 10.5

250 $5.71 Packing: 58% Column: 33%

∆P: 3.4%

1.56 15.1 10.6

500 $5.11 1.72 20.3 11.6 Packing: 64%

Column: 25% ∆P: 5.3%

44

19

Conclusions

M250X, MP252Y, and M125Y were characterized and contrasted with the standard M250Y. The packing geometry greatly impacted the vapor side of the gas-liquid contacting process; pressure drop scaled directly with specific area (M125Y) and was significantly reduced by relatively minor modifications to the flow channel angles (M250X and MP252Y). The liquid side, on the other hand, appeared to be primarily influenced by the specific area. Despite their varying configurations, all three 250-series packings (M250Y, M250X, and MP252Y) displayed similar hold-ups, with both M250X and MP252Y being only marginally lower (10%) on average than M250Y. Furthermore, the 250-series packings had essentially the same mass transfer area. M125Y exhibited much lower hold-ups (60%) relative to M250Y. Its fractional area was higher (10%) than M250Y as well, indicating a tendency toward random packing-like behavior (i.e. effective areas well in excess of the specific area) with increasing structured packing coarseness.

A ten-fold increase in solution viscosity did not appreciably affect pressure drops but did increase liquid hold-ups and decrease the capacities of the packings. Mass transfer experiments, performed via absorption of CO2 into 0.1 M NaOH, showed no perceivable effect of viscosity on the packing effective area. A reduction in surface tension (30 dynes/cm) resulted in a 10% increase in area. All of these findings were consistent with our past conclusions.

The mass transfer area database (updated to include the new M250X, MP252Y, and M125Y data sets) continued to be represented well (±15%) by the correlation that was regressed as a function of (WeL)(FrL)

-1/3, further validating the proposed form of our model. The model was checked under distillation conditions (cyclohexane/n-heptane) and appeared to be capable of handling this type of systems as well.

The hold-up database collapsed when plotted as a function of (ReL)(Gap)-2/3. The regressed

model offered an overall improved fit of our data (±25%) compared to the Suess and Spiegel correlation.

Optimum cost estimates of $5–7/tonne CO2 for a 100–500 MW absorber were obtained from the economic analysis. It is believed that a good economic model foundation was established, but obviously, much more work will be necessary to obtain results that we would feel comfortable reporting in an official setting.

Future Work

Primary goals for the near future include the refinement of the global mass transfer model and more rigorous analysis of the hydraulic data – chiefly, the pressure drop results. The economic model will also be improved, per the advice of industrial consultants. Journal publications that cover these topics will hopefully be completed later on in the year.

Nomenclature

ae = effective area of packing, m2/m3

ap = specific (geometric) area of packing, m2/m3

B = packing channel base, m

F = gas flow factor, (m/s)(kg/m3)0.5 or Pa0.5

g = gravitational constant; 9.81 m/s2

45

20

h = packing crimp height, m

hL = (total) liquid hold-up, m3/m3

kG = gas-side mass transfer coefficient, kmol/(m2·Pa·s)

kL0 = physical liquid-side mass transfer coefficient, m/s

Lp = wetted perimeter in cross-sectional slice of packing, m

∆P = pressure drop, Pa

Q = volumetric flow rate, m3/s

S = packing channel side, m

u = velocity, m/s

Z = packed height, m

Greek Symbols

α = corrugation angle (with respect to the horizontal), deg

δ = characteristic length, m

µ = viscosity, kg/(m-s)

ρ = density, kg/m3

σ = surface tension, N/m

Subscripts

G = gas phase

L = liquid phase

Dimensionless Groups

af = fractional area of packing, ae/ap

Fr = Froude number, δg

u 2

Ga or NGa = Galileo number, 2

23

µρδg

NRe = Reynolds number as defined by Shetty and Cerro (1997), pL

Q

µρ4

Re = Reynolds number, µδρ u

We = Weber number, σδρ 2u

46

21

References

Alix P, Raynal L. "Liquid Distribution and Liquid Hold-up in Modern High Capacity Packings". Chem Eng Res Des. 2008;86:585–591.

Aroonwilas A. Mass-Transfer with Chemical Reaction in Structured Packing for CO2 Absorption

Process. University of Regina. Ph.D. Thesis. 2001.

Billet R, Schultes M. "Predicting Mass Transfer in Packed Columns". Chem Eng Technol. 1993;16(1):1–9.

Brunazzi E et al. "Interfacial Area of Mellapak Packing: Absorption of 1,1,1-Trichloroethane by Genosorb 300". Chem Eng Technol. 1995;18(4):248–255.

Green CW et al. "Novel Application of X-ray Computed Tomography: Determination of Gas/Liquid Contact Area and Liquid Holdup in Structured Packing". Ind Eng Chem Res. 2007;46(17):5734–5753.

Henriques de Brito M et al. "Effective Mass-Transfer Area in a Pilot Plant Column Equipped with Structured Packings and with Ceramic Rings". Ind Eng Chem Res. 1994;33(3):647–656.

Kohl A, Nielsen R. Gas Purification. Houston, Gulf Publishing Co.: 1997.

Olujic Z et al. "Influence of Corrugation Geometry on the Performance of Structured Packings: An Experimental Study". Chem Eng Process. 2000;39(4):335–342.

Peters MS, Timmerhaus KD. Plant Design and Economics for Chemical Engineers. New York, McGraw-Hill, Inc.: 1991.

Petre CF et al. "Pressure Drop through Structured Packings: Breakdown into the Contributing Mechanisms by CFD Modeling". Chem Eng Sci. 2003;58(1):163–177.

Rocha JA et al. "Distillation Columns Containing Structured Packings: A Comprehensive Model for Their Performance. 2. Mass-Transfer Model". Ind Eng Chem Res. 1996;35(5):1660–1667.

Rochelle GT et al. "Integrating MEA Regeneration with CO2 Compression and Peaking to Reduce CO2 Capture Costs. Final Report for Trimeric Corp. Subcontract of DOE contract DE-FG02-04ER84111." June, 2005.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report 2006." Luminant Carbon Management Program. The University of Texas at Austin. 2006.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report 2006." Luminant Carbon Management Program. The University of Texas at Austin. 2007.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2008." Luminant Carbon Management Program. The University of Texas at Austin. 2008.


Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009." Luminant Carbon Management Program. The University of Texas at Austin. 2009.

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Shetty S, Cerro RL. "Fundamental Liquid Flow Correlations for the Computation of Design Parameters for Ordered Packings". Ind Eng Chem Res. 1997;36(3):771–783.

Stichlmair J et al. General Model for Prediction of Pressure Drop and Capacity of Countercurrent Gas/Liquid Packed Columns". Gas Sep Purif. 1989;3:19-28.

Suess P, Spiegel L. "Hold-up of Mellapak Structured Packings". Chem Eng Process. 1992;31(2):119–124.

Tsai RE et al. "Influence of Surface Tension on Effective Packing Area". Ind Eng Chem Res. 2008;47(4):1253–1260.

Tsai RE et al. "Influence of Viscosity and Surface Tension on the Effective Mass Transfer Area of Structured Packing". GHGT-9, Washington D.C. 2008.

Wang GQ et al. "Review of Mass-Transfer Correlations for Packed Columns". Ind Eng Chem Res. 2005;44(23):8715–8729.

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1

Modeling Stripper Performance for CO2 Removal with Amine Solvents


by David Van Wagener


and the




July 1, 2009

Abstract Since Hilliard developed thermodynamic models for various amine solvents, additional experimental data has been collected at new conditions. The data primarily of interest have been for concentrated piperazine (PZ). The Hilliard model performed well for low concentrations, 0.9 m–5 m, but 8 m PZ will be used in future simulations. VLE data collected by Dugas as well as heat capacity data collected by Nguyen for PZ was incorporated into previous parameter regression files. Additionally, heat of absorption data was collected by Freeman. The parameters to be regressed were reconsidered, and more focus was put on the heat capacity parameters of the dominant species at relevant loadings. This quarter the dielectric constant of PZ was also included. The original value used by Hilliard was for piperidine, a molecule similar to PZ with one amine group instead of two. Including the dielectric constant in the regression greatly improved the fit, and the new value of the dielectric constant is between that of piperidine and MEA. The theoretical difference between the heat of absorption for 8 m MEA at 40 °C and 120 °C was calculated using an energy balance, and the difference was found to be approximately 3.2 kJ/mol CO2. This difference is much smaller than what is observed in the experimental data, so the data collection for heat of absorption should be reevaluated.

Introduction Piperazine (PZ) is of interest as a solvent for CO2 capture because it has significantly higher capacity than monoethanolamine (MEA), the baseline and industry standard. A PZ molecule has two amine groups, which leads to this increased capacity. High solvent capacity leads to less solvent being circulated between the absorber and stripper, so the stripper reboiler duty decreases because the sensible heat input for the solvent is lowered. The CO2 absorption rate for PZ is enhanced over MEA as well, also possibly due to the two amine groups per molecule. As an added benefit, PZ has no detectable thermal degradation up to 150 °C. Many explored stripper configurations operate more efficiently at high temperatures, so it is expected that PZ will perform better than MEA (Freeman et al., 2008).

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Previously, a thermodynamic model was developed for PZ (Hilliard, 2008), and it was used to simulate a simple stripper with the accompanying rich and lean pumps, cross heat exchanger, and multi-stage compressor. The simulations produced results with few convergence errors; however, the behavior while varying the lean loading specification was unexpected. Typically the calculated equivalent work of the stripper has a single distinct optimum lean loading (Oyenekan, 2007). Conversely, the PZ simulation demonstrated both a local and global optimum (Rochelle et al., 2008). The local optimum was at a lean loading of 0.30, an expected value based upon the measured VLE at absorber conditions. The global optimum was at a lean loading of 0.15, and the temperature profile was very hot; reaching temperatures over 120 °C. A suggested source for this unusual behavior was the accuracy of the model predictions of thermodynamic values for the solvent. The predictions that seemed particularly questionable were the solvent heat capacity and heat of absorption of CO2. Prior work generated heat capacity data for 8 m PZ by using trends in lower concentration piperazine solvents. The model parameters were regressed to better predict heat capacity. However, the fit was not improved. The heat capacity data used for the regression were extrapolated from values at lower PZ concentrations. Though not an ideal source of data, these values were the only ones available at the time.

More recently, heat capacity data for 8 m PZ was collected by Nguyen. These data were used to improve the heat capacity predictions in a new regression incorporating A-E heat capacity parameters. The fit was much improved because the regression only focused on heat capacity data. The work this quarter continues with the perfection of the PZ model for the use in simulating absorption/stripping for CO2 capture.

Methods and Results The final PZ model from the last quarter was evaluated for overall accuracy of predictions. The predictions of CO2 solubility and heat capacity fit the laboratory data very well, and the speciation followed expected trends. However, the heat of absorption predictions did not follow typical heat of absorption trends and were lower than collected experimental data on average. The data collected by Freeman and Aspen Plus® predictions from the PZ model corrected for heat capacity are shown below in Figure 1.

The heat of absorption predictions decreased with increasing temperature, so the 150 °C predictions were extremely low, dropping as far as 30–40 kJ/mol CO2. This model will be used to develop both absorber and stripper simulations. The predictions between 40 °C–70 °C are the most important for the absorber simulations, but the stripper will be modeled up to 150 °C due to the expected improvement in performance at high temperature with relatively minimal thermal degradation. In the previous quarter, correlations for PZ were used to calculate the approximate energy consumption for 2- and 3-stage flash configurations, operating isothermally at 150 °C. Since there are no sensible heat effects between flashes when operating isothermally, only the heat of absorption matters for this section. It is essential to achieve relatively accurate heat of absorption predictions in Aspen Plus® to accompany the accurate VLE and heat capacity. For this reason, further regressions were performed to obtain accurate VLE, heat capacity, heat of absorption, and speciation.

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Figure 1: Heat of Absorption for 8 m PZ. Data from Freeman (points) and predictions in

Aspen Plus® for heat capacity corrected model (lines). A substantial database of properties for piperazine has been collected by other contributors, shown below in Table 1.

Table 1: Available Data Sets for Piperazine Regression

Data Type Temperature (°C) PZ Concentration (m) CO2 Loading

VLE 40-180 2-12 0.148-0.424 Heat Capacity 40-120 3.6-12 0.159-0.400

Volatility 40-60 3.6-5 0.148-0.415

Heat of Absorption 40-120 2.42-8 0.041-0.488

In addition to a wide range of data sets, there are many parameters that can be varied. These parameters included:

• Ionic heat capacities • Enthalpy of formation for ionic species • Gibbs free energy of formation for ionic species • Tau interaction parameters

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Both the ionic heat capacities and tau interaction parameters are temperature dependent, so each has multiple parameters for each component. The most dominant species in loaded solutions were determined to be:

• HPZ+ • HPZCOO • PZCOO-

The regressions using the DRS package in Aspen Plus® were not straightforward. They often failed to converge, and even completed regressions yielded many flash errors when analyzing the fit of the new model. What was first planned to be a simple regression activity to match the available high concentration PZ data turned into a search for regression conditions which yielded a converging regression, had few to no flash errors after completing, and adequately fit the data available for 8 m PZ. After many combinations of the regressed parameters and included data sets, a run with adequate results and no flash errors was eventually stumbled upon. The solution started with the original Hilliard regression and added the following data to the appropriate data sets:

• VLE: 8 m PZ, 0.23–0.40 loadings, 40 °C–100 °C • Heat Capacity: 8 m PZ, 0.21–0.40 loadings, 40 °C–150 °C

Including these data in new data sets did not yield good results. An assortment of parameters for the dominant species were chosen; see the regressed values below in Table 2. As alluded to above, this regression converged and had no flash errors when analyzing the fit for 8 m PZ. The behavior of the VLE, heat capacity, and heat of absorption are shown below in Figures 2–4. The most apparent misfit is the VLE, with significant inaccuracies at very low and very high loadings.

Table 2: Regressed Parameter Values - 1

Parameter Component i Component j Value (SI units) σ 1 ΔGaq

form PZCOO- -205423628 70058650 2 ΔHaq

form PZCOO- -482049819 1313460810 3 Cp-A PZCOO- -6128880 209089229 4 Cp-B PZCOO- 21341 565138 5 Cp-C PZCOO- -0.140 20.332 6 ΔGaq

form HPZCOO -274324954 403173 7 ΔHaq

form HPZCOO -510325070 3313114 8 Cp-A HPZCOO 1597232 2951470 9 Cp-B HPZCOO -8765 16428 10 Cp-C HPZCOO 14.2 22.7 11 Cp-A PZH+ 5085443 32344691 12 Cp-B PZH+ -14469 174471 13 Cp-C PZH+ 2.98 232.48 14 τm,ca-A H2O (PZH+,PZCOO-) 7.37 45.07 15 τm,ca-B H2O (PZH+,PZCOO-) 1682 17945 16 τca,m-A (PZH+,PZCOO-) H2O -181 76

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Parameter Component i Component j Value (SI units) σ 17 τca,m-B (PZH+,PZCOO-) H2O 71192 31106 18 τm,ca-A H2O (PZH+,HCO3-) 9.04 118055.40 19 τm,ca-B H2O (PZH+,HCO3-) -538 20327596 20 τm,ca-C H2O (PZH+,HCO3-) -0.978 35923.919 21 τca,m-A (PZH+,HCO3-) H2O -77.4 104442.7 22 τca,m-B (PZH+,HCO3-) H2O 186 874129 23 τca,m-C (PZH+,HCO3-) H2O -484 1744760 24 τm,ca-A PZ (PZH+,PZCOO-) 3.96 7.92 25 τca,m-A (PZH+,PZCOO-) PZ -11.3 2.9

Figure 2: CO2 Solubility Predictions for Regression 1. Points - Experimental Data. Lines -

Model Predictions.

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Figure 3: Heat Capacity Predictions for Regression 1. Points - Experimental Data. Lines -

Model Predictions.

Figure 4: Heat of Absorption Predictions for Regression 1. Points - Experimental Data.

Lines - Model Predictions.

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This model converged and produced no flash errors in the model analysis, indicating that it could be a stable thermodynamic model to be used for simulations. However, the VLE was very inaccurate. The spacing between temperatures appeared roughly correct, but the slope of the Aspen Plus® predictions was too shallow. The dielectric constant is a variable parameter that mostly affects the slope of the VLE. The magnitude of this constant is relatable to a component's ability to stabilize in an ionic solution. The value of this parameter had been specified by Hilliard to be equal to that of piperidine because the dielectric constant is not available for piperazine in the literature. Since the value of the dielectric constant for piperazine is uncertain, it was varied to attempt to better match all sets of data. The regressed constants are displayed in Table 3, and the predictions are shown in Figures 5–7.

Table 3: Regressed Parameter Values - 2

Parameter Component i Component j Value (SI units) σ 1 ΔGaq

form PZCOO- -219389572 609502.7542 ΔHaq

form PZCOO- -479875330 6857582.963 Cp-A PZCOO- -2331715 2081603.714 Cp-B PZCOO- 9202 6131 5 Cp-C PZCOO- -0.0162 0.1439 6 ΔGaq

form HPZCOO -273197501 420092 7 ΔHaq

form HPZCOO -516582958 2735043.358 Cp-A HPZCOO -14434 67471 9 Cp-B HPZCOO 1003 245 10 Cp-C HPZCOO 0.000417 0.1982529911 Cp-A PZH+ 1435790 535810 12 Cp-B PZH+ -4323 1462 13 Cp-C PZH+ 0.0288 0.2337 14 εr-A PZ 23.5 85.0 15 εr-B PZ 6.20 1034.55 16 τm,ca-A H2O (PZH+,PZCOO-) -18.028 2094.437 17 τm,ca-B H2O (PZH+,PZCOO-) -5737.179 704096.22418 τca,m-A (PZH+,PZCOO-) H2O -99.4 70.6 19 τca,m-B (PZH+,PZCOO-) H2O 47711 32337 20 τm,ca-A H2O (PZH+,HCO3-) 18.6 0.5 21 τm,ca-B H2O (PZH+,HCO3-) 8.94 144.90 22 τm,ca-C H2O (PZH+,HCO3-) -3.40 6.87 23 τca,m-A (PZH+,HCO3-) H2O -7.44 0.40 24 τca,m-B (PZH+,HCO3-) H2O 11.5 130.9 25 τca,m-C (PZH+,HCO3-) H2O 0.831 2.980 26 τm,ca-A PZ (PZH+,PZCOO-) 54.9 9109.8 27 τca,m-A (PZH+,PZCOO-) PZ -4.73 0.71

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Figure 5: CO2 Solubility Predictions for Regression 2. Points - Experimental Data. Lines -

Model Predictions.

Figure 6: Heat Capacity Predictions for Regression 2. Points - Experimental Data. Lines -

Model Predictions.

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Figure 7: Heat of Absorption Predictions for Regression 2. Points - Experimental Data.

Lines - Model Predictions.

The fit is reasonable overall. The VLE fits very well, displaying little discrepancy between the Aspen Plus® predictions and the experimental data. The vertical spacing for each 20 °C increment should be equal, so the 160 °C predictions may be too low. The heat of absorption follows adequate trends. Based on the VLE, the heat of absorption is expected to be about 65–70 kJ/mol CO2, but the predictions in the relevant loading range of 0.3–0.4 display a range of 60–80 kJ/mol CO2. However, the predictions are well behaved and are tighter packed than in previous models. The heat capacity predictions are also very close to the data, but start to show deviation at 150 °C.

Since the piperazine dielectric constant was varied with free bounds, its final value was analyzed. Figure 8 compares the final regressed dielectric constant as a function of temperature to those provided by Hilliard (2008) and Bishnoi (2002). In Bishnoi's work, the dielectric constant was assumed to be equal to that of MEA, though the value used is lower than the current documented literature value, which is listed in the CRC handbook as 31.94 at a temperature of 20 °C. Figure 8 shows that the regressed value in the current work approaches the value used by Bishnoi, but is significantly different than the value used by Hilliard. Without direct measurement of the exact dielectric constant of PZ, it cannot be determined whether the regressed value is accurate, but most importantly it allows the Aspen Plus® predictions of solvent properties to closely follow experimental data.

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Figure 8: PZ Dielectric Constant Used by Various Models

The accuracy of collected heat of absorption data has also been questioned recently. The data are collected from an apparatus at NTNU in Trondheim, Norway, and it is very consistent between runs. However, the data can be scattered and may be too high. As stated before, the expected heat of absorption for 8 m PZ is approximately 65 kJ/mol CO2, given by the Gibbs-Helmholtz relation with the VLE. On the other hand, the data collected for 8 m PZ, shown in Figure 1 are scattered between 80–120 kJ/mol CO2 before dropping off at a loading of 0.45. The spread of this data is also worrisome.

The cycle used for this calculation is shown in Figure 9. It is not meant to simulate an actual absorption/stripping process, but merely quantify the theoretical difference between the heat of absorption at absorber and stripper temperatures. Below are several equations which were used: an energy balance rearranged to calculate the heat of absorption difference, and 3 equations to calculate the heat capacity of the solvent.

1

2

3

4

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The heat capacity correlation used for calculating the heat duty for cooling the lean and heating the rich streams was developed by Nguyen. Shown above, it calculates the heat capacity for concentrated piperazine solvents as a function of temperature and PZ, CO2, and water mass fraction. Not shown, the water heat capacity was calculated using the DIPPR correlation for liquid water. This overall heat capacity was integrated over the range of the absorber and stripper temperatures. The ideal gas heat capacity for CO2 from DIPPR was used to determine the sensible heat required to cool the desorbed CO2 back to the absorption temperature. The specifications and result of the calculation is shown below in Table 4.

Figure 9: Energy Balance on CO2 Absorption/Desorption

Table 4: Calculation of Heat of Absorption Increase with Temperature

Calculation specifications Heat input/outputs

Rich loading 0.4 Rich sensible heat 568.64 kJ Lean loading 0.3 Lean sensible heat 568.71 kJ CO2 absorbed 1 mol CO2 cooling 3.18 kJ Solvent 8 m PZ Cold side 40 °C Calculated Difference Hot side 120 °C dHabs120‐dHabs40 3.25 kJ

This calculation demonstrated that the difference between the heats of absorption in 8 m PZ at 40 °C and 120 °C should be approximately 3.25 kJ/mol CO2. This result revealed uncertainty in the collected heat of absorption data. Not only are the values consistently higher than expected, but they should be more tightly packed as well.

Cool CO2 to 40°C

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Conclusions The PZ model developed by Hilliard in Aspen Plus® was regressed to better fit recent VLE, heat capacity, and heat of absorption data. The key to a successful model seems to be fixing the PZ dielectric constant. Trials with many combinations of regressed data and parameters were evaluated, but none produced an accurate model with converging flashes. Including the dielectric constant in the regression yielded a promising model, and the final value for the dielectric constant was realistic. In the next quarter, the model will be further evaluated to potentially adjust the 150 °C–160 °C VLE and volatility predictions.

The calculation of the theoretical difference between the heats of absorption at absorber and stripper temperatures verifies that the heat of absorption at 40 °C should only be approximately 3.2 kJ/mol CO2 lower than the value at 120 °C. This calculation raises concerns about the accuracy of the apparatus for heat of absorption data collection.

Future Work The PZ model in Aspen Plus® will be further evaluated to determine if it is ready for use in simulations. The volatility and high temperature VLE will be addressed. If the dielectric constant of 8 m PZ or pure PZ is measured, it can be compared against the value obtained by the regression.

Once the thermodynamic model is completed, configurations with varying levels of complexity will be analyzed with an end goal of correlating configuration complexity with performance. The analysis will begin with simple flowsheets like a single flash, a simple stripper, and a stripper with a preflash. The analysis will continue through more complex options like a double matrix.

References Bishnoi S, Rochelle GT. Thermodynamics of Piperazine/Methyldiethanolamine/Water/Carbon

Dioxide. Ind Eng & Chem Res. 2002:604–612.

Freeman SA, Dugas RE, Van Wagener DH, Nguyen T, Rochelle GT. CO2 capture with concentrated, aqueous piperazine. GHGT-9. Washington, DC: Elsevier, 2008.

Hilliard MD. A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue Gas. The University of Texas, Austin. Ph.D. Dissertation. 2008.

Oyenekan, Babatunde. Modeling of Strippers for CO2 Capture by Aqueous Amines. Ph.D. Dissertation, The University of Texas, Austin. Ph.D. Dissertation. 2007.

Rochelle GT et al. “CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report 2008.” Luminant Carbon Management Program. The University of Texas, Austin. 2008.

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Solvent Management of MDEA/PZ


by Fred Closmann


the


and the

Process, Science, & Technology Center



July 10, 2009

Abstract A thermal degradation experiment was conducted on 7 m MDEA in the second quarter. The compounds dimethylaminoethanol (DMAE), diethanolamine (DEA), N,N-dimethyl ethanamine (DMEA), dimethyl piperazine, 1-(2-hydroxyethyl)-4-methylpiperazine (HMP), and triethanolamine (TEA) were tentatively identified in degraded solvent samples through ion chromatography (IC) and IC-mass spectrometry (IC-MS) methods. We calculate an activation energy for the degradation of MDEA of approximately 47 kJ/gmol, and rates of degradation of MDEA of 66 and 112 mmolal/day were calculated at 150 °C and loadings of 0.1 and 0.2 moles CO2/mole alkalinity, respectively. Proposed degradation mechanisms for the MDEA loss include the protonation of MDEA, the formation of an MDEA-carbamate, and the disproportionation of MDEA. All three mechanisms are followed by subsequent reactions and could involve PZ when present in a solvent blend. However, because the MDEA loss rate is similar to rates calculated for MDEA in 7 m MDEA/2 m PZ, we believe the role of PZ in the degradation of the solvent is of lesser importance.

The construction of the integrated solvent cycling/degradation apparatus is near completion, and basic temperature metrics for amine cycling have been met. Modifications to the system to achieve accurate temperature measurement are ongoing. Amine cycling degradation experiments will be completed in 3rd quarter 2009.

Introduction During the 2nd Quarter 2009, a thermal degradation experiment was performed on 7 m MDEA for comparison to similar experiments utilizing 7 m MDEA/2 m PZ. Cation chromatography was used to determine the loss rate of MDEA, and to identify breakdown products through comparison to standards. Mass spectrometry (MS) coupled with IC was used to confirm the identity of breakdown byproducts. Degradation of samples was performed at 120, 135, and 150 °C, and loadings of 0.1 and 0.2 moles CO2/mole alkalinity. The degradation rate of MDEA

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at 135 °C was 53 to 55 mmolal/day at loadings of 0.1 and 0.2 moles CO2/mole alkalinity, and 66 and 112 mmolal/day at loadings of 0.1 and 0.2 moles CO2/moles alkalinity, respectively, at 150 °C.

An additional thermal degradation experiment was initiated using 7 m MDEA/2 m PZ treated with H2SO4 in sufficient quantity to neutralize 10% of alkalinity. The solution was not loaded with CO2. All sample cylinders failed and the experiment was stopped pending fabrication of a new set of sample cylinders.

Thermal Experiments

Thermal No. 10 A single thermal experiment was initiated in March, with final samples to be pulled in July 2009. MDEA degradation rates of 39, 55, and 66 mmolal/day were measured for a loading of 0.1 moles CO2/mole alkalinity at 120, 135, and 150 °C, respectively (Table 1). Tables 2, 3, and 4 include the raw data for MDEA concentration by time and sample cylinder number. MDEA degradation rates of 53 and 112 mmolal/day were measured for a loading of 0.2 moles CO2/mole alkalinity at 135 and 150 °C, respectively (Figure 1). Insufficient data points were obtained at 120 °C and a loading of 0.2 due to sample cylinder failure. Previous thermal degradation studies (Thermals No. 7 and 8) indicated that the rate of loss of MDEA in MDEA/PZ blends may decline after the PZ is degraded, but this newly reported data suggests that at loadings in the range of 0.1 to 0.2 moles CO2/mole alkalinity, the MDEA loss rate in 7 m MDEA is equivalent to that reported in 7 m MDEA/2 m PZ. Figure 2 presents the data from degradation experiments with 7 m MDEA/2 m PZ and the new MDEA degradation data (large blue diamonds) from Thermal No. 10 at a loading of 0.2 moles CO2/mole alkalinity and 150 °C. The degradation rate in the MDEA alone experiment is similar to that for MDEA in the blend, suggesting that the MDEA degradation mechanism will occur independently of the presence or concentration of PZ.

The compounds dimethylaminoethanol (DMAE)(89.1), diethanolamine (DEA)(105.1), N,N-dimethyl ethanamine (DMEA)(73.1), dimethyl piperazine (114.2), and 1-(2-hydroxyethyl)-4-methylpiperazine (HMP)(144.2) were all tentatively identified in degraded 7 m MDEA samples through a combination of IC standard comparison and quantification of mass using MS. We have made tentative identification of triethalolamine (TEA)(149.1) in a sample degraded at 150 °C for 21 days with MS, which corroborates the findings of Dow Chemical (Bedell, 2009); Bedell reports that at 180 °C, MDEA disproportionates to form TEA and DMEA. In degraded samples of 7 m MDEA, we have observed both TEA and DMEA. Note that Bedell reports that disproportionation of MDEA does not occur when the same experiment is conducted with nitrogen in the sample container headspace.

Other key peaks appearing in degraded MDEA samples include those appearing after MDEA in IC-MS analysis with masses of 133.1, 103.1, 176.1, 144.1, and 146.1. The peak with mass of 103.1 is possibly 2-(ethylmethylamino)ethanol; a standard for this peak has been ordered and will be analyzed using cation chromatography to compare peak retention times. Compound identification was compared to the work of Chakma & Meisen (1997), Dawodu & Meisen (1996), and Bedell. In that body of literature, the presence of DEA, DMAE, and TEA were reported in degraded MDEA samples.

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Degradation mechanisms have been postulated for 7 m MDEA/2 m PZ that are consistent with degradation mechanisms in MDEA alone. A possible mechanism postulated by Dr. Grant Willson is the protonation of MDEA which then reacts with a strong base (PZ or other amine) at the nitrogen to form a product (methyl-PZ or hydroxyethyl PZ) plus DEA (Figure 3). One other mechanism that has been suggested (Rochelle) is the initial formation of DEA-carbamate upon loading either the blend or MDEA alone, which can then polymerize to form the oxazolidone ring (similar to MEA mechanism). This step is followed by PZ attacking the carbon adjacent to the ester-oxygen and the formation of a PZ compound with a molecular weight of 187.1 which has been observed in IC-MS analyses of degraded blended solvent samples. This same mechanism could also result in a compound with a mass of 173.1 which has also been observed. In the absence of PZ, this mechanism may lead to the formation of a polymeric diamine with a mass of 206.1 (Figure 4). This mass was observed in at least two degraded MDEA samples (Bombs No. 41 and 44) at a retention time of approximately 35 minutes which is consistent with the appearance of other diamines using the current IC-MS elution program. This latter mechanism also supports the finding that PZ plays little or no role in the degradation of MDEA.

Figure 1: Thermal No. 10, 7 m MDEA/2 m PZ, 120, 135, and 150 °C, α = 0.1 and 0.2

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Table 1: MDEA and PZ Thermal Degradation Rates

Solvent Temp (°C)

Duration (Days)

MDEA Deg Rate (mmolal/day)

PZ Deg Rate (mmolal/day)

Diamine Appearance Rate (mmolal/day)

α = 0.1 α = 0.2 α = 0.1 α = 0.2 α = 0.1 α = 0.2

7 m MDEA 100 63 6 ± 6 18 ± 52 NA NA NA NA

120 63 0.3 ± 11 31 ± 16 NA NA NA NA

7 m MDEA 120 45 39 NA NA NA NA NA

7 m MDEA 135 69 55 53 NA NA NA NA

7 m MDEA 150 69 66 112 NA NA NA NA

7 m MDEA/2 m PZ

100 54 3 ± 13 19 ± 4 2 ± 4 6 ± 1 1 ± 2 2 ± 2

120 54 11 ± 11 7 ± 20 7 ± 3 9 ± 5 2 ± 2 5 ± 2

7 m MDEA/2 m PZ w/ 1 mM Fe2+

100 42 NA 3 ± 13 NA 2 ± 5 NA 2 ± 3

120 49 NA 18 ± 28 NA 11 ± 10 NA 12 ± 3

7 m MDEA/2 m PZ

135 42 9 ± 8 30 ± 15 31 ± 3 44 ± 2 20 ± 4 16 ± 6

7 m MDEA/2 m PZ*

150 28 8 ± 57 66 ± 21 79 ± 20 59 ± 25 NA NA

* Experimental loadings were 0.1 and 0.26.

Table 2: Thermal No. 10 MDEA Degradation Data (120 °C)

Temperature = 120 °C, Loading = 0.1 Time (Days) Bomb No.

MDEA Pk Area

MDEA Conc. (m)

0 0.812 7.003 32 0.802 6.427 1 0.677 4.74

21 30 0.784 5.7735 31 0.89 6.2545 33 0.737 5.27


MDEA Pk Area

MDEA Conc. (m)

0 0.899 7.003 38 0.78 5.647 136 0.774 5.46

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Table 3: Thermal No. 10 MDEA Degradation Data (135 °C) Temperature = 135 °C, Loading = 0.1

Time (Days) Bomb No.

MDEA Pk Area

MDEA Conc. (m)

0 0.812 7.003 4 0.769 6.177 34 0.737 5.38

21 49 0.757 5.7135 57 0.705 5.46


MDEA Pk Area

MDEA Conc. (m)

0 0.899 7.003 53 0.796 5.757 55 0.606 3.927 50 0.573 4.03

21 58 0.697 5.0835 46 0.632 4.1445 50 0.616 4.4569 48 0.607 4.85

Table 4: Thermal No. 10 MDEA Degradation Data (150 °C) Temperature = 150 °C, Loading = 0.1

Time (Days) Bomb No.

MDEA Pk Area

MDEA Conc. (m)

0 0.812 7.003 68 0.757 5.853 47 0.775 5.88

21 61 0.703 5.6335 65 0.689 6.0869 132 0.231 1.85


MDEA Pk Area

MDEA Conc. (m)

0 0.899 7.003 59 0.74 5.417 41 0.681 4.46

21 44 1.671 11.6935 69 0.171 1.0969 70 0.066 0.52

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Figure 2: 7 m MDEA Degradation – Comparison to 7 m MDEA/2 m PZ

MDEA/PZ Degradation Protonation Pathway

MDEA MDEAH+

DEA

CO2

protonation

polymerization

+H2O

DEA-carb

+

Methyl-PZ

PZ

Figure 3: MDEA Protonation Pathway

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Degradation Pathway – Formation of DEA-Carbfrom MDEA

+

MDEA

CO2

polymerization

+ H2O

DEA‐carbamate

+ CO2

+

MDEA

Figure 4: DEA-Carbamate Formation from MDEA

Cycling/Integrated Solvent Degradation Apparatus During the second quarter of 2009, we continued the construction of the Integrated Solvent Cycling/Degradation Apparatus (Figure 5). The system consists of an oxidative reactor which is a hybrid of the Rochelle group low gas flow reactor; the reactor includes a liquid hold-up section below the stirred section to allow entrained air bubbles to migrate into the stirred section above, and to mimic hold-up in an absorber unit. At a liquid flow rate of 100 ml/min, the residence time in the hold-up section is approximately three minutes. Amines are pumped through the system by a Cole-Parmer positive displacement pump at a nominal rate of 100 to 150 ml/min to an off-the-shelf heat exchanger manufactured by Exergy operated in a countercurrent flow pattern with hot amine from the thermal reactor, and into two oil bath preheaters to gain heat, and into a jacketed thermal reactor to achieve a temperature of 120+ °C. Hot amines are returned to the heat exchanger to give up heat to the amine pumped from the oxidative reactor (countercurrent flow) and then pumped to an additional heat exchanger (Exergy) for trim cooling purposes. The trim cooler is provided with tap water on the outer tube side which exits the heat exchanger to the fume hood drain. Finally, the cooled amine passes through a back-pressure valve used to control pressure and prevent flashing of solvent components (i.e., water, CO2, amine) in the heated section of the system.

To date, the system has been operated to meet the basic performance goals of 55 °C in the oxidative reactor and 120+ °C in the thermal reactor, simultaneously, when loaded with water. A larger heat exchanger was acquired from Exergy during June after testing of the system indicated that insufficient heat transfer was taking place in the existing heat exchanger. The heat exchanger design (tube-in-tube) is the same for the small and large heat exchangers, but the

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length and overall heat transfer area is 4 times greater for the larger heat exchanger. The smaller heat exchanger is currently being used as the trim cooler.

A solvent consisting of MDEA and PZ in excess of 7 molal MDEA and 2 molal PZ with a loading of approximately 0.1 moles CO2/mole alkalinity was charged into the system. The system reached a stable operating condition whereby the oxidative reactor temperature was approximately 50 °C and the thermal reactor temperature was 114 °C simultaneously. The temperature at the inlet to the first preheater was 78 °C, providing a heat exchanger hot-side temperature approach of >35 °C. Two heat exchanger flow path configurations have been attempted: (1) the hot liquid leaving the thermal reactor is passed through the outer tube of the cross exchanger, and (2) the hot liquid leaving the thermal reactor is passed through the inner tube of the cross exchanger. The latter configuration was recommended by the manufacturer as being more efficient and is the present configuration for the system. System performance is similar for both configurations.

The installation of an additional bimetal analog thermometer in-line after the trim cooler yielded temperature measurements of approximately 30 °C, whereas a thermometer placed in the oxidative reactor read 55+ °C, suggesting that the in-line bi-metal analog thermometers purchased from Arthur Fluids do not give accurate readings of bulk fluid temperature. The critical and immediate task for system troubleshooting entails the installation of K-type bi-metal thermocouples/thermowells for in-line temperature measurement on an electronic readout device for measurement of the fluid temperature at the outlet of the thermal reactor; it is currently believed that the temperature of the fluid leaving the thermal reactor is above 125 °C when cycling amines. We will acquire a minimum of two accurate temperature measurement thermowells for this purpose in the immediate term to confirm that our stated temperature goals of 55 °C in the oxidative reactor and 120+ °C in the thermal reactor are being simultaneously met.

Finally, an important finding of the shake-out tests has been that for 7 m MDEA/2 m PZ loaded to approximately 0.1 moles CO2/mole alkalinity, the hot section of the cycling process will need to be maintained at a minimum pressure of 78 psig to prevent flashing. Initial degradation studies will likely focus on 7 m MEA at lean loadings (<0.3 moles CO2/mole alkalinity). The total operating pressure of this amine is unknown at this time.

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Heat XOxidative Reactor

PreHeaterNo.1

PreHeaterNo.2

ThermalReactor

Cooling water

To sink

Trim Cooler

Reactor Hold‐up

55 °C

T2

T1

Mixer

Cycling/Integrated Degradation Apparatus

Temperature Performance (MDEA/PZ):T1 – 89 °CT2 – 109 °C (*)* With water, T2 ~ 125 °C

P1

Positive Displacement Pump

Figure 5: Integrated Solvent Degradation/Cycling Apparatus

Conclusions The latest experimental work indicates that : (1) MDEA rates of 66 and 112 mmolal/day were calculated at 150 °C and loadings of 0.1 and 0.2 moles CO2/mole alkalinity, respectively; (2) degradation rates for degraded 7 m MDEA are similar to those for MDEA in 7 m MDEA/2 m PZ, suggesting that PZ plays a secondary role in the overall degradation process in 7 m MDEA/2 m PZ; (3) the activation energy for MDEA degradation was kJ/gmol assuming an Arrhenius relationship; and (4) the construction of the integrated solvent cycling/degradation apparatus is complete, and we are currently completing shake-out testing including installation of improved temperature sensing equipment.

Future Work Future work will focus on achieving accurate measurement of bulk liquid temperatures for the integrated degradation/solvent cycling apparatus through installation of K-type bi-metal thermowells/thermocouples. A thermowell will be installed at the outlet of the thermal reactor as this fitting will accommodate the passage of a probe into the reactor. It is currently believed that the system achieves the basic goals of 55 °C in the oxidative reactor and 120+ °C at the outlet of the thermal reactor. Confirmation of these temperatures is essential to the operation of the system. Once final shake-out is complete, each experiment will be conducted for a period of up

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to three weeks, requiring some level of automation. Automation of the system will be achieved through the installation and integration of temperature probes and a pressure gauge with PicoLog software for continuous capture of data. Planned experiments for the integrated degradation system include 7 m MEA and 7 m MDEA/2 m PZ.

During the third quarter 2009, we will initiate a new acid treatment experiment similar to the H2SO4 experiment stopped in the second quarter due to sample cylinder failure. This experiment will be conducted with 7 m MDEA/2 m PZ. Based on a recommendation by Dr. Willson, this experiment will utilize toluene sulfonic acid to neutralize 10% of solvent alkalinity. We will also use new sample cylinders to eliminate failures in the experiment.

Experimental work with HPLC, IC, and IC-MS will continue in order to identify products in degraded 7 m MDEA and 7 m MDEA/2 m PZ. We will continue to use our knowledge of the degradation products to derive mechanisms for the degradation processes that take place in these solvents. In particular, we will make an effort to understand whether disproportionation, protonation, and/or the formation of carbamates are the leading mechanisms for MDEA loss. Once the MDEA degradation process is understood, we will use this knowledge to determine the role of PZ in the degradation of 7 m MDEA/2 m PZ. As in the second quarter, future efforts will include confirming the presence of the DEA-based urea compound with HPLC. Analytical methods developed for the batch thermal and oxidative degradation experiments over the last year will be utilized to analyze samples collected from the cycling apparatus.

References Bedell S. "Autoxidation of methyldiethanolamine - Lab studies and implications for flue gas

carbon capture". Dow Chemical Company, Trondheim CCS Conference, June 17, 2009.

Chakma A, Meisen A. "Methyl-Diethanolamine Degradation - Mechanism and Kinetics". Can J Chem Eng. 1997:75.

Dawodu OF, Meisen A. "Degradation of Alkanolamine Blends by Carbon Dioxide". Can J Chem Eng. 1996:74.

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Oxidation and Thermal Degradation of Concentrated Piperazine


by Stephanie A. Freeman


and the




July 1, 2009

Abstract The Cation IC-Mass Spectrometer was used to identify numerous masses of unidentified degradation products in 8 m PZ thermal degradation experiments. Although not yet positively identified, possible candidates include N-formyl PZ, N-(hydroxymethyl) PZ, 1-methyl PZ, and others. Further work is needed to provide positive identification.

Quantification of heavy metals in solution has been used to analyze the corrosion of 316 stainless steel. Initial results show that thermally degraded PZ solutions contain less metal than 7 m MEA solutions. After 18 weeks at 150 °C, the iron and nickel concentrations in an 8 m PZ solution were 0.9 and 0.7 mM, respectively. For a 7 m MEA experiment at 135 °C, the final concentrations of iron and nickel were 13.7 and 4.2 mM, respectively, after four weeks. Further work is needed to understand if the amines themselves or their degradation products are responsible for this corrosion.

Concentrated, aqueous PZ solutions oxidized 3 to 5 times slower than 7 m MEA in systems with iron, copper, or stainless steel metals (chromium, nickel, and iron). The thermal degradation rate in concentrated PZ is 23 to 70 times less than 7 m MEA.

Introduction Concentrated aqueous piperazine (PZ) is being investigated as a possible alternative to 30 wt % (or 7 m) MEA in absorber/stripper systems to remove CO2 from coal-fired power plant flue gas. Aqueous PZ has been given a proprietary name of ROC20 for 10 m PZ and ROC16 for 8 m PZ. Previous reports include the proprietary name, while the concentration of PZ will be explicitly used in this document.

Preliminary investigations of PZ have shown numerous advantages over 7 m MEA systems (Freeman, 2008a). PZ solutions have less oxidative and thermal degradation, as previously shown at concentrations of 5 and 8 m PZ (see previous quarterly reports). The kinetics of CO2 absorption are faster in concentrated PZ (Cullinane and Rochelle, 2006; Dugas, 2008). The capacity of concentrated PZ is greater than that of MEA while the heat of absorption and volatilities are comparable (Freeman, 2008).

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This quarter was focused on completing the blank oxidation experiments started last quarter. The oxidation experiments were conducted in an attempt to quantify the volatility of PZ and other mechanical issues with the low gas flow apparatus. Work began in earnest this quarter on quantifying metals in solution and using the mass spectrometer to identify degradation products. A long-term thermal degradation experiment was planned and begun.

Analytical Methods

Total Inorganic Carbon Analysis (TIC) Quantification of CO2 loading was performed using a total inorganic carbon analyzer. In this method, a sample is acidified with 30 wt % H3PO4 to release the CO2 present in solution (Hilliard, 2008). The CO2 is carried in the nitrogen carrier gas stream to the detector. PicoLog software was used to record the peaks produced from each sample. A calibration curve was prepared at the end of each analysis using a TIC standard mixture of K2CO3 and KHCO3. The TIC method quantifies the CO2, CO3

-2, and HCO3- present in solution. These species are in

equilibrium in the series of reactions shown below.

CO32−

+ 2H+ ↔ HCO3− + H+ ↔ H2CO3 ↔ CO2 + H2O

Acidification of the sample shifts the equilibrium toward CO2 which bubbles out of solution and is detected in the analyzer.

Acid pH Titration Titration with 0.2 N H2SO4 was used to determine the concentration of amines in experimental samples. The automated Titrando apparatus (Metrohm AG, Herisau, Switzerland) was used for this method. A known mass of sample was diluted with water and the autotitration method was then used. The Titrando titrates the sample with acid while monitoring the pH. The equivalence points are recorded. The equivalence point around a pH of 3.9 corresponds to basic amine species in solution (Hilliard, 2008). The test is not sensitive to the type of amine, so if PZ has degraded to ethylenediamine (EDA), the titration test will detect the sum of contributions from the species.

Anion IC The anion IC was used to determine the concentration of glycolate, acetate, formate, chloride, nitrite, sulfate, oxalate, and nitrate in experimental samples. A Dionex ICS-3000 instrument with AS15 IonPac column, 4-mm Anionic Self-Regenerating Suppressor (ASRS), carbonate removal device (CRD), and carbonate removal from eluent generation was used as previously described by Andrew Sexton using a linear KOH eluent concentration (Sexton, 2008). No major modifications have been made to the method in this quarter.

Cation IC The cation IC was used to determine the concentration of PZ and ethylenediamine (EDA) in experimental samples. A Dionex ICS-2500 instrument with CS17 IonPac column with 4-mm Cationic Self-Regenerating Suppressor (CSRS) was used as previously described by Andrew Sexton with a linear increase of methanesulfonic acid (MSA) concentration in the eluent (Sexton, 2008). No major modifications have been made to the method in this quarter.

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NaOH Treatment for Amides An analytical test for the formation of amides was developed by Andrew Sexton and has been included in the results shown here. Experimental samples were treated with 5 N NaOH (in equal gravimetric amounts) and allowed to sit overnight. The anion IC analytical method was then used to quantify increases in the concentrations of analytes as compared to the original samples (Sexton, 2008). In most cases, the main increases were in the production of formate and oxalate following NaOH treatment.

The addition of strong base reverses the amide formation reaction that has occurred during the experiment. As an example, the formation of N-formylPZ is shown in Eqn. 1 below:

The addition of NaOH hydrolyzes the bond between the amine group and the carbon of the formyl group to reverse the reaction. In this way, the free formate created from reversing this reaction can be used to identify the formate bound as N-formylPZ. The same process can be used to identify the oxalate amine of PZ.

Atomic Absorption Spectrophotometry (AA) The concentration of individual heavy metals in solution has been measured using a Perkin Elmer 1100B flame atomic absorption spectrophotometer (AA). The AA uses a lamp producing a specific wavelength of light for each metal of interest. The AA also uses a flame fueled by acetylene with air added at either a rich or lean ratio. For iron and nickel, a lean (blue) flame is used that requires 2.5 L/min of acetylene with 8 L/min of air. Chromium requires a rich (yellow) flame with a higher acetylene to air ratio. Samples analyzed for chromium also require the addition of 2% ammonium chloride (NH4Cl) to suppress interference from iron and nickel. The AA atomizes the liquid sample and mixes it with fuel and air that is then sent to the flame. In the flame, the elements of interest are reduced to unexcited atoms which absorb the light at the characteristic wavelength of that metal. The absorbance of light is measured on the opposite end of the lamp to determine the amount of light absorbed by the analyte of interest. This is correlated to concentration using a set of known standards and a third order polynomial calibration curve.

Cation IC – Mass Spectrometry (MS) Mass spectrometry (MS) with cation IC is used to help identify unknown peaks on cation IC chromatography or other unknown cations in solution. A thermal TSQ MS is attached to a Dionex ICS-2500 IC with an IonPac CS17 analytical column (4 x 250 mm), IonPac CG17 guard column (4 x 50 mm), Dionex AS-25 autosampler, and 4-mm CSRS is used as described above. The main modification is that the suppressor does not run in regeneration mode, the outlet analyte stream is sent to the MS and an additional water stream is added into the suppressor.

After separation on the cation IC column, the sample stream enters the MS, which uses electrospray ionization (ESI) to ionize the molecules in solution. The ions then enter the mass

+

→

(1)

(PZ) (Formate) (N-formylPZ)

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4

analyzer of the MS which sorts ions based on their mass to charge ratio (M/Z) using an electric field. The standard conditions used are a range of 50 to 300 M/Z. There is a 2.2 minute delay in the resolution of the cation IC chromatogram and the MS spectrum that is taken into account while analyzing data.

Results

Oxidative Degradation Continuing work on the oxidation of PZ has been completed this quarter. Samples from OE5, OE5B, OE6 and OE6B were reanalyzed this quarter due to unexpected trends in the data. The revised data is presented. Also, most of this quarter focused on trying to understand the limitations of the low gas flow apparatus through four blank experiments. Finally, all the past PZ oxidation data was reanalyzed in preparation for the 5th Trondheim Conference on Carbon Capture and Sequestration (TCCCS) in June.

Reanalysis of OE5, OE5B, OE6, and OE6B Samples

Samples from OE5, OE5B, OE6, and OE6B were reanalyzed this quarter because of some discrepancies in the trends of the initial data.

For OE5/OE5B, the concentration profiles reported in the 4th quarter of 2008, shown below in Figure 1, had some unusual results (Rochelle, 2009b). The main issues were in the PZ, post NaOH PZ, and ethylenediamine (EDA) profiles. All the OE5 and OE5B samples were re-run on the cation IC to reevaluate the PZ and EDA concentrations before and after NaOH treatment.

The new concentration profiles are shown in Figure 2 below, with similar axes ranges for better comparison. The three profiles mentioned demonstrate the improved analysis. No EDA was found in OE5B, which matched the lack of EDA in OE5. It was suspect that the initial analysis shown EDA in the final set of samples, suggesting that the removal of the solution from reactor and the foaming test initiated the creation of EDA. The PZ concentration profile is improved in terms of trends but no shows a larger discrepancy at 70 hours, the point in between the two experiments. Finally, the post NaOH PZ profile is slightly improved. The original data showed an unexplained dip in the concentration during OE5, which is now removed. The anion IC was not rerun as the profiles for formate, oxalate, acetate, and nitrite were relatively clear in their trends.

For OE6/OE6B, the concentration profiles reported in the 4th quarter of 2008, shown below in Figure 3, also had some unusual results (Rochelle, 2009b). The main issues were in the post NaOH formate, PZ, and post NaOH PZ profiles. All the OE6 and OE6B samples were re-run on the cation IC to reevaluate the PZ concentrations before and after NaOH treatment. The formate results were reported incorrectly, so a re-analysis on the anion IC was not necessary.

The original reporting of the concentration profiles is shown in Figure 3 with the new, updated profiles shown in Figure 4.

The post NaOH formate was reported incorrectly in the 4th quarter of 2008. A miscalculation made the formate appear to reach upwards of 15 mM while the actual concentration is closer to 3.0 mM. The profiles for PZ and post NaOH PZ improved with the new analysis. For PZ, the concentrations for OE6B (after 70 hrs) are more smooth while for post NaOH PZ the concentrations for OE6 (before 70 hrs) show a more reasonable curve.

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Figure 1: Concentration Profiles for OE5 and OE5B (8 m PZ, 1 mM Fe2+, α=0.3, 55°C)

reported Q4 2008. OE5 ended and OE5B started at 70 hours.

Figure 2: Reanalyzed Concentration Profiles for OE5 and OE5B (8 m PZ, 1 mM Fe2+,

α=0.3, 55°C). OE5 ended and OE5B started at 70 hours.

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Figure 3: Concentration Profiles for OE6 and OE6B (8 m PZ, 1 mM Fe2+, 100 mM Inhibitor A, α=0.3, 55°C) reported Q4 2008. OE5 ended and OE5B started at 70 hours.

Figure 4: Reanalyzed Concentration Profiles for OE6 and OE6B (8 m PZ, 1 mM Fe2+, 100

mM Inhibitor A, α=0.3, 55°C). OE5 ended and OE5B started at 70 hours.

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Limitations of the Low Gas Flow Apparatus Previous experiments with PZ in the system showed losses of PZ in the range of 10 to 20% while the production of measureable degradation products is very low. This apparent disconnect between PZ loss and the production of products is what is currently being investigated.

Last quarter, the first experiment in a set of four was reported. This quarter, the remaining three experiments were performed and the results of all are reported here. The first two, OE9 and OE10, looked at PZ degradation in the presence of a N2/CO2 gas stream without any catalysts or additives. The second two, OE11 and OE12, looked at PZ loss in the presence of an O2/CO2 gas stream without catalysts or additives.

Samples obtained were analyzed for PZ concentration (sulfuric acid titration), CO2 concentration (TIC), and degradation products (anion and cation IC). The results from PZ titration (orange line with filled circles) and IC analysis for each experiment are shown in Figures 5 through 8.

Figure 5: Concentration Profiles for OE9 (8 m PZ, 55 °C, a=0.3, 100 mL/min N2/CO2)

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Figure 6: Concentration Profiles for OE10 (8 m PZ, 55 °C, a=0.3, 100 mL/min N2/CO2)

Figure 7: Concentration Profiles for OE11 (8 m PZ, 55 °C, a=0.3, 100 mL/min O2/CO2)

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Figure 8: Concentration Profiles for OE12 (8 m PZ, 55 °C, a=0.3, 100 mL/min O2/CO2)

Overall, very few degradation products were formed in any experiment while the PZ concentration decreased. There are points that appear erroneous in each graph. For OE9 (Figure 5), the apparent spike in EDA concentration at a time of 150 hours does not seem correct. The post-NaOH treatment sample did not display this increase in EDA concentration and this is assumed to be an artifact of something that went wrong in the cation IC at that point.

For OE10 (Figure 6), there is a definite increase in the concentration of PZ and the detected degradation products. This increase was caused by the plug in the rubber stopper on the top of the reactor being left off after sampling at 150 hours. This open hole allowed a large amount of water to evaporate, concentrating all the species in solution. For further analysis, the experiment will be cut off at 250 hours when compared to OE9.

For OE11 (Figure 7) and OE12 (Figure 8), the curves have unexpected increases in the total formate concentration but these issues are due to the inaccuracy of the experimental method and will not be adjusted.

Last quarter, a method for analyzing the data in terms of loss from the vapor phase was introduced (Rochelle, 2009a). In this method, the known partial pressure of water and PZ at 55 °C is used along with the known PZ and CO2 concentrations to estimate the loss of mass from the reactor during the experiment. A mass balance was created for each sampling point based on knowing the initial total mass in the reactor, the sampling mass, the mass of water added each time, and the final total mass of the reactor. The estimated mass lost from the reactor is shown for each of the four oxidation experiments in Figures 9 and 10.

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Figure 9: Estimated Total Mass Lost During OE9 and OE10 (8 m PZ, 55 °C, a=0.3, 100

mL/min N2/CO2)

Figure 10: Estimated Total Mass Lost During OE11 and OE12 (8 m PZ, 55 °C, a=0.3, 100

mL/min O2/CO2)

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The estimated mass lost from each reactor appears to be a very inconsistent and diverse calculation. There are a few assumptions in the estimate that may have led the values towards an inconsistent answer. The large loss of water from the reactor during OE10 at 250 hours is captured, however, in an estimated loss of 75 g. Although the data points are scattered, the overall average of their values may still prove useful. To get an average value, the mass loss recorded at each sampling data point was divided by the hours between it and the previous point. This was done to normalize the time in between sampling and these values were the hourly loss of mass in grams per hour. Then, and average and standard deviation was calculated and reported in Table 1 as the estimated mass loss.

To compare this value with the other set of data that is known, the water balance loss was calculated as well. The water balance loss assesses the differences in the overall mass balance of the reactor by knowing the initial mass, how much water was added at each sampling point and the final mass. This value gives an estimate of how much mass loss there is hourly in the low gas flow apparatus operating at 55 °C. These values are also shown in Table 1.

Table 1: Comparison of Mass Losses in OE9 through OE12

Expt Estimated Mass Loss

(g/hr)

Water Balance Loss

(g/hr)

OE9 0.32 ± 0.16 0.30

OE10 0.39 ± 0.54 0.42

OE11 0.23 ± 0.12 0.31

OE12 0.30 ± 0.13 0.32

The estimation procedure predicted losses ranging from 0.23 to 0.39 g/hr with a standard deviation up to 0.53 gram per hour. The data for OE10 clearly reflects the error in the rubber stopper that allowed a large amount of water to evaporate at the end of the experiment. Excluding this experiment, the range of 0.23 to 0.32 gram per hour is seen in the remaining three experiments. The water balance loss values are in better agreement, with between 0.30 and 0.32 gram per hour expected, excluding OE10. Although this data does not help explain the losses of PZ seen in these experiments, it does indicate the proper amount of water to be added to the reactors to replace the water that evaporates. Before, a rule of thumb of 15 grams every two days was used without any specific data to back this up. It was close, as these new values indicate 14.4 grams of water every 48 hours is the best estimate to maintain the level in the reactor.

A secondary goal of these four experiments was to determine the repeatability of a low gas flow experiment. As OE10 and OE12 were repeats of the same experiment as OE9 and OE11, a comparison of the results provides an idea of how repeatable the low gas flow procedure is. The concentration profile of PZ and the main degradation products (formate, total formate, and EDA) for OE9 and OE10 is shown in Figure 11. The same is shown for OE11 and OE12 in Figure 12.

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Figure 11: Comparison of OE9 and OE10 Concentration Profiles (Closed Points = OE9,

Open Points = OE10)

Figure 12: Comparison of OE11 and OE12 Concentration Profiles (Closed Points = OE11,

Open Points = OE12)

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Although the repeatability of the low gas flow experiment is only average based on the concentration profile, it is not unexpected given the limitations of the apparatus. The concentration of degradation products is so low that most of the results are within the detection limit of the equipment being used, generally understood to be near 5 mM for our samples. The production of EDA in the four experiments is suspect. In OE9 there is very little detectable EDA while none was found in OE12. OE10 had a large spike of EDA in the middle of the experiment while OE11 showed a steady production of EDA during the experiment. The detection of EDA using the cation IC is difficult for PZ-based experiments because a high dilution (10000X) is needed to reduce the PZ peak to reasonable levels. This dilutes the EDA concentration making detection difficult. EDA and PZ have nearly identical response factors on the cation IC, but the small concentration makes EDA more problematic. Also, there is a bump in the baseline exactly where EDA elutes, adding to the difficulty.

The PZ concentration, on the other hand, should be easily measured in the Cation IC and the scattered results indicate the propagation of error in the low gas flow experiment that is not well understood. There are numerous opportunities for error while running the experiment and a few while analyzing the samples. These results show that more care is needed in operating the reactors so that large errors are not introduced due to human error or water balance issues.

Reanalysis of Past Oxidative Degradation Data In preparation for the TCCCS in Trondheim, Norway, all of the PZ oxidation data collected so far was revisited. The presentation for the conference focused on comparing oxidation of PZ with MEA, and the effect of Inhibitor A on oxidation. All of the following figures have the same axes, the percent of the initial amine against the time in hours. All of the scales in the figures are the same to allow direct comparison. The improvement in each case is demonstrated going from the red data series to the blue data series on each figure.

The difference in the metal-catalyzed oxidation of PZ and MEA is demonstrated in the following three figures. In each, an 8 to 10 m PZ solution is compared to a 7 m MEA solution with the sample metal catalyst. All experiments were performed using the low gas flow apparatus at 55 °C with agitation at 1400 rpm.

The effect of iron, copper, and stainless steel metals are shown in Figures 13, 14, and 15, respectively, below.

In all three cases, the oxidation of PZ is less than that of a comparable 7 m MEA experiment. This improvement results in approximately 3 to 5 times less amine loss. The effect is especially pronounced in the case of stainless steel metals where there is essentially no loss of PZ up until 275 hours.

Inhibitor A has been shown to effectively reduce the oxidation of MEA in the presence of metals such as iron and copper (Goff, 2005; Sexton, 2008). The effect of Inhibitor A on iron-catalyzed degradation of PZ is shown in Figure 16 below. The effect of Inhibitor A on copper-catalyzed degradation of PZ is shown in Figure 17.

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Figure 13: Loss of Amine for Iron-Catalyzed Degradation for PZ and MEA

Figure 14: Loss of Amine for Copper-Catalyzed Degradation for PZ and MEA

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Figure 15: Loss of Amine for Stainless Steel-Catalyzed Degradation for PZ and MEA

Figure 16: Loss of PZ for Iron-Catalyzed Degradation with and without Inhibitor A

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Figure 17: Loss of PZ for Copper-Catalyzed Degradation with and without Inhibitor A

Thermal Degradation Thermal degradation data for concentrated PZ was also reanalyzed. A new comparison was created to illustrate the difference in degradation rates of PZ and MEA. This new comparison is shown in Figure 18. In this figure, the amine loss rate as a percent per week is plotted against the inverse temperature in Kelvin. The labels of degrees Celsius were added as a reference for the reader.

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Figure 18: Comparison of Thermal Degradation Rates for Concentrated PZ and 7 m MEA

Solutions. In this comparison, the logarithmic rate of thermal degradation is linearly related to the inverse temperature, indicating an Arrhenius relationship between temperature and amine loss. Using an Arrhenius relationship (Equation 2), the activation energy can be calculated from the slope of each linear trendline. In Equation 2, the rate is represented by the rate constant k which is directly proportional to the pre-exponential factor, A. the rate is also directly proportional to the quantity of the exponent of the activation energy, EA, divided by the universal gas constant, R, and the temperature, T. Taking the natural logarithm of each side gives a form directly related to the trendlines in Figure 18 (Equation 3). The activation energies shown in Figure 18 were calculated in this way.

(2)

(3)

The activation energy of concentrated PZ and MEA is nearly the same, both around 130 kJ per mole. From the data at 135 and 150 °C, the degradation rate of concentrated PZ is 23 to 70 times less than that of 7 m MEA.

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Additional thermal degradation data from screening work of Davis and new data for 8 m EDA from Shan Zhou were also analyzed (Davis, 2009; Rochelle, 2009a). The thermal degradation rates for 7 m (AMP), 7 m diglycolamine (DGA), 7 m hydroxyethylpiperazine (HEP), 7 m methyldiethanolamine (MDEA)/2 m PZ, 8 m EDA, and 7 m diethylenetriamine (DETA) were compared to the PZ and MEA data in Figure 19. These experiments were all performed at 135 °C and a loading of 0.4 mole CO2 per mole alkalinity. As Davis found, DETA has a faster thermal degradation rate than 7 m MEA while the other amines fall between PZ and MEA in terms of degradation. The additional EDA data for 100 and 120 °C from Zhou show that the thermal degradation of EDA is similar to MEA systems, although the activation energy is slightly lower at 97 kJ per mole.

Figure 19: Comparison of Thermal Degradation Rates for Screened Amines Compared to

Concentrated PZ and 7 m MEA Solutions

Trace Metals Analysis Analysis for the concentration of trace metals in solution using AA was started this quarter. This work investigates the leaching of metals into solution in thermal degradation experiments as a way to quantify and understand corrosion in our stainless steel cylinders. Iron, nickel, and chromium will be the target species first as they are the primary components of stainless steel. The first samples analyzed were TE9, a thermal degradation experiment at 150 °C for 18 weeks. The iron and nickel concentrations were measured and are shown in Figure 20 below. Iron is quickly dissolved into solution, with the first sample showing a concentration only slightly lower than that at 18 weeks. The iron concentration overall does not increase significantly, remaining around 0.9 to 1.0 mM. Nickel, on the other hand, appears to increase slightly throughout the experiment, reaching about 0.7 mM.

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Figure 20: Iron and Nickel Concentration for TE9

Alex Voice measured the iron and nickel concentrations for one of Davis’ experiments on 7 m MEA (Davis, 2009). This experiment at 135 °C and a loading of 0.4 mole CO2 per mole alkalinity had an amine loss of 6.7%/week. The results of the metals concentration analysis are shown alongside the previously reported PZ data in Figure 21. The PZ loss for that experiment was 0.4%/week.

Figure 21: Comparison of Iron and Nickel Concentrations for PZ and MEA

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The concentration of iron and nickel is significantly higher for MEA than PZ and appear to be constantly increasing. The concentration did not reach a level value within the four weeks of the experiment. The reason for this difference is not yet clear. One explanation is that the MEA itself is more corrosive than PZ and causes higher metal concentrations in solution. Another possible explanation is that the MEA is degrading much faster than the PZ and the degradation products are responsible for initiating and continuing corrosion. Both the apparent corrosion rate (as read through the metals concentration directly) and the degradation rate are higher for MEA and the two effects cannot be separated at this point.

Mass Spectrometry (MS) for Identification of Unknown Compounds The MS-Cation IC was used this quarter to attempt to identify unknown degradation compounds. The cation IC was used to separate the samples to allow compounds to be separated before entering the MS. Three samples were run this quarter and the results analyzed.

First, the final sample from TE9 was analyzed. TE9 was a thermal degradation experiment with 8 m PZ solution at a loading of 0.3 mole CO2 per mole alkalinity at 150 °C for 18 weeks. This experiment lost 6.3% of the initial PZ, a loss of 0.4%/week. The results from the MS analysis are listed in Table 2. The retention time on the cation IC column is listed in the first column and the mass-to-charge ratio (M/Z) in the second column. When analyzing the MS data, any significant peak of a single mass was recorded, even if a peak on the cation IC did not appear. In this way, masses of molecules that may have been separated by the column but do not register with the conductivity meter because they are neutral are also seen. The third column lists any species that were positively identified, which is only PZ at this point. The final column lists proposed degradation products based solely on the predicted molecular weight from the M/Z ratio.

Table 2: MS Results for the Final Sample of TE9

Retention Time (min) M/Z Ratio Species

Identified Possible Species??

13.1 120.1 - MDEA

15.2 115.1 - N-formyl PZ, 2,5-piperazinone, N-ethyl PZ, dimethyl PZ

15.2 229.2 - - 30.5 87.1 PZ - 30.5 105.1 - - 30.5 187.1 - - 32.5 101.1 - N-methyl PZ, 2-piperazinone 32.5 117.0 - N-(Hydroxymethyl) PZ, N,N’-diformyl EDA

33.2 115.1 - N-formyl PZ, 2,5-piperazinone, N-ethyl PZ, N,N’-dimethyl PZ

33.2 113.1 - Triethylenediamine 33.2 117.1 - N-(Hydroxymethyl) PZ, N,N’-diformyl EDA 34.4 117.1 - N-(Hydroxymethyl) PZ, N,N’-diformyl EDA 34.6 130.1 - PZ-carbamate 35.6 199.1 - 1,1'-carbonylbis PZ 35.6 197.1 - - 35.6 200.1 - -

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37.8 197.1 - - 39.9 117.1 - N-(Hydroxymethyl) PZ

The results show a variety of different masses exist in this sample but few are identified yet. The MS-Cation IC chromatogram is shown in Figure 22. The bottom panel in the figure is the cation IC chromatogram, as would be expected from cation IC run alone. The top panel is the total MS signal that has been adjusted to match the position on the cation IC chromatogram.

The mass of 119.1 g/mol (M/Z of 120.1) at the beginning of the sample is most likely contamination of methyldiethanolamine (MDEA) from the other users of the Cation IC-MS machine. A mass of 116.1 (M/Z of 117.1) was found at multiple points on the chromatogram which was possibly an N-(Hydroxymethyl) PZ. This molecule has been previously proposed as a product of formaldehyde reacting with PZ and a precursor of the formation of N-formylPZ (Freeman, 2009). The remaining masses found have not been positively identified but appear to be PZ-based degradation products in very small amounts. Some are expected, although the mechanism is not clear, such as 1-methyl PZ, dimethyl PZ, N,N’-diformyl EDA, or 1,1’-carbonylbis PZ. Others, such as triethylenediamine, are not very likely, but are listed since the molecular mass matches. The chemical structures of the proposed degradation products are shown below in Figure 23 for reference.

Figure 22: Cation IC-MS Result for Final Sample of TE9; Top panel - Total MS Signal,

Bottom Panel - Cation IC Chromatogram

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22

or

N-formyl PZ

(114.1 g/mol)

2,5-piperazinone

(114.0 g/mol)

N-ethyl PZ (or 2-ethyl PZ)

(114.2 g/mol)

or

or

N,N’-dimethyl PZ (or 2,5-

dimethyl PZ)

(114.2 g/mol)

N-methyl PZ (or 2-methyl PZ)

(100.2 g/mol)

2-piperazinone

(100.0 g/mol)

N-(Hydroxymethyl) PZ

(116.1 g/mol)

N,N’-diformyl EDA

(116.0 g/mol)

Triethylenediamine

(112.2 g/mol)

PZ carbamate

(129.1 g/mol)

1,1'-carbonylbisPZ

(198.1 g/mol)

Figure 23: Chemical Structures of Possible Degradation Products

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The final samples of OE9 and OE10 were also analyzed by Cation IC-MS. These two experiments were low gas flow oxidation experiments using 8 m PZ with a loading of 0.3 mole per mole alkalinity at 55 °C for 14 days. The gas phase of the experiment was 98% N2/2% CO2 as the goal of the experiment was to assess the volatility losses of the apparatus under standard operating conditions. These two experiments were duplicates but yielded slightly different results for the liquid phase analysis, as shown in prior sections of this report.

The Cation IC-MS results are shown in Tables 3 and 4 and Figures 24 and 25 below. The OE9 sample contained eleven different masses but only PZ was positively identified. The early peaks of 61.1 and 119.1 g/mol (M/Z of 62.1 and 120.1) are most likely monoethanolamine (MEA) and MDEA, respectively, contamination from other users of the MS machine. OE10 had only two strong masses in the sample, one of which was clearly PZ. The other strong mass of 116.1 g/mol (M/Z of 117.1) may be N-(Hydroxymethyl) PZ as discussed for the TE9 sample.

Table 3: MS Results for the Final Sample of OE9



10.6 62.1 - Carbamic Acid, MEA 11.4 90.1 - - 13.2 120.1 - MDEA 15.6 134.1 - - 30.8 87.1 PZ - 30.8 105.1 - - 30.8 187.1 - - 32.6 101.1 - N-methyl PZ 32.6 99.1 - - 35.5 204.1 - - 35.5 202.1 - -

Table 4: MS Results for the Final Sample of OE10



30.6 87.1 PZ - 34.0 117.1 - N-(Hydroxymethyl) PZ

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Figure 24: Cation IC-MS Result for Final Sample of OE9; Top panel - Total MS Signal,


Figure 25: Cation IC-MS Result for Final Sample of OE10; Top panel - Total MS Signal,


Discussion The MS work started this quarter demonstrated the utility of the machine to elucidate the soup of degradation products present in heavily degraded PZ samples. Very few of the degradation products of PZ have been identified so far as this MS work has just begun. There is a lot of work to be done, especially with thermally degraded samples, to identify the major products. Unfortunately, despite the presence of a variety of masses detected by the MS, there are no or very few significant, unidentified peaks on the cation IC chromatogram. This leads to the assumption that either the PZ is not degraded and is being lost through physical means (entrainment, evaporation, volatility, water balance issues, etc) or it is degrading to neutral molecules. Either way, more work is needed to identify and quantify the degradation products of PZ.

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Conclusions The current low gas flow experiment is imprecise in terms of water balance and repeatability. The low gas flow was first proven on systems that heavily degrade whereas PZ may require an experimental apparatus with higher sophistication to allow the quantification of a system that oxidizes slowly, producing few degradation products.

The MS has already proved to be a useful tool for identifying unknown compounds. There are numerous masses identified in the thermal degradation of PZ that have yet to be positively identified. Possible candidates include N-formyl PZ, N-(hydroxymethyl) PZ, 1-methyl PZ, N,N’-diformyl EDA, and others. Further work is needed to provide positive identification.

Quantification of heavy metals in solution has been shown to be an important tool for analyzing corrosion at a basic level. Initial results on PZ show that thermally degraded PZ corrodes 316 stainless steel less than 7 m MEA. After 18 weeks at 150 °C, the iron and nickel concentrations in an 8 m PZ solution were 0.9 and 0.7 mM, respectively. For a 7 m MEA experiment at 135 °C, the final concentrations of iron and nickel were 13.7 and 4.2 mM, respectively, after four weeks. Further work is needed to understand if the amines themselves or their degradation products are responsible for this corrosion.

Concentrated, aqueous PZ solutions oxidized 3 to 5 times slower than 7 m MEA in systems with iron, copper, or stainless steel metals (chromium, nickel, and iron). The thermal degradation rate in concentrated PZ is 23 to 70 times less than 7 m MEA.

Future Work Thermal degradation of PZ has been analyzed with only a small number of experiments to date. Multiple long-term thermal degradation studies have been started this quarter and will finish next year. These experiments will be continuously monitored as samples are taken periodically. A summary of the experiments started this quarter is shown in Table 5. Already, there has been some failure of cylinders for various reasons, so the experiments may not finish exactly as planned. Next quarter, an update will be given as to the status of these experiments.

Table 5: Thermal Degradation Experiments Started this Quarter

Expt Solution Loading (mol CO2/mol alk)

Temperature (°C)

Duration (weeks)

Expected End Date

TE10 8 m PZ 0.3 135 72 Oct 2010 TE11 8 m PZ 0.4 135 72 Oct 2010 TE12 8 m PZ 0.3 175 12 Aug 2009 TE13 8 m PZ 0.4 175 12 Aug 2009 TE14 8 m PZ 0.3 150 30 Jan 2010 TE15 8 m PZ 0.4 150 30 Jan 2010

Additional thermal degradation experiments are planned to begin in the next two quarters. These experiments will look at the effect of 100 mM Inhibitor A and PZ concentration on PZ thermal degradation. The second experiment will look at 5 and 12 m PZ.

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Corrosion studies using the AA to measure the concentration of heavy metals in solution will continue next quarter. Samples from the MEA thermal degradation work of Davis are being analyzed for iron, nickel, and chromium (Davis, 2009). The samples from the PZ pilot plant campaign from last fall will be analyzed as well as other PZ thermal degradation samples. The goal of this work is to understand the corrosion characteristics of PZ in terms of temperature, loading, and amine concentration.

References Cullinane JT, Rochelle GT. "Kinetics of carbon dioxide absorption into aqueous potassium

carbonate and piperazine." Ind & Engr Chem Res. 2006;45(8):2531–2545. Davis J. Thermal Degradation of Aqueous Amines Used for Carbon Dioxide Capture. The

University of Texas at Austin. Ph.D. Dissertation. 2009. Dugas R. “Absorption and desorption rates of carbon dioxide with monoethanolamine and

piperazine”. GHGT-9. Washington, DC. 2008. Freeman SA. Degradation of Concentrated Piperazine Used for Carbon Dioxide Capture. The

University of Texas at Austin. Research Proposal. 2009. Freeman SA, Dugas R, Van Wagener D, Nguyen T, Rochelle GT. "Carbon dioxide capture with

concentrated, aqueous piperazine." IJGGC. Accepted for publication, 2008. Goff GS. Oxidative Degradation of Aqueous Monoethanolamine in CO2 Capture Processes: Iron

and Copper Catalysts, Inhibition, and O2 Mass Transfer. The University of Texas at Austin. Ph.D. Dissertation. 2005.

Hilliard MD. A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue Gas. The University of Texas at Austin. Ph.D. Dissertation. 2008.

Rochelle GT. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009." Luminant Carbon Management Program. The University of Texas at Austin. 2009a.

Rochelle GT. "CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report 2008." Luminant Carbon Management Program. The University of Texas at Austin. 2009b.

Sexton A. Amine Oxidation in CO2 Capture Processes. The University of Texas at Austin. Ph.D. Dissertation. 2008.

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1

Ethylenediamine as a Solvent for CO2 Capture

Quarterly Report for January 15 – June 30, 2009

by Shan Zhou


and the




July 7, 2009

Abstract Thermal and oxidative degradation products of 8 m EDA (Ethylenediamine) were measured by cation IC, anion IC, HPLC, and TIC. Foaming was measured in samples from oxidative degradation. Vapor liquid equilibrium and amine volatility of 8 m and 12 m EDA were measured using hot gas FTIR. CO2 solubility and absorption/desorption rates were measured in the wetted-wall column. Viscosity of 8 m and 12 m EDA was measured at different temperatures.

About 35% of the EDA was lost after 4 weeks at 135 oC. Only about 10% EDA degraded after 16 weeks at 100 oC. EDA urea was the most concentrated product from thermal degradation, representing about 20% of the degraded EDA.

10% of the EDA was oxidized after 168 hours with 1mM Fe2+ at 55 oC. With 100 mM inhibitor A the oxidation of EDA was insignificant. DETA (Diethylenetriamine) and formate were the most concentrated in the quantified oxidative degradation products of EDA.

The foaminess coefficient and foam break time of EDA are close to that of 7 m MEA, and much better than those of 8 m PZ.

Although EDA is volatile at low CO2 loading, 8 m and 12 m EDA have amine partial pressure comparable to 7 m MEA and 8 m PZ in expected lean loading. However, the CO2 flux normalized by partial pressure driving force of 12 m EDA solution was lower than that of 7 m MEA at the same conditions. VLE models for 12 m EDA were regressed with experiment data from the wetted-wall column and FTIR. CO2 capacity and enthalpy of CO2 absorption were calculated. The Habsorption of CO2 in EDA is similar to MEA at operating conditions, higher than most of the other amine systems.

Introduction Ethylenediamine (EDA) is a primary diamine, which is reported to react rapidly with CO2 at low CO2 loading. Table 1 summarizes measurements in the literature of the reaction kinetics of CO2 and aqueous EDA. Most of this work focuses on unloaded solution. The kinetics are all reported as a second-order reaction between aqueous EDA and CO2. The last row is reaction

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between CO2 and MEA. Sharma (1965) used the same experimental method for EDA and MEA. EDA has a higher reaction constant than MEA according to Sharma.

Table 1: Literature on the Reaction between CO2 and Aqueous EDA

Authors k2 adjusted

to 303K (m3 mol-1s-1)

Method Experiment T(K)

CEDA (mol l-1)

Activation Energy

(kJ/mol) Jensen and Christensen

(1955)* 22.3 Competitive reaction 291 0.1** -

Sharma (1965)* 21.6 Liquid jet 298 - - Weiland and Trass (1971)* 138 Liquid jet 298.5 0.1~0.9 -

Sada et al.(1977)* 25.0 Liquid jet 298 0.4~1.2 - Hikita et al.(1977) 17.9 Rapid mixing 278.5~317 0.007~0.03 53.6

Li et al. (2007) 17.7 Stopped-flow 298~313 0.03~0.07 50.56 MEA: Sharma(1965) 10.6 Liquid jet 298 - -

*The rate constant values were calculated from measurements at the other temperatures by Sada et al. (1985). The activation energy of the reaction was 53.6 kJ mol-1 according to Hikita et al. (1977).

**The author did not offer concentration units expressly.

Jensen and Christensen (1955) introduced 15% CO2 and 85% atmospheric air into aqueous solution containing both 0.1 M EDA and 0.2 M NaOH at 18 °C. The gaseous mixture was depleted, and the solution was immediately analyzed after being shaken for 2 minutes. Rate constant for the reaction (1) was calculated.

NHCOOHCHCHNHCONHCHCHNH 22222222 ⋅⋅⋅⎯→⎯+⋅⋅⋅ (1)

According to the other authors' calculation, the second-order reaction rate constant should be 22.3 m3mol-1s-1 adjusted to 303K.

Sharma (1965) used several types of apparatus to obtain the rate constants for the reaction between CO2 and various substances. The jet apparatus was used for 0.05~102 m3mol-1s-1. The second-order rate constant of CO2 and EDA in this work is 15.1 m3mol-1s-1 (104.18 lmol-1s-1) at 298K. However, the solvent concentration and CO2 partial pressure were not mentioned in the paper.

Weiland and Trass (1971) solved the following equilibrium equations using a method due to Rosenbrock (1960). The dicarbamate form was calculated as monocarbamate.

( ) ( ) −+ +⋅⋅⎯→←+⋅⋅ OHNHCHNHOHNHCHNH 322222222 (2)

( ) ( ) −+++ +⋅⋅⎯→←+⋅⋅ OHNHCHNHOHNHCHNH 322323222 (3)

( ) ( ) +−−+ +⋅⎯→←⋅⋅ HNHCOOCHNHNHCOOCHNH 222223 (4)

( ) ( ) -322222222 HCONHCHNHOHNHCOOCHNH +⋅⎯→←+⋅⋅ −

(5) −− ⎯→←+ 32 HCOOHCO (6)

OHCOOHHCO 22

33 +⎯→←+ −−−

(7)

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3

Most of the carbamate is in zwitterion form. Weiland and Trass also calculated an extremely low equilibrium partial pressure of CO2.

A laminar liquid jet was used to measure the physical and chemical absorption rates. Chemical absorption experiments were done using uncarbonated EDA of several concentrations under vacuum. Exposure time was varied by adjusting the jet velocity. The pseudo-first-order rate constants found from the slopes of the data had a linear relation with EDA concentration. The second-order rate constant at 25.5 oC is 100.4 m3mol-1s-1 ±4% for a 95% confidence interval.

Sada et al. (1977) measured the rates of chemical absorption of carbon dioxide into aqueous EDA using a laminar liquid jet with and without a surface-active agent (Phensol NP-100 (0.025 %)). The absorption rates were measured volumetrically at 298K and at atmospheric pressure. The first-order reaction rate constant was derived by comparing the observed absorption rates under the fast reaction regime with the penetration theory solution. Solvent concentration was varied from 0.396 mol/l to 1.145 mol/l. Liquid flow rate was maintained constant. CO2 was depleted. The value of the rate constant for the second order reaction was derived as 17.5 m3mol-1s-1.

Hikita et al. (1977) used the rapid mixing method to investigate the kinetics of reaction of carbon dioxide with EDA. The extent of the reaction at any point along the observation tube was calculated from the observed temperature difference. The concentration of EDA varied from 0.00744 to 0.0284M. The CO2 concentration was in the range of 0.00446–0.00742M. The temperature was varied from 5.5 to 40.4 oC. The activation energy was found to be 53.6 kJ mol-1 (12800 cal gmol-1). The second-order rate constant was correlated by the empirical equation:

logk2=13.49-2799/T (8)

The average deviation of equation 8 is 4.1%.

Li et al. (2007) observed pseudo-first-order rate constant (k0) for the reaction between CO2 and EDA aqueous solution at 298, 303, 308, 313K. EDA concentration was varied from 26.2 mol/m3 to 67.6 mol/m3. The standard model SF-51 stopped-flow equipment manufactured by Hi-Tech Scientific, Ltd. (UK) was used. The second-order rate constant can be expressed by equation 9.

⎟⎠⎞

⎜⎝⎛ −×=

T6080exp107.8 9

2k (9)

or logk2=9.9395-2651/T.

Most of the previous reports of the second-order rate constant agreed with each other except Weiland and Trass (1971). The second-order rate constant was observed for fresh EDA solution. EDA aqueous solution with CO2 loaded has not been investigated. High EDA concentration with CO2 loaded solution which is close to industrial application needs to be studied.

A few papers discuss stability of EDA with CO2. Mulvaney and Evans (1948) introduced a method of producing imidazolidone from EDA and CO2 directly. At 200~230oC, 400~900 psi, conversion from EDA to imidazolidone can reach 80%. This process relies on high temperature and high pressure and may play an important role in EDA thermal degradation.

Sexton (2008) and Davis (2009) have reported a few degradation experiments with 3.5 m EDA. This current work focused on a broader investigation of more concentrated EDA solution. Both unloaded and loaded 8 m EDA solutions were used in thermal degradation at different

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temperatures. 1 mM Fe2+ was added into the low gas flow oxidative degradation system as a catalyst and the effect of inhibitor A was investigated. Oxidative degradation was also measured with high gas flow. VLE data were measured with both the hot gas FTIR and the wetted-wall column. CO2 capacity and heat of CO2 absorption was calculated from the VLE model and compared with 7 m MEA and 8 m PZ. Viscosity of partial loaded EDA aqueous solution was measured and compared with PZ.

Experimental Methods Thermal degradation experiments were performed as detailed in the Davis dissertation (2009). EDA was sealed in 10 mL stainless steel bombs. All the bombs were put in a forced convection oven at 100oC to 135oC for different times.

The low gas flow apparatus described by Sexton (2008) was used to measure oxidative degradation. 350 mL solution was kept in a glass vessel and maintained at 55 oC. A gas flow of 100 mL/min of 2% CO2/98% O2 was introduced at the top of the gas liquid interface, while the solution was agitated at 1400 RPM. 1 mM Fe2+ was added as a catalyst for both oxidation experiments.

The high gas flow apparatus was also described detailed by Sexton (2008). Solution volume was approximate 350 mL. A gas flow of 5 L/min of 2% CO2/15% O2/83%N2 was bubbled into the solution. The agitator in the solution was operated at 1400 RPM at most time. Fe2+, Cu2+ and inhibitor A was added during the experiment. The hot gas FTIR was used to measure the vapor-liquid equilibrium. The detailed experiment method and procedure have been described in Hilliard (2008). The wetted-wall column was used for CO2 rate measurement. Details of the apparatus have been described by Bishnoi & Rochelle (2000). A foaming experiment was performed using an adapted ASTM D892 method. Detailed apparatus and experiment procedure have been described in previous reports. (Rochelle et al. 2008). Foaminess (F) is defined in equation 10:

GVV

GV

F tg 0−==

(10) where Vg is the total steady volume (m3) of gas trapped in the liquid, V0 is the original liquid volume (m3), Vt is the total steady volume (m3) of content in the cylinder during foaming, G is the gas flow rate (m/s). The break time of foam was measured as the period required for the foam to break completely after the gas flow was discontinued.

Analytical Methods Cation IC: A Dionex ICS-2500 Ion Chromatography System with CS17 IonPac column with CSRS 4 mm self-regenerating suppressor was used to determine concentrations of EDA and the cationic species in degraded solutions. The system and analytical method are the same as described by Davis (2009). All the samples were diluted about 10000 times for cation analysis.

Anion IC: A Dionex ICS-3000 Dual RFIC Ion Chromatography System with AS15 IonPac column and ASRS 4 mm self-regenerating suppressor were used to quantify glycolate, acetate, formate, nitrite, oxalate, and nitrate in the degraded solutions. The system and analytical method are the same as described by Sexton (2008). The samples were diluted 50 times to get signals

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5

from the detector.

HPLC: HPLC analysis of nonionic species was performed using the PL-ELS 2100 evaporative light scattering detector. The analytical method is the same as described in Sexton (2008). Degraded solutions were diluted 10 to 50 times to protect the detector from high signal.

TIC: The concentration of carbon dioxide in the solution was measured using the Total Inorganic Carbon Analyzer. CO2 loading of solutions can be calculated from the result of TIC.

Acid pH Titration: Metrohm 835 Titrando with an 801 stirrer was used to determine the total concentration of amines in the solution. The analytical method is the same as described by Freeman.

NaOH Treatment for Amides: Oxidative degradation samples were treated with 5 N NaOH and then analyzed using anion IC. The increase of carboxylic acid concentration between pre- and post-NaOH treatment samples is taken to be amide. This analytical method has been described by Sexton (2008).

Viscosity measurement: Viscosity of solutions was measured using one Physica MCR 300 cone and plate rheometer. The measurement method has been described by Freeman (Rochelle et al., 2008) in a previous report, with the slight modification that at 80 oC the viscosity was measured for 5 seconds rather than 10 seconds.

Material Table 2: Chemical Reagent Specifications

Reagent CAS# Supplier Molecular Weight Assay % Lot #

Ethylenediamine1 107-15-3 Strem Chemicals 60.10 99 A3566128, A4879029(v)

Ethylenediamine2 107-15-3 Fisher Chemical 60.10 100.2 064516 2-Imidazolidone 120-93-4 Sigma-Aldrich 86.09 96 07806DH

1 Used in most of the experiments and measurements except Wetted Wall Column. 2 Used in Wetted Wall Column.

Results All the raw experiment data compiled from liquid phase analysis for thermal and oxidative degradation experiments and raw data from the other experiments and measurements are listed below.

Table 3: Thermal degradation of 8 m EDA at 100 oC with 0.4 mols CO2/2 mols EDA (Analyzed in May, 2009)

Experiment Time (weeks) 2 4 6 8** 10 13 16 Ethylenediamine Urea (mM)* 1.95 5.49 12.10 19.63 18.85 26..33 32.03

DETA (mM) 0 0 0 0 0 0 0 Formate (mM) 1.38 2.16 3.01 4.52 22 6.66 6.43

Imidazolidone (mM) 0 0 0 0.03 0.02 0.12 0.14 EDA (M) 4.2 4.2 4.1 4.1 4.1 4.1 4.0

* Calculated from EDA standard curve **This point is from analysis in March

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Table 4: Thermal degradation of 8 m EDA at 120 oC with 0.4 mols CO2/2 mols EDA Experiment Time (weeks) 1 2 3 4 5 6 7 8

Ethylenediamine Urea (mM)* 17.63 37.21 44.62 56.65 64.77 74.84 81.07 93.58 DETA (mM) 0 0 2.39 2.37 1.17 2.35 2.52 2.38

Formate (mM) 5.09 13.23 10.97 29.05 26.09 23.25 25.85 35.04 Oxalate (mM) 0.01 0 0.01 0.01 0.01 0 0.01 0 Acetate (mM) 0 0 0 0 0 0 4.07 0

Glycolate (mM) 0 0 0 0.14 0 0.15 0.21 0.26 Nitrate (mM) 0.06 0.02 0.04 0.17 0.11 0.01 0.14 0.02 Nitrite (mM) 0.03 0.01 0.01 0.06 0.05 0.01 0.07 0.02 Sulfate (mM) 0.09 0.12 0.15 0.17 0.16 0.13 0.17 0.11

Chloride (mM) 0.24 0.05 0.31 0.38 0.23 0.05 0.22 0.04 Imidazolidone (mM) 4.42 4.21 4.26 4.42 4.62 4.63 5.08 5.78

EDA (M) 4.2 4.0 3.9 3.9 3.8 3.7 3.5 3.5 RT=3.7 min on Cation (mM)** 15.87 19.14 19.28 19.12 23.63 23.78 28.00 31.29 RT=4.0 min on Cation (mM)** 4.54 9.57 9.64 7.17 9.45 14.27 15.27 16.85 RT=4.5 min on Cation (mM)** 2.27 4.79 4.82 14.34 11.82 11.89 12.73 19.25 * Calculated from EDA standard curve ** Calculated from MEA standard curve

Table 5: Thermal degradation of 8 m EDA at 135 oC with 0.4 mols CO2/2 mols EDA Experiment Time (weeks) 1 2 3 4

Ethylenediamine Urea (mM)* 66.16 98.01 109.27 113.74 DETA (mM) 2.42 3.64 4.75 4.86

Formate (mM) 11.65 35.66 42.36 48.93 Oxalate (mM) 0 0 0.01 0.02 Acetate (mM) 0 0 0 0

Glycolate (mM) 0 0 0.08 0.43 Nitrate (mM) 0.03 0.04 0.23 0.10 Nitrite (mM) 0.01 0.01 0.02 0.02 Sulfate (mM) 0.07 0.12 0.15 0.11

Chloride (mM) 0.13 0.05 0.60 0.05 Imidazolidone (mM) 7.39 14.59 17.83 22.64

EDA (M) 3.7 3.2 3.1 2.9 RT=3.7 min on Cation (mM)** 29.37 46.56 62.37 76.17 RT=4.0 min on Cation (mM)** 17.13 26.96 35.98 41.77 RT=4.5 min on Cation (mM)** 4.90 17.15 19.19 27.03

* Calculated from EDA standard curve ** Calculated from MEA standard curve

Table 6: Thermal degradation of 8 m EDA at 100 oC with 0.2 mols CO2/2 mols EDA Experiment Time (weeks) 2 4 8

Ethylenediamine Urea (mM)* 3.33 5.76 10.46 DETA (mM) 0 0 0

Formate (mM) 20.74 22.49 27.82 Oxalate (mM) 0.03 0.11 0.07 Acetate (mM) 0 0 0

Glycolate (mM) 0 0 0 Nitrate (mM) 0 0 0

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Nitrite (mM) 0 0 0 Sulfate (mM) 0.11 0.10 0.18

Chloride (mM) 0.05 0.02 0.04 Imidazolidone (mM) 0 0 0

EDA (M) 4.8 4.8 4.7 RT=3.7 min on Cation (mM)** 10.92 11.19 10.68 RT=4.0 min on Cation (mM)** 2.31 1.16 2.63 RT=4.5 min on Cation (mM)** 0.51 0.51 0.75


Table 7: Thermal degradation of 8m EDA at 120 oC with 0.2 mols CO2/2 mols EDA Experiment Time (weeks) 1 2 3 5 8

Ethylenediamine Urea (mM)* 8.76 18.70 27.14 40.33 67.01 DETA (mM) 0 0 0 1.68 2.65

Formate (mM) 23.46 23.90 26.64 30.77 53.38 Oxalate (mM) 0.04 0.03 0.06 0.04 0.07 Acetate (mM) 0 0 0 0 0

Glycolate (mM) 0 0 0 0 0 Nitrate (mM) 0 0 0 0 0.75 Nitrite (mM) 0 0 0 0 0 Sulfate (mM) 0.14 0.11 0.17 0.22 0.36

Chloride (mM) 0.06 0.02 0.04 0.10 0.40 Imidazolidone (mM) 0 0 0 0 0

EDA (M) 4.8 4.8 4.7 4.7 4.7 RT=3.7 min on Cation (mM)** 11.28 13.24 13.19 14.89 17.03 RT=4.0 min on Cation (mM)** 1.86 1.78 2.26 6.69 5.09 RT=4.5 min on Cation (mM)** 0.53 0.51 1.13 1.64 3.30


Table 8: Thermal degradation of 8m EDA at 135 oC with 0.2 mols CO2/2 mols EDA Experiment Time (weeks) 1 2 3 4 6 8

Ethylenediamine Urea (mM)* 39.70 79.30 101.83 124.01 151.50 162.72 DETA (mM) 1.95 4.18 4.06 4.85 5.93 7.06

Formate (mM) 30.94 45.48 50.44 61.26 68.87 82.44 Oxalate (mM) 0.04 0.04 0.04 0.04 0.02 0 Acetate (mM) 0 0 0 0 0 0

Glycolate (mM) 0 0 0 0 0 0 Nitrate (mM) 0 0 0 0 0 0 Nitrite (mM) 0 0 0 0 0 0 Sulfate (mM) 0.13 0.12 0.09 0.12 0.15 0.16

Chloride (mM) 0.03 0.03 0.03 0.04 0.03 0.09 Imidazolidone (mM) 0 0 7.46 7.73 8.72 8.70

EDA (M) 4.7 4.6 4.5 4.4 4.2 4.2 RT=3.7 min on Cation (mM)** 17.75 22.39 27.45 33.26 39.60 46.38 RT=4.0 min on Cation (mM)** 4.47 8.48 12.27 14.40 19.73 21.40 RT=4.5 min on Cation (mM)** 1.28 4.55 6.07 10.44 15.30 24.73

* Calculated from EDA standard curve

103

8

** Calculated from MEA standard curve

Table 9: Thermal degradation of 8m EDA at 135 oC with 0.4 mols CO2/2 mols EDA (02/09) Experiment Time (weeks) 1 2 3 4 5 6 7 8

Ethylenediamine Urea (mM)* 107.62 202.78 202.78 215.03 244.41 248.93 254.32 242.06 DETA (mM) 3.46 4.25 5.90 7.07 7.29 9.37 9.34 10.82

Formate (mM) 38.02 57.88 58.89 70.27 75.41 67.53 68.46 81.06 Oxalate (mM) 0.00 0.02 0.02 0.02 0.03 0.02 0.06 0.03 Acetate (mM) 0 0 0 0 0 0 0 0

Glycolate (mM) 0.07 0.18 0.27 0.43 0.43 0.45 0.49 0.48 Nitrate (mM) 0 0 0 0 0 0 0 0 Nitrite (mM) 0 0 0 0 0 0 0 0 Sulfate (mM) 0.06 0.09 0.11 0.13 0.11 0.11 0.19 0.11

Chloride (mM) 0.03 0.04 0.04 0.06 0.03 0.04 0.03 0.02 Imidazolidone (mM) 7.61 10.06 14.35 18.42 22.97 17.01 46.48 30.34

EDA (M) 4.0 3.8 3.5 3.3 3.2 3.0 2.9 2.9 RT=3.7 min on Cation (mM)** 29.69 42.21 56.90 69.39 79.55 97.15 107.65 115.42 RT=4.0 min on Cation (mM)** 0 0 32.02 40.46 42.02 46.42 47.63 45.60 RT=4.5 min on Cation (mM)** 0 0 0 25.52 32.33 24.19 25.15 41.66


Table 10: Oxidative degradation of 8 m EDA at 55 ºC with 1 mM Fe2+

Experiment Time (hours) 0 24 48 73 96 120 144 168 DETA (mM) 0 5.67 10.78 18.18 21.48 26.04 29.39 33.41

Formate (mM) 0.33 2.08 4.08 5.22 6.43 7.83 8.25 10.53 Formamide (mM) 3.50 10.34 19.01 22.64 26.49 26.45 34.51 40.43

Oxalate (mM) 0.01 0 0.01 0.01 0.02 0.03 0.05 0.08 Nitrite (mM) 0 0.60 0.93 1.27 1.62 2.07 3.11 3.96 Sulfate (mM) 0.91 0.90 0.88 0.88 0.85 0.84 0.82 0.90

Chloride (mM) 0 0.04 0.05 0.04 0.04 0.04 0.04 0.04 EDA (M) 4.3 4.2 4.2 4.1 4.0 3.8 3.8 3.8

RT=3.7 min on Cation (mM)* 11.55 41.23 48.84 53.54 64.22 75.40 70.84 69.60 RT=4.5 min on Cation (mM)* 0.55 2.98 4.99 6.56 7.80 9.95 12.26 14.34

* Calculated from MEA standard curve

Table 11: Oxidative degradation of 8 m EDA at 55 oC with 1 mM Fe2+/100 mM A Experiment Time (hours) 0 24 48 72 96 120 144 168

DETA (mM) 0 2.53 1.90 2.05 1.94 1.97 2.38 2.12 Formate (mM) 0.16 1.23 1.74 2.57 3.48 4.13 5.03 6.25

Formamide (mM) 2.53 10.58 12.45 13.35 12.80 15.10 15.88 14.54 Oxalate (mM) 0 0.06 0.01 0 0.01 0.01 0.01 0.01 Nitrite (mM) 0 0.08 0.06 0.08 0.09 0.09 0.08 0.10 Sulfate (mM) 1.01 0.93 1.00 0.89 0.96 0.88 0.88 0.97

Chloride (mM) 0.02 0.03 0.05 0.05 0.07 0.06 0.10 0.04 EDA (M) 4.2 4.3 4.3 4.1 4.3 4.1 4.1 4.1

RT=3.7 min on Cation (mM)* 8.90 12.45 10.10 8.29 7.38 5.10 9.96 5.76 RT=4.5 min on Cation (mM)* 0 2.45 2.89 3.06 3.92 3.54 3.77 3.99

* Calculated from MEA standard curve

104

9

Table 12: High gas flow oxidation of 8 m EDA at 55 ºC with multiple additives Experiment date 0406

Experiment Time (hours) 137 Concentration at the end(mM) Average rate(mM/hr)

EDA loss 440 3.21 NH3 yield 470 3.43 CO yield 8 0.06 CH4 yield 14 0.10 NO2 yield 10 0.07

CH3COH yield 17 0.12 Volatile EDA 28 0.20

Formate 10.49 0.077 Nitrite 1.59 0.012 Oxalate 0.091 0.0007 Nitrate 1.60 0.012 Sulfate 4.34 0.032

Chloride 0.04 0.00029 DETA 22.57 0.16

Formamide* 31.37 0.23 RT=3.5 min on Cation ** 106.33 0.78 RT=4.5 min on Cation** 21.6 0.16

* Formate standard curve does not cover the formate concentration in the samples. It is calculated from lower concentration's standard curve. **Calculated from MEA standard curve

Table 13: Vapor-Liquid Equilibrium and Volatility Data of EDA Solution from FTIR Solution Addition F (10-3m2s) t(s)

8m EDA LGF 1mM Fe 31 14 8m EDA LGF 1mM Fe/100 mM A 10 6 8m EDA HGF 1mM Fe/5mM Cu100 mM A 23 9 8m EDA HGF 1mM Fe/5mM Cu 26 9

8m EDA 0.4 ldg - 27 7 8m EDA 0.4 ldg 0.01 mM Fe 30 8 8m EDA 0.4 ldg 0.1 mM Fe 31 8 8m EDA 0.4 ldg 0.2 mM Fe 28 8 8m EDA 0.4 ldg 0.3 mM Fe 30 10 8m EDA 0.4 ldg 0.5 mM Fe 29 10 8m EDA 0.4 ldg 1 mM Fe 36 10 8m EDA 0.4 ldg 200 mM CH2O 36 15

Table 14: Vapor-Liquid Equilibrium and Volatility Data of EDA Solution from FTIR EDA

conc.(m) T(°C) T(K) Loading(mol CO2/

2 mols EDA) pCO2(kPa) pEDA(kPa)

7.914 40.016 313.166 0 0 2.414E-2 7.914 60.033 333.183 0 0 1.457E-1 7.932 40.016 313.166 0.411 0.249 9.37E-4 7.932 60.006 333.156 0.411 2.695 6.189E-3 7.816 40.014 313.164 0.487 4.544 0.459E-3 7.816 60.025 333.183 0.487 24.054 2.905E-3

105

10

11.566 39.995 313.145 0 0 7.305E-2 11.566 60.000 333.150 0 0 4.312E-1 11.904 40.007 313.157 0.424 0.203 1.412E-3 11.904 59.991 333.141 0.424 2.420 8.637E-3 11.578 40.004 313.154 0.501 20.728 0.160E-3 11.578 60.046 333.196 0.501 33.060 1.199E-3

Table 15: Vapor-Liquid Equilibrium and Rate Data from Wetted Wall Column

Conc.(m) Temp(◦C) CO2 Loading PCO2 kg' 12 40 0.362 28 2.60E-06 12 40 0.429 190 1.03E-06 12 40 0.486 4031 1.71E-07 12 60 0.219 9.3 12 60 0.29 27 1.12E-05 12 60 0.367 203 2.04E-06 12 60 0.429 1816 7.58E-07 12 60 0.491 23756 1.41E-07 12 80 0.22 49 12 80 0.292 242 5.61E-06 12 80 0.353 1522 1.67E-06 12 80 0.43 9621 7.73E-07 12 100 0.219 220 12 100 0.288 1643 5.00E-06 12 100 0.351 7128 1.99E-06 12 100 0.432 41621 5.19E-07

Table 16: Viscosity of EDA solution

Conc.(m) CO2 Loading Temp(◦C) Viscosity(cP) 8 0.2 25 5.24 8 0.2 40 3.47 8 0.2 60 2.06 8 0.2 80 1.49 8 0.3 25 6.35 8 0.3 40 4.21 8 0.3 60 2.66 8 0.3 80 1.91 8 0.4 25 7.88 8 0.4 40 5.42 8 0.4 60 3.33 8 0.4 80 2.39 8 0.5 25 9.78 8 0.5 40 6.72 8 0.5 60 4.16 8 0.5 80 3.17

106

11

12 0.2 25 10.45 12 0.2 40 6.43 12 0.2 60 3.60 12 0.2 80 2.40 12 0.3 25 14.64 12 0.3 40 9.11 12 0.3 60 5.34 12 0.3 80 3.43 12 0.4 25 21.17 12 0.4 40 13.10 12 0.4 60 7.41 12 0.4 80 4.75 12 0.5 25 27.13 12 0.5 40 16.48 12 0.5 60 9.72 12 0.5 80 6.19

Thermal Degradation Seven experiments were performed for thermal degradation with 8 m EDA loaded with 0.2 or 0.4 moles CO2 /2 moles EDA at 100–135 oC (Tables 3–9). Unloaded EDA solution did not degrade. Table 17 summarizes these results.

Table 17: Summary of thermal degradation results EDA

concentration (m) CO2 Loading Temp (◦C) Experiment time (weeks)

EDA remaining after 8 weeks (%)

8 0.4 100 16 94 8 0.4 120 8 79 8 0.4 135 4 - 8 0.4 135 8 58 8 0.2 100 8 95 8 0.2 120 8 94 8 0.2 135 8 83

Figures 1 to 3 are typical chromatograms of thermally degraded 8 m EDA. There are several unknown product peaks. Molecular weights of the products on cation IC were determined through IC-Mass spectra by Davis. The peak whose retention time is about 14.0 min on cation IC should be EDA urea (N,N'-bis(2-aminoethyl) urea, shown in Figure 1). However, this species cannot be quantified accurately because we have no standard sample.

The two peaks whose retention times are 15 min and 25.3 min on the anion IC can also be seen on the chromatogram of the initial solution with CO2 loaded but not on that of unloaded solution. The size of the peaks changes little over 8 weeks. That means they are products of the reaction between EDA and CO2, but not degradation products.

There are two peaks of imidazolidone on the HPLC chromatogram of degraded EDA solution. Both peaks appear even in the chromatogram of standard imidazolidone. The summation of the two peak areas is used to quantify imidazolidone in the samples.

107

12

-0.5

0.5

1.5

2.5

3.5

4.5

5.5

6.5

0 5 10 15 20Time/min

Res

pons

e/μs

*min

Figure 1: Cation chromatogram of thermally degraded EDA, α=0.4, T=135 oC, t=4weeks

-1

1

3

5

7

9

11

13

15

0 5 10 15 20 25 30 35Time/min

Res

pons

e/μs

*min

Figure 2: Anion chromatogram of thermally degraded EDA, α=0.4, T=135 oC, t=4weeks

Formate

RT=29.8

EDA MW=60

DETA MW=103

RT=3.7 MW=86/172

RT=4.0 MW=61/14

RT=4.5 MW=88 Background

Background

Product of reaction between CO2 and EDA

EDA urea MW=146

O

NH2NH NH

NH2

EDA urea

108

13

0

50

100

150

200

250

300

0 5 10 15 20Time/min

Res

pons

e/m

V

Figure 3: HPLC chromatogram of thermally degraded EDA, α=0.4, T=135 ºC, t=4weeks

Figure 4 shows EDA as a function of time in the thermal degradation experiment. As in other amine systems, EDA degrades faster at higher temperatures and with higher CO2 loading. About 40% of 8 m EDA (0.4 loading) was lost after 4 weeks at 135 oC, which agrees with Davis (2009), who screened several amines in thermal degradation experiments. At the same conditions, amine loss of 8 m EDA (0.4 loading) is similar to 7 m MEA (0.4 loading), which Davis found to be 37%. At 120 oC after 8 weeks EDA loss was about 20%. Absorbent loss dropped to 10% at 100 oC after 16 weeks. Ideal stripper temperature for EDA should not be higher than 110 oC. Degradation process is slower with 0.2 CO2 loading. 8 m EDA at 0.2 loading degraded 20% after 8 weeks at 135 oC, which is similar to 8 m 0.4 loading at 120 oC.

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8Time/week

EDA

Rem

aini

ng/%

100 C120 C135 C I135 C II

Figure 4a: Thermal degradation of partially loaded 8 m EDA; α=0.4

EDA and other amine products

Imidazolidon

NH NH

O

109

14

Figure 4b: Thermal degradation of partially loaded 8 m EDA; α=0.2

Table 18 lists carbon mass balance of three thermally degraded samples. Each unknown molecule is assumed to consume 2 EDA molecules. Imidazolidone, formate, and DETA consume little EDA. The unknowns from cation IC are more concentrated. As there are no standards for the unknowns and EDA urea, there could be a significant error in the estimates of their concentrations. Thermally degraded samples were not treated with NaOH, so there is no formamide information in table 18.

Table 18: Carbon mass balance of 8 weeks thermally degraded samples Actual concentration (mM) Equated EDA/Total EDA Loss (%)

Concentrations

(mM)

135 oC 0.4 loading

135 oC 0.2 loading

120 oC 0.4 loading

135 oC 0.4 loading

135 oC 0.2 loading

120 oC 0.4 loading

EDA urea 328 163 94 32 39 20 Formate 81 82 35 2 5 2

IM 30 9 6 1 1 1 DETA 11 7 2 1 2 1

Unknowns 203 93 67 20 22 14 Imbalance 44 31 63 EDA loss 2055 832 940

EDA and CO2 are known to produce imidazolidone at higher temperature and higher pressure (Mulvaney and Evans, 1948). EDA urea may be developed from imidazolidone before the samples are analyzed. However, imidazolidone cannot be detected in less degraded samples. The EDA urea is always the most concentrated degradation product. After 8 m 0.2 EDA was mixed with imidazolidone and left in a 120 oC oven overnight. Significant EDA urea was present then. Detailed data are in Table 19.

Table 19: Experiments with Imidazolidone and 8 m 0.2 loading EDA at 120 oC Initial Concentration Post reaction concentration

# EDA (M) Imidazolidone (M) EDA(M) EDA urea (M)* 1 4.32 1.39 3.87 0.24 2 4.30 0.64 4.22 0.08

*EDA urea concentration was calculated from EDA standard curve

110

15

Since the main products of EDA thermal degradation are EDA urea and other amines, CO2 capacity of the solution does not decrease as much as EDA concentration, while the reverse reaction of EDA urea composition may also happen when CO2 is stripped. Oxidative degradation Low gas flow and high gas flow apparatus were set up for the EDA oxidative degradation experiment. All the solutions in this section were 8 m EDA. CO2 was loaded before the experiment, but loading varied during the experiment because CO2 gas was introduced into the reactor. All the oxidative experiments are summarized in Table 20.

Table 20: Summary of oxidative degradation results Apparatus Additives Experiment time

(hr) Degradation rate

(mM/hr) Low gas flow 1 mM Fe 2+ 168 3.2 Low gas flow 1 mM Fe2+/100 mM A 168 0.1 High gas flow 1 mM Fe2+/5 mM Cu2+/100 mM A 140 3.2

Inhibitor A has been reported to be an effective degradation inhibitor for many amine systems. A was investigated with catalyst Fe2+ together for EDA. The oxidative degradation of 8 m EDA is fast with 1 m Fe2+, and much slower with 100 mM inhibitor A. EDA loss decreases significantly, as shown in Figure 5.

3.7

3.8

3.9

4

4.1

4.2

4.3

4.4

0 20 40 60 80 100 120 140 160

Time/hour

EDA

Con

cent

ratio

n/M

Figure 5: EDA loss in oxidative degradation, 55 oC, 98% O2/2% CO2

Hydroxyethyl-formamide, DETA, and formate were the most concentrated oxidation products of 3.5 m EDA in Sexton’s study (2008). Sexton used the HPLC-UV to detect hydroxyethyl-formamide, which method was not used in this work. Formate is the most concentrated known product of anion IC. The increase of formate between pre- and post-NaOH treated samples is taken to be formamide. Glycolate and acetate were not detected in either of the low gas flow oxidative degradation experiments. DETA (diethylenetriamine) is the most concentrated known

1 mMFe 2+/100 mM A

1 mMFe2+

111

16

product of cation IC. However, monoamines, whose retention times are close to MEA, are present in greater concentrations. All of the main degradation products in the two experiments are shown in Figure 6. The monoamines were quantified using the MEA standard curve.

0

20

40

60

80

0 20 40 60 80 100 120 140 160Time/hour

Con

cent

ratio

n/m

M

Figure 6: Oxidative degradation products with low gas flow, 8 m EDA, α=0.4, 55 oC, 1 mM Fe2+

Formate decreased about 50% when A was added into the solution, while the DETA decreased about 90%. Inhibitor A can play an important role in protecting EDA from oxidative degradation.

Fe2+, Cu2+ and inhibitor A were added into the reactor at different times during the high gas flow oxidative degradation experiment, so that the effect of theses compounds on EDA loss is difficult to quantify. Generally, there is low NOx, but high NH3 in the degradation products. CO, CH4, and acetaldehyde are also present in the gas phase. The unknown monoamines are the most concentrated products in the liquid phase following LGF experiment. Formamide, formate, and DETA are also present. Cu2+ is a stronger catalyst for EDA oxidative degradation than Fe2+, following the other amines. Inhibitor A can protect EDA from degradation with both Fe2+ and Cu2+. Detailed data for all kind species found in the HGF experiment are listed in Table 21. NH3 production rate in the gas phase is showed in Figure 7. Where there is a red line in the figure is the time additives were added in to the solution. There are some other rapid changes in NH3 rate, which was caused by system changes, e.g. a reduction in N2.

All Formamide /A

Rt=3.7 min on Cation

DETA

Rt=3.7 min on Cation /A

All Formamide

DETA /A

112

17

Table 21: High gas flow oxidation of 8 m EDA at 55 ºC with multiple additives

Species Concentration

at the end (mM)

Average rate (mM/hr) Species

Concentration at the end

(mM)

Average rate (mM/hr)

EDA loss 440 3.21 Nitrate 1.60 0.012 NH3 yield 470 3.43 Sulfate 4.34 0.032 CO yield 8 0.06 Chloride 0.04 0.00029 CH4 yield 14 0.10 DETA 22.57 0.16 NO2 yield 10 0.07 Formamide* 31.37 0.23

CH3COH yield 17 0.12 Volatile EDA 28 0.20

RT=3.5 min on Cation ** 106.33 0.78

Formate 10.49 0.077 Nitrite 1.59 0.012

RT=4.5 min on Cation** 21.6 0.16

Oxalate 0.091 0.0007 * Formate standard curve does not cover the formate concentration in the samples. It is calculated from lower concentration's standard curve. **Calculated from MEA standard curve

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0 1000 2000 3000 4000 5000 6000 7000 8000 9000Time (min)

NH

3 po

duct

ion

rate

(mM

/hr)

Figure 7: NH3 production rate in HGF experiment

Foaming tendency All of the final samples from oxidative degradation were tested for foaming. Fe2+ and CH2O (formaldehyde) were added into undegraded, loaded EDA to detect their effect. All the tests were performed at 40 oC.

The EDA molecule has a short carbon chain like MEA, and has two amino groups like PZ. 8 m

+0.1 mM Fe2+

+1 mM Fe2+

+5 mM Cu2+ +50 mM A +50 mM A

113

18

EDA (0.4 loading) foamed like 7 m MEA (0.4 loading) at 40 oC. At the same conditions, both foaminess and break time of EDA was much less than that of 8 m PZ (0.3 loading) as compared in Table 22.

Table 22: Foaming tendency compare at G=2 mm/s, t=40 oC, V0=400 mL Samples Foaminess coefficient (10-3m2s) Break time (s)

7 m 0.4 loading MEA* 21 5 8 m 0.3 loading PZ* 90 30 8 m 0.4 loading EDA 27 7

* Rochelle et al. (2008)

Fe2+ is one of the main corrosion products in the CO2 capture process. Fe2+ was varied from 0.01 mM to 1 mM (Figure 8). With Fe2+ less than 0.5 mM, EDA foaminess is not sensitive. Foaminess increased by 35% at 1 mM Fe2+, but break time changed little. This behavior is more similar to that with MEA. MEA foaming tendency was not sensitive to small amounts of Fe2+

(Rochelle et al., 2008). Formaldehyde was considered to be one of the degradation products and added into the solution. Foaminess increased by 35% with 200 mM formaldehyde, while break time doubled. EDA foaminess was not sensitive to degradation.

26

28

30

32

34

36

38

0 0.2 0.4 0.6 0.8 1Ferrous concentration /mM

F /1

0-3m

2s

Figure 8: Foaminess coefficient as function of [Fe2+] at 40 oC

CO2 Solubility and Amine Volatility 8 m and 12 m EDA solutions were investigated using an equilibrium experiment with the hot gas FTIR. VLE data for 12 m EDA were also obtained with CO2 absorption rate data from the wetted wall column. The VLE model of 12 m EDA was given as equation 11 by regressing VLE data from both wetted wall column and FTIR.

Lnp=49.17-16290/T-50.19α+15607α/T+48.08α2 (11)

where p is CO2 partial pressure (Pa), T is temperature (K), and α is loading (mol CO2/equivalent amine). This is only an estimated model, relative error between experiment data and calculated value can be 50%.

114

19

Figure 9 shows the CO2 solubility for 12 m and 8 m EDA with CO2 loading. The predicted lines are also present in Figure 9. The experiment points and predicted lines of 12 m EDA seem to agree with each other well except the two points from FTIR at 0.5 loading. Assume p*

CO2 at rich loading is 5 kPa, rich loading of EDA should be around 0.5. According to the absorption reactions, two amine functions can capture one CO2. Loading of 0.5 is a critical value. CO2 partial pressure on rich EDA solution is very sensitive at lower temperatures as shown in Figure 9.

Assume p*CO2 for lean loading is 0.5 kPa, CO2 capacity (mol CO2 /kg EDA + Water) can be

derived from equations 11–12.

( )1000/MWMolarity1

Molarityloadinglean -loadingrich Capacity COamineamine

amine2 ×

×+

= (12)

CO2 capacity of 12 m EDA is calculated to be 0.78 mol CO2/ kg EDA + Water, almost the same with 8 m PZ and higher than 7 m MEA.

0.001

0.01

0.1

1

10

100

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55Loading / mol CO2/equivalent amine

CO

2 pa

rtial

pre

ssur

e / k

Pa

Figure 9: CO2 solubility in 12 m and 8 m EDA

CO2 partial pressure over 12 m EDA is compared with 8 m PZ in Figure 10. p*CO2 over 8 m PZ is much higher than over 12 m EDA at the same loading and same temperature. As CO2 loading at operation temperature is close to critical value, CO2 absorption rate in EDA is measured to be about one third of PZ.

40 °C

100 °C

80 °C

60 °C

—Lnp=49.17-16290/T-50.19α+15607α/T+48.08α2

● WWC 12 m ■ FTIR 8 m ▲ FTIR 12 m

115

20

0.001

0.01

0.1

1

10

100

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55Loading / mol CO2/equivalent amine

CO

2 pa

rtial

pre

ssur

e / k

Pa

Figure 10: Comparison of CO2 solubility between 12 m EDA and 8 m PZ.

*Work with Chen **Dugas (2009)

CO2 heat absorption can also be estimated from Equations 11 and 13. Heat absorption formula is given as Equation 14.

(13)

ΔH=(14313-9849α)R (14)

where ΔH is CO2 absorption heat (J / mol CO2), R is universal gas constant (8.314 J/mol/K).

ΔH is independent of T in Equation 13. The ΔH value calculated from eq. 14 is considered as the value at 70 oC, because the temperature range in the wetted wall column experiment is 40~100 oC. Figure 11 shows ΔH in EDA from other authors. ΔH in EDA solution should be a positive function of T, and higher than that of PZ solutions.

( ) RΔH=T

p

p

2CO //1

ln−⎟

⎟⎠

⎞⎜⎜⎝

⎛

∂

∂

100 °C 80 °C 60 °C 40 °C ● 12 m EDA* ■ 8 m PZ**

116

21

60

70

80

90

100

110

120

130

10 30 50 70 90 110 130t /°C

ΔH /

kJ/m

ol C

O2

Figure 11: CO2 heat of absorption in EDA

Figure 12 compares the measured values of EDA volatility to those of PZ and MEA. Pure EDA is volatile and flammable. The EDA partial pressure over unloaded solution is also much higher than MEA and PZ. However, EDA partial pressure decreases quickly as CO2 is loaded into the solution. In the operating range of CO2 loading, which is about 0.4~0.5 for EDA, amine partial pressure over 12 m EDA solution is lower than 7 m MEA. As shown above, the heat of CO2 absorption in EDA is higher than MEA and PZ. The interaction of CO2 and EDA is strong. EDA and CO2 exist as carbamate in the solution. As EDA has two basic groups, there is little free EDA molecule in the solution when the loading is 0.5 and EDA partial pressure drops sharply. At the same time, CO2 partial pressure increases dramatically. At operating loading, amine partial pressure of 8 m PZ solution is similar to 8 m EDA.

HIKITA,1977

Trass and Weiland, 1971

Rochelle MEA

PZ

117

22

0.1

1

10

100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7loading / mol CO2/mol equivalent amine

pam

ine

/ Pa

Figure 12: EDA volatility in partial loaded solutions, 40 oC.

*Hilliard (2008), **Nguyen (2008), ***Work with Nguyen.

Reaction Rate The fast reaction rate between EDA and CO2 is one of the reasons we want to explore EDA as a solvent for CO2 capture. Nevertheless, most of the literature is focused on the reaction between unloaded EDA and CO2, which differs from the operating conditions in a typical absorber.

The absorption rate was measured in the wetted wall column. The experimental and data analysis methods are the same as those used by Ross Dugas and Xi Chen for other amines. Generally, all experiment runs are at less than 50% gas film control. The overall mass transfer coefficient KG was calculated from the flux and driving force. The gas phase mass transfer coefficient kg was calculated from a correlation (Cullinane, 2005). The liquid film mass transfer coefficient, kg', was calculated using Equation 15.

'ggG k

+k

=K

111

(15)

where kg' is obtained liquid film mass transfer coefficient, which is a function of both physical diffusion of reactants and products and reaction kinetics.

Results with12 m EDA are presented in Figure 13. The CO2 partial pressure was too low to be measured at low CO2 loading. CO2 partial pressure is very sensitive to CO2 loading in EDA solution when loading is greater than 0.4. Conformation of carbamate at one side of EDA weakens the other amino group. The CO2 absorption rate decreases rapidly with increased loading. Figure 14 shows the obtained kg' of 7 m MEA, 8 m PZ, and 12 m EDA. kg' of 12 m EDA is higher than 7 m MEA when CO2 partial pressure is low, but lower than 7 m MEA when the p*

CO2 is in operation range.

8m PZ

7m MEA 8m EDA

12m EDA

118

23

10 100 1000 10000 100000p*CO2(Pa)

kg'(m

ol/s

Pa m

2)7m MEA 40°C 8m PZ 40°C 12m EDA 40°C7m MEA 60°C 8m PZ 60°C 12m EDA 60°C

Figure 13: Liquid film mass transfer coefficient of 12 m EDA, 7 m MEA, and 8 m PZ

Viscosity The viscosity of 8 m and 12 m EDA with 0.2 to 0.5 moles CO2/2 EDA was measured using an Anton Parr cone and plate viscometer (Figures 14a –d). Temperature varied from 25 °C to 80 °C. Generally, the measured time for each sample is 100 s. Since evaporation is much faster, the measuring time for samples at 80 °C was 50 s. As with other amines, the viscosity increases with EDA and CO2 loading. The logarithm of viscosity has a linear relationship with the reciprocal of temperature.

1

10

0.1 0.2 0.3 0.4 0.5 0.6Loading / mol CO2/equivalent amine

Visc

osity

/ cP

25°C40°C60°C80°C

Figure 14a: Viscosity of 8 m EDA solution with loading

1E-7

1E-6

1E-5

1E-4

119

24

1

10

0.0028 0.0029 0.0030 0.0031 0.0032 0.0033 0.0034 1/T / 1/K

Visc

osity

/ cP

0.2 loading0.3 loading0.4 loading0.5 loading

Figure 14b: Viscosity of 8 m EDA solution with temperature

1

10

100

0.1 0.2 0.3 0.4 0.5 0.6Loading / mol CO2/equivalent amine

Visc

osity

/ cP

25°C40°C60°C80°C

Figure 14c: Viscosity of 12 m EDA solution with loading

120

25

1

10

100

0.0028 0.0029 0.0030 0.0031 0.0032 0.0033 0.0034 1/T / 1/K

Visc

osity

/ cP

0.2 loading0.3 loading0.4 loading0.5 loading

Figure 14d: Viscosity of 12 m EDA solution with temperature

Function groups have an important effect on the viscosity of aqueous amines. Additional groups may increase viscosity (Hartono, 2009). Higher concentrations of basic groups also increase viscosity linearly (Freeman, 2008). 12 m EDA solution has a higher viscosity value than 8 m EDA solution with the same loading because of higher concentrated basic groups. 8 m EDA solution seems to have lower viscosity than 8 m PZ at the same loading because of the smaller molecule weight.

Conclusions Thermal loss of 8 m EDA (0.4 loading) is about 35% at 135 oC after 4 weeks, but only 10% at 100 oC after 16 weeks. EDA urea is the most concentrated product, representing only 20% of the lost EDA.

Oxidation of EDA at 55 oC with 2%/98% CO2/O2 consumed 10% after 168 hours with 1 mM Fe2+. However EDA loss was negligible after 168 hours with 100 mM inhibitor A.

Foaminess of 8 m EDA (0.4 loading) is comparable to 7 m MEA (0.4 loading) with or without formaldehyde.

With an equilibrium CO2 partial pressures range of 500 to 5000 Pa t 40 oC, the lean loading of 8 or 12 m EDA is greater than 0.4 and the rich loading is less than 0.5, giving a capacity with 12 m EDA of 0.78 moles CO2/kg EDA + H2O.

Amine partial pressure of 12 m EDA is comparable with 7 m MEA and 8 m PZ at operating conditions.

CO2 absorption rate in 12 m EDA is about 50% that of 7 m MEA at rich conditions.

The viscosity of 8 m EDA is a little less than 8 m PZ and comparable with other amines.

EDA is not a perfect absorbent for CO2 capture. Slow absorption rate and high amine demand for equivalent CO2 is the most disadvantageous.

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26

Future Work Density is one important property of partially loaded EDA. The density of EDA at different loadings will be measured at different temperatures. Further summary and analysis will be the last part of the solvent evaluation.

References Bishnoi S, Rochelle GT. “Absorption of CO2 into aqueous piperazine: reaction kinetics, mass

transfer and solubility”. Chem Engr Sci. 200;55:5532-5542.

Cullinane JT. Thermodynamics and Kinetics of Aqueous Piperazine with Potassium Carbonate for CO2 Absorption. The University of Texas at Austin. Ph.D. Dissertation. 2005.

Davis J. Thermal Degradation of Amines Used in Carbon Dioxide Removal Applications. The University of Texas at Austin. Ph.D. Dissertation. 2009.

Dugas R. “Absorption and desorption rates of carbon dioxide with monoethanolamine and piperazine”. Energy Procedia. 2009;1:1163–1169.

Freeman S et al. “Carbon dioxide capture with concentrated, aqueous piperazine” Energy Procedia. 2009;1:1489–1496.

Hartono A. Characterization of diethylenetriamine (DETA) as absorbent for Carbon Dioxide. Norwegian University of Science and Technology. Ph.D. Dissertation. 2009.

Hikita H, Asai S, Ishikawa H, Honda M. “The kinetics of reactions of carbon dioxide with monoisopropanolamine, diglycolamine and ethylenediamine by a rapid mixing method.” Chem Eng J. 1977;14:27–30.

Jensen A, Christensen R. “Studies on Carbamates XI the Carbamate of Ethylenediamine.” Acta Chem Scand. 1955;9(3):486–492.

Li J, Henni A, Tontiwachwuthikul P. “Reaction kinetics of CO2 in aqueous ethylenediamine, ethyl ethanolamine, and diethyl monoethanolamine solutions in the temperature range of 298–313K, using the stopped-flow technique.” Ind Eng Chem Res. 2007;46:4426–4434.

Mulvaney JF, Evans RL. “Synthesis of Ethylene Urea (Imidazolidone-2)”. Ind & Engr Chem. 1948;40:393–397.

Rochelle GT et al. “CO2 Capture by Aqueous Absorption, Second Quarterly Progress Report 2008.” The University of Texas at Austin. 2008.

Rochelle GT et al. “CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report 2008”. The University of Texas at Austin. 2008.

Rosenbrock HH. Computer Journal. 1960;3:175.


Sharma MM. “Kinetics of reactions of carbonyl sulphide and carbon dioxide with amines and catalysis by Brönsted bases of the hydrolysis of COS.” Trans Faraday Soc. 1965;6:681–688.

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Sada E, Kumazawa H, Butt MA. “Absorption of carbon dioxide into aqueous solutions of ethylenediamine: Effect of interfacial turbulence.” Chem Engr J. 1977;13:213–217.

Sada E, Kumazawa H, Han ZQ. “Kinetics of Reaction between carbon dioxide and ethylenediamine in non-aqueous solvents.” Chem Engr J. 1985;31:109–115.

Trass O, Weiland RH. “Absorption of carbon dioxide in ethylenediamine solutions II. Pilot Plant Study of Absorption and Regeneration”. Can J Chem Engr. 1971;49:773–781.

Weiland RH, Trass O. “Absorption of carbon dioxide in ethylenediamine solutions I. Absorption kinetics and equilibrium.” Can J Chem Engr. 1971;49:767–772.

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Mass Transfer and CO2 Partial Pressure Results

Quarterly Report for April 1 – June 30, 2009 (Dissertation Chapter 4)

by Ross Dugas Supported by the Luminant Carbon Management Program

and the Industrial Associates Program for CO2 Capture by Aqueous Absorption

Department of Chemical Engineering The University of Texas at Austin

July 10, 2009

This chapter includes the experimental results of the diaphragm cell and the wetted wall column. The diaphragm cell measures diffusion coefficients in CO2 loaded MEA and PZ solutions. The wetted wall column measures CO2 partial pressure and CO2 absorption/desorption rate in CO2 loaded MEA and PZ. The chapter concludes with a section predicting CO2 reaction rates in MEA solutions. 4.1 NECESSITY OF EXPERIMENTS

4.1.1 Need for Diaphragm Cell Experiments Work by Versteeg and Van Swaaij (1988) has shown that the diffusion of N2O and CO2 in aqueous amines generally follow the viscosity dependence in Equation 4.1. Snijder et al. (1993) have shown that alkanolamine diffusion in aqueous alkanolamine solutions follow the viscosity dependence in Equation 4.2. ( ) ( )WaterONeSolutionAON DCONSTANTD 8.0

2min8.0

2 ηη == (4.1)

( ) ( )WatereAeSolutionAeA DCONSTANTD 6.0minmin

6.0min ηη == (4.2)

The N2O and CO2 diffusivity relationship in Equation 4.1 was confirmed with MDEA solutions but resulted in less satisfactory results for AMP (Tomcej and Otto, 1989; Xu, Otto et al., 1991). If the diffusion relationships are dependent on amines, the relationship in Equation 4.1 may not directly apply to MEA, PZ, or MEA/PZ. The current work uses a diaphragm cell to measure diffusion coefficients in MEA and PZ.

4.1.2 Need for the Current Wetted Wall Column Experiments A significant amount of data is available on rate studies concerning the reaction of CO2 and monoethanolamine. Numerous references are compiled in Table 2.1 of the Literature Review. Almost all of this data was obtained at low MEA concentration in unloaded solutions. Unfortunately, these data do not allow for the prediction of CO2 absorption/desorption rates in concentrated, CO2 loaded monoethanolamine solutions. Concentrated, CO2 loaded MEA solutions are non-ideal solutions which can have significant activity coefficient and ionic strength effects not seen in the present literature data. Therefore, to predict CO2 absorption/desorption rates in concentrated, CO2 loaded MEA solutions, rate experiments with concentrated, CO2 loaded MEA solutions must be performed.

124

2

Currently, only Aboudheir (2003) has provided a major data source on the CO2 reaction rates in concentrated, loaded MEA. Dang (2003) provides a few more data points for comparison. This work provides a second major data source of CO2 reaction rates in concentrated, CO2 loaded MEA. As Table 2.2 of the Literature Review summarized, there is little CO2 rate data in piperazine solutions. Of the 4 literature sources, none have been tested near industrial conditions. 1.5 m PZ was the most concentrated solution studied. None of the CO2 rate data in piperazine have been obtained using CO2 loaded solutions. The current work measures CO2 rates at high CO2 loading in 2, 5, 8, and 12 m PZ. These data should provide a much greater insight into the CO2 capture performance of industrial systems.

4.2 AMINE CONCENTRATION BASIS – MOLALITY, MOLARITY, AND WT % Wetted wall column rate experiments were conducted on 7, 9, 11, and 13 m MEA, 2, 5, 8, and 12 m PZ, and 7 m MEA/2 m PZ. A molality basis is convenient in experimentation because it does not change with the addition of other components and does not require density measurements. However, many other researchers are accustomed to molarity or amine mass fraction. Table 4.1 shows the experimental amine concentrations on each basis. Molarity and mass fraction are presented on a CO2-free basis. Calculated molarities use the density at 25 ˚C. The correlation by Weiland (1998) was used to determine MEA densities. PZ densities were obtained by extrapolating density measurements by Freeman back to zero loading (Rochelle, Dugas et al., 2008). A measured density of 1.02 was used for 7 m MEA/2 m PZ.

Table 4.1: Concentration conversions for the wetted wall column experiments Molality Molarity Mass

m M wt%7 5.0 309 5.9 3511 6.7 4013 7.4 442 1.7 155 3.6 308 4.9 4112 6.2 51

MEA

/PZ

7 - MEA2 - PZ

4.5 - MEA1.3 - PZ

27 - MEA11 - PZ

MEA

PZ

Molality is defined as mol/kg water while molarity is defined as mol/l solution. Molality and molarity do not scale linearly. 4.3 DIAPHRAGM CELL RESULTS Diffusion experiments were carried out in a diaphragm cell for 7, 9, and 13, m MEA and 2, 5, and 8 m PZ. Table 4.2 summarizes results for each experiment.

The membrane-cell integral diffusion coefficient, D , is a complex concentration and time averaged value which is somewhat different from the fundamental diffusion coefficient, D. The fundamental diffusion coefficient is defined with respect to one species. The membrane-cell integral diffusion coefficient is the effective diffusion coefficient of all of the CO2 species in solution. More details are given in Experimental Methods, section 3.1.3 of the dissertation.

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3

Table 4.2: Diaphragm cell results for monoethanolamine and piperazine solutions CO2 Loading Temp Time Visc Approach to Material Balance(mol/molalk) (C) (h) (cP) (m2/s) Equilibrium (%) Error (%)0.25-0.35 236 2.8 2.2E-10 34 70.45-0.55 261 3.3 4.7E-10 62 40.25-0.35 93 3.8 3.7E-10 19 160.44-0.49 138 4.5 3.2E-10 22 25

13m MEA 0.16-0.31 261 5.8 3.8E-10 58 70.24-0.32 72 1.7 6.1E-10 24 140.35-0.41 146 1.6 5.8E-10 37 260.25-0.32 166 5.2 2.5E-10 20 320.33-0.39 308 5.4 2.7E-10 48 30.25-0.29 237 14.5 1.2E-10 20 270.34-0.41 409 16.5 8.9E-11 27 4

30

Solution

7m MEA

9m MEA

2m PZ

5m PZ

8m PZ

D

The viscosity in Table 4.2 is the viscosity of the average loading of the solutions in the two chambers. For MEA, the viscosity was obtained from correlations produced by Weiland (1998). For PZ, the viscosity was obtained from a regression by Plaza (Rochelle, Chen et al., 2009) using viscosity measurements by Freeman (Rochelle, Sexton et al., 2008). Plaza regressed PZ viscosity to a similar form used by Weiland (1998) and shown in Equation 4.3. In Equation 4.3, Ω refers to the mass fraction of the amine and α refers to the CO2 loading in molCO2/molalk. In the correlation by Weiland (1998) Ω refers to the mass percent of the amine. Weiland uses a CO2-free basis for the mass percentage. Plaza incorporates the CO2 mass into the mass fraction.

⎟⎠⎞

⎜⎝⎛ Ω+++Ω+Ω++Ω

= 22

]1)()][()[(expT

gfTedcTba

OH

αηη (4.3)

The obtained constants from the correlation are shown in Table 4.3. Table 4.3: Viscosity parameters for PZ solutions

Parameter Valuea 487.52b 1389.31c 1.58d 4.50e 8.73f ‐0.0038g ‐0.30

Table 4.2 also shows an approach to equilibrium and a material balance for each experiment. The material balance was calculated by comparing the change in CO2 loading of the bottom chamber to the change in CO2 loading in the top chamber. It does not represent the total amount of CO2 which was lost during an experiment. A 25% material balance error could be represented as the top CO2 loading changing from 0.20 to 0.215 while the bottom chamber CO2 loading changed from 0.30 to 0.28. The approach to equilibrium is the change in CO2 loading in a chamber divided by half the difference in CO2 loading of the original two solutions. If 0.2 and 0.3 CO2 loading solutions reach 0.225 and 0.275 CO2 loadings by the end of the experiment, then the approach would be 50%. A 100% approach would result in both solutions achieving a 0.25 CO2 loading.

126

4

12 m PZ was also tested in the diaphragm cell but meaningful results were not obtained. The Mettler Toledo DE40 density meter was not able to reproducibly analyze the 12 m PZ samples. The solutions may have been too viscous or may not have been homogeneous. 12 m PZ at 20 ˚C (the temperature of the density measurement) is about 50–60 cP depending on the CO2 loading (Rochelle, Sexton et al., 2008). Diffusion coefficients are typically a function of viscosity. Figure 4.1 plots the diffusion coefficient and viscosity data in Table 4.2. The diffusion coefficient of 1 m piperazine is shown for comparison (Sun, Yong et al., 2005).

1x10-10

1x10-9

1 10

Diff

usio

n C

oeffi

cien

t (m

2 /s)

Viscosity (cP)

Sun (2005)1 m PZ

Circles - 7, 9, 13 m MEADiamonds - 2, 5, 8 m PZ

m = -0.72

30C

Figure 4.1: Diffusion coefficient-viscosity relationship for MEA and PZ (Sun, Yong et al.,

2005) The data seem to show a slope of -0.72 although a fair amount of data scatter is apparent. This 0.72 value seems reasonable compared to a 0.8 dependence for N2O and a 0.6 dependence for amines cited by Versteeg and Van Swaaij (1988). The membrane-cell integral diffusion coefficient cited here most likely refers to the carbon dioxide carrying species since CO2 loading changes were measured. In that case the measured diffusion coefficient would most closely represent the diffusion coefficient of the carbamate species. The data also compare favorably to the piperazine diffusion coefficient data point measured by Sun (2005). Extrapolating the trend line in Figure 4.1 to the Sun data point viscosity would show the trend line slightly under predicting the diffusion coefficient. However, the diffusion coefficient of a PZ carbamate may be slightly lower than PZ due to the larger size and possibly more hydrogen bonding on the ionic species. Overall, the 0.72 dependence the diaphragm cell provides seems reasonable and can be used in the rate model.

127

5

4.4 WETTED WALL COLUMN RESULTS

4.4.1 Tabulated Wetted Wall Column Data Tables 4.4–4.6 provide tabulated kg’ rate data and equilibrium CO2 partial pressure data. Section 2.3.1 explains why rate data are presented in terms of kg’ rather than rate constants. kg’ is the liquid film mass transfer coefficient in gas film units, defined by Equation 4.4. Figure 2.1 in the Literature Review graphically defines kg’.

)( *

,2,2

2'

bCOiCO

COg PP

Nk

−= (4.4)

Each row of the following tables represents the results of 6 experimental inlet CO2 partial pressures. Much more detailed data including gas flow rates, pressures, and inlet and outlet CO2 partial pressures can be found in Appendix A. Appendix A also includes the liquid film physical mass transfer coefficient, kl

o, and the gas film resistance percentage of each experiment. Experiments were designed to be less than 50% gas film controlled. In some experiments kl

o may be limiting such that CO2 mass transfer is restricted by diffusion limitations in the system.

Table 4.4: CO2 equilibrium partial pressure and rate data obtained from the wetted wall column with aqueous MEA

MEA Temp CO2 Loading P*CO2 kg' MEA Temp CO2 Loading P*CO2 kg'm C mol/molalk Pa mol/s.Pa.m2

m C mol/molalk Pa mol/s.Pa.m2

0.252 15.7 3.34E-06 0.261 14.0 3.36E-060.351 77 1.40E-06 0.353 67 1.76E-060.432 465 7.66E-07 0.428 434 7.14E-070.496 4216 3.47E-07 0.461 1509 4.34E-070.252 109 2.92E-06 0.261 96 3.35E-060.351 660 1.70E-06 0.353 634 1.80E-060.432 3434 9.28E-07 0.428 3463 8.71E-070.496 16157 3.76E-07 0.461 8171 5.02E-070.271 1053 2.85E-06 0.256 860 4.35E-060.366 4443 1.87E-06 0.359 3923 1.93E-060.271 5297 2.98E-06 0.256 4274 3.72E-060.366 19008 1.40E-06 0.359 18657 1.56E-060.231 10.4 - 0.252 12.3 3.08E-060.324 34 1.86E-06 0.372 84 1.28E-060.382 107 1.40E-06 0.435 491 6.96E-070.441 417 8.36E-07 0.502 8792 1.62E-070.496 5354 3.02E-07 0.252 100 2.98E-060.231 61 3.80E-06 0.372 694 1.54E-060.324 263 2.44E-06 0.435 3859 7.56E-070.382 892 1.47E-06 0.502 29427 1.93E-070.441 2862 9.57E-07 0.254 873 4.21E-060.496 21249 3.24E-07 0.355 3964 1.85E-060.265 979 3.24E-06 0.254 3876 3.66E-060.356 4797 1.75E-06 0.355 18406 1.56E-060.265 4940 3.40E-060.356 21534 1.33E-06

7

9

80

100

40

60

40

60

80

100

11

13

80

100

40

60

40

60

80

100

12 m PZ experiments at 40 ˚C could not be run in the wetted wall column due to the high viscosity of the solution. A thin liquid film on the surface of the stainless steel rod could not be maintained. Also 12 m PZ samples with approximately 0.40 CO2 loading were not tested due to solubility limitations.

128

6

Table 4.5: CO2 equilibrium partial pressure and rate data obtained from the wetted wall column with aqueous PZ

PZ Temp CO2 Loading P*CO2 kg' PZ Temp CO2 Loading P*CO2 kg'm C mol/molalk Pa mol/s.Pa.m2

m C mol/molalk Pa mol/s.Pa.m2

0.240 96 3.32E-06 0.231 68 4.27E-060.316 499 2.04E-06 0.305 530 1.98E-060.352 1305 1.39E-06 0.360 1409 1.14E-060.411 7127 5.55E-07 0.404 8153 3.53E-070.240 559 3.33E-06 0.231 430 4.41E-060.316 2541 2.06E-06 0.305 2407 2.02E-060.352 5593 1.38E-06 0.360 7454 9.57E-070.411 25378 3.84E-07 0.404 30783 3.20E-070.239 2492 3.34E-06 0.253 3255 3.61E-060.324 12260 1.32E-06 0.289 9406 1.97E-060.239 9569 2.40E-06 0.253 13605 2.18E-060.324 39286 9.12E-07 0.289 32033 1.20E-060.226 65 4.39E-06 0.231 331 4.19E-060.299 346 2.57E-06 0.289 1865 1.85E-060.354 1120 1.69E-06 0.354 6791 7.73E-070.402 4563 7.93E-07 0.222 2115 4.24E-060.226 385 4.75E-06 0.290 9141 1.48E-060.299 1814 2.62E-06 0.222 7871 3.78E-060.354 5021 1.80E-06 0.290 33652 8.30E-070.402 17233 6.59E-070.238 2192 4.67E-060.321 9699 1.91E-060.238 8888 3.52E-060.321 36960 1.02E-06

60

2

5

80

100

80

100

40

60

40

40

60

60

8

12

80

100

80

100

Table 4.6: CO2 equilibrium partial pressure and rate data obtained from the wetted wall

column with 7 m MEA/2 m PZ

MEA PZ Temp CO2 Ldg P*CO2 kg'm m C mol/molalk Pa mol/s.Pa.m2

0.242 27 3.45E-060.333 166 1.96E-060.416 1425 8.76E-070.477 7418 4.32E-070.242 178 4.00E-060.333 1256 2.03E-060.416 7122 9.08E-070.477 33704 3.75E-070.242 1138 4.29E-060.333 6174 2.12E-060.242 4340 4.83E-060.333 26571 1.23E-06

7 2

40

60

80

100

4.4.2 Equilibrium CO2 Partial Pressure The figures in the following sections graphically represent the data in Tables 4.4–4.6 along with applicable literature data.

129

7

4.4.2.1 Monoethanolamine Figure 4.2 shows wetted wall column obtained CO2 equilibrium partial pressure values in 7, 9, 11, and 13 m MEA compared to Jou (1995) and Hilliard (2008) values. Hilliard used an equilibrium cell to measure CO2 partial pressures with an FTIR (Fourier transform infrared spectroscopy) analyzer to quantify the CO2 concentration. Jou also measured the equilibrium partial pressure with an equilibrium cell.

1

10

100

1000

10000

100000

1000000

0.05 0.15 0.25 0.35 0.45 0.55

CO2 Loading (mol/molalk)

P*C

O2 (

Pa)

Hilliard 3.5 m MEAHilliard 7 m MEAHilliard 11 m MEA7 m MEA9 m MEA11 m MEA13 m MEAJou 7 m MEA

Open Points – Hilliard (2008) – 3.5, 7, 11 m MEADashes – Jou (1995) – 7 m MEAFilled Points – Current Work – 7, 9, 11, 13 m MEA

100˚C

80˚C

60˚C

40˚C1

10

100

1000

10000

100000

1000000

0.05 0.15 0.25 0.35 0.45 0.55


P*C

O2 (

Pa)

Hilliard 3.5 m MEAHilliard 7 m MEAHilliard 11 m MEA7 m MEA9 m MEA11 m MEA13 m MEAJou 7 m MEA

Open Points – Hilliard (2008) – 3.5, 7, 11 m MEADashes – Jou (1995) – 7 m MEAFilled Points – Current Work – 7, 9, 11, 13 m MEA

100˚C

80˚C

60˚C

40˚C

Figure 4.2: Equilibrium CO2 partial pressure measurements in MEA solutions at 40, 60,

80, and 100 ˚C (Jou, Mather et al., 1995; Hilliard, 2008) The 3.5, 7, and 11 m MEA data by Hilliard (2008), the 7 m MEA data by Jou (1995), and the current work at 7, 9, 11, and 13 m MEA agree well at each of the 4 temperatures. The current data represented by the filled data points seem to show a little deviation from the other data near 0.5 loading. These data seem to show a dependence with respect to amine concentration. At both 40 and 60 ˚C near 0.5 loading the 13 m data has a higher CO2 partial pressure than the 11 m MEA data which is higher than the 7 m MEA data. The 11 m MEA data by Hilliard both at 40 and 60 ˚C show a higher CO2 partial pressure than 7 or 3.5 m MEA data at high CO2 loading. However, the 7 m MEA data from the wetted wall column provide higher CO2 partial pressure values than the 7 m MEA data by Hilliard (2008) or Jou (1995). The effect of amine concentration on the CO2 partial pressure of the MEA system at high loading is not necessarily erroneous. Amine concentration should not affect CO2 equilibrium partial pressures for carbamate producing systems when compared at equivalent CO2 loading. However, amine concentration is extremely important in bicarbonate producing systems. MEA systems begin producing significant bicarbonate concentrations approaching 0.5 loading. This difference is based on the stoichiometry of the carbamate and bicarbonate reactions. The mathematics of the difference are explained in Appendix B.

130

8

The increased CO2 partial pressure of the higher MEA concentration near 0.5 loading is likely due to an increased concentration of bicarbonate. At lower CO2 loading bicarbonate concentration is insignificant and MEA concentration has no effect on the equilibrium CO2 partial pressure of the system. 4.4.2.2 Piperazine Figure 4.3 shows wetted wall column obtained CO2 equilibrium partial pressure values in 2, 5, 8, and 12 m PZ compared to Ermatchkov (2006a) and Hilliard (2008). Hilliard used an equilibrium cell to measure CO2 partial pressure with an FTIR (Fourier transform infrared spectroscopy) analyzer to quantify the CO2 concentration. Ermatchkov measured the equilibrium partial pressure using headspace gas chromatography (2006b).

10

100

1000

10000

100000

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45


P*C

O2 (

Pa)

Hilliard 0.9 m PZHilliard 2 m PZHilliard 2.5 m PZHilliard 3.6 m PZHilliard 5 m PZ8 m PZ5 m PZ12 m PZ2 m PZErmatchkov 1-4 m PZ

Open Points – Hilliard (2008) – 0.9, 2, 2.5, 3.6, 5 m PZDashes – Ermatchkov (2006) – 1-4.2 m PZFilled Points – Current Work – 2, 5, 8, 12 m PZ

100˚C

80˚C

60˚C

40˚C10

100

1000

10000

100000

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45


P*C

O2 (

Pa)

Hilliard 0.9 m PZHilliard 2 m PZHilliard 2.5 m PZHilliard 3.6 m PZHilliard 5 m PZ8 m PZ5 m PZ12 m PZ2 m PZErmatchkov 1-4 m PZ

Open Points – Hilliard (2008) – 0.9, 2, 2.5, 3.6, 5 m PZDashes – Ermatchkov (2006) – 1-4.2 m PZFilled Points – Current Work – 2, 5, 8, 12 m PZ

100˚C

80˚C

60˚C

40˚C

Figure 4.3: Equilibrium CO2 partial pressure measurements in PZ solutions at 40, 60, 80,

and 100 ˚C (Ermatchkov, Perez-Salado Kamps et al., 2006a; Hilliard, 2008) All the data in Figure 4.3 matche very well at 40, 60, and 80 ˚C. Neither Ermatchkov (2006a) or Hilliard (2008) provide data at 100 ˚C but the 100 ˚C data look reliable based upon the spacing from the 80 ˚C data and the overlap of the amine concentrations. Unlike the CO2 partial pressure measurements in the MEA system, the PZ system does not show a dependence of amine concentration at high loading. This is because the CO2 loading is not high enough to see appreciable quantities of bicarbonate. Since only carbamates are produced, none of the data show an effect of amine concentration when plotted versus CO2 loading. 4.4.2.3 7 m MEA/2 m PZ Very little data for equilibrium CO2 partial pressure are available for 7 m MEA/2 m PZ. Figure 4.4 includes the current data (filled points) compared against Hilliard (2008) represented as the

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9

open points. Again, Hilliard used an equilibrium cell to measure CO2 partial pressure with an FTIR analyzer to quantify the CO2 concentration.

1

10

100

1000

10000

100000

0.05 0.15 0.25 0.35 0.45


P CO

2* (P

a)

Open Points – Hilliard (2008) Filled Points – Current Work

100˚C

80˚C

60˚C

40˚C7 m MEA/2 m PZ

Figure 4.4: Equilibrium CO2 partial pressure measurements in 7 m MEA/2 m PZ at 40, 60,

80, and 100 ˚C (Hilliard, 2008) Although there are limited data for 7 m MEA/2 m PZ, the available equilibrium CO2 partial pressure data show a very good match despite using two very different experimental apparatuses. Other MEA/PZ concentrations were not studied due to significant concerns on thermal degradation discovered by Davis (2009). Davis found that the more reactive PZ will react preferentially with an oxazolidone intermediate formed by thermally degrading MEA. Essentially, PZ protects MEA in the thermal degradation of the blended system. PZ in the absence of MEA will not thermally degrade significantly because there is no pathway to produce oxazolidone. 4.4.3 CO2 Capacity The equilibrium CO2 partial pressures in Figures 4.2–4.4, allow for the determination of the CO2 capacity of the systems. The CO2 capacity is defined as the difference in the CO2 concentration from the rich to the lean amine streams, not the total CO2 concentration in any particular steam. The CO2 capacity leads to the amount of CO2 that would be removed from the system during a circulation of the amine solution. The CO2 capacity is important because of energy tradeoffs of the sensible heat and the heat of absorption. Circulating less solvent reduces the sensible heat duty since the stripper must heat all the solution from the cross-exchanger outlet temperature to the stripper temperature. This temperature difference is the same as the cross-exchanger temperature approach. However, circulating too little solvent to achieve a very high CO2 capacity generally results in a very low

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lean loading or CO2 partial pressure. Stripping to very low CO2 partial pressures increases the stripping steam required per mole of CO2 and can cause inefficient operation of the stripper. The optimal operating lean loading and thus CO2 capacity for a given amine system requires a significant optimization with a complex model since CO2 reaction rates will change drastically with changing CO2 loading. Since the optimal lean loading and thus CO2 partial pressure of that lean loading cannot be easily determined, Figure 4.5 is constructed to compare the CO2 capacity of 8 m PZ and 7 m MEA at 40 ˚C for any lean partial pressure. Alternative amine systems allow for an increase in the CO2 capacity of the system without requiring the system to strip to lower CO2 partial pressures. Figure 4.5 includes CO2 loading values next to some of the data points. Since CO2 capacity relates to the sensible heat of the solution and the total dissolved CO2 has a negligible partial heat capacity, CO2 capacities are calculated on a molCO2/kg (water+amine) basis. It is not appropriate to include the CO2 in the weight of the solution since it has an effective negligible sensible heat. Essentially, a mole of MEA has almost the same heat capacity as MEA carbamate (Hilliard, 2008). Nguyen has seen the same effect in PZ systems (Rochelle, Chen et al., 2009).

0

0.5

1

1.5

2

2.5

101001000

CO

2 Cap

acity

with

a 5

kPa

Ric

h So

ln(m

ol C

O2/k

g(w

ater

+am

ine)

)

Lean Partial Pressure (Pa)

8 m PZ

7 m MEA.36 Ldg

.31

.23

.15 Ldg

.47

.31

.19

40C

.39

.54

.20

.30

.37

.49

11 m MEA

Figure 4.5: Operating CO2 capacity of 8 m PZ and 7 and 11 m MEA assuming a 5 kPa rich

CO2 partial pressure at 40 ˚C (7 and 11 m MEA data from Hilliard (2008)) Figure 4.5 assumes a 5 kPa CO2 partial pressure rich solution. In a coal-fired power plant CO2 enters the absorber near 12 mole %, or 12 kPa since it is near atmospheric pressure. Therefore, the assumption of a 5 kPa CO2 partial pressure rich solution at 40 ˚C represents a 5/12 or a 42% approach to saturation at the bottom of the absorber if the solution exits at 40 ˚C. With the stated assumptions detailed above, 8 m PZ exhibits about a 70% greater CO2 capacity than 7 m MEA and about a 50% greater CO2 capacity than 11 m MEA. Some of the increased

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11

capacity arises from the increased amine strength of the piperazine, 8 m versus 7 m for MEA. The majority of this increased CO2 capacity is due to the fact that each mole of piperazine has two functional nitrogen groups. This allows PZ to react twice in the CO2 reaction, whereas MEA can only react once. PZ solutions allow for much greater CO2 capacities than MEA and thereby lower required liquid flow rates and the sensible heat input requirement of the reboiler. 4.4.4 CO2 Reaction Rates As previously stated in section 2.3.1 (Ch. 2), CO2 absorption rates should be reported in terms of kg’. The definition of kg’ is reiterated in Equation 4.5. kg’ is a the liquid film mass transfer coefficient converted to gas phase units.

)( *

,2,2

2'

bCOiCO

COg PP

Nk

−= (4.5)

Obtained kg’ values for each MEA experiment are plotted against the measured equilibrium partial pressure at the temperature of the experiment in Figure 4.6. Figure 4.6 includes 7, 9, 11, and 13 m MEA rate data at 40, 60, 80, and 100 ˚C.

1E-07

1E-06

1E-05

10 100 1000 10000 100000

P*CO2 (Pa)

k g' (

mol

/s. Pa

. m2 )

40˚C 60˚C 80˚C 100˚C

7, 9, 11, 13 m MEA

1E-07

1E-06

1E-05

10 100 1000 10000 100000

P*CO2 (Pa)

k g' (

mol

/s. Pa

. m2 )

40˚C 60˚C 80˚C 100˚C

7, 9, 11, 13 m MEA

Figure 4.6: CO2 absorption/desorption rates in MEA solutions at 40, 60, 80, and 100 ˚C

Each shape in Figure 4.6 represents a different MEA concentration but the MEA concentration does not seem to significantly affect the measured kg’. This is interesting and was unexpected, considering kg’ is often represented by the pseudo first order approximation result shown in Equation 4.6.

2

22' ][

CO

bCOg H

AmkDk = (4.6)

Equation 4.6 includes a term for the amine concentration in the numerator. For Equation 4.6 to hold true, other terms in Equation 4.6 must change with concentration to offset the change in the concentration term. The diffusion coefficient and the Henry’s constant are both affected by

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12

changes in concentration. The “Henry’s constant” shown in Equation 4.6 is not the true thermodynamic Henry’s constant which refers to the solubility in water. The Henry’s constant shown in Equation 4.6 refers to the CO2 solubility in the solution. It is a function of the amine concentration, CO2 loading and temperature (Browning and Weiland, 1994; Hartono, 2009). The diffusion coefficient of CO2 will go down slightly with increasing MEA concentration due to the viscosity effect. The CO2 solubility decreases (HCO2 increases) with increasing amine concentration and this change cancels most of the increasing MEA concentration term. Contrary to the data, Equation 4.6 does predict an amine concentration effect on kg’. Figure 4.6 seems to imply that kg’ in MEA solutions increases with increasing temperature. However, that assertion is wrong. Rather than each increasing temperature curve having a higher kg’, it has a higher CO2 equilibrium partial pressure. A close look at Figure 4.6 reveals that the kg’ is almost identical with increasing temperature. The 7 m MEA (circles) data point at 40 ˚C near 15 Pa has a kg’ of approximately 3.3*10-6 mol/s.Pa.m2. The lowest loading data points for 7 m MEA at 60, 80, and 100 ˚C each show a kg’ of approximately 3.0*10-6 mol/s.Pa.m2. Each of these 4 data points has a similar CO2 loading and kg’, verified in Table 4.4. Since temperature has little effect on the measured kg’, the temperature dependent terms in Equation 4.6 must somehow cancel each other. The diffusion coefficient, rate constant, and Henry’s constant are all temperature dependent. The diffusion coefficient will decrease with increasing temperature due to viscosity changes. The rate constant will increase with increasing temperature as shown by regressed literature data (Versteeg, Van Dijck et al., 1996). The solubility of CO2 and N2O in water decreases with increasing temperature (Versteeg and Van Swaaij, 1988). Like concentration, Equation 4.6 does not predict kg’ to be independent of temperature as the data indicate. It would be convenient to show Figure 4.6 in terms of CO2 loading on the x-axis but the CO2 loading basis would prohibit the MEA data from being compared to other amines. Different amines can only be compared on a partial pressure basis since the definition of CO2 loading is somewhat arbitrary and each amine has a different CO2 loading operating range. However, we can plot the x-axis in terms of the equilibrium CO2 partial pressure at a given temperature. This results in two points with the same CO2 loading being plotted at the same value on the x-axis regardless of temperature. In this respect it is similar to plotting the x-axis on a CO2 loading basis. However, this basis has the advantage that it also allows a fair comparison of the CO2 reaction rates with different amines since it is on a partial pressure basis. The equilibrium CO2 partial pressure at 40 ˚C can be viewed as a surrogate for CO2 loading. The MEA rate data is plotted versus the equilibrium CO2 partial pressure at 40 ˚C in Figure 4.7.

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13

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

7, 9, 11, 13 m MEA

100˚C80˚C60˚C40˚C

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

7, 9, 11, 13 m MEA

100˚C80˚C60˚C40˚C

Figure 4.7: CO2 absorption/desorption rates in MEA solutions at 40, 60, 80, and 100 ˚C,

plotted against the 40 ˚C equilibrium CO2 partial pressure The MEA data clearly show that the amine concentration and temperature do not affect the kg’ of the MEA solution. These points fall nicely on each other and make the determination of kg’ for MEA solutions relatively simple. It is important to note that measured kg’ values drastically decrease with increasing equilibrium CO2 partial pressure at 40 ˚C (CO2 loading). This decrease of about a factor of 10 in the value of kg’ from approximately 0.25 to 0.50 CO2 loading is primarily due to the reduction of free MEA available for reaction. PZ rate data at 40, 60, 80, and 100 ˚C are compared in Figure 4.8. 12 m PZ data is not included in the plot since the equilibrium partial pressures of 12 m PZ at 40 ˚C could not be determined using the wetted wall column. These solutions were too viscous for wetted wall column operation.

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14

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

2, 5, 8 PZ

100˚C80˚C60˚C40˚C

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

2, 5, 8 PZ

100˚C80˚C60˚C40˚C

Figure 4.8: CO2 absorption/desorption rates in PZ solutions at 40, 60, 80, and 100 ˚C,

plotted against the 40 ˚C equilibrium CO2 partial pressure The PZ data do not converge quite as nicely as the MEA data. The measured kg’ values of the PZ data do not seem to be dependent on the amine concentration. However, there are some temperature effects. At the lowest CO2 loading near 70–100 Pa, the black 100 ˚C data points seem to drop below the trend of the other data. At the next higher CO2 loading near 300–500 Pa, the green 80 ˚C data points drop from the trend while the 100 ˚C data points drop far below the trend. At the two highest loadings only 40 and 60 ˚C data is available but the red 60 ˚C data points routine fall slightly below the 40 ˚C data points. The observed temperature effects in the PZ data suggest that diffusion of products and reactants may be limiting the reaction of the CO2 with the amine. At the lowest CO2 loading, there is plenty of free amine at the interface and CO2 fluxes are generally small at the lower temperatures. Recall, tested CO2 partial pressures range roughly from 0–2 times the equilibrium partial pressure, not the equilibrium partial pressure at 40 ˚C. Therefore fluxes at 100 ˚C are very high compared to the other temperatures and it is possible that these fluxes, combined with fast CO2 reaction rates, are depleting the interface of reactive PZ and PZ carbamate. At the next highest loading, there is less free PZ carbamate at the interface while CO2 fluxes are higher due to the increased equilibrium partial pressure of the solutions. At this loading it seems as though the 80 ˚C data are now being somewhat restrained by diffusion limitations while 100 ˚C seem severely hampered by the diffusion of reactants and products near the interface. At the higher loadings, the PZ carbamate concentration continues to decrease while tested partial pressures continue to increase, thereby possibly slowing the measured mass transfer coefficients.

137

15

It is important to keep in mind that although the PZ rate data suggest this diffusion limiting phenomenon, a comprehensive model is required to verify it. Alternatively, the MEA experiments do not seem to be limited by the diffusion of reactants and products. It is also important to keep in mind that the proposed diffusion limitation for the PZ experiments in the wetted wall column may not be seen in industrial columns. The wetted wall column has a somewhat smaller liquid film physical mass transfer coefficient, kl

o, than a typical industrial column. This is due to the 9.1 cm stainless steel contactor. In a packed industrial column, either structured or random packing, the mean flow path of the solvent is probably closer to 2–4 cm. The more frequent mixing of the solvent will refresh the interface and discourage depletion of the reactants at the gas-liquid interface. The MEA, PZ, and the MEA/PZ data are compared in Figure 4.9. MEA is represented by the empty points. PZ is represented by the filled data points. 7 m MEA/2 m PZ data is shown as Xs.

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

Filled Points – 2, 5, 8 m PZEmpty Points – 7, 9, 11, 13 m MEA

100˚C80˚C60˚C40˚C

X’s – 7 m MEA/2 m PZ

1E-07

1E-06

1E-05

10 100 1000 10000

P*CO2 @ 40C (Pa)

k g' (

mol

/s. Pa

. m2 )

Filled Points – 2, 5, 8 m PZEmpty Points – 7, 9, 11, 13 m MEA

100˚C80˚C60˚C40˚C

X’s – 7 m MEA/2 m PZ

Figure 4.9: CO2 absorption/desorption rates in MEA, PZ, and MEA/PZ solutions at 40, 60,

80, and 100 ˚C, plotted against the 40 ˚C equilibrium CO2 partial pressure Most of the PZ data points form a trend line above the MEA data. These data show that kg’ for PZ is 2–3 times faster than MEA. This means PZ reacts with CO2 2–3 times faster than MEA. To a first approximation 1/2 to 2/3 less packing in the absorber would be required for PZ compared to MEA. The 7 m MEA/2 m PZ rate data generally fall between the MEA and PZ. The CO2 loading near 200 Pa seems to show some diffusion limitations at the 100 ˚C condition.

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16

4.4.4.1 Rate Comparisons with Literature 4.4.4.1.1 Monoethanolamine Rate data obtained in this work are compared to literature values in this section. As previously stated, there is limited rate data on highly loaded concentrated amines. For a proper comparison on a kg’ basis, some raw data is required. Figure 4.10 shows a comparison of 7 m MEA rate data at 40 and 60 ˚C. Aboudheir (2003) provides rate data obtained from a laminar jet absorber. At each condition a few measurements were made and these data overlap nicely. Figure 4.10 also includes 4 wetted wall column data points obtained by Dang (2003). Dang used the same wetted wall column used in this work. A single 40 ˚C data point from Hartono (2009) is also included in Figure 4.10.

4x10-7

6x10-7

8x10-7

1x10-6

3x10-6

0 0.1 0.2 0.3 0.4 0.5

k g' (m

ol/s

. Pa. m

2 )

CO2 Loading (mol/mol)

7 m MEA

Circle - Harono (2009)X's - Aboudheir (2003)Squares - Dang (2003)Triangles - Current Work

60C40C

Figure 4.10: CO2 reaction rate comparison on a kg’ basis for 7 m MEA at 40 and 60 ˚C (Aboudheir, Tontiwachwuthikul et al., 2003; Dang and Rochelle, 2003; Hartono, 2009)

The data by Dang fit very nicely with the newly obtained wetted wall column data. The data by Aboudheir also fit nicely at the two higher CO2 loadings. The data by Aboudheir (2003) near 0.1 loading show a lower kg’ value than an extrapolation of the wetted wall column data would predict. However, the unloaded rate data by Hartono (2009) supports these 0.1 CO2 loading values and suggests that the liquid film mass transfer coefficient, kg’, may not change significantly from 0 to 0.25 CO2 loading. No wetted wall column experiments were conducted below 0.2 CO2 loading. The wetted wall column cannot accurately obtain rate data in MEA solutions at CO2 loading much lower than 0.25 because the system becomes dominated by the gas film mass transfer coefficient. Recall the gas film mass transfer coefficient of the column was originally characterized using unloaded MEA (Pacheco, 1998).

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17

The data by Aboudheir (2003) seem to show a fairly reproducible effect of temperature. In each of the three CO2 loadings, the 60 ˚C data points exhibit about 50% higher kg’ values. The wetted wall column data including Dang (2003) and the current work do not clearly show a trend. Figure 4.7 more clearly shows that there is no discernable temperature effect on the CO2 absorption/desorption rates in MEA solutions. Unloaded MEA rate data found in the literature can also be compared to the highly loaded, highly concentrated MEA rate data presented here. As the Literature Review detailed, there are numerous literature sources which report rate data in unloaded, relatively dilute MEA solutions. However, most of these data sources only report obtained rate constants and do not detail values used for the Henry’s constant or the diffusion coefficient. Neither do they include fluxes and driving forces which allow for the calculation of kg’. Laddha and Danckwerts (1981) provide calculated rate constants along with the solubility and diffusion parameters which allow for the calculation of the measured flux and KG. No gas film mass transfer coefficients were given for the stirred cell experiments. The rate constants (expressed through Equation 4.7) for the 6 tested amine concentrations ranged from 5.49 to 6.28 m3/(mol.s) at 25 ˚C. These rate constants compare favorably with 5.99 m3/(mol.s) value predicted by a correlation developed from a review of literature data (Versteeg, Van Dijck et al., 1996).

[ ][ ]222 COMEAkrCO =− (4.7) Hartono (2009) provides all the important experimental data from his CO2 absorption into MEA. This allows for the calculation of KG and then kg’. The rate experiments performed using a string of discs were determined to be 5–18% gas film controlled. The calculated kg’ from the experiments by Hartono (2009) and the calculated KG values from Laddha (1981) are shown in Figure 4.11.

7x10-78x10-79x10-71x10-6

2x10-6

3x10-6

4x10-6

0 1 2 3 4 5

KG o

r kg' (

mol

/s. P

a. m2 )

MEA Concentration (Molarity)

25C

25C30C

40C

50CKG - Laddha and Danckwerts (1981)

kg' - Hartono (2009)

Figure 4.11: CO2 reaction rates in unloaded MEA solutions (Laddha and Danckwerts,

1981; Hartono, 2009)

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18

Figure 4.11 shows the Laddha data at 25 ˚C below the Hartono data at 25 ˚C. This is expected since the Laddha data does not remove the gas film resistance from the system. Recall, the liquid film mass transfer coefficient, kg’, must be larger than the overall mass transfer coefficient, KG. In cases where the gas film mass transfer coefficient, kg, is limiting, KG can be significantly lower than kg’. In a stirred cell experiment with unloaded MEA, it is quite likely that gas film mass transfer resistance is significant since stirred cells often have this concern. The Laddha data in Figure 4.11 is not as descriptive of CO2 rates into MEA as the Hartono data because gas film resistances due to operating conditions and the geometry of the apparatus cannot be extracted from the reported data. Figure 4.11 shows a couple of interesting points. First of all it shows a dependence of kg’ on the MEA concentration at lower MEA concentrations. At higher MEA concentrations kg’ becomes independent of concentration, although at different amine concentrations for different temperatures. This independence of concentration on kg’ is also seen in the current MEA rate data (Figure 4.7) which was taken at high MEA concentrations. 4.4.4.1.2 Piperazine Although Table 2.2 of the Literature Review only lists 5 references for CO2 reaction rates into aqueous PZ solutions, all provide a fair amount of raw experimental data. Sun (2005), Derks (2006), Cullinane (2006) and Samanta (2007) include unloaded PZ rate data while Bishnoi provides CO2 loaded rate data. All five data sources use low piperazine concentrations. Derks uses a stirred cell and a “semi-continuous” gas phase operation. Numerous CO2 partial pressures were tested for each amine to determine when the pseudo first order condition applies. At high CO2 partial pressures, diffusion in the liquid phase limits CO2 mass transfer. For 1.0 M PZ at 40 ˚C, approximately 1.5 kPa CO2 was the threshold for the onset of the pseudo first order condition. Inlet CO2 partial pressures above 1.5 kPa showed a distinct effect of the partial pressure on the measured KG. Below the threshold, the overall mass transfer coefficient is independent of the inlet partial pressure. Sun (2005) and Samanta (2007) each measured the absorption into unloaded PZ solutions using wetted wall columns. However, each uses very high CO2 partial pressures, typically about 5 kPa. At these high CO2 partial pressures and amine concentrations below 1 M, CO2 fluxes into the liquid phase may be restricted by diffusion. In fact, Sun (2005) tested a few lower CO2 partial pressures and these data verify that the system is not operating in the pseudo first order regime at the 5 kPa CO2 pressure experiments, which comprise most of the data. Although we cannot extract a meaningful kg’ value from these raw data, they are still valuable data. These data require a model to properly account for the diffusion limitations in the system. Cullinane provides all the required data to directly calculate kg’. At each condition, 5 measurements were made. Obtained kg’ values were shown to range ± 30% from the mean due to the high dependence of the gas film mass transfer coefficient. The 1.2 m PZ experiments were 54–73% gas film controlled. Only 25 and 60 ˚C experiments were tested. The Cullinane experiments all use very low CO2 partial pressures (< 250 Pa) so the pseudo first order condition should apply. Figure 4.12 shows a comparison of the obtained 2 m PZ wetted wall column rate data with some literature obtained values. Figure 4.12 includes an unloaded 1.0 M PZ data point from Derks. This point is actually the obtained overall mass transfer, KG, not the liquid film mass transfer coefficient, kg’. Derks does not provide a gas film mass transfer coefficient correlation to quantify if or how much gas phase resistance limits CO2 absorption into the solution. For

141

19

purposes of comparison, the KG obtained from Derks is plotted alongside the kg’ data and the kg’ model prediction from Cullinane (2005).

6x10-7

8x10-7

1x10-6

3x10-6

5x10-6

7x10-6

0 0.1 0.2 0.3 0.4

k g' (m

ol/s

. Pa. m

2 )

CO2 Loading (mol/mol

alk)

Triangle - Derks (2006) (KG)1.0 M PZ

Squares - Bishnoi (2000) 0.06-0.31 m PZLine - Cullinane (2005) 1.8 m PZ Model PredictionX's - Cullinane (2006) 1.2 m PZ (25 and 60C)Circles - Current Work 2 m PZ

40CPZ

60C

25C

Figure 4.12: CO2 reaction rate comparison on a kg’ basis for PZ solutions at 40 ˚C (Bishnoi and Rochelle, 2000; Cullinane, 2005; Cullinane and Rochelle, 2006; Derks, Kleingeld et al.,

2006) Figure 4.12 shows a good match of the current 2 m PZ rate data with the 1.8 m PZ model prediction by Cullinane (2005). The loaded Bishnoi data shows mass transfer coefficients somewhat below the current data. This is expected due to the very low PZ concentration (0.06 to 0.31 m PZ) in these experiments. Interestingly, these data show the same trend as the 2 m PZ data. Very low amine concentrations also exhibited a reduced kg’ in MEA solutions (Figure 4.11). The unloaded data in Figure 4.12 is difficult to analyze. The 25 and 60 ˚C data points by Cullinane show a significant temperature effect at zero loading. These 1.2 m PZ data points show a decent fit to the 1.8 m 40 ˚C model prediction. The Derks overall mass transfer coefficient falls significantly below the other unloaded data, which is not unexpected. This suggests that the gas film mass transfer coefficient is likely limiting mass transfer into the PZ solution. The limitation of the gas film mass transfer coefficient is a disadvantage of stirred cell reactors to measure CO2 reaction rates of very fast amines.

4.5 PREDICTING KG’ FOR MEA WITH THE PSEUDO FIRST ORDER ASSUMPTION The pseudo first order conditions are fulfilled when the bulk amine concentration is the same as the amine concentration at the interface. At this condition, the diffusion of reactants and products does not hinder CO2 mass transfer and the mass transfer rate can be determined analytically by the rate equation. However, an analytical expression to calculate kg’ at highly concentrated, highly loaded conditions has previously remained elusive and thus required experimentation to determined CO2 mass transfer rates.

142

20

Equation 4.8 yields the rate equation typically used for CO2 reaction with MEA. The rate expression can be used in a material balance to determine the CO2 flux. The grouping of terms in Equation 4.10 can be treated as the liquid film mass transfer coefficient, kg’.

[ ][ ]22 COMEAkr fCO −= (4.8)

0]][[][22

22

2 =−∂

∂ COAmkxCOD fCO (4.9)

)(][ *

,2,22

22 bCOiCO

CO

COCO PP

HAmkD

N −= (4.10)

2

2' ][

CO

bCOg H

AmkDk = (4.11)

The rate constant in Equation 4.11 is known from the literature data. The amine concentration can be chosen. The diffusion coefficient and Henry’s constant can be estimated to predict kg’. This method has not been effective at predicting kg’ in highly concentrated, highly loaded MEA solutions. This disconnect required a closer look into the assumptions in Equation 4.11. 4.5.1 Activity Coefficients The rate expression is determined by the activity of the reactants, not the concentration. It cannot be assumed that activity coefficients are near 1.0 in highly loaded, highly concentrated MEA solutions. These solutions are highly ionic and should be treated accordingly. Accounting for activity coefficients in the rate expression (Equation 4.12) leads to a modified kg’ expression shown in Equation 4.13.

[ ] [ ]222 COMEAkr COMEAfCO γγ−= (4.12)

2

22' ][

CO

CObMEACOg H

AmkDk

γγ= (4.13)

Including activity coefficients into the rate expression requires an understanding of what the activity coefficients of MEA and CO2 are in these solutions. MEA activity coefficients can be obtained from amine volatility experiments. CO2 activity coefficients can be obtained from Henry’s solubility data. 4.5.1.1 Monoethanolamine Amine volatility data is very scarce but Hilliard (2008) provides a nice sample of 3.5, 7, and 11 m MEA volatility data. These experiments coincide with the CO2 partial pressure experiments Hilliard ran in an equilibrium cell. The FTIR analyzer he used can simultaneously measure gas phase concentrations of multiple components. The MEA volatility data was treated via the modified Raoult’s Law in Equation 4.14. DIPPR database obtained value of 164 and 666 Pa were used for the equilibrium partial pressure of pure MEA at 40 and 60 ˚C (1979).

*MEAMEAMEAMEAMEA PxPPy γ== (4.14)

Equation 4.14 requires the mole fraction of MEA, which is easy to determine accurately below 0.4 CO2 loading. Above a 0.45 CO2 loading bicarbonate concentrations can become significant while free MEA concentration become very small. At these high CO2 loadings it is very difficult to accurately determine the free MEA concentration. Due to this uncertainty, no data above 0.45 CO2 loading was used in the determination of MEA activity coefficients. Figure 4.13 shows the calculated MEA activity coefficients from the Hilliard (2008) data.

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21

y = 1.115x + 0.339

y = 0.679x + 0.294

0.0

0.4

0.8

1.2

1.6

2.0

0.1 0.2 0.3 0.4 0.5CO2 Loading (mol/mol)

MEA

Act

ivity

Coe

ffici

ent

7 m MEA, 40C 7 m MEA, 60C11 m MEA, 40C 11 m MEA, 60C3.5 m MEA, 40C 3.5 m MEA, 60C

Figure 4.13: Calculated MEA activity coefficients for 3.5, 7, and 11 m MEA at 40 and 60 ˚C

(Hilliard, 2008). The large errors in Figure 4.13 seen at high loading are not particularly important. These large errors are the result of fairly small deviations in either the vapor-liquid equilibrium model or the CO2 loading reported by Hilliard. The Hilliard data show an increasing MEA activity coefficient with increasing CO2 loading. The MEA activity coefficient also seems to be a function of temperature, with higher temperatures having lower activity coefficients. Amine concentration does not seem to be a major factor in the determination of the activity coefficient. The 3.5, 7 and 11 m MEA data sets each tend to overlap fairly well. Figure 4.13 includes trend lines for the 40 and 60˚C data. The equations of the trend lines reproduced in Equation 4.15 and 4.16 give a pretty good estimation of the MEA activity coefficient as a function of CO2 loading.

( ) 339.0115.1 240, += LoadingCOP CMEA (4.15) ( ) 294.0679.0 260, += LoadingCOP CMEA (4.16)

4.5.1.2 Carbon Dioxide The activity of CO2 in loaded MEA solutions can be obtained from Henry’s solubility data. Unfortunately very little N2O solubility data has been reported in concentrated, CO2 loaded MEA systems. Browning and Weiland (1994) present 12 N2O solubility data points in 10, 20, and 30, wt % MEA solutions up to 0.4 CO2 loadings at 25 ˚C. No other N2O solubility data varying amine concentration and CO2 loading is known. The N2O solubility data was regressed into Equation 4.17. Equation 4.17 includes the MEA concentration in wt %. ))((7.294)(1115)(31.184093 2225,2 LdgCOMEALdgCOMEAH CON +−+= (4.17)

Figure 4.14 shows the N2O solubility data points from Browning as well as regressed curves for 10, 20, and 30 wt % MEA.

60˚C

40˚C

144

22

4000

4500

5000

5500

6000

6500

7000

7500

8000

0 0.1 0.2 0.3 0.4


HN

2O (k

Pa. L/

mol

)

Figure 4.14: N2O solubility data (Browning and Weiland, 1994) and model (lines) in 10, 20,

and 30 wt % MEA solutions at 25 ˚C. Figure 4.14 shows that Equation 4.17 does a very good job of predicting the N2O solubility as a function of amine concentration and CO2 loading. Figure 4.14 also illustrates how drastically the N2O solubility decreases with increasing loading and amine concentration. It is imperative that both the amine concentration and CO2 loading are considered in the estimation of the Henry’s constant. The literature suggests these factors have generally been ignored. Equation 4.17 allows for the calculation of the solubility of CO2 via the N2O analogy. The CO2 and N2O solubility data in water as a function of temperature has been compiled and regressed (Versteeg and Van Swaaij, 1988). The implementation of the N2O analogy assumes that N2O and CO2 solubility in concentrated, loaded amines has the same temperature dependence as N2O and CO2 in water. Fortunately this assumption is no longer required for MEA solutions. Hartono (2009) recently published N2O solubility data in loaded 30 wt % (7 m) MEA solutions. He measured N2O solubility from 25–87 ˚C for 0, 0.2, 0.4, and 0.5 CO2 loading solutions. Figure 4.15 illustrates the N2O solubility results for each of the 4 CO2 loadings.

10 wt% MEA

30 wt% MEA

20 wt% MEA

25˚C

145

23

8

8.5

9

9.5

10

10.5

0.0027 0.0029 0.0031 0.0033

1/Temp (1/K)

ln (H

N2O

) (Pa

. m3 /m

ol)

Figure 4.15: N2O solubility data (points) and trend lines for 0, 0.2, 0.4, and 0.5 CO2 loaded

7 m MEA solutions (Hartono, 2009) The natural log of the N2O solubility plotted versus inverse temperature yields straight lines for each of the 4 CO2 loadings. The slope of the lines corresponds to the temperature behavior of N2O solubility in 7 m MEA. The slopes of the four lines are approximately equal with an average value of -1905. The N2O solubility temperature dependence in loaded MEA solutions can be added to Equation 4.17 which is valid at 25 ˚C. Equation 4.18 should be valid from 25 to at least 87 ˚C, the temperature range of the regressed data.

[ ]⎟⎠⎞

⎜⎝⎛ −

⎟⎠⎞

⎜⎝⎛ −

+−+=

15.2981905exp

1905exp))((7.294)(1115)(31.184093 222

TLdgCOMEALdgCOMEAH ON (4.18)

Similar to the N2O solubility from Browning (1994), Hartono shows the N2O solubility decreasing with increasing CO2 loading. Unfortunately the data do not agree completely. Both Hartono and Browning measure N2O solubility at 25˚ C for 7 m MEA. Figure 4.16 shows the disagreement in the two data sets.

0.5 CO2 Loading

0.4 0.2 0

7 m MEA

146

24

4500

5000

5500

6000

6500

7000

7500

8000

0 0.1 0.2 0.3 0.4 0.5


H N2O

(kPa

. L/m

ol)

Figure 4.16: N2O solubility in 7 m MEA at 25 ˚C (Browning and Weiland, 1994; Hartono,

2009) Since these are the only two data sets for N2O solubility in loaded MEA solutions, it is not possible to tell which data set is erroneous. In this work the Browning (1994) data set has been used to quantify the effects of CO2 loading and MEA concentration on N2O solubility. The Hartono (2009) data set has been used to quantify the effect of temperature on N2O solubility. Using the N2O analogy, the solubility of CO2 in loaded MEA solutions can be determined using Equation 4.18 along with the solubility of CO2 and N2O in water. An accurate calculation of the Henry’s constant of CO2 allows for the determination of the activity coefficient of CO2 via Equation 4.19.

OHCOCOCO HH 2,222 γ= (4.19)

2COH gives the effective solubility of CO2 in the solution. OHCOH 2,2 is the true thermodynamic Henry’s constant which refers to the solubility of CO2 in water. The activity coefficient of CO2 varies between 1.3 and 2.5 for 7–13 m MEA wetted wall column experiments. 5.4.2 Diffusion Coefficient of CO2 Work by Versteeg and Van Swaaij (1988) has shown that the diffusion of N2O and CO2 in aqueous amines generally follows the viscosity dependence in Equation 4.20. ( ) ( )WaterONeSolutionAON DCONSTANTD 8.0

2min8.0

2 ηη == (4.20) The N2O and CO2 diffusivity relationship in Equation 4.20 was confirmed with MDEA solutions but resulted in less satisfactory results for AMP (Tomcej and Otto, 1989; Xu, Otto et al., 1991). Diaphragm cell experiments in loaded MEA and PZ solutions yield a viscosity dependence of 0.72 compared to the 0.8 obtained by Versteeg and Van Swaaij (1988) for N2O. Although the 0.72 dependence obtained from the diaphragm cell experiments does necessarily represent CO2 diffusion, or diffusion of any other specific species, the 0.72 dependence was used for calculation of the diffusion coefficient in the pseudo first order expression.

7 m MEA

Browning (1994)

Hartono (2009)

25˚C

147

25

( ) ( )( ) eSolutionA

WaterCOeSolutionACO

DD

min72.0

72.02

min2 ηη

= (4.21)

The viscosity of water at the wetted wall column experimental temperatures was found from Watson (1986). MEA solution viscosity values were obtained from Weiland (1998). 5.4.3 Monoethanolamine Order With accurate estimations for the activity coefficients of MEA and CO2, the MEA concentration dependence on kg’ can be examined. It was found that the rate data show a 2nd order dependence on the MEA concentration. This 2nd order dependence can be satisfied from either the zwitterion or termolecular mechanism, although the termolecular mechanism is more likely for MEA. The termolecular mechanism allows for the following base catalysis reaction expression below.

[ ] [ ]( ) [ ] [ ]2222 COMEAOHkMEAkr OHMEACO ⋅⋅+−= (4.22) For the 2nd order dependence to be observed [ ]MEAkMEA must be much greater than [ ]OHk OH 22 . Cullinane (2005) used Bronsted Theory to relate pKa’s to rate constants. According to the pKa-rate constant relationship found for piperazine, an amine with the pKa of MEA would produce

OH

MEA

kk

2

ratios near 180, thereby making catalysis by water negligible in almost all the wetted wall

column experimental conditions. In 7 m MEA the base catalysis by water would approach that by MEA only after more than 95% of the total MEA had been reacted.

In order for the 2nd order amine dependence to submit to the zwitterion mechanism, rk must be much greater than [ ]∑ Bkb in Equation 4.23 yielding Equation 4.24. This is generally not accepted as true for MEA (Danckwerts 1979). However, in the previous treatment of literature data leading to this conclusion, activity coefficients and the Henry’s constant of CO2 were not considered rigorously. At least at these highly concentrated, highly loaded MEA systems, these parameters are very important.

∑+

−=

][1

]][[ 22

Bkkk

k

COAmr

bf

r

f

CO (4.23)

[ ][ ]∑−= ][22 BkCOMEAkk

r br

fCO (4.24)

5.4.4 Predicting kg’ Results Instituting the 2nd order amine dependence into the pseudo first order condition result yields Equation 4.25

OHCOco

bMEACO

CO

CObMEACOg H

MEAkDH

MEAkDk

2,25.02

222

2

222

2' ][][γ

γγγ== (4.25)

The rate constant in the simple form of the pseudo first order expression (Equation 4.11) assuming 1st order amine kinetics was compiled into Equation 4.26 (Versteeg, Van Dijck et al., 1996).

smol

mT

k⋅

⎟⎠⎞

⎜⎝⎛ −×=

38 5400exp104.4 (4.26)

148

26

The temperature dependence of this expression did not require readjustment. The pre-exponential factor was scaled until the calculated kg’ values from Equation 4.26 matched wetted wall column measured values. Figure 4.17 is a parity plot quantifying the fit of the modified pseudo first order expression. Figure 4.17 also includes 0.5, 1, 2, 3, 4, and 5 M unloaded MEA rate data (Hartono, 2009).

1E-07

1E-06

1E-05

1E-07 1E-06 1E-05

Measured kg' (mol/s.Pa.m2)

Cal

cula

ted

k g' (

mol

/s. Pa

. m2 )

Figure 4.17: Parity plot of measured kg’ versus calculated kg’ from modified pseudo first order expression for 7, 9, 11, and 13 m MEA at 40 and 60 ˚C and 0.5–5 M unloaded MEA

at 40 ˚C (Hartono, 2009) Figure 4.17 shows an excellent agreement between the wetted wall column measured and calculated kg’ values from the modified pseudo first order expression in Equation 4.25. After adjusting the kg’ expression to include 2nd order MEA dependence, the amine concentration dependence was perfectly accounted for by modifying the Henry’s constant, the diffusion coefficient, and the activity coefficients of MEA and CO2 according the available literature data. The model only compares 40 and 60 ˚C wetted wall column kg’ data due to the lack of available MEA volatility data above 60 ˚C. However these data match very well and no unaccounted temperature effect seems to be present. The higher temperature increases the rate constant, decreases the MEA activity coefficient, decreases the CO2 activity coefficient, and increases the effective Henry’s constant. The effects of each of these parameters (obtained from literature data) essentially cancel in the kg’ expression. The same was seen in the wetted wall column experiments where the temperature effect did not significantly affect kg’. Although this comparison only includes 40 and 60 ˚C, the model will likely do a fair job extrapolating to the 80 and 100 ˚C experimental data. CO2 reaction rates with unloaded MEA can also be predicted using the modified pseudo first order expression. At higher MEA concentration, 2–5 M, the expression very closely predicted the experimental kg’. At the two lower MEA concentrations, 0.5 and 1 M, the expression under

7, 9, 11, 13 m MEA …. 40˚C - Circles…..…… 60˚C – Triangles X’s – 0.5-5 M MEA, 40˚C …….Hartono (2009)

0.5 M

1 M

2 M3 M

4 M5 M

149

27

predicted the experimental kg’. This underestimation is partially due to the neglected H2O catalysis in the modified pseudo first order expression. According to Equation 4.22, base catalysis by water can become significant or even dominant at very low amine concentrations. It is important to remember that the activity coefficient correlations (Equations 4.15, 4.18, and 4.19) are likely less accurate for unloaded, dilute MEA data than the highly concentrated, highly loaded data. This analysis was able to quantitatively show how the temperature and concentration dependent parameters in the modified pseudo first order expression cancel each other to show negligible temperature and concentration effects in kg’. It is important to note that all the parameters were estimated from available literature data and only the implementation of 2nd order MEA kinetics was required to account for the lack of temperature and concentration dependences in the rate data of the highly loaded, 7–13 m MEA experiments.

References (1979). Vapor Pressures and Critical Points of Liquids XIV: Aliphatic Oxygen-Nitrogen

Compounds. Item 79030. London, Engineering Sciences Data. Aboudheir A, Tontiwachwuthikul P, et al. "Kinetics of the reactive absorption of carbon dioxide

in high CO2-loaded, concentrated aqueous monoethanolamine solutions." Chem Engr Sci. 2003;58:5195–5210.

Bishnoi S, Rochelle GT. "Absorption of CO2 into Aqueous Piperazine: Reaction Kinetics, Mass Transfer and Solubility." Chem Engr Sci. 2000;55:5531-5543.

Browning GJ, Weiland RH. "Physical Solubility of CO2 in Aqueous Alkanolamine via Nitrous Oxide Analogy." J Chem Eng D. 1994;39:817-822.

Cullinane JT. Thermodynamics and Kinetics of aqueous piperazine with potassium carbonate for CO2 absorption. The University of Texas at Austin. Ph.D. Dissertation. 2005;295.

Cullinane JT, Rochelle. "Kinetics of CO2 Absorption into Aqueous Potassium Carbonate and Piperazine." I&ECR. 2006;45(8):2531-2545.

Danckwerts PV. "The reaction of CO2 with ethanolamines." Chem Engr Sci. 1979;34(4):443-446.

Dang H, Rochelle GT. "CO2 Absorption Rate and Solubility in MEA/PZ/H2O." Sep Sci & Tech. 2003;38(2):337-357.

Davis JD. Thermal Degradation of Aqueous Amines Used for CO2 Capture. The University of Texas at Austin. Ph.D. Dissertation. 2009;278.

Derks PWJ, Kleingeld T et al. "Kinetics of Absorption of CO2 in Aqueous Piperazine Solution." Chem Engr Sci. 2006;61(20):6837-6854.

Ermatchkov V, Perez-Salado Kamps A et al. "Solubility of CO2 in Aqueous Solutions of N-Methyldiethanolaine in the Low Gas Loading Region." Ind Eng Chem Res. 2006b;45:6081–6091.

Ermatchkov V, Perez-Salado Kamps A et al. (2006a). "Solubility of Carbon Dioxide in Aqueous Solutions of Piperazine in the Low Gas Loading Region." J Chem Eng D. 2006a;51(5):1788–1796.

150

28

Hartono A. Characterization of diethylenetriamine (DETA) as absorbent for CO2. Trondheim, Norway, Norwegian University of Science and Technology. Ph.D. Dissertation 2009;243.

Hilliard M. A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for CO2 Capture from Flue Gas. The University of Texas at Austin. Ph.D. Dissertation 2008;1025.

Jou F-Y, Mather AE et al. "The Solubility of CO2 in a 30 Mass Percent Monoethanolamine Solution." Can J Chem Engr. 1995;73(1):140–147.

Laddha SS, Danckwerts PV. "Reaction of CO2 with Ethanolamines: Kinetics from Gas Absorption." Chem Engr Sci. 1981;36:479–482.

Pacheco MA. (1998). Mass Transfer, Kinetics and Rate-Based Modeling of Reactive Absorption. The University of Texas at Austin. Ph.D.: 291.

Rochelle GT et al. CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009. Luminant Carbon Management Program. The University of Texas at Austin. 2009.

Rochelle GT et al. CO2 Capture by Aqueous Absorption, Second Quarterly Progress Report 2009. Luminant Carbon Management Program. The University of Texas at Austin. 2009.



Samanta A, Bandyopadhyay SS. "Kinetics and Modeling of CO2 Absorption into Aqueous Solutions of Piperazine." Chem Engr Sci. 2007;62(24):7312–7319.

Snijder ED, te Riele MJM et al. "Diffusion Coefficients of Several Aqueous Alkanolamine Solutions." J Chem Engr Data. 1993;38(3): 475-480.

Sun W-C, Yong C-B et al. "Kinetics of the Absorption of CO2 into Mixed Aqueous Solutions of 2-amino-2methyl-1-propanol and Piperazine." Chem Engr Sci. 2005;60(2):503–516.

Tomcej RA, Otto FD. "Absorption of CO2 and Nitrous Oxide into Aqueous Solutions of Methyldiethanolamine." AIChE J. 1989;35(5):861–864.

Versteeg GF, Van Dijck LAJ, et al. "On the Kinetics between CO2and Alkanolamines both in Aqueous and Non-aqueous Solutions. An Overview." Chem Engr Comm. 1996;144:113–158.

Versteeg GF, Van Swaaij WPM. "Solubility and diffusivity of acid gases (CO2, nitrous oxide) in aqueous alkanolamine solutions." J Chem Engr Data. 1988;33(1):29–34.

Watson JR,. Sengers JV. "Improved International Formullations for the Viscosity and Thermal Conductivity of Water Substance." J Phys Chem Ref Data. 1986;15:1291.

Weiland RH, Dingman JC et al. "Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine Solutions and Their Blends." J Chem Engr Data. 1998;43(3):378–382.

Xu S, Otto FD, et al. "Physical Properties of Aqueous AMP Solutions." J Chem Engr Data. 1991;36(1):71–75.

151

1

Modeling CO2 Absorption Using Aqueous Amines


by Jorge M. Plaza


and the




July 1, 2009

Abstract

A CO2 absorber model for the 8 m PZ solvent is under development. It uses a modified version

of the Hilliard (2008) thermodynamic representation by Van Wagener version 02/06/09

(Rochelle et al., 2009). It includes a reduced reaction set based on the more relevant species

present at the expected operating loading (0.3–0.4 mol CO2/mol alkalinity) Kinetics will be

based on Cullinane (2005) and regressed to include data generated by Dugas (Rochelle et al.,

2008a). Initial work accesses proper representation of solvent physical properties. Density and

viscosity were regressed to match experimental data generated by Freeman (Rochelle et al.,

2008b: Rochelle et al., 2009). The activity coefficient of CO2 was also examined and compared

to values found in Cullinane (2005) as a function of amine concentration and loading.

Density and viscosity match the experimental data with a deviation of maximum 10%. The

model represents correctly the expected change in the activity coefficient of CO2 with amine

concentration and its values are consistent with the available low concentration experimental

data. However, the effect of loading is not represented correctly. This issue needs to be

addressed. Kinetics will be implemented once the effect of loading on CO2 activity coefficients

is resolved.

The overall gas–side mass transfer coefficient (Kg) was calculated for the November 2008 PZ

pilot plant data and compared to kg’ data by Dugas (Rochelle et al., 2008a) and previous pilot

plant campaigns. Results show a higher Kg for 5m PZ.

PZ Model Development

Thermodynamic Model

Thermodynamics are based on a re-regression of the Hilliard (2008) PZ model by Van Wagener

version 02/06/09 (Rochelle et al., 2009) to include data generated at amine concentration above

5m. The reaction set was reduced to four reactions involving the most significant species present

at the loading range proposed for operation for this solvent (0.3–0.4 mol CO2/mol alkalinity)

152

2

based on speciation by Hilliard (2008). Species that are present in small concentrations have

been omitted from the model (CO3=, H

+, OH

-, PZH2

++). The final reaction set is as follows:

2 𝑃𝑍 + 𝐶𝑂2 ↔ 𝑃𝑍𝐻+ + 𝑃𝑍𝐶𝑂𝑂− (1)

2 𝑃𝑍𝐶𝑂𝑂− + 𝐶𝑂2 ↔ 𝑃𝑍 𝐶𝑂𝑂− 2 + 𝐻+𝑃𝑍𝐶𝑂𝑂− (2)

𝑃𝑍𝐶𝑂𝑂− + 𝐶𝑂2 + 𝐻2𝑂 ↔ 𝐻𝐶𝑂3− + 𝐻+𝑃𝑍𝐶𝑂𝑂− (3)

𝑃𝑍 + 𝐻+𝑃𝑍𝐶𝑂𝑂− ↔ 𝑃𝑍𝐻+ + 𝑃𝑍𝐶𝑂𝑂− (4)

The vapor-liquid equilibrium (VLE) was verified with the new reaction set (Figure 1) and it

continues to adequately represent the behavior of the partial pressure of CO2 with loading.

Figure 1: VLE verification for the Hilliard modified by Van Wagner version 02/06/09

(Rochelle et al., 2009) for 8 m PZ. Points are by Dugas (Rochelle et al., 2008a). Lines

generated with the reduced reaction set in Aspen Plus® RateSep

TM.

10

100

1000

10000

100000

1000000

0.2 0.25 0.3 0.35 0.4 0.45

PC

O2

*(P

a)

Loading (mol CO2/mol alkalinity)

40oC

60oC

80oC

100oC

153

3

Physical Properties Regression

Density and viscosity were fit to data generated in the lab by Freeman (Rochelle et al., 2008b:

Rochelle et al., 2009) for PZ from 5m to 9 m PZ which corresponds to the range of operation of

the pilot plant.

Density: Density data for 5 m, 7 m, 8 m and 9 m PZ and from 0.20 to 0.46 mol CO2/mol

alkalinity was regressed using the Clarke model for liquid molar volume for electrolyte solutions.

The liquid molar volume for the solution (Vml) is calculated using the following:

𝑉𝑚𝑙 = 𝑉𝑠𝑜𝑙𝑣 + 𝑥𝑐𝑎𝑉𝑐𝑎 (5)

where:

Vsolv is the liquid molar volume for the solvent;

Vca is the electrolyte effective partial molar volume;

xca is the apparent electrolyte mole fraction.

The liquid molar volume for the solvent is calculated using the following expression:

𝑉𝑠𝑜𝑙𝑣 = 𝑥𝑤𝑉𝑤∗ + 𝑥𝑛𝑤𝑠 𝑉𝑛𝑤𝑠

𝑙 (6)

where:

Vw* is the water volume from the steam tables.

xnws is the sum of the mole fractions of all non-water solvents.

Vnwsl is the liquid molar volume for the mixture of all non-water solvents calculated

using the Rackett equation.

The Rackett expression was used for PZ and CO2:

𝑉𝑛𝑤𝑠𝑙 =

𝑅𝑇𝑐 𝑍𝑚𝑅𝐴 1+(1−𝑇𝑟)

27

𝑃𝑐 (7)

where:

𝑇𝑐 = 𝑥𝑖𝑥𝑗𝑉𝑐𝑖𝑉𝑐𝑗 𝑇𝑐𝑖𝑇𝑐𝑗

0.5 1 − 𝑘𝑖𝑗 𝑗𝑖

𝑉𝑐𝑚2 (8)

𝑇𝑐𝑃𝑐

= 𝑥𝑖

𝑇𝑐𝑖

𝑃𝑐𝑖𝑖

(9)

𝑍𝑚𝑅𝐴 = 𝑥𝑖𝑍𝑖

𝑅𝐴

𝑖

(10)

𝑉𝑐𝑚 = 𝑥𝑖

𝑖

𝑉𝑐𝑖 (11)

𝑇𝑟 =𝑇

𝑇𝑐 (12)

154

4

The electrolyte contribution in the molar volume is determined using the apparent mole fraction

(xca) calculated from the true ionic composition based on the proposed system chemistry.

𝑉𝑐𝑎 = 𝑉𝑐𝑎∞ + 𝐴𝑐𝑎

𝑥𝑐𝑎

1 + 𝑥𝑐𝑎

(13)

The parameters Vca∞, and Aca for equation 13 were regressed for the cation PZH

+ and the other

ionic species along with the values for ZiRA

in equation 10 for CO2 and PZ using Aspen Plus®

Data Regression System (DRS). Table 1 shows the resulting values.

Table 1: Regressed parameters for the density of PZ-CO2-H2O

Parameter Component(s) Value

ZiRA

PZ 0.2459

CO2 0.2799

Vca∞

PZH+ - PZCOO

- 0.2165

PZH+ - PZ(COO

-)2 0.3198

PZH+ - HCO3

- 0.1651

Aca

PZH+ - PZ(COO

-)2 -0.2324

PZH+ - HCO3

- -0.5939

The regressed values adequately represent the change in density with loading at the selected

amine concentrations (Figures 2-5)

Viscosity: Data available for 5 m, 7 m and 9 m PZ was fit using the expression of viscosity as a

function of loading, amine concentration and temperature proposed by Weiland et al. (1998).

𝜇𝑠

𝜇𝑤= 𝑒𝑥𝑝

𝑎𝛺 + 𝑏 𝑇 + 𝑐𝛺 + 𝑑 𝛼 𝑒𝛺 + 𝑓𝑇 + 𝑔 + 1 𝛺

𝑇2 (14)

where:

µs ,µw are the viscosity of the solution and water, respectively (cP);

Ω is the weight fraction of PZ;

α is the loading of the solution (mol CO2/mol alkalinity);

T is the temperature (K).

155

5

Figure 2: Density fit for 5 m PZ. Points by Freeman (Rochelle et al., 2009). Lines are fit in

Aspen Plus®.


Aspen Plus®.

1.04

1.06

1.08

1.1

1.12

1.14

0.10 0.20 0.30 0.40 0.50

r(g

/cm

3)


40oC

60oC

20oC

1.06

1.08

1.1

1.12

1.14

1.16

0.15 0.25 0.35 0.45

r(g

/cm

3)


40oC

60oC

20oC

156

6


Aspen Plus®


Aspen Plus®.

Table 2 shows the regressed parameters for this correlation. This expression was selected

because it was not possible to adequately fit the viscosity data with the available correlations in

Aspen Plus®. It was implemented in Aspen Plus

® using a FORTRAN subroutine. The obtained

results are presented in Figures 6–8. The 80 oC extrapolated line is added to show that the

viscosity correlation is well behaved at the absorber operating temperature range.

1.06

1.08

1.1

1.12

1.14

1.16

1.18

0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

r(g

/cm

3)


20oC 40oC

60oC

1.08

1.1

1.12

1.14

1.16

1.18

1.2

0.15 0.20 0.25 0.30 0.35 0.40 0.45

r(g

/cm

3)


20oC

40oC

60oC

157

7

Table 2: Regressed viscosity parameters for PZ.

Parameter a b c d e f g

Value 487.52 1389.31 1.58 4.50 8.73 -0.0038 -0.30

Figure 6: Viscosity results for 5 m PZ. Points by Freeman (Rochelle et al., 2008b). Lines

are fit in Aspen Plus®.



1

10

0.20 0.25 0.30 0.35 0.40 0.45

m(c

P)


25oC

40oC

60oC

80oC (extrapolated)

2

20

0.20 0.25 0.30 0.35 0.40 0.45

m(c

P)


25oC

40oC

60oC

80oC (extrapolated)

158

8



CO2 Activity Coefficient:

The activity coefficient of CO2 (γCO2) was evaluated in Aspen Plus® for variable amine

concentration and CO2 loading. Model results are compared against experimental points by

Cullinane (2005). Figure 9 shows that the model adequately represents the change in γCO2 with

amine concentration. However, for 8m PZ at various loadings an erratic behavior can be

observed with a maximum around a loading of 0.30 (Figure 10).

2

20

0.20 0.25 0.30 0.35 0.40 0.45

m(c

P)


25oC

40oC

60o C

80oC (extrapolated)

159

9

Figure 9: CO2 Activity coefficient change for unloaded solutions. VLE by Van Wagener

version 02/06/09 (Rochelle et al., 2009). Points from Cullinane (2005). Lines Aspen Plus®

model results.

0.95

1.05

1.15

1.25

1.35

1.45

0 2 4 6 8 10

g CO

2

PZ concentration (m)

25oC

40oC

160

10

Figure 10: Change in CO2 activity coefficient with loading for 8 m PZ. VLE by Van

Wagener version 02/06/09 (Rochelle et al., 2009). Lines Aspen Plus® model results.

PZ Pilot Plant Analysis

PZ at 5 m, 8 m and 9 m was tested at the Pickle Research Center in November 2008. The

absorber was packed with Mellapak 2X and operated at volume liquid-gas ratios (L/G) from 4.6

x10-3

to 6.9 x 10-3

. Inlet CO2 gas concentration varied from 10–12%.

The overall gas-side mass transfer coefficient (Kg) was calculated using absorber data and the

following expression:

𝐾𝐺 =𝑁𝐶𝑂2

𝑎𝑒𝑓𝑓𝑉𝑝𝐿𝑀𝑃𝐷 (15)

NCO2 is the absorbed CO2 (gmol/s). Vp is the column packing volume (m3) and aeff is the

packing effective area calculated using the Tsai et al. (2008) correlation:

𝑎𝑒𝑓𝑓

𝑎𝑝= 1.329

𝜌𝐿

𝜎𝑔

13

𝑄

𝐿𝑝

43

0.116

(16)

where:

ap is the nominal packing area (m2/m

3);

ρL is the liquid density (kg/m3);

σ is the surface tension (N/m);

0.0

0.5

1.0

1.5

2.0

2.5

0.1 0.2 0.3 0.4 0.5

g CO

2


25oC

40oC

161

11

Q is the liquid flow (m3/s);

Lp is the wetter perimeter in cross sectional slice of packing (m).

LMPD is the Log Mean Pressure Difference calculated between absorber inlet and outlet

conditions:

𝐿𝑀𝑃𝐷 =

∆𝑃𝑖𝑛 − ∆𝑃𝑜𝑢𝑡

ln ∆𝑃𝑖𝑛

∆𝑃𝑜𝑢𝑡

(17)

ΔP is the difference between the actual CO2 partial pressure and the CO2 pressure in equilibrium

with the solvent loading.

The calculated Kg was plotted against the arithmetic average between inlet and outlet CO2

equilibrium pressures. Results were plotted in Figures 11 and 12 along with values for kg’ by

Dugas (Rochelle et al., 2008a) and previous pilot plant studies (Cullinane, 2005: Plaza et al.,

2008).

Figure 11. Comparison between Kg and kg’ data for 5 m PZ. Lines are kg

’ data by Dugas

(Rochelle et al., 2008a). Diamond shape points are for 5 m PZ (large diamond is the

average of the runs). Squares are average results for other concentrations tested in the

November 2008 campaign. Circles are data from other campaigns and solvents (Cullinane,

2005: Plaza et al., 2008).

1.E-07

1.E-06

1.E-05

100 1000 10000

Kg

-k

g'(g

mo

l/P

a-m

2-s

)

PCO2* (Pa)

5m PZ 60oC

5m PZ 40oC 5m PZ

9m PZ

8m PZ

7.5m PZ

6.4m K+/1.6m PZ7 MEA

5m K+/2.5 m PZ 9m MEA

162

12

Figure 12: Comparison between Kg and kg’ data for 8 m PZ. Lines are kg

’ data by Dugas

(Rochelle et al., 2008a). Diamond shape points are for 8 m PZ (large diamond is the

average of the runs). Squares are average results for other concentrations tested in the

November 2008 campaign. Circles are data from other campaigns and solvents (Cullinane,

2005: Plaza et al., 2008).

Conclusions

PZ model development

The VLE for CO2–PZ was accurately represented with the new reduced reaction set. Density

and viscosity were also adequately implemented for the PZ model at absorber conditions.

The CO2 activity coefficients for unloaded solutions at low PZ concentrations are consistent with

the experimental data presented by Cullinane (2005) and behave adequately at higher

concentrations (>2 m). However, experimental data at higher concentrations is required to verify

the accuracy of the extrapolation.

The effect of loading on the CO2 activity coefficient is not being represented correctly by the

model at the desired PZ concentrations. The presence of a maximum at a mid loading point

needs to be addressed and experimental data is necessary for the regression of the activity

coefficient at the proposed PZ concentrations (> 5 m) and loadings.

PZ Pilot Plant Analysis

The calculated Kg data from the pilot plant campaign is consistent with the values of kg’ obtained

by Dugas (Rochelle et al., 2008a) in the wetted wall column (Figures 11 and 12).

1.E-07

1.E-06

1.E-05

100 1000 10000

Kg

-k

g'(g

mo

l/P

a-m

2-s

)

PCO2* (Pa)

8m PZ 40oC

8m PZ 60oC

5m PZ

9m PZ

8m PZ

7.5m PZ

5m K+/2.5 m PZ9m MEA

7 MEA

8m PZ 40oC

8m PZ 60oC

5m PZ

9m PZ

8m PZ

7.5m PZ

5m K+/2.5 m PZ9m MEA

7 MEA

6.4m K+/1.6m PZ

163

13

The 5 m PZ run exhibits the highest Kg values (Figure 12). It is 5 times higher than the data for

7 m MEA, 4 times higher than 5 m K+/2.5 m PZ and 20% higher than 8 m PZ.

Future Work

Work will continue to develop a rigorous PZ model for high concentration PZ (> 5 m). Issues

related to the activity coefficient of CO2 with respect to loading will be addressed by modifying

some of the τ parameters necessary to calculate γCO2. The values of γCO2 at high loadings will be

approximated using values by Cullinane (2005) for K+/PZ.

Kinetics will be developed using the reduced reaction set, values reported by Cullinane (2005)

and recent data by Dugas (Rochelle et al., 2008a)

Data from the November 2008 campaign will be used to validate the PZ model. Data

reconciliation in Aspen Plus® will be used for this purpose. Additional absorber configurations

including intercooling will be evaluated. The critical L/G (Plaza et al., 2009) for the upcoming

pilot plant campaign will be predicted.

The amine water wash and the flue gas blower will be included in the developed model and the

most recent model for MEA.

References

Cullinane JT. Thermodynamics and Kinetics of Aqueous Piperazine with Potassium Carbonate

for Carbon Dioxide Absorption. The University of Texas at Austin. Ph.D. Dissertation.

2005

Hilliard MD. A Predictive Thermodynamic Model for an Aqueous Blend of Potassium

Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue

Gas. The University of Texas at Austin. Ph.D Dissertation. 2008

Plaza JM et al., "Modeling CO2 Capture with Aqueous Monoethanolamine" in 9th International

Conference on Greenhouse Gas Control Technologies. Elsevier: Washington D.C. 2008.

Plaza JM et al. "Absorber Intercooling in CO2 Absorption by Piperazine Promoted Potassium

Carbonate". Submitted to AIChE J. 2009.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report

2008". Luminant Carbon Management Program. The University of Texas at Austin.

2008a

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report

2008". Luminant Carbon Management Program. The University of Texas at Austin.

2008b

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009".

Luminant Carbon Management Program. The University of Texas at Austin. 2009

Tsai R et al., "Influence of Viscosity and Surface Tension on the Effective Mass Transfer Area

of Structured Packing" in 9th International Conference on Greenhouse Gas Control

Technologies. Elsevier: Washington D.C. 2008.

Weiland R et al. "Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine

Solutions and Their Blends". J. Chem. Eng. Data. 1998. 43: p. 378-382.

164

1

Total P of CO2 Loaded Aqueous Amines at High T


by Qing Xu, Martin Metzner


and the




July 7, 2009

Abstract

In this quarter a series of total pressure measurements were conducted with CO2 loaded monoethanolamine (MEA) or piperazine (PZ) at 100 to 160 oC (for MEA) or 190 ºC (for PZ). A 500 mL 316 SS autoclave was used as the equilibrium cell. The total pressure of 8 m PZ with 0.465 CO2 loading varied from 7.4 to 25.8 bar at 100 to 147 ºC. At 100 to 150 ºC, for 5 m PZ with 0.293 CO2 loading Pt varied from 1.1 to 7.2 bar; for 7 m MEA with 0.316 CO2 loading Pt is from 1.1 to 6.7 bar; for 7 m MEA with 0.479 CO2 loading Pt is from 3.3 to 15.9 bar.

The partial pressure of CO2 at each experimental condition was estimated by subtracting partial pressures of water and amines. The calculated results for 7 m MEA match well with literature data. The regression based on data from 40 to 190 ºC gives empirical models for CO2 partial pressure over loaded aqueous PZ and MEA respectively and the models predict the data well. Heat of absorption for CO2 loaded aqueous PZ and MEA was calculated from these empirical models.

For PZ:

2

21ln 38.4 ( 102,000 / ) 20.6 13,200 3.23COP J molRT T

αα α= + − ⋅ − + + (1)

ln ( 102,000 13,200 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂ (2)

For MEA:

2

21ln 44.2 ( 116,000 / ) 29.7 11,600 17.3COP J molRT T

αα α= + − ⋅ − + + (3)

ln ( 116,000 11,600 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂ (4)

165

2

Introduction

Figure 1 shows the conditions in a typical post-combustion CO2 capture process with aqueous MEA solution.

Figure 1: High Temperature Parts in CO2 Capture Process

For concentrated PZ solution, thermal degradation is negligible up to 150 ºC (Freeman et al., 2008). Many stripper configurations operate more efficiently at high temperatures. Thus for concentrated PZ solutions, better energy performance may be obtained by increasing stripper pressure without degradation of PZ (Rochelle et al., 2008). The stripper pressure of the pilot plant campaign in 2008 approached 60 psia (Rochelle et al, 2009a). The non-ideal behavior of concentrated PZ at high temperatures might make its volatility comparable to that of MEA (Freeman et al., 2008).

For MEA solutions, due to the high thermal degradation rate, the stripper must operate below 120 ºC. However, the temperature in the reboiler is about 120 ºC and it can increase to 160 ºC in the thermal reclaimer. Many other aqueous amines also have relatively high temperature and pressure processes in CO2 capture. VLE research at high T and P can help understand these processes.


In this period, the total pressure measurement only involved static operation. An autoclave was used as the equilibrium cell. 2 runs for 7 m MEA, 4 runs for 8 m PZ, and 3 runs for 5 m PZ were conducted. All the experiments were performed by Martin Metzner, an undergraduate research assistant, supervised by Qing Xu.

CO2

Waste

Flue gas

ABS

HX

STRIP

Lean

Rich

Purified gas

Lean

Rich

Reclaimer

Reboiler

40-50 ºC 1 atm

100-110 ºC 1-2 atm Up to 160 ºC

166

3

Figure 2: Total Pressure Measurement with an Autoclave

An autoclave (ZipperClave®, by Autoclave Engineers) was used. Its designed operating range is up to 2000 psia and 232 ºC. The 500 mL pressure vessel is made of 316SS stainless steel. Closure is effected by a resilient spring member (the “Zipper”) inserted through a circumferential groove in the body and cover (Autoclave Engineers). A quick release/safety lock ensures that the spring is fully inserted and makes it easy to open and close the equilibrium cell. A magnetic agitator (MAG075, MagneDrive II Series, by Autoclave Engineers) was used to get equilibrium without leaking to the atmosphere. It was driven by a compressed air motor (2AM-NCC-16, by Gast®). The agitator is good for both liquid and vapor phases. It has a hollow shaft, which draws the gas into the middle of the shaft when the agitation starts. It is then dispersed through the impeller hub and mixes with the liquid.

A platinum resistance thermometer (model 5622-16, by Hart Scientific), installed inside the thermal well of the vessel, was used for temperature measurement. It was connected to a series data logger (PT-104, by Picotech), and the temperature was recorded by Picolog software. Temperature was controlled by a Fuji Electric PXZ-4 temperature controller, with connection to an Omega® K-type thermocouple placed between the autoclave vessel wall and the heating jacket. With 5 m PZ and with 8 m PZ at 0.465 CO2 loading 8, the PRT thermometer was replaced by an Omega® K-type thermocouple, which was used for both measurement and temperature control.

A pressure transducer (Validyne® DP15) was connected to a pressure indicator (Validyne® CD379). Because the indicator does not display pressure directly, calibration was performed by heating water and correlating the readings with known vapor pressures. About 330 to 350 mL water was added to the vessel, the cover was tightly sealed, and the autoclave was heated up. At about 110 ºC, vapor was released from a valve on top of the vessel directly to the back of hood until temperature dropped to 100 ºC. This was processed 3 times to purge all the air. Then the autoclave was heated to certain temperatures up to 200 ºC. Both temperature and the readings of the pressure indicator were recorded. The vapor pressures of pure water at each temperature were found from DIPPR Chemical Database. A calibration curve which relates the pressure indicator reading and the real

Data Acquisition

system

Autoclave

Vent air Compressed air

P transducer Thermocouple

or PRT

Heating jacket Temperature

control

Liq.

Vap.

P indicator Air motor for the agitator

167

4

pressure in the vessel was regressed and used for further experiments. The calibration method and results can be found in Appendix 1.

Before each run, about 330 to 350 mL of the CO2 loaded aqueous amine solution was prepared and added into the autoclave. To avoid the effects of O2, N2 was used to purge air and then the cell was sealed. Initial pressure and temperature readings were recorded for later data correction. Then the cell was heated. Data recording of both temperatures and pressures started at around 100 ºC and the intervals of the data points were about 10 ºC. After it reached 160 ºC for MEA and up to 190 ºC for PZ, heating was stopped and the system started to cool down, but the heating jacket was still used from time to time to maintain certain temperatures. Data were also recorded when cooling down. Liquid samples were collected before and after each experiment and analyzed by TIC and acid titration. The agitation rate varied from 1500 RPM to 2500 RPM, depending on the viscosity of the mixture.

Analytical Methods

Total Inorganic Carbon (TIC) The concentration of CO2 in solution was determined by TIC analysis. The liquid samples collected before and after each run were diluted by a factor of 100. About 10–15 μL diluted sample was injected into a CO2 analyzer (Model 525, Horiba PIR 2000). Details can be found in Appendix B.2 of Hilliard’s dissertation (2008).

Acid Titration The total alkalinity of solution was determined by acid titration using a Metrohm-Peak 835 Titrando equipped with an automatic dispenser, Metrohm-Peak 801 stirrer, and 3M KCl pH probe. Details are available in Appendix A.3 of Hilliard (2008) and Appendix F of Sexton (2008).

Results

Total Pressure Table 1 shows the measured total pressure for each run, including the data from last quarter.

Table 1: Measured Total Pressure in This Work

Amine Total Pressure(bar)Type Concentration(m)

ldg @100ºC @150ºC

6.97 0.427 1.8 14.9 6.86 0.479 3.3 15.9 6.82 0.389 1.1 9.1

MEA

6.86 0.316 1.1 6.7 7.78 0.314 1.9 13.0 7.43 0.312 1.4 11.8 7.93 0.33 N/A 11.9 7.92 0.306 0.9 9.0

PZ

7.94 0.424 3.9 20.6

168

5

Amine Total Pressure(bar)Type Concentration(m)

ldg @100ºC @150ºC

7.75 0.378 3.2 18.8 7.93 0.374 N/A 17.7 7.86 0.252 0.7 6.7 8.00 0.465 7.4 25.8* 4.93 0.293 1.1 7.2 4.97 0.339 1.1 8.7 4.96 0.374 1.8 12.6

* at 147 ºC.

Aqueous PZ In this work only total pressure can be measured directly. The partial pressure of CO2 was obtained by subtracting Pwater and Pamine from the corrected Ptotal. The raw data and calculation examples are in Appendices 2 and 4. Table 2 shows the partial pressure of CO2 in aqueous PZ from this work, including data from the last period. CO2 loading is defined as:

2 2moles of CO ( )moles of equivalent amine ( ) 2 ( )

n COldgn MEA n PZ

= =+ ⋅

(5)

Table 2: Partial Pressure of CO2 in Aqueous PZ in This Work

PZ T CO2 ldg PCO2 Pt PZ T CO2 ldg PCO2 Pt PZ T CO2 ldg PCO2 Pt

m ºC mol/molalk Pa Pa m ºC mol/molalk Pa Pa m ºC mol/molalk Pa Pa

7.78 110 0.314 122004 250946 7.94 130 0.424 890663 1134289 8.00 146.7 0.465 2190458 2583187

7.78 120 0.314 206448 385261 7.94 140 0.424 1232321 1558442 8.00 140.5 0.465 1890708 2221231

7.78 130 0.314 261222 504652 7.94 146 0.424 1439628 1825179 8.00 128.3 0.465 1415352 1646638

7.78 140 0.314 507117 832979 7.94 150 0.424 1631711 2061510 8.00 117.8 0.465 1061530 1228207

7.78 150 0.314 881085 1310544 7.94 157 0.424 1893836 2410800 8.00 112.2 0.465 886736 1025533

7.78 160 0.314 1230159 1788109 7.94 150 0.424 1624908 2054707 8.00 100.6 0.465 593660 686342

7.78 150 0.314 851237 1280696 7.94 139 0.424 1217419 1534395 4.93 110.6 0.293 37363 174033

7.78 140 0.314 536965 862826 7.94 130 0.424 936923 1180550 4.93 131.1 0.293 77414 236125

7.78 130 0.314 335841 579272 7.94 120 0.424 676762 855720 4.93 138.9 0.293 145351 338550

7.78 120 0.314 206448 385261 7.94 110 0.424 474636 603683 4.93 150.0 0.293 263278 473104

7.78 110 0.314 122004 250946 7.94 100 0.424 318940 410152 4.93 159.4 0.293 435371 708830

7.43 120 0.312 90102 270140 7.94 90 0.424 195049 258119 4.93 169.4 0.293 649478 1380592

7.43 130 0.312 277354 522437 7.94 82 0.424 125038 171188 4.93 180.0 0.293 970061 1909424

7.43 140 0.312 521298 849354 7.75 100 0.378 170068 262580 4.93 191.1 0.293 1409788 2614787

7.43 150 0.312 863332 1295661 7.75 110 0.378 276202 407013 4.93 180.6 0.293 1014296 1966727

7.43 160 0.312 1299707 1861361 7.75 120 0.378 436736 618119 4.93 170.0 0.293 694638 1436458

7.43 170 0.312 1930831 2650918 7.75 134 0.378 711388 989360 4.93 160.6 0.293 444675 1032286

7.43 180 0.312 2976199 3888194 7.75 140 0.378 898772 1229235 4.93 150.0 0.293 286258 731810

7.43 169 0.312 1351290 2054103 7.75 151 0.378 1396338 1843641 4.93 140.0 0.293 169141 507307

169

6

PZ T CO2 ldg PCO2 Pt PZ T CO2 ldg PCO2 Pt PZ T CO2 ldg PCO2 Pt


7.43 160 0.312 956457 1518110 7.75 160 0.378 1894297 2460012 4.93 130.0 0.293 100479 353178

7.43 150 0.312 639473 1071803 7.75 160 0.378 1867497 2433213 4.93 120.0 0.293 56453 242135

7.43 140 0.312 416830 744886 7.75 150 0.378 1486603 1922081 4.93 108.9 0.293 28143 157210

7.43 130 0.312 187810 432894 7.75 140 0.378 1070682 1401145 4.93 100.6 0.293 18161 114694

7.43 119 0.312 140659 315053 7.75 130 0.378 787574 1034471 4.97 130.0 0.339 155641 408218

7.43 110 0.312 111869 241702 7.75 120 0.378 570081 751464 4.97 140.0 0.339 261383 599378

7.93 104 0.330 122886 289325 7.75 110 0.378 414122 544933 4.97 150.6 0.339 431175 883729

7.93 111 0.330 177648 347203 7.93 121 0.374 298160 482894 4.97 160.0 0.339 664657 1243089

7.93 120 0.330 289389 462958 7.93 137 0.374 750749 1050095 4.97 170.0 0.339 957045 1698510

7.93 130 0.330 465124 643168 7.93 146 0.374 1093812 1479419 4.97 180.6 0.339 1362623 2314623

7.93 140 0.330 755280 937817 7.93 152 0.374 1353818 1807285 4.97 183.9 0.339 1465469 2491812

7.93 150 0.330 1192741 1379792 7.93 161 0.374 1796602 2369457 4.97 180.0 0.339 1314079 2253030

7.93 160 0.330 1777501 1969091 7.93 163 0.374 1913345 2515848 4.97 170.0 0.339 974279 1715745

7.93 167 0.330 2472784 2667568 7.93 150 0.374 1301407 1731268 4.97 160.0 0.339 679019 1257451

7.93 161 0.330 1736266 1928314 7.93 139 0.374 890953 1207975 4.97 148.9 0.339 433183 865522

7.93 149 0.330 1183986 1370584 7.93 129 0.374 601494 837945 4.97 140.0 0.339 278617 616613

7.93 139 0.330 747838 929925 7.93 119 0.374 384305 557678 4.97 130.0 0.339 167130 419707

7.93 125 0.330 326615 502420 7.93 110 0.374 219026 348092 4.97 118.9 0.339 86984 266187

7.92 130 0.306 127680 371356 7.86 129.9 0.252 51537 294670 4.96 100.0 0.374 74309 168981

7.92 140 0.306 245208 571395 7.86 140.9 0.252 113667 448507 4.96 109.4 0.374 137126 268343

7.92 150 0.306 446316 876201 7.86 151.5 0.252 222010 669834 4.96 120.0 0.374 239208 424820

7.92 160 0.306 704144 1262643 7.86 163.4 0.252 426793 1035847 4.96 130.0 0.374 383427 636038

7.92 170 0.306 1135744 1851814 7.86 171.9 0.252 638306 1388487 4.96 140.0 0.374 570972 909014

7.92 174 0.306 1394408 2182589 7.86 180.5 0.252 925639 1843775 4.96 150.0 0.374 825638 1271034

7.92 170 0.306 1226905 1942975 7.86 191.8 0.252 1440747 2623128 4.96 160.0 0.374 1136407 1714917

7.92 160 0.306 830679 1389179 7.86 182.9 0.252 1059682 2029640 4.96 170.0 0.374 1522077 2263643

7.92 150 0.306 495978 925863 7.86 173.2 0.252 723127 1497001 4.96 175.0 0.374 1756862 2592581

7.92 140 0.306 280584 606771 7.86 159.8 0.252 398841 954906 4.96 170.6 0.374 1564217 2316616

7.92 130 0.306 148769 392445 7.86 149.5 0.252 238340 662796 4.96 160.6 0.374 1181920 1769326

7.92 120 0.306 83481 262476 7.86 141.1 0.252 154799 491536 4.96 148.3 0.374 814471 1239909

7.94 81 0.424 92604 136936 7.86 131.1 0.252 91982 343991 4.96 140.0 0.374 599696 937738

7.94 89 0.424 149554 210261 8.00 100.0 0.465 709231 799937 4.96 130.6 0.374 410284 667468

7.94 94 0.424 198010 271311 8.00 110.0 0.465 876977 1005924 4.96 120.6 0.374 268515 457686

7.94 101 0.424 274363 368890 8.00 120.0 0.465 1169529 1348350 4.96 110.0 0.374 167312 301209

7.94 111 0.424 442568 576028 8.00 129.4 0.465 1499210 1738306 4.96 100.6 0.374 100818 197539

7.94 120 0.424 617576 796534 8.00 140.6 0.465 1917040 2248496

CO2 partial pressure data over 0.9–12 m PZ have been previously reported by Hilliard (2008), Dugas and Nguyen (Rochelle et al., 2008), and Ermatchkov (2006). The original data can be found in Appendix 2. Based on those data and the high temperature data in this work, an empirical model was developed:

170

7

2

21ln 38.4 ( 102,000 / ) 20.6 13, 200 3.23COP J molRT T

αα α= + − ⋅ − + +

ln ( 102,000 13, 200 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

2 0 9902R .=

Figures 3 and 4 show the prediction of PCO2 by this empirical model. Pcalc was calculated using the model while Pexp was measured in experiments.

0.4

0.8

1.2

1.6

2.0

40 60 80 100 120 140 160 180 200T (C)

Pca

lc/P

exp

Figure 3: Prediction of PCO2 over Aqueous PZ by Empirical Model, T Dependence

0.4

0.8

1.2

1.6

2.0

0.1 0.2 0.3 0.4 0.5CO2 loading (mole/equiv PZ)

Pca

lc/P

exp

Figure 4: Prediction of PCO2 over Aqueous PZ by Empirical Model, Ldg Dependence

Data at low T New data at high T

Data at low T

New data at high T

171

8

Figure 5 shows CO2 solubility in aqueous PZ at temperatures from 40 to 160 ºC and the heat of absorption over 0.2–0.5 CO2 loading range. The points are experimental data and curves in different colors are the values predicted by the empirical model. According to this figure the model fairly predicts the data at both high and low temperature, and the CO2 partial pressure strongly depends on loading and temperature. Points at 80, 100, and 120 ºC indicate that results from this work match well with former data. The heat of absorption

decreases at increased CO2 loading. ΔHabs is 47 to 80 kJ/mol at 0.5 to 0.2 loading. Both

PCO2 and ΔH are independent of PZ concentration in this empirical correlation.

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

0.20 0.25 0.30 0.35 0.40 0.45 0.50

CO2 loading (moles/equiv PZ)

PC

O2 (

Pa)

45

50

55

60

65

70

75

80

ΔH

abs (

kJ/m

ol)

Figure 5: CO2 solubility over aqueous PZ Aqueous MEA In this work, PCO2 was obtained using the same method as with aqueous PZ. The raw data and calculation samples are in Appendices 3 and 4. Table 3 shows the partial pressure of CO2 in aqueous MEA from this work.

Table 3: Partial Pressure of CO2 in MEA in This Work

MEA T CO2 ldg PCO2 Pt MEA T CO2 ldg PCO2 Pt MEA T CO2 ldg PCO2 Pt


6.97 100 0.427 100613 191250 6.86 160 0.479 1426541 1983628 6.82 120 0.389 108188 286547

6.97 110 0.427 137624 265870 6.86 166 0.479 1568194 2216066 6.82 110 0.389 60929 189520

6.97 120 0.427 237224 415109 6.86 161 0.479 1441713 2013184 6.82 100 0.389 34293 125176

6.97 130 0.427 351956 594196 6.86 149 0.479 997132 1414288 6.86 100.5 0.316 15881 108340

6.97 140 0.427 613050 937446 6.86 139 0.479 715741 1031654 6.86 111.3 0.316 22059 156294

6.97 150 0.427 1061899 1489631 6.82 111 0.389 22177 155167 6.86 121.8 0.316 45436 228767

Filled points: this work Open points: former work

40 ºC

160 ºC

80 ºC

120 ºC

ΔH

172

9

MEA T CO2 ldg PCO2 Pt MEA T CO2 ldg PCO2 Pt MEA T CO2 ldg PCO2 Pt


6.97 140 0.427 463811 788207 6.82 120 0.389 62433 240791 6.86 131.9 0.316 73217 330103

6.97 130 0.427 292260 534500 6.82 131 0.389 153209 403473 6.86 141.4 0.316 130830 468989

6.97 120 0.427 192452 370337 6.82 140 0.389 288705 613945 6.86 150.2 0.316 213155 644022

6.97 110 0.427 122700 250946 6.82 150 0.389 463202 892032 6.86 159.0 0.316 332091 875068

6.97 100 0.427 85689 176326 6.82 160 0.389 723822 1281241 6.86 152.0 0.316 235784 687920

6.86 101 0.479 94696 188823 6.82 170 0.389 1090007 1805101 6.86 142.3 0.316 144827 491656

6.86 111 0.479 170514 303420 6.82 170 0.389 1050788 1765882 6.86 129.9 0.316 77312 319308

6.86 121 0.479 283022 466998 6.82 160 0.389 731665 1289085 6.86 120.4 0.316 50175 230689

6.86 130 0.479 448092 690820 6.82 150 0.389 500460 929291 6.86 109.0 0.316 35729 159965

6.86 140 0.479 683104 1008146 6.82 140 0.389 307661 632901 6.86 101.1 0.316 12239 106695

6.86 150 0.479 1011870 1440443 6.82 129 0.389 176362 412044

CO2 partial pressure data over 3.5–13 m MEA have been reported by Hilliard (2008), Jou (1995), and Dugas (Rochelle et al., 2008). The original data can be found in Appendix 3. Based on those data and the high temperature data in this work, an empirical model was developed:

2

21ln 44.2 ( 116,000 / ) 29.7 11,600 17.3COP J molRT T

αα α= + − ⋅ − + +

ln ( 116,000 11,600 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

2 0 9884R .=

Figures 6 and 7 show the prediction of PCO2 by this empirical model. Pcalc was calculated using the model while Pexp was measured in experiments.

0.2

0.7

1.2

1.7

2.2

40 60 80 100 120 140 160 180T(C)

Pca

lc/P

exp

Figure 6: Prediction of PCO2 over Aqueous MEA by Empirical Model, T Dependence


173

10

0.2

0.7

1.2

1.7

2.2

0.05 0.15 0.25 0.35 0.45 0.55Loading (mole CO2/mole MEA)

Pca

lc/P

exp

Figure 7: Prediction of PCO2 over Aqueous MEA by Empirical Model, Ldg Dependence

Figure 8 shows CO2 solubility in aqueous MEA solutions at temperature from 40 to 160 ºC and the heat of absorption over 0.3-0.5 CO2 loading range. The points are experimental data and curves in different colors are the predicted values by the empirical model. The heat of

absorption is about 67 to 86 kJ/mol at 0.5 to 0.3 loading. Both PCO2 and ΔH are

independent on MEA concentration.

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

0.30 0.35 0.40 0.45 0.50

Loading (moles CO2/mole MEA)

PC

O 2 (P

a)

65

70

75

80

85

90Δ

Hab

s (k

J/m

ol)

Figure 8: CO2 solubility over aqueous MEA


40 ºC

80 ºC

120 ºC

160 ºC ΔH

Filled points: this work Open points: former work

174

11

Comparison with literature data Jou et al. (1995) has reported the solubility of CO2 in 7 m MEA between 0 and 150 ºC at partial pressure of CO2 ranging from 0.001 to 20,000 kPa. Table 4 shows their data at 100, 120, 150 ºC.

Table 4: Solubility of CO2 in 7 m MEA by Jou et al, 1995

100 ºC 120 ºC 150 ºC Loading PCO2(kPa) Loading P CO2 (kPa) Loading P CO2 (kPa)

0.941 19812 0.863 17723 0.755 16441 0.914 14842 0.829 14741 0.727 11504 0.856 9871 0.7815 9770 0.6505 8525 0.81 5891 0.7205 5809 0.583 4544 0.705 2899 0.639 2804 0.484 570 0.6135 909 0.536 822 0.388 560 0.571 509 0.473 422 0.358 420 0.589 376 0.444 222 0.301 123 0.481 109 0.4115 122 0.153 34.2 0.477 69 0.349 46.8 0.0509 2.63 0.422 39 0.119 2.29 0.0134 0.184 0.381 19 0.0247 0.0984 0.00992 0.0843 0.188 1.43 0.0112 0.0221 0.00496 0.0239 0.0566 0.136 0.00333 0.00202 0.00199 0.00477 0.0117 0.00724

Figure 9 shows the comparison of data in this work and in Jou’s work. The CO2 loading range is restricted to 0.3–0.5 in this graph. The curves are from the empirical model. The new data fairly match Jou’s work, and the model predicts CO2 solubility well. The data at 100 ºC are not as good because the CO2 partial pressure estimation method in this work gets closer results to the true values when it is at high pressure.

175

12

1.E+04

1.E+05

1.E+06

1.E+07

0.3 0.35 0.4 0.45 0.5

Loading (mole CO2/mole MEA)

PC

O2(P

a)

Figure 9: CO2 solubility over aqueous MEA, comparison with literature data

Conclusions

The total pressure of 8 m PZ with 0.465 CO2 loading varied from 7.4 to 25.8 bar at 100 to 147 ºC. At 100 to 150 ºC, for 5 m PZ with 0.293 CO2 loading Pt varied from 1.1 to 7.2 bar; for 7 m MEA with 0.316 CO2 loading Pt is from 1.1 to 6.7 bar; for 7 m MEA with 0.479 CO2 loading Pt is from 3.3 to 15.9 bar.

The calculated CO2 partial pressure matches well with previous data at lower temperatures. The calculated results for 7m MEA also match well with the work by Jou et al (1995) at high temperature.

For a specific amine solution PCO2 is a function of temperature and CO2 loading. A new empirical model for aqueous PZ was developed based on data from 40 to 190 ºC:

2

21ln 38.4 ( 102,000 / ) 20.6 13,200 3.23COP J molRT T

αα α= + − ⋅ − + +

Another empirical model for aqueous MEA was developed based on data from 40 to 160 ºC:

2

21ln 44.2 ( 116,000 / ) 29.7 11,600 17.3COP J molRT T

αα α= + − ⋅ − + +

Both the models predict the data well. Heat of absorption for CO2 loaded aqueous PZ and MEA were calculated from these empirical models.

For PZ:

ln ( 102,000 13, 200 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

Filled points: this work Open points: Jou et al, 1995

120 ºC

150 ºC

100 ºC

176

13

For MEA:

ln ( 116,000 11,600 )( / )1( )

PH R R J mol

T

α∂Δ = − ⋅ = − − + ⋅ ⋅

∂

Future Work

The work planned for the next period includes modifying the total pressure measurement, adding LabView® into the data acquisition system, as well as using a deadweight tester to do independent calibration for the pressure transducer. We will then conduct more experiments at various CO2 loadings for more concentrated PZ and other aqueous amines. The empirical model for PCO2 over aqueous PZ will be modified.

Based on the total pressure data, the Aspen PZ model, developed by Hilliard (2008) and modified by Van Wagener for PZ will be tested and further modified.

We will build an apparatus for (x, y, P, T) measurement at high temperature and pressure. A framework has been developed, but detail problems need to be fixed. The same autoclave will be used as the equilibrium cell; vapor sample will be depressurized, diluted with nitrogen, and analyzed by FTIR; liquid sample will be drawn out, cooled, and analyzed by TIC and titration.

Further goals include exploring the temperature dependence of heat of absorption of CO2 loaded amine solutions.

References

Autoclave Engineers®, Zipperclave® 500&1000 mL stirred reactor, http://www.autoclaveengineers.com/ae_pdfs/SR_500_1000_Zip.pdf

DIPPR, 1998-Provo, UT: BYU DIPPR, Thermophysical Properties Laboratory, 1998-Version 13.0.

Ermatchkov V et al. "Solubility of Carbon Dioxide in Aqueous Solutions of Piperazine in the Low Gas Loading Region." J Chem Engr Data. 2006;51(5):1788–1796.

Freeman SA et al. "Carbon dioxide capture with concentrated, aqueous piperazine." GHGT-9, Washington D.C. 2008.

Hilliard MD. A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue Gas. The University of Texas at Austin. Ph.D. Dissertation. 2008;1083.

Jou F-Y, Mather AE et al. "The Solubility of CO2 in a 30 Mass Percent Monoethanolamine Solution." Can J Chem Eng. 1995;73(1):140–147.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Third Quarterly Progress Report 2008." Luminant Carbon Management Program. The University of Texas at Austin. 2008.

177

14

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report 2008". Luminant Carbon Management Program. The University of Texas at Austin. 2009a.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009". Luminant Carbon Management Program. The University of Texas at Austin. 2009b.

Sexton AJ. Amine Oxidation in CO2 Capture Processes. The University of Texas at Austin. Ph.D. Dissertation. 2008.

Appendices

Appendix 1: Calibration

Calibration for Autoclave - nitrogen The following calibration was used for run MEA-3 and PZ-6. About 1 atm of nitrogen at room temperature was purged into the autoclave before calibration and before each experiment run. Thus corrections were made to the measured total pressure.

Table 5: Calibration for Autoclave - nitrogen

T(°C) Indicator reading P water(kPa) P N2(Pa) P(Pa) T(°C)Indicator readingP water(kPa) P N2 (Pa) P(Pa)

105 0.2225 120.7 128513 249213 200 1.357 1551.6 160798 1712398

110 0.242 143.12 130212 273332 190 1.131 1252.5 157400 1409900

121 0.295 204.64 133950 338590 180 0.942 1000.5 154001 1154501

131 0.351 277.88 137349 415229 169 0.769 771.45 150263 921713

140 0.417 360.75 140407 501157 160 0.6505 616.82 147204 764024

150 0.502 475.09 143806 618896 150 0.541 475.09 143806 618896

160 0.613 616.82 147204 764024 140 0.4505 360.75 140407 501157

171 0.7655 809.66 150942 960602 130 0.3795 269.71 137009 406719

180 0.923 1000.5 154001 1154501 120 0.322 198.29 133610 331900

190 1.1215 1252.5 157400 1409900 110 0.2775 143.12 130212 273332

200 1.364 1551.6 160798 1712398 99 0.239 97.702 126474 224176

178

15

y = 13.07 x ‐ 0.65

R2 = 1.00

2

4

6

8

10

12

14

16

18

0.2 0.4 0.6 0.8 1 1.2 1.4

Transducer reading

Pressure (b

ar)

Figure 10: Calibration for Autoclave – Nitrogen

Calibration Calculation Example: At 105 °C, the vapor pressure of water is 120700 kPa, the transducer reading is 0.2225 (average value). At 25 °C before calibration, the 1 atm nitrogen inside the vessel has a reading of 0.113. Assume the nitrogen behaves as an ideal gas during calibration, according to PV=nRT, ignore the volume change, P/Pi=T/Ti, thus:

2

105 273.15( ) 101325( ) 128513( )25 273.15( )N

KP Pa PaK

+= ⋅ =

+

The total pressure in the equilibrium cell at 105 °C:

2128513 120700 249213( )total N waterP P P Pa= + = + =

Then correlate the Ptotal with readings from the indicator and get the calibration curve in Figure 10.

Calibration for Autoclave - vacuum The following calibration was used for run MEA-5 and PZ 7-12. In the beginning of this calibration at about 110 ºC water vapor was released from a valve on top of the vessel directly to the back of hood until temperature dropped to 100 ºC. This was processed 3 times to purge all the air. So no correction was conducted to the total pressure measured in these experiments.

179

16

Table 6: Calibration for Autoclave - vacuum

Temperature (°C) Transducer Reading Pressure (kPa)

102.0 0.155 108.70

120.0 0.219 198.29

139.3 0.319 353.66

159.4 0.487 607.46

173.0 0.744 849.35

200.1 1.153 1554.90

220.8 1.719 2351.00

226.0 1.899 2593.40

209.1 1.378 1869.80

189.3 0.958 1233.40

171.2 0.659 813.56

150.6 0.434 482.78

130.9 0.292 277.05

y = 1436.2x - 128.17R2 = 0.9985

0

500

1000

1500

2000

2500

3000

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Transducer Reading

Pres

sure

(kPa

)

Figure 11: Calibration for Autoclave – Vacuum

180

17

Appendix 2: PZ Data Tables Raw data for PZ 1 to 5 can be found in the section by Xu in the last quarterly progress report. (Rochelle et al., 2009)

Table 7: PCO2 from Hilliard Dissertation (Table D.5, 2008)

PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 m ºC mol/molalk Pa m ºC mol/molalk Pa m ºC mol/molalk Pa 0.9 40 0.208 44 2 40 0.227 106 3.6 60 0.385 13600 0.9 40 0.217 70.5 2 40 0.257 184 3.6 60 0.4 19300 0.9 40 0.241 103 2 40 0.309 526 3.6 40 0.146 21.1 0.9 40 0.284 234 2 40 0.372 1950 3.6 40 0.217 62.8 0.9 40 0.344 987 2 40 0.431 10100 3.6 40 0.272 211 0.9 40 0.418 4850 2.5 40 0.166 31.7 3.6 40 0.318 687 0.9 60 0.111 29 2.5 40 0.228 88.4 3.6 40 0.384 4370 0.9 60 0.217 299 2.5 40 0.278 247 3.6 40 0.412 8420 0.9 60 0.242 841 2.5 40 0.328 662 5 40 0.172 28.7 0.9 60 0.325 1930 2.5 40 0.423 7510 5 40 0.22 60.5 0.9 60 0.37 8290 2.5 40 0.437 10600 5 40 0.274 211 0.9 60 0.383 14700 2.5 60 0.164 141 5 40 0.339 798 2 60 0.132 92.4 2.5 60 0.196 263 5 40 0.409 5710 2 60 0.193 296 2.5 60 0.251 725 5 40 0.413 6990 2 60 0.275 1400 2.5 60 0.341 3960 5 60 0.164 137 2 60 0.33 3950 2.5 60 0.4 16900 5 60 0.226 365 2 60 0.37 9910 2.5 60 0.443 27400 5 60 0.296 1290 2 60 0.412 24700 3.6 60 0.158 129 5 60 0.33 3310 2 60 0.169 142 3.6 60 0.217 431 5 60 0.386 18300 2 60 0.383 13700 3.6 60 0.277 1050 5 60 0.417 51400 2 40 0.146 21.5 3.6 60 0.338 3490

Table 8: PCO2 from Ermatchkov (2006)

PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 m ºC mol/molalk Pa m ºC mol/molalk Pa m ºC mol/molalk Pa

2.281 80 0.067 111 1.969 80 0.362 25620 4.168 80 0.264 4470 2.281 80 0.154 870 1.969 80 0.368 28420 4.168 80 0.288 6860 2.281 80 0.251 3580 1.969 80 0.382 37950 4.168 80 0.308 10150 2.156 80 0.271 5600 3.95 80 0.077 154 4.199 80 0.342 19850 2.156 80 0.312 10820 3.95 80 0.138 480 4.199 80 0.362 31400 2.156 80 0.340 18360 3.95 80 0.201 1530 4.199 80 0.404 77630

181

18

Table 9: PCO2 from Dugas (Rochelle et al., 2008)

PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 PZ Temp. CO2 ldg PCO2 m ºC mol/molalk Pa m ºC mol/molalk Pa m ºC mol/molalk Pa 2 40 0.240 96 5 40 0.402 4563 8 60 0.360 7454 2 40 0.316 499 5 60 0.226 385 8 60 0.404 30783 2 40 0.352 1305 5 60 0.299 1814 8 80 0.253 3255 2 40 0.411 7127 5 60 0.354 5021 8 80 0.289 9406 2 60 0.240 559 5 60 0.402 17233 8 100 0.253 13605 2 60 0.316 2541 5 80 0.238 2192 8 100 0.289 32033 2 60 0.352 5593 5 80 0.321 9699 12 60 0.231 331 2 60 0.411 25378 5 100 0.238 8888 12 60 0.289 1865 2 80 0.239 2492 5 100 0.321 36960 12 60 0.354 6791 2 80 0.324 12260 8 40 0.231 68 12 80 0.222 2115 2 100 0.239 9569 8 40 0.305 530 12 80 0.290 9141 2 100 0.324 39286 8 40 0.360 1409 12 100 0.222 7871 5 40 0.226 65 8 40 0.404 8153 12 100 0.290 33652 5 40 0.299 346 8 60 0.231 430 5 40 0.354 1120 8 60 0.305 2407

Table 10: Raw Data for Run PZ-6

T(ºC) transducer reading Pt(Pa) T(ºC) transducer

reading Pt(Pa) T(ºC)

transducer

reading Pt(Pa)

100 0.251 262580 151 1.4605 1843641 130 0.8415 1034471

110 0.3615 407013 160 1.932 2460012 120 0.625 751464

120 0.523 618119 160 1.9115 2433213 110 0.467 544933

134 0.807 989360 150 1.5205 1922081 100 0.3335 370432

140 0.9905 1229235 140 1.122 1401145




transducer

reading Pt(Pa)

121 0.538 482894 161 1.863 2369457 129 0.7875 837945

137 0.9375 1050095 163 1.9655 2515848 119 0.5895 557678

146 1.239 1479419 150 1.4155 1731268 110 0.441 348092

152 1.469 1807285 139 1.048 1207975

182

19




transducer

reading Pt(Pa)

100.3 0.22 74518.1 163.4 0.875 1035847 159.8 0.815 954906

110.1 0.251 139819 171.9 1.12 1388487 149.5 0.612 662796

118.5 0.288 193348 180.5 1.437 1843775 141.1 0.493 491536

129.9 0.359 294670 191.8 1.98 2623128 131.1 0.39 343991

140.9 0.466 448507 182.9 1.564 2029640

151.5 0.62 669834 173.2 1.193 1497001




transducer

reading Pt(Pa)

100.0 0.705 799937 140.6 1.720 2248496 117.8 1.006 1228207

110.0 0.850 1005924 146.7 1.954 2583187 112.2 0.864 1025533

120.0 1.090 1348350 140.5 1.701 2221231 100.6 0.626 686342

129.4 1.363 1738306 128.3 1.299 1646638




transducer

reading Pt(Pa)

101.1 0.222 100211 169.4 1.125 1380592 140.0 0.512 507307

110.6 0.275 174033 180.0 1.495 1909424 130.0 0.403 353178

121.1 0.320 236125 191.1 1.988 2614787 120.0 0.324 242135

131.1 0.393 338550 180.6 1.535 1966727 108.9 0.263 157210

138.9 0.488 473104 170.0 1.164 1436458 100.6 0.232 114694

150.0 0.654 708830 160.6 0.881 1032286

159.4 0.862 1005288 150.0 0.670 731810




transducer

reading Pt(Pa)

100.0 0.232 103304 170.0 1.356 1698510 140.0 0.597 616613

109.4 0.280 169679 180.6 1.787 2314623 130.0 0.458 419707

122.2 0.364 286831 183.9 1.911 2491812 118.9 0.349 266187

130.0 0.450 408218 180.0 1.744 2253030 110.0 0.284 175261

140.0 0.585 599378 170.0 1.368 1715745 100.0 0.236 109049

150.6 0.785 883729 160.0 1.047 1257451

160.0 1.037 1243089 148.9 0.772 865522

183

20




transducer

reading Pt(Pa)

100.0 0.279 168981 160.0 1.367 1714917 140.0 0.822 937738

109.4 0.350 268343 170.0 1.751 2263643 130.6 0.632 667468

120.0 0.461 424820 175.0 1.981 2592581 120.6 0.484 457686

130.0 0.610 636038 170.6 1.788 2316616 110.0 0.373 301209

140.0 0.802 909014 160.6 1.405 1769326 100.6 0.299 197539

150.0 1.056 1271034 148.3 1.034 1239909

Appendix 3: MEA Data Tables Raw data for MEA-1 and MEA-2 can be found in the section by Xu in the last quarterly progress report. (Rochelle et al., 2009)

Table 17: PCO2 from Hilliard Dissertation (D4, 2008)

MEA T CO2 Loading PCO2 MEA T CO2 Loading PCO2 MEA T CO2 Loading PCO2

(m) (C) (mol/molalk) (Pa) (m) (C) (mol/molalk) (Pa) (m) (C) (mol/molalk) (Pa)

3.5 40 0.121 5.55 7 40 0.153 5.7 7 40 0.501 1870

3.5 40 0.212 14 7 40 0.17 7.21 7 40 0.491 1100

3.5 40 0.3 36.2 7 40 0.163 6.64 7 40 0.518 3030

3.5 40 0.369 116 7 40 0.194 9.85 7 40 0.326 48.5

3.5 40 0.467 879 7 40 0.191 9.95 7 40 0.348 66.2

3.5 40 0.552 8560 7 40 0.272 22.4 11 40 0.115 5.05

3.5 60 0.159 21.2 7 40 0.232 14.6 11 40 0.201 10.8

3.5 60 0.219 78 7 40 0.246 19.1 11 40 0.298 29.5

3.5 60 0.307 244 7 40 0.269 23.1 11 40 0.373 104

3.5 60 0.38 794 7 40 0.36 96.6 11 40 0.485 1620

3.5 60 0.477 4320 7 40 0.35 72.1 11 40 0.545 22300

3.5 60 0.504 14800 7 40 0.386 120 11 60 0.136 15.5

7 60 0.114 19.4 7 40 0.389 113 11 60 0.225 73.1

7 60 0.191 58.9 7 40 0.4 128 11 60 0.291 199

7 60 0.291 209 7 40 0.382 131 11 60 0.415 847

7 60 0.386 763 7 40 0.466 574 11 60 0.464 6980

7 60 0.485 4860 7 40 0.591 28300 11 60 0.502 26500

7 60 0.544 25800 7 40 0.481 883 11 40 0.115 5.05

7 60 0.565 50200 7 40 0.464 750 11 40 0.201 10.8

184

21

Table 18: PCO2 from Jou et al., 1995



7 40 0.0888 1.47 7 60 0.438 2010 7 100 0.0566 136

7 40 0.203 8.96 7 60 0.504 11000 7 100 0.188 1430

7 40 0.365 67.7 7 60 0.565 34100 7 100 0.381 19000

7 40 0.461 604 7 80 0.118 99.2 7 100 0.422 39000

7 40 0.513 2570 7 80 0.187 278 7 100 0.477 69000

7 40 0.557 8090 7 80 0.348 2670 7 100 0.481 109000

7 60 0.119 19.3 7 80 0.46 16000 7 100 0.589 376000

7 60 0.206 57.9 7 80 0.517 56000

7 60 0.389 528 7 80 0.576 235000

Table 19: PCO2 from Dugas, (Rochelle et al., 2008)



7 40 0.252 15.7 9 60 0.231 61 11 80 0.256 860

7 40 0.351 77 9 60 0.324 263 11 80 0.359 3923

7 40 0.432 465 9 60 0.382 892 11 100 0.256 4274

7 40 0.496 4216 9 60 0.441 2862 11 100 0.359 18657

7 60 0.252 109 9 60 0.496 21249 13 40 0.252 12.3

7 60 0.351 660 9 80 0.265 979 13 40 0.372 84

7 60 0.432 3434 9 80 0.356 4797 13 40 0.435 491

7 60 0.496 16157 9 100 0.265 4940 13 40 0.502 8792

7 80 0.271 1053 9 100 0.356 21534 13 60 0.252 100

7 80 0.366 4443 11 40 0.261 14.0 13 60 0.372 694

7 100 0.271 5297 11 40 0.353 67 13 60 0.435 3859

7 100 0.366 19008 11 40 0.428 434 13 60 0.502 29427

9 40 0.231 10.4 11 40 0.461 1509 13 80 0.254 873

9 40 0.324 34 11 60 0.261 96 13 80 0.355 3964

9 40 0.382 107 11 60 0.353 634 13 100 0.254 3876

9 40 0.441 417 11 60 0.428 3463 13 100 0.355 18406

9 40 0.496 5354 11 60 0.461 8171

Table 20: Raw Data for Run MEA-3

T(ºC) transducer reading Pt(Pa) T(ºC) transducer reading Pt(Pa) T(ºC) transducer reading Pt(Pa)

101 0.2235 91477 160 1.15 1281241 129 0.4765 412044

111 0.275 155167 170 1.5535 1805101 120 0.378 286547

120 0.343 240791 170 1.5235 1765882 110 0.301 189520

131 0.4705 403473 160 1.156 1289085 100 0.249 125176

140 0.634 613945 150 0.878 929291

150 0.8495 892032 140 0.6485 632901

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Table 21: Raw Data for Run MEA-5

T(ºC) transducer reading Pt(Pa) T(ºC) transducer reading Pt(Pa) T(ºC) transducer reading Pt(Pa)

100.5 0.255 108340 150.2 0.64 644022 120.4 0.345 230689

111.3 0.291 156294 159.0 0.803 875068 109.0 0.293 159965

121.8 0.344 228767 152.0 0.671 687920 101.1 0.254 106695

131.9 0.417 330103 142.3 0.532 491656

141.4 0.516 468989 129.9 0.409 319308

Appendix 4: Total Pressure Correction and CO2 Partial Pressure Calculation In this period all the experiments used nitrogen to purge air. The total pressure of the solution was corrected by subtracting PN2.

Calculation example: In run PZ-6, at 100 °C, the transducer reading is 0.251. Based on the equation from calibration for calorimeter - air: P=1307301*(reading)-64677.3 (Pa). The initial pressure reading of nitrogen is 0.05 at 20 °C, which corresponds to 688 Pa. Assume nitrogen behaves as an ideal gas during the run and ignore the vapor volume change,

2

100 273.15( ) 688( ) 875( )20 273.15( )N

KP Pa PaK

+= ⋅ =

+

The vapor pressure at 100 °C for pure water is 101260 Pa, and for pure PZ it is 21296 Pa. These can be found in DIPPR Chemical Database. Assume an ideal mixture in the solution, and CO2 is combined with PZ (or MEA, in MEA case), then the vapor pressure of water and PZ in vapor can be calculated by Raoult’s Law:

* 101260 0.8775 88853( )water water waterP P x Pa= ⋅ = × =

*, , .

21296 0.1225 2609( )PZ PZ PZ PZCOO etcP P x Pa−= ⋅ = × =

The partial pressure of CO2:

2 2263455 875 88853 2609 171118( )CO N water PZP P P P P Pa= − − − = − − − =

186

1

Amine Volatility

Progress Report for April 1 – June 30, 2009

by Thu Nguyen


and the




July 1, 2009

Abstract Amine volatility is a crucial screening criterion which affects fugitive emission and calls for appropriate water wash design. At a lean CO2 partial pressure of 500 Pa at 40 ºC, the ranking of amine volatility is: 7 m MDEA/2 m PZ (7/6 ppm) < 12 m EDA (9 ppm) < 8 m PZ (14 ppm) < 7 m MEA (28 ppm) < 5 m AMP (112 ppm). The less volatile amines appear to have higher heats of amine solution than the more volatile amines. There is no apparent correlation between volatility and the amine heat of desorption.

Introduction This report discusses the volatility of amine solvents at the entrance to the water wash section at 40 ºC and nominal lean loading. Amine volatility is analyzed in terms of apparent activity coefficient. Both the heats of solution and vaporization of the amines are also estimated. The amine systems that have been studied include: monoethanolamine (MEA), piperazine (PZ), methyldiethanolamine/piperazine (MDEA/PZ), ethylene diamine (EDA), and 2-amino-2-methyl-1-propanol (AMP).

Experimental Apparatus Amine volatility is measured by using a setup which includes a stirred reactor coupled with a hot gas FTIR analyzer (Fourier Transform Infrared Spectroscopy technique) manufactured by Gasmet Inc. Figure 1 shows this VLE experimental setup.

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Figure 1. Amine Volatility Experimental Setup The 1L glass reactor is well-stirred and kept isothermal by use of dimethylsilicone oil circulating from the oil bath. The reactor is insulated from the surrounding with aluminum foil. As the experiment proceeds, vapor from the headspace of the reactor is continually being drawn off into a heated line kept at an elevated temperature of 180 ºC which is also the FTIR operating temperature. The gas sampling rate is between 5 and 10 L/min. Both the line and analyzer are maintained at 180 ºC to prevent possible condensation or adsorption of vapor amine to any of the inner surfaces. The FTIR is capable of multi-component analysis as it is able to measure both CO2 solubility and volatility of the rest of the gaseous species present, including the amines of interest. After the gas passes through the FTIR, it is taken back to the reactor via a different line kept at approximately 55 ºC higher than the equilibrium reactor temperature. It was determined that the 55 ºC difference is sufficient for two reasons: (1) to ensure that the return gas does not upset the solution that is in equilibrium with the gas inside the reactor; (2) to prevent potential heat loss at the bottom of the reactor.

Loading is initially determined gravimetrically by weighing the amount of CO2 that is sparged into the amine solution. At the end of the VLE experiment, the loading is again verified by the Total Inorganic Carbon method which measures the amount of CO2 evolution into 30 wt % H3PO4.

Theory Amine volatility is quantified by the apparent activity coefficient (γamine) as defined by the modified Raoult’s law. γamine = Pamine / [xamine * Pamine

sat] The reference value for γamine is 1 which is the case of a solution having ideal species interaction. A coefficient that is less than 1 generally indicates a low volatile system while a value greater than 1 indicates the opposite. Note that the activity coefficients presented in this report are

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apparent values, instead of being actual values, as they are computed using xamine that are not the true liquid phase mole fractions of free amine present in solution, but only estimates.

The heat of amine solution, and similarly, the heat of amine vaporization (desorption from solution), are calculated by the Gibb’s-Helmholtz relations. d (ln Pamine) / d (1/T) = -∆Hvaporization / R d (ln γamine) / d (1/T) = -∆Hsolution / R

Data Table 1: 3.5 m, 7.0 m, 11.0 m MEA Volatility

MEA (m) T ( °C ) Loading PCO2 (Pa) PMEA (Pa) γMEA 3.50 60.0 0.00 0.0 13.20 0.34 3.57 59.9 0.16 21.2 11.00 0.28 3.63 60.1 0.22 78.0 9.26 0.23 3.53 60.0 0.31 244.0 7.20 0.19 3.57 60.0 0.38 794.0 5.08 0.13 3.55 59.9 0.48 4320.0 3.23 0.08 3.54 60.0 0.50 14800.0 2.19 0.06 3.50 40.0 0.00 0.0 4.19 0.44 3.53 40.0 0.12 5.6 3.91 0.41 3.46 40.0 0.21 14.0 3.41 0.36 3.51 39.9 0.30 36.2 2.81 0.30 3.54 40.1 0.37 116.0 2.24 0.24 3.57 40.0 0.47 879.0 1.68 0.18 3.49 40.0 0.55 8560.0 0.98 0.11 7.00 40.0 0.00 0.0 10.00 0.55 6.88 40.0 0.15 5.7 6.58 0.37 6.98 40.0 0.17 7.2 6.36 0.36 6.95 40.1 0.16 6.6 6.36 0.36 6.85 40.0 0.19 9.9 6.45 0.37 6.97 40.1 0.19 10.0 6.23 0.35 6.93 40.4 0.27 22.4 5.11 0.29 7.06 40.0 0.23 14.6 5.63 0.32 7.08 40.1 0.25 19.1 5.53 0.31 7.10 40.0 0.27 23.1 5.16 0.29 7.12 39.9 0.36 96.6 3.55 0.20 7.05 40.0 0.35 72.1 4.23 0.24 7.06 39.9 0.39 120.0 3.62 0.21 7.05 39.9 0.39 113.0 3.38 0.19 7.05 40.0 0.40 128.0 3.50 0.20 7.58 40.1 0.38 131.0 3.32 0.18 7.00 39.9 0.47 574.0 2.70 0.16 7.11 40.0 0.59 28300.0 1.46 0.08 7.06 40.0 0.48 883.0 2.47 0.14 7.17 40.0 0.46 750.0 2.66 0.15 7.06 40.0 0.50 1870.0 1.99 0.12 7.11 39.9 0.49 1100.0 1.93 0.11

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7.06 40.0 0.52 3030.0 1.72 0.10 7.06 39.9 0.33 48.5 4.58 0.26 7.04 39.9 0.35 66.2 4.23 0.24 7.00 60.0 0.00 0.0 27.10 0.37 7.00 59.9 0.11 19.4 21.50 0.29 7.08 60.0 0.19 58.9 18.60 0.25 7.07 60.0 0.29 209.0 14.10 0.20 7.03 59.9 0.39 763.0 10.00 0.14 7.14 59.8 0.49 4860.0 4.94 0.07 7.17 60.1 0.54 25800.0 3.16 0.04 7.38 59.9 0.57 50200.0 2.88 0.04 11.00 40.0 0.00 0.0 12.00 0.45 11.00 40.0 0.12 5.1 10.40 0.40 10.75 40.0 0.20 10.8 8.42 0.33 10.90 39.9 0.30 29.5 6.03 0.24 11.28 40.1 0.37 104.0 4.39 0.17 11.06 40.0 0.49 1620.0 1.98 0.08 11.12 40.0 0.55 22300.0 0.95 0.04 11.00 60.0 0.00 0.0 40.20 0.37 11.21 60.0 0.14 15.5 36.09 0.33 11.17 60.0 0.23 73.1 28.38 0.27 11.12 60.0 0.29 199.0 22.52 0.21 11.36 60.0 0.42 847.0 14.30 0.14 11.32 59.9 0.46 6980.0 6.55 0.06 10.98 60.0 0.50 26500.0 4.16 0.04

Table 2: 2 m, 5 m, 8 m PZ Volatility

8 m PZ T ( °C ) Loading PCO2 (Pa) PPZ (Pa) γPZ

40 0.00 0.0 14.21 0.10 40 0.28 512.6 2.14 0.02 40 0.38 6500.8 1.07 0.01 60 0.00 0.0 109.31 0.25 60 0.28 4930.0 16.31 0.04 60 0.38 30620.8 7.39 0.02


40 0.00 0.0 5.12 0.05 40 0.17 28.7 3.12 0.03 40 0.22 60.5 2.88 0.03 40 0.27 211.0 2.20 0.02 40 0.34 798.0 1.03 0.01 40 0.41 5710.0 0.82 0.01

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5

40 0.41 6990.0 0.86 0.01 60 0.00 0.0 17.20 0.06 60 0.16 137.0 10.20 0.04 60 0.23 365.0 7.45 0.03 60 0.30 1290.0 5.59 0.02 60 0.33 3310.0 4.86 0.02 60 0.39 18300.0 2.86 0.01 60 0.42 51400.0 2.23 0.01


40 0.00 0.0 2.17 0.06 40 0.15 21.5 2.12 0.05 40 0.23 106.0 1.80 0.04 40 0.26 184.0 1.68 0.04 40 0.31 526.0 1.49 0.04 40 0.37 1950.0 1.38 0.04 40 0.43 10100.0 1.09 0.03 60 0.00 0.0 6.78 0.06 60 0.13 92.4 5.55 0.05 60 0.19 296.0 4.80 0.04 60 0.28 1400.0 2.93 0.02 60 0.33 3950.0 2.24 0.02 60 0.37 9910.0 1.77 0.02 60 0.41 24700.0 1.28 0.01 60 0.17 142.0 5.13 0.05 60 0.38 13700.0 1.87 0.02

Table 3: 7 m MDEA/2 m PZ Blend Volatility

T (°C ) Loading PCO2 (Pa) PMDEA (Pa) PPZ (Pa) γMDEA γPZ

40 0.00 0.0 0.81 2.04 1.78 0.06 40 0.10 398.9 0.69 0.59 1.55 0.02 40 0.19 2580.9 0.60 0.09 1.37 0.00 60 0.00 0.0 7.37 12.37 2.90 0.12 60 0.10 2967.1 6.53 4.80 2.61 0.05 60 0.19 18639.7 6.48 2.47 2.63 0.02

Table 4: 8 m and 12 m EDA Volatility

EDA (m) T (°C ) Loading PCO2 (Pa) PEDA (Pa) γEDA 8 40 0.00 0.0 24.14 4.7E-02 8 40 0.41 248.1 0.93 1.8E-03 8 40 0.49 4090.2 0.46 9.2E-04 8 60 0.00 0.0 165.69 1.2E-01

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6

8 60 0.41 2695.6 6.19 4.4E-03 8 60 0.49 25054.1 2.90 2.1E-03

12 40 0.00 0.0 57.00 7.9E-02 12 40 0.42 203.5 1.41 2.0E-03 12 40 0.50 10747.8 0.16 2.3E-04 12 60 0.00 0.0 430.31 2.1E-01 12 60 0.42 2420.2 8.64 4.4E-03 12 60 0.50 32959.6 1.20 6.2E-04

Table 5: 5 m AMP Volatility

T (°C ) Loading PCO2 (Pa) PAMP (Pa) γAMP 40 0.30 1090.7 9.84 0.68 40 0.55 7768.1 4.57 0.33 60 0.30 6816.2 55.53 0.86 60 0.55 30818.2 26.40 0.42

Results Figure 2 shows MEA partial pressure for 7.0 m and 11.0 m MEA systems at 40 ºC and 60 ºC, respectively.

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6

Loading (mol CO2/mol total alkalinity)

PMEA

(Pa)

7m MEA 40ºC

11m MEA_40ºC

7m MEA_60ºC

Figure 2: MEA Partial Pressure with respect to T and Loading

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7

MEA partial pressure is greater for higher amine concentration. Evidently, 11 m MEA solution has greater partial pressure than 7 m MEA at the same temperature. The presence of greater amine in solution gives rise to higher partial pressure. Additionally, partial pressure increases with temperature. At 60 ºC MEA partial pressure is higher than at 40 ºC for the 7 m solution. With higher temperature there is more heat energy present to volatilize more amine into the vapor phase; therefore, the partial pressure is greater.

Figure 3 displays MEA volatility for 3.5 m, 7 m, and 11 m solutions in terms of MEA apparent activity coefficient at 40 ºC and 60 ºC.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.0 0.1 0.2 0.3 0.4 0.5 0.6


App

aren

t MEA

Act

ivity

Coe

ffici

ent

28 ppm

40ºC

60ºC

Figure 3: 3.5 m, 7 m, 11 m MEA Volatility The effect of MEA concentration has been normalized as volatility is now expressed as an apparent activity coefficient instead of partial pressure. Therefore, all the data points representing different MEA concentrations (3.5 m, 7 m, and 11 m) collapse onto individual lines representing only the effect of temperature. Furthermore, the effect of temperature has also been greatly reduced by presenting the results as apparent activity coefficient.

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8

0.001

0.01

0.1

1

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40


App

aren

t PZ

Act

ivity

Coe

ffici

ent

60ºC

40ºC21.4 ppm

Figure 4: 8 m PZ Volatility The apparent PZ activity coefficient in 8 m PZ also decreases with loading (Figure 4), as free PZ is converted by reaction with CO2 to other nonvolatile species. The apparent PZ activity coefficient is somewhat greater at 60 ºC than at 40 ºC, exhibiting endothermic behavior. In subsequent plots, it can be seen that the temperature behavior for lower concentrations of PZ (5 m and 2 m) is different from 8 m due to unique and complex speciation occurring at each concentration. At the absorber operating condition of interest (40 ºC and nominal lean loading of ~0.30), 8 m PZ volatility is approximately 21.4 ppm.

With 5 m PZ (Figure 5) there is little effect of temperature on the apparent activity coefficient.

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9

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45


App

aren

t PZ

Activ

ity C

oeffi

cien

t

40ºC

60ºC

Figure 5: 5 m PZ Volatility With 2 m PZ (Figure 6) there is a reversed effect of temperature on the apparent activity coefficient.

195

10

0

0.01

0.02

0.03

0.04

0.05

0.06

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45


App

aren

t PZ

Act

ivity

Coe

ffici

ent

40ºC

60ºC

Figure 6: 2 m PZ Volatility

In this case, the apparent PZ activity coefficient is greater at 40ºC than at 60ºC which indicates an exothermic behavior. In summary, as PZ concentration is lowered from 8 m to 5 m and then to 2 m, the solution transitions from being endothermic to exothermic as is indicated by the temperature behavior of the apparent PZ activity coefficient in each case. This is possibly due to different speciation effects occurring in each solution. At zero loading, however, the apparent PZ activity coefficient is always higher for 60ºC than for 40ºC, due to the lack of speciation from the CO2 reactions. The volatility of PZ in 7 m MDEA (methyldiethanolamine) / 2 m PZ is presented in Figure 7.

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11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 0.05 0.1 0.15CO2 Loading (mol CO2/mol tot alk)

App

aren

t PZ

Act

ivity

Coe

ffici

ent

40º C

60º C

5.9 ppm

Figure 7: PZ Volatility in 7 m MDEA/2 m PZ Blend

7 m MDEA/2 m PZ shows temperature and loading effects similar to those of MEA. The apparent PZ activity coefficient of the blend decreases with loading as there is less free amine present in solution in the presence of greater CO2 at higher loading. PZ in this blend shows an endothermic behavior with the apparent PZ activity coefficient being greater at 60 ºC than at 40 ºC. At the operating condition of interest, PZ volatility for this blend is roughly 6 ppm which is much less than that of 7 m MEA and 8 m PZ.

Figure 8 exhibits the volatility of MDEA in the 7 m MDEA/2m PZ.

197

12

1

10

0 0.05 0.1 0.15

CO2 Loading (mol CO2/mol tot alk)

App

aren

t MD

EA A

ctiv

ity C

oeffi

cien

t

40º C

60º C

6.9 ppm

Figure 8: MDEA Volatility in 7 m MDEA/2 m PZ Blend

Unlike PZ, MDEA in the 7 m MDEA/2 m PZ blend does not show a very strong dependence on loading as the apparent MDEA activity coefficient remains rather constant throughout the loading range shown. This behavior is due to CO2 reacting more preferentially with PZ than with MDEA in the blend, and thereby MDEA volatility stays roughly constant. Again the temperature behavior is endothermic. The volatility of MDEA for this blend is about 6.9 ppm at the relevant operating condition.

Figure 9 presents ethylenediamine (EDA) volatility for the 8 m EDA and 12 m EDA at 40 ºC and 60 ºC.

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0.0001

0.001

0.01

0.1

1

0 0.1 0.2 0.3 0.4 0.5

CO2 Loading (mol CO2/equiv EDA)

App

aren

t ED

A A

ctiv

ity C

oeffi

cien

t

8m EDA_40C12m EDA_40C

8m EDA 60C

14.1 ppm

12m EDA_60C

Figure 9: EDA Volatility

The apparent EDA activity coefficient is greater at higher temperature than at lower temperature – again an indication of endothermic behavior. Moreover, at the same temperature, EDA volatility is greater at 12 m EDA than at 8 m EDA as there is more free amine present in the former to volatilize. As loading is increased, EDA volatility decreases as has been seen with all the other amines. At the operating condition of interest, 12 m EDA volatility is approximately 14.1 ppm.

Figure 10 displays 5 m AMP (2-amino-2-methyl-1-propanol) volatility.

199

14

0.1

1

0.3 0.35 0.4 0.45 0.5 0.55CO2 Loading (mol CO2/mol AMP)

App

aren

t AM

P A

ctiv

ity C

oeffi

cien

t

40º C

60º C

97 ppm

Figure 10: 5 m AMP Volatility

5 m AMP displays an endothermic temperature behavior as far as the apparent amine activity coefficient is concerned. Volatility for this solution is seen to decrease with CO2 loading as has always been expected. At 40 ºC and a nominal lean loading of 0.3, 5 m AMP volatility is as much as 97 ppm which is remarkably the highest of all the solutions studied so far.

The following graph summarizes the volatility of all the systems in terms of amine partial pressure as a function of CO2 partial pressure at 40 ºC.

200

15

0.1

1

10

100

0 2000 4000 6000 8000 10000 12000PCO2 (Pa) at 40C

P am

ine (

Pa)

7m MDEA/2m PZ

8m PZ

7m MEA

5m AMP

12m EDA

Figure 11: Amine Volatility Comparison The least volatile system observed thus far is 7 m MDEA/2m PZ. The system with the next higher volatility is 12 m EDA; however, surprisingly enough, unloaded 12 m EDA has a greater volatility than all of the systems. 8 m PZ is seen to have comparable volatility to the baseline 7 m MEA solvent. 5 m AMP is noted to be the most volatile system studied to date.

Conclusions Table 6 ranks the amine systems in order of increasing volatility starting from the top. It also tabulates estimated figures of the amine heats of solution (the amount of heat given off as the amine comes into solution with water and CO2) and the heats of vaporization (the amount of heat it takes to desorb the amine from the solution). Calculations for these quantities are done per the Gibb’s-Helmholtz relations presented in the Theory section.

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Table 6. Summary of Amine Volatility

Note that these amine systems are all ranked on the same basis – this being the nominal lean loading that corresponds to a CO2 partial pressure of approximately 500 Pa at 40ºC in each case. In order of increasing volatility, 7 m MDEA / 2 m PZ is the least volatile system, followed by 12 m EDA, 8 m PZ, baseline 7 m MEA, and 5 m AMP being the most volatile. In regard to the amine heat of solution, it appears that the less volatile systems have higher heats of solution than those that are more volatile. Meanwhile, there is no apparent correlation between amine volatility and the amine heat of vaporization.

Future Work Screening of additional amine systems is to be continued. Future systems that will be considered for screening include: 2-PE, MAPA, DGA, and HEP. Furthermore, there is plan to resume collecting additional experimental data (unloaded) for the MDEA/PZ blend to support Aspen Plus® modeling activity. Heat capacity and NMR speciation for the blend at different loadings will also be studied. In the long run, an attempt will be made to construct a generalized amine volatility model.

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Degradation of Amine Solvents for Carbon Capture


by Alexander Voice


and the




July 17, 2009

Abstract Oxidative degradation experiments in the high gas flow (HGF) apparatus continued this quarter. An experiment was conducted to determine the sensitivity of the oxidative degradation rate of 7 m MEA under kinetically limited conditions in the high gas flow apparatus on various operating conditions. Temperature was by far the most important parameter, showing a 100–500% change in ammonia production for a 10 °C temperature change.

Amine screening for oxidative degradation rate was also conducted in the HGF apparatus. Ethylenediamine (EDA, 8 m) degraded at a rate of 1 mM/hr for 0.1–1 mM Fe as evidenced by NH3 production. Addition of 1 mM Fe produced a burst of 0.15 mM of NH3, although the steady state rate remained unchanged. 4.8 m 2-amino-2-methyl-1-propanol (AMP) and 17.7 m diglycol amine (DGA®) did not degrade to produce NH3 in significant quantities. AMP did not produce significant quantities of heat stable salts. The presence of other degradation products, including NO2 and formaldehyde, had a low signal to noise ratio in the FTIR spectrum, and will require verification. 8 m PZ did not produce NH3 or heat stable salts in the HGF. 11 m MEA produced ammonia at a rate of 4.1 mM/hr, compared with 1.8 mM/hr for previous 7 m experiments conducted by Sexton.

Formic acid and 7 m MEA at 0.4 loading reacted rapidly to produce hydroxyl-ethyl-formamide at 135 °C. Formate and formamide concentrations were stable after 24 hours (the first sample taken). The formate to formamide ratio was 1:1 in a solution which initially contained only formic acid. In the solution loaded with formic acid and formaldehyde, the ratio was 2:1. Formate was also detected in the solution containing only formaldehyde at a concentration of 22 mM, well above the ~1 mM formate detected in the neat solution.

The metal dissolution rate (metal concentration divided by time) in thermal degradation experiments was found to be a clean Arrhenius function of T.

Introduction Degradation is a serious problem for most carbon capture systems employing amine absorption stripping technology. Solvent degradation can be divided into two main categories: oxidative

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degradation, which occurs primarily in the absorber (due to the presence of dissolved oxygen), and thermal degradation, which occurs primarily in the stripper (due to elevated temperature). In the case of monoethanolamine (MEA), oxidative degradation produces ammonia and formate (Sexton, 2008; Goff, 2005). Thermal degradation is not known to produce any heat stable salts. Instead, MEA undergoes a polymerization reaction to produce larger molecules which have not been observed in oxidative degradation experiments (Davis, 2009).

Degradation reduces the amount of solvent available for absorbing carbon dioxide, thus reducing the capacity of the solution and the performance of the system. In addition, degradation products must eventually be removed and disposed of. Solvent reclaiming systems can be used to remove heat stable salts from solution; however it is preferable to prevent degradation from occurring in the first place.

Oxidative degradation of MEA has been shown to be mass transfer controlled in situations where high concentrations of catalysts are present (Goff, 2005). Iron, Cr, and Ni are all catalysts for oxidative degradation, and have been observed in high concentrations in thermal degradation experiments using stainless steel reactors (Rochelle et al., 2008). High temperature, low loading, high agitation rate, high MEA concentration, and high oxygen concentration also increase the degradation rate (Goff, 2005). Degradation can be limited by selecting a solvent with a low affinity for dissolved oxygen, which limits the driving force for oxygen mass transfer. Inhibitors which scavenge oxygen, chelate metals, or absorb free radicals can reduce the degradation rate. Proprietary inhibitors A and B have been shown to decrease the rate of ammonia production from MEA and EDA solutions in the presence of dissolved iron and copper. Oxidative degradation can be different for different solvents and inhibitors; thus it is an important factor to consider in selecting a solvent.


Oxidative Degradation Experiments A procedure for characterizing oxidative degradation of various amine solvents in the high gas flow apparatus was developed this quarter. The procedure and apparatus are similar to those employed by Goff and Sexton. 350mL of loaded amine solution were placed in a 1L glass reactor with a heated dimethylsilicone oil jacket. The jacket is set at 63 °C to maintain a reactor temperature of 55 °C. Air and carbon dioxide (98%v./2%v.) are fed into the bottom of the reactor by two Brooks mass flow controllers at a rate of approximately 0.23 mol/min (dry). The total gas flow rate is calculated from the water content of the gas (measured by an FTIR analyzer), the temperature of the reactor, and by assuming ideal gas behavior and total pressure to be 1 atmosphere (1).

Gas rate = n*R*T/P/(1-f) = 6.19L/min (1)

where n = molar dry gas rate (.23 mol/min) R = gas constant (.08206 L*atm/mol/K) T = Reactor temperature (328.15K) P = ambient pressure (1 atm) f = water fraction (.1 - .2)

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The rate of species produced from the solution (including volatile losses and degradation products) can be calculated using the gas rate and concentration reported by the FTIR.

Production rate [=] mM/hr = gas rate (gmol/min) * concentration (µmol/mol) * (1 mol/1e6 µmol) / solution volume (liters)

* 1000 (mmol/mol) * 60min/hour (2) The water balance was maintained on the reactor in one of two ways: by passing the dry gas through a saturator bath before entering the reactor or by feeding dry gas and makeup water directly to the reactor. Liquid samples were taken every 24–48 hours. An FTIR analyzer provided online analysis of the gas phase, allowing degradation products in the gas phase and volatile losses to be well accounted for. Liquid phase samples were analyzed using anion and cation chromatography using the procedure described by Sexton. Operating conditions for the high gas flow apparatus are summarized in Table 1

Table 1: Operating Conditions for the HGF System

Operating Condition Operating Range

Agitation rate (rpm) 1000 ± 5

Temperature (oC) 55±1

Pressure (mm Hg) 760±15

Dry gas composition (%) 2 CO2, 98 air

Dry gas flow rate (gmol/min) 0.23 ± .04

Saturator temperature*(oC) 53-63C

Water makeup rate* (ml/min) 0.1-0.5

*Continuous water makeup is only used as a replacement for saturating the reactor gas

Thermal Degradation Experiments A new apparatus was designed for thermally degrading large quantities of amine solutions. The device is a stainless steel container with a sampling port, valve, and quick-connect sampling tube. When the container is heated pressure builds inside the device. When the valve on the sampling port is opened, pressure forces liquid into the sampling tube. The sampling tube is cooled and then disconnected. The device was designed to facilitate degradation of relatively large quantities (up to 400 mL) of amine at a time (Figure 1).

Figure 1: Schematic of Thermal Degradation Device

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4

A thermal degradation experiment was designed to determine the effect of formate and formaldehyde on the thermal degradation rate. 10 mL SS316L tubes with Swagelok endcaps (“thermal bombs”) were filled with one of four different solutions. A batch of neat 7 m MEA with 0.4mol CO2/mol MEA was used to prepare all four solutions (Table 2).

Table 2: Thermal Bomb Solutions Summary

Solution Contents

1 7 m MEA 0.4mol/CO2 per mol MEA 0.1mol formic acid/mol MEA

2 7 m MEA 0.4mol/CO2 per mol MEA 0.1mol formaldehyde acid/mol MEA

3 7 m MEA 0.4mol/CO2 per mol MEA 0.05mol formic acid/mol MEA 0.05mol formaldehyde/mol MEA

4 7 m MEA 0.4mol/CO2 per mol MEA

Samples were taken in roughly geometric intervals (1, 2, 4, 8…days) and analyzed for the presence of formate, formamide (by sodium hydroxide treatment), total inorganic carbon content (by acid evolution), and dissolved iron (by flame atomic absorption). Samples will eventually be analyzed by cation chromatography to determine amine loss and the presence of thermal degradation products.

Analytical Methods Analytical methods have been described extensively in previous quarterly reports (Rochelle et al., 2008; Rochelle et al., 2009). Methods used this quarter are summarized in Table 3.

Table 3: Analytical Methods Overview

Method Analyte (s)

Total inorganic carbon (by phosphoric acid release)

Dissolved carbon dioxide

Cation chromatography Amines (ethanolamine, ethylene diamine, piperazine, etc.)

Thermal degradation products (MEA-oxazolidone, MEA-urea)

Anion chromatography Organic carboxylic acids (formate, oxalate, glycolate, acetate)

Amides (formamide, oxalamide, glycolamide, acetamide)

Inorganic salts (nitrates, nitrites, sulfates,

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chloride)

Cation chromatography mass spectroscopy Unknown cation degradation products

Titration Total alkalinity ≈ amine concentration, exact for unloaded neat soln’s

High pressure liquid chromatography Hydroxyethyl imidazole, N-formyl-ethanolamine (MEA-formamide)

Fourier transform infrared analyzer Gas phase degradation products (ammonia, N2O, NOx)

Gas phase inerts (water, carbon dioxide)

Other gas phase components (including, not limited to, methane, ethylene, formaldehyde, acetaldehyde, methanol, methylamine)

Results

Degradation of 7 m MEA – Kinetic Control MEA was degraded under kinetically controlled conditions: 0.1-1 mM Fe with 50–100 mM inhibitor A. Although NH3 production rates are not a good estimate of the absolute degradation rate under these conditions, the data demonstrate that temperature is a major factor in oxidative degradation of MEA.

Table 4: Ammonia Production from MEA

Agit.

(RPM) Fe (mM) “A” (mM)

Dry Gas O2

(%)

Temp.

(C) NH3 Rate (mM/hr) Change (%)

1500 0 50 17.2% 54 0.99 --

1500 .1 50 17.2% 54 1.16 17

1000 .1 50 17.2% 54 1.01 -12

1000 .1 50 20.5% 54 1.20 18

1000 .1 50 15.0% 54 1.00 -16

1000 .1 50 17.2% 64 3.04 204

500 .1 50 17.2% 64 1.66 -45

1500 1 100 17.2% 64 0.63 -62

500 1 100 17.2% 64 0.5 -20

1500 1 100 17.2% 46 -0.03 -106

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1700 1 100 17.2% 54 0.04 -233

1700 1 100 17.2% 64 0.23 475

Corrosion Data Metals concentrations found in Davis’ SS-316L thermal degradation bombs were evaluated using flame AA last quarter. The results were re-interpreted this quarter to explore the metal dissolution rate (metal concentration in µg/g divided by the time in days) as a function of temperature. For this particular data set, the dissolution rate was a clean Arrhenius function for 100–150 °C (Figure 2). For the 135 °C data, the rate was calculated using the slope retrieved using the LINEST function in MS Excel. The slope of ln(µg/g*day) vs. 1/T was -7698, -10408, and -7708 for Fe, Ni, and Cr, respectively.

Degradation Bombs

Figure 2: Metal Dissolution Rate as a Function of Temperature

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Table 5: Present in Thermally Degraded MEA Metals Present in Thermally Degraded MEA

Time (Days)

Temperature (C) Fe (ppm)

Ni (ppm)

Cr (ppm)

28 100 116 26 38

14 120 167 49 69

4 135 196 57 78

8 135 302 111 142

14 135 409 142 184

28 135 765 241 310

14 150 673 386 217

Oxidative Degradation Screening Experiments The following is a summary of the results of screening studies conducted in the HGF system. 8 m EDA, 4.8 m AMP, 8 m PZ, 11 m MEA, and 17.7 m DGA were degraded oxidatively. Liquid samples were taken every 24–48 hours and analyzed using anion chromatography. Cation chromatography will be conducted on all samples to gauge amine loss.

EDA was the only amine besides MEA to produce significant quantities of ammonia (Figure 4).

Figure 4: Oxidative Degradation of EDA in the HGF

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Table 6: 8 m EDA – Degradation Product Concentrations (mmols/kg solution)

Time (min) 0 207 1227* 1762** 3202 4547§ 7131� 8687

Formate 0.36 0.67 0.77 0.72 0.76 2.03 7.40 10.49

Formamide 2.26 1.52 1.97 2.02 3.56 7.15 25.33 31.38

Oxalate 0.04 0.06 0.07 0.04 0.03 0.04 0.08 0.13

Oxamide 0.00 0.00 0.02 0.03 0.04 0.12 0.69 0.97

Nitrate 0.04 0.06 0.13 0.17 0.47 1.03 1.56 1.60

Nitrite 0.09 0.20 0.28 0.33 0.51 0.99 1.55 1.59

Volatility 0.00 0.47 2.61 3.84 6.05 13.32 22.11 27.89

EDA (%wt.) 32.05 23.61

*1 mM Fe added, **5 mM Cu added, §50 mM “A” added, �50 mM “A” added

EDA produced ammonia at a rate of 1 mM/hr for concentrations of 0.1 and 1.1 mM Fe. 5 mM copper caused a roughly 4-fold increase in the steady state ammonia production rate. Addition of 50 mM Inhibitor A reduced the ammonia rate to 2.2 mM/hr; 100 mM Inhibitor A reduced the ammonia rate to 1.2 mM/hr in the presence of 1.1 mM Fe and 5 mM Cu.

MEA produced significant concentrations of ammonia (quantified in the gas phase with the FTIR) and formate (quantified using anion chromatography), in addition to lower concentrations of other products (Table 6). Of the heat stable salts, formate, formamide, oxalate, oxalamide, nitrate, and nitrite all exhibited a linear trend over the course of the experiment. The ratio of formamide to formate fluctuated between 7 and 12, with no clear trend. Glycolate and acetate were observed in low concentrations, and appeared to remain constant over the course of the experiment.

Table 7: 11 m MEA – Degradation Product Concentrations (mmols/kg solution)

Time (hours) 0 22.82 40.03 47.30 64.97

Glycolate 0.00 3.35 3.05 3.81 2.68

Glycolamide 0.00 0.66 1.87 2.89 3.50

Acetate 0.00 2.41 2.00 1.83 2.28

Acetamide 0.00 0.00 0.00 0.00 0.00

Formate 0.28 2.34 3.60 5.41 8.06

Formamide 0.00 19.85 42.46 49.19 61.49

Oxalate 0.00 0.04 0.08 0.12 0.22

Oxalamide 0.00 1.45 4.47 6.09 10.26

Nitrate 0.00 2.24 3.92 5.25 6.59

Nitrite 0.00 0.49 0.78 1.00 1.44

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NH3 0.00 96.86 169.71 199.59 267.75

MEA 0.00 30.08 50.00 58.08 76.63

N2O 0.00 0.31 0.50 0.58 0.73

NO2 0.00 3.21 5.81 6.83 9.14

Table 8: 4.8 m AMP – Degradation Product Concentrations (mmols/kg solution)

Time (hrs) 0 3.1 10.5 44.5 50.8 67.9 72.4

Formate 0.12 1.11 1.00 0.56 0.89 0.75 1.13

Volatility 0.00 46.05 133.77 570.81 636.44 799.91 844.12

Finally samples from oxidatively degraded amine solutions were evaluated using HPLC. The program uses acetonitrile-H2O from 2% to 20% acetonitrile. No significant peaks (besides the amine peak) were observed in any of the solutions except MEA, which had well defined peaks at t = 3.069, 3.315, 4.159, and 4.292 min. None of these peaks correspond with those qualified by Sexton (n-formyl-ethanolamine t = 2.66min, hydroxyethyl-imidazole t = 5.91min). Therefore, these peaks will be qualified by LC/MS.

PZ and DGA had some peaks in the 6–7min and 6–11min noise range. These peaks were not well defined and will be difficult to qualify using LC/MS. Methanol was investigated as an alternative solvent for the HPLC. The same gradient was employed using methanol instead of water as the polar solvent in the mobile phase. This program did not achieve better separation of peaks in the MEA sample.

Thermal Degradation Experiments Formate and total formamide concentrations were determined in solutions of 7 m MEA at 0.4 loading after thermal degradation for t = 1, 2, 3, and 6 days at 135 °C. Solutions 1 and 3 were spiked with 0.1 and 0.05 mols of formic acid per mol of MEA, respectively. Solution 2 contained 0.1mol of formaldehyde per mol MEA, and solution 4 contained only 7 m MEA. Solutions 1 and 3 quickly equilibrated converting some formate to formamide in less than 24 hours. Solution 2, which initially contained no formate, had converted some formaldehyde to formate and formamide in less than 24 hours. Formate and formamide were observed in trace concentrations in the neat solution (Figures 5 and 6)

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Figure 5: Formate concentrations in formic acid spiked MEA solutions (1 and 3), 7m MEA

at 0.4 loading at 135 °C

Figure 6: Formate concentrations in MEA solutions (2 and 4), 7 m MEA at 0.4 loading at

135 °C

Table 9: Formate (mmol/kg solution) in MEA Solutions

Time (Days)

Solution 1 Solution 2 Solution 3 Solution 4

0 559 3 330 1

1 362 22 189 1

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11

2 377 22 182 1

3 331 22 173 2

6 359 24 200 --

Table 12: Formamide (mmol/kg solution) in MEA Solutions

Time (Days)

Solution 1 Solution 2 Solution 3 Solution 4

0 41 11 0 5

1 268 15 98 0

2 265 13 117 1

3 222 14 100 4

6 314 13 76

Total inorganic carbon content was determined for the first four samples. For the neat solution, 4.40mol base/kg solution corresponds to 7.07 m MEA.

Table 11: Total Inorganic Carbon in Thermal Bombs (mol CO2/kg solution)

Sol’n t=1 days t=2 days t=3 days t=6 days

1 1.657 1.539 1.681 1.630

2 1.683 1.821 1.765 1.690

3 2.022 1.729 1.716 1.683

4 1.785 1.829 1.811 --

Table 12: Total Alkalinity of 7 m MEA with Additives

Solution Total Alkalinity (mol base / kg solution)

Additive

1 3.78 Formic acid

2 4.26 Formaldehyde

3 3.93 Formic acid and formaldehyde

4 4.40 None

Discussion The high gas flow apparatus provides a valuable tool for evaluating the volatility and oxidative degradation products of amines. The FTIR analyzer provides information about the gas phase exiting the reactor that cannot otherwise be easily obtained. The device can be calibrated to detect any gas phase compound. However, if unknown components are in the gas phase, they can interfere with the analysis. Once the existence of a particular compound has been verified,

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the FTIR is an excellent tool for quantification. Volatile losses from the HGF can be reliably accounted for because the instrument can be calibrated to detect the amine, and because the gas phase contains significant quantities of amine. Furthermore, analysis of FTIR spectra using various settings (i.e. having the machine simultaneously analyze for different components) has shown the amine concentration to be relatively insensitive to the analysis settings. In summary, the FTIR can quantify amine volatility and gaseous degradation products, but the former measurement is more reliable because the component is known and exists in significant quantities.

Cation chromatography is the most reliable way to interpret oxidative degradation, because it relies on amine loss rather than the identification and quantification of unknown degradation products. However, significant degradation must occur for this method to yield useful results for several reasons. The error in the cation analysis may be 100 mM, which corresponds to roughly 2% for 7 m MEA at 0.4 loading, therefore amine losses should be at least 5% to be significant. In addition, volatile losses, which may exceed degradative losses, must be accurately quantified. Volatile losses are calculated from the concentrations of amine given by the FTIR analyzer, the gas rate, and the solution volume. The amine concentration and gas rate can be precisely determined, but the solution volume may fluctuate if water is evaporated or condensed during the experiment. Changes in the water balance affect both the total solution volume and the amine concentration. Cation chromatography can therefore only be used to quantify oxidative degradation losses in experiments where significant amounts of degradation occur and where the water balance has been well maintained.

Conclusions Temperature is the most important factor in oxidative degradation experiments where the reaction is kinetically limited. It outweighs catalyst, reagent, and inhibitor concentration, as well as agitation rate and loading. In 7 m MEA with 0.1 Fe++/50 mM A, the NH3 production rate increased from 1 to 3 mM/hr as T increased from 54 to 64 oC.

EDA degrades to produce ammonia with 0.1mM Fe. High Fe concentrations do not increase the degradation rate. 5 mM Cu produces roughly 4x the degradation rate of 0.1-1mM Fe. 11 m MEA produces ammonia at a 2x higher rate than 7 m MEA. DGA, AMP, and PZ do not produce significant quantities of ammonia or heat stable salts.

Formate reacts with MEA to form n-formyl-ethanolamine in less than 24 hours. Formaldehyde also reacts with MEA to form formate and formaldehyde in less than 24 hours.

The metal dissolution rate in thermal experiments can be fitted with an Arrhenius function. The slope of the Arrhenius function was similar for Fe and Cr.

Future Work LC/MS is a top priority to qualify unknowns in oxidatively degraded MEA solutions. LC/MS will offer an opportunity to identify additional unknowns in MEA degradation and potentially close the gap in the mass balance.

In the thermal degradation experiments, formate was observed to react with MEA to form formamide faster than anticipated. A second experiment will be conducted to evaluate formamide formation over a shorter time period (<24hrs). In addition, the formation of formate in the MEA/formaldehyde solution was unexpected. This result will be explored further.

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Screening of additional amine solvents will continue. Cation chromatography will be the main tool employed in this study. Amines studied will include MDEA/PZ blend, 2-piperadine ethanol (2-PE), 2-methyl piperazine, 2,5-methyl piperazine, hydroxy-ethyl-piperazine (HEP), glycine, and methyl-amino-propyl amine (MAPA).

References Davis J. Thermal Degradation of Amines Used in Carbon Dioxide Removal Applications. The

University of Texas at Austin. Ph.D. Dissertation. 2009.

Goff G. Oxidative Degradation of Aqueous Monoethanolamine in CO2 Capture Processes: Iron and Copper Catalysis, Inhibition, and O2 Mass Transfer. Ph.D. Dissertation. 2005.

Rochelle GT et al. CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report 2008. Luminant Carbon Management Program. The University of Texas at Austin. 2009.

Rochelle GT et al. CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009. Luminant Carbon Management Program. The University of Texas at Austin. 2009.


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Model Validation and Dynamic Simulation of Absorption System in Aspen Custom Modeler®


by Sepideh Ziaii Fashami


and the




July 9, 2009

Abstract The dynamic model of the absorber created in ACM® has been run at the design and operating conditions of a pilot plant run with 9 m MEA. The steady state results give 58.8 % CO2 removal, which is 1.7% less than a reconciled value (59.9%). The temperature profile is not completely matched with experimental data because of inaccurate calculation of heat of absorption for 9 m MEA.

In addition, the dynamic model of the absorber has been run for two stripper ratio control strategies in flexible CO2 capture. The CO2 removal, rich loading and temperature profiles were calculated for each strategy and overall dynamic and steady state behavior were compared.

Pilot Plant Model Validation The dynamic model of the absorber in ACM® was run at the design basis of a pilot plant run with 9 m MEA at the University of Texas at Austin. In this simulation, the inlet conditions of the lean solvent and flue gas are set at the reconciled values by Plaza et al. (2009). (For details of the absorber model, see Rochelle et al., 2009)

The absorber packing (Flexipac AQ Style 20) was modeled using Flexipac 1Y with 29 mixed flow model. The segments were spaced with a Gaussian distribution pattern with Lmax/Lmin=10, which was shown by Rochelle et al. (2009). The interfacial area was calculated using a new correlation developed by Tsai et al. (2009).

Reconciled inlet conditions by Plaza et al. (2009) for simulation of pilot plant run along with the resulting values from this model are presented in Table 1.

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Table1: Pilot Plant Model Validation, 9 m MEA, 6.1 m packing, 0.43m diameter

Variable Reconciled value by Plaza (2009)

Inlet and resulting values

Inlet gas (mol/hr)

33346 33346

YCO2-In 0.1192 0.1192 YCO2-Out 0.0501 0.044 YH2O-In 0.022 0.022 TG-In(°C) 25.1 25.1 TG-Out(°C) 46.1 55.18 TL-In(°C) 38.2 38.2 TL-Out(°C) 46.7 43.03 Water-Lean(mol/hr) 143700 143700 CO2-Lean(mol/hr) 8307 8307 CO2 removal (%) 59.9 58.9 Rich loading (mol CO2/mol MEA)

0.469 0.457

As indicated in Table 1, resulting values give 58.9% CO2 removal, which is 1.7% less than the reconciled value. The outlet temperature values and profiles (Figure 1) do not match the data, which may reflect an inaccurate heat of absorption for 9 m MEA.

Figure1: Temperature profiles, (Δ) reconciled values, (-) and

(..) resulting values from the model

25

30

35

40

45

50

55

60

65

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Z/Ztotal

T(C

) Tvapor

Tliquid

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3

In this model, the heat of absorption is calculated from Clausius-Clapeyron equation using a Peq

CO2 regression from Hilliard’s Aspen Plus® model points (2008). The equation is a linear function of MEA concentration and is regressed around 7 m MEA, consequently it gives an independent equation for ΔHabs from MEA concentration. Therefore, the model could not predict an accurate ΔHabs for a broad range of solvent. In order to get better results, the calculated ΔHabs is multiplied by a correction factor, which is [MEA] (m) divided by 7 m.

Dynamic Analysis As discussed in previous quarterly reports, flexible capture is a system that is dynamically operated at variable load based on electricity market conditions. An important part of this discussion is to find a control strategy that minimizes the energy consumption and maximizes the profits. Some dynamic strategies have been proposed and analyzed on the stripper in the absence of an absorber model in our previous reports.

In this report, two strategies were simulated with the current dynamic model of the absorber with the same design and inlet conditions presented in the pilot plant model validation section. This report assumes that stripper is operated with ratio control: when the reboiler duty decreases, only a portion of rich solvent (proportional to the stripper vapor rate) is regenerated and the remaining portion is by-passed from the stripper or stored. Using this stripper strategy, there are three options for running the absorber.

1. Run the absorber with constant lean solvent rate and variable lean loading by returning to the stripper by-passed rich solution and regenerated solvent to the absorber (full flue gas flow rate).

2. Run the absorber with variable lean solvent rate and constant lean loading by returning the regenerated portion of solvent to the absorber (full flue flow gas rate).

3. Run the absorber with constant lean loading and reduced solvent and flue gas rate, which is option 2 with partially by-passed flue gas from the absorber.

In options 2 and 3 we kept the lean loading constant because we have shown that it remains almost unchanged if we use ratio-control strategy for the stripper when the reboiler heat duty is reduced (Ziaii et al., 2009). In this report, we looked at steady state results and dynamic behavior of the absorber for options 2 and 3.

Option 2: 50% step decrease in solvent flow rate To simulate option 2 with 50% reduction in reboiler heat duty, a negative 50% step change was made to the lean solution rate. As shown in Figure 2, CO2 removal goes down, rich loading goes up as liquid rate decreases, and both reach steady state in 5–6 minutes. A delay is seen for the rich loading at the initial point, which represents the time needed for the liquid to get to the bottom and changes to be sensed.

In Figure 3, the liquid temperature profile is shown at 0, 1.8 min, and 8 min. As also shown in this figure, the temperature at the top of the column initially increases and then decreases while from the middle to the bottom, temperature goes down monotonically. The initial temperature rise can be interpreted with the faster response of the liquid hold-up relative to the absorption of CO2, especially at the top of the column. Furthermore,

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4

the upward movement of the temperature bulge with the decreasing liquid rate is clear in this figure.

Figure 2: CO2 removal and rich loading responses to a -50% step change in lean

solution rate

Figure 3: Liquid temperature profile at 0, 1.8, and 8 min in response to -50% step

change in lean solution

Option 3: 50% ramp decrease in both solvent rate and flue gas rate To simulate option 3 with 50% reduction in reboiler heat duty, a -50% ramp change in 3 min was made to both lean solution rate and inlet gas rate with fixed inlet compositions. A 50% step change is not a reasonable change and causes the absorber to show very sharp changes versus time. In order to get smooth dynamic results, we have chosen the ramp change. Similar to option 2, for option 3 rich loading and CO2 removal responses are plotted in Figure 4. CO2 removal takes less than 30 seconds to reach the steady state after 3 mins ramping. However, rich loading takes 5–6 min to get to the steady state. This means that the CO2 hold-up time should be much smaller in the vapor phase than the liquid phase.

0.45

0.46

0.47

0.48

0.49

0.5

0.51

0.52

0.53

0 0.6 1.2 1.8 2.4 3 3.6 4.2 4.8 5.4 6 6.6 7.2 7.8 8.4 9

Time (min)

Ric

h lo

adin

g

0.46

0.48

0.5

0.52

0.54

0.56

0.58

0.6

CO

2 R

emov

al

CO2

Rich loading

30

35

40

45

50

55

60

65

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Z/Ztot

Liqu

id T

empe

ratu

re (C

)

t= 0 (Initial state)

t= 1.8

t= 8 min (Final state)

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5

Temperature increases at first along the column and then decreases as shown in Figure 5. When gas and liquid rates are changed at the same time, no temperature bulge movement is observed.

Figure 4: CO2 removal and rich loading responses to -50% ramp change in lean

solution rate and flue gas rate

40

45

50

55

60

65

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Z/Ztotal

Liqu

id T

empe

ratu

re (C

)

Figure 5: Liquid temperature profile at 0, 1.8, and 8 min in response to -50% ramp

change in lean solution rate and flue gas rate

Steady State comparison of options 2 and 3 Table 2 summarizes initial and final steady state values for options 1 and 2. Comparing the steady state values for the options, bypassing the gas (option 3) results in much lower final overall CO2 removal and lower rich loading compared to option 2. The lower the lean loading, the more specific energy required for solvent regeneration. Therefore option 2 may result in a lower regeneration energy consumption and higher compression energy due to more CO2 being compressed, provided stripper operating conditions do not change significantly.

0.45

0.455

0.46

0.465

0.47

0.475

0 1.2 2.4 3.6 4.8 6 7.2 8.4 9.6 10.8

Time (min)

Ric

h lo

adin

g

0.58

0.6

0.62

0.64

0.66

0.68

CO

2 R

emov

al

Rich loading

CO2 Removal

t= 0 (Initial state)

t= 3 min t= 8 min (Final state)

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6

Table 2: Steady state results of dynamic options 2 and 3

Variable Initial value Final value Option 2

Final value Option 3

Rich loading 0.457 0.495 0.471 CO2 removal 0.589 0.513 0.335 TG-Out (°C) 55.18 56.25 57.22 TL-Out (°C) 43.03 31.88 43.55 Dynamic responses indicate that both rich loading and CO2 removal reach steady state in about 5 mins with 98% approach when the lean solvent rate is decreased (option 2). Soon after both gas and liquid rate are ramped in option 3, it takes about 3.8 mins for the removal and 4.8 mins for rich loading to reach steady state with 98% approach.

Conclusions and Future Work The ACM® time-variant model for the absorber was validated with steady state reconciled pilot plant data with 9 m MEA. A 1.7% difference between reconciled measured, and calculated values was achieved for CO2 removal. Temperature profiles do not match the reconciled data well because of the lack of an accurate model to calculate ΔHabs for 9 m MEA.

Two dynamic strategies were run with the current absorber model to simulate varying steam consumption. The first option, where the absorber works at the full load of flue gas and partial solvent rate, may give lower specific energy consumption compared to the second option where the absorber operates at partial flue gas load. However, option 2 needs a bigger and more expensive heat exchanger and shows slower response time when the system changes from full load to the partial load and vice versa. Comparing the response time of the above dynamic options, option 2 indicates about 1 min faster response for the removal (3.8 mins). Rich loading gets to the steady state in about 5 mins in both strategies.

Next quarter, I plan to provide realistic multi-stage compressor and steam pressure characteristic curves and incorporate them into the while integrated system. I plan to use optimization tools in ACM® to do multivariable optimization to optimize the lean loading, stripper pressure, and other variables.

References Hilliard MD. Predictive Thermodynamic Model for an Aqueous Blend of Potassium

Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue Gas. The University of Texas at Austin. Ph.D. Dissertation. 2008.

Plaza J, Van Wagener D, Rochelle GT. "Modeling CO2 Capture with Aqueous Monoethanolamine". In 9th International Conference on Greenhouse Gas Control Technologies. Washington D.C., Elsevier. 2008.

Rochelle GT et al. "CO2 Capture by Aqueous Absorption, First Quarterly Progress Report 2009". Luminant Carbon Management Program. The University of Texas at Austin. 2009.

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Tsai R, Seibert F, Eldridge B, Rochelle GT. "Influence of Viscosity and Surface Tension on the Effective Mass Transfer Area of Structured Packing". 9th International Conference on Greenhouse Gas Control Technologies. Elsevier: Washington D.C., 2008.

Ziaii S, Cohen S, Rochelle GT, Edgar TF, Webber ME. "Dynamic operation of amine scrubbing in response to electricity demand and pricing". 9th International Conference on Greenhouse Gas Control Technologies. Washington D.C., Elsevier. 2008.

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Electric Grid-Level Implications

of Flexible CO2 Capture Operation


by Stuart Cohen

Supported by EPA STAR Fellowship



July 13, 2009

Abstract A flexible carbon dioxide (CO2) capture system with large-scale solvent storage allows continuous high CO2 removal from flue gas while power output is increased by turning stripping and CO2 compression systems to partial- or zero-load. In such a system, the basic tradeoff is that longer solvent storage times provide more opportunity to increase plant output when electricity prices are high, but at the expense of additional energy cost to regenerate stored solvent when electricity prices are low as well as additional capital costs of solvent inventory, storage tanks, and larger stripping, compression, and auxiliary equipment.

For storage times of 1–12 hours using monoethanolamine (MEA) solvent, 10s million kg MEA, 10s–100s m3 storage capacity, and tens to hundreds of millions of dollars in incremental capital costs are required. Storage tanks are a relatively insignificant capital cost relative to that of solvent inventory and larger stripping/compression equipment. A preliminary optimization study finds that solvent storage of a few hours or more is attractive when the electricity market experiences large differences between high and low electricity prices, even if only in the form of infrequent price spikes. Lower capital costs can improve the economics of a solvent storage system, but capital cost reductions are insufficient to justify installation of a solvent storage system without a favorable electricity price distribution. However, 2008 annual average electricity prices in the Electric Reliability Council of Texas (ERCOT) along with base case parameters and the preliminary modeling methodology do not provide the conditions required for solvent storage to be desirable.

Background and Introduction to Flexible CO2 Capture Flexible operation of a post-combustion amine absorption and stripping system could allow plant operators to recover some or all of the energy required for carbon dioxide (CO2) capture to increase power output under appropriate electricity market conditions. Flexible operation would consist of redirecting some or all of the steam being used for solvent regeneration back to the power cycle, thereby also reducing the need for energy to compress CO2.

In this manner, operating the energy intensive components of the amine absorption/stripping process at partial- or zero-load could allow the plant operator to choose the CO2 capture

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operating point that provides the most economical combination of cost and output for the current electricity market conditions. For instance, it may be profitable to operate CO2 capture at partial- or zero-load when electricity prices are high if additional electricity sales are greater than any increase in CO2 emissions costs under a CO2 regulatory framework (Ziaii, Cohen et al., 2008).

In addition, operating CO2 capture at zero-load during annual peak electricity demand can eliminate the need to spend billions of dollars to replace generation capacity lost when CO2 capture operates at full-load (Cohen, Rochelle et al., 2008). If a flexible CO2 capture system was able to respond fast enough that the energy typically used for CO2 capture is considered available to the electric grid as reserve capacity, CO2 capture systems may not even have to turn to zero-load during annual peak demand time periods unless the grid experienced an unplanned power plant outage or other reliability event.

There are two basic concepts for flexible CO2 capture with an amine absorption/stripping system. My previous work has almost exclusively analyzed the concept shown in Figure 1, where the steam and rich solvent flow rates to the stripper are reduced equally and simultaneously during partial- or zero-load. At partial-load, rich solvent that is not sent to the stripper is recycled to the absorber, so CO2 removal in the absorber will decrease as solvent becomes saturated with CO2. Zero-load could involve recirculating all solvent through the absorber, or the CO2 capture system could be bypassed completely. The incremental capital cost of flexibility is relatively low in this configuration and would primarily consist of costs for additional piping and possibly valves and control devices. This design’s primary disadvantage is increased CO2 emissions at partial- or zero-load, which would incur additional CO2 costs under a CO2 regulatory regime.

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Figure 1: Simultaneously reducing steam and rich solvent flow to the stripper allows

increased output but at the expense of additional CO2 emissions.

Figure 2 describes another concept for flexible CO2 capture. At high electricity prices, stripping and compression systems turn to partial- or zero-load while the absorber remains at full-load (Chalmers, Chen et al., 2006). The absorber is fed lean solvent from a lean solvent storage system and sends rich solvent to a rich solvent storage system in what may be termed “storage mode.” When electricity prices are low, stored rich solvent is then regenerated and the product CO2 is compressed in “regeneration mode.” In “regeneration mode,” stripping and compression systems, including appropriate pumps and heat exchange devices, must treat both the current process stream as well as stored solvent, so these systems would be larger and total energy requirements would be greater than in a baseline system built for inflexible CO2 capture or

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flexible CO2 capture with CO2 venting. This configuration is environmentally attractive because it maintains low CO2 emissions; however, total energy requirements for CO2 capture may be greater in “regeneration mode,” and there are significant additional capital costs for solvent inventory, solvent storage tanks, and larger stripping, compression, and auxiliary equipment.

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Figure 2: Including large-scale solvent storage incurs significant capital costs but allows

continued high CO2 removal at partial- and zero-load.

Optimizing Solvent Storage Time Problem Description This report focuses on recent work involving the solvent storage configuration in Figure 2 where the analysis seeks to find the optimal amount of time that a plant should operate in “storage mode” (storage time) by finding the most profitable combination of time spent in “storage mode” and “regeneration mode.” Previous calculations, some of which are presented in the Luminant Program 2nd Quarterly Report in 2008, find that solvent inventory costs for monoethanolamine (MEA) are on the order of tens of millions to hundreds of millions of dollars (tens of millions of kgMEA at $2/kgMEA), and tank volumes are on the order of tens to hundreds of cubic meters

(5-50 million gallons) for storage times of 1-12 hours (Rochelle, 2008). A baseline amine absorption/stripping CO2 capture system has capital costs on the order of several hundreds of millions of dollars or more depending on system size, so solvent storage is assumed to be cost prohibitive for time periods longer than one day (USNETL, 2007).

Figure 3 describes the general optimization problem by plotting the average diurnal variation in electricity prices in the Electric Reliability Council of Texas (ERCOT) in 2008 along with several hypothetical output curves for a 500MW plant with 0, 2, 6, or 10 hours of solvent storage. Zero storage hours represents an inflexible CO2 capture facility. As solvent storage time increases, more time can be spent at maximum output during high electricity price periods, but because of additional stripping and compression requirements in regeneration mode, the output in regeneration mode decreases with solvent storage time. Thus, there is a basic tradeoff: as solvent storage time increases, more profits are earned during high electricity prices, but capital costs increase along with the energy costs of CO2 capture and compression in regeneration mode.

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hypothetical output curves for different amounts of solvent storage to illustrate the tradeoff between storage mode and regeneration mode (ERCOT, 2008).

Modeling Methodology and Assumptions A first-order MATLAB model is created to find the number of storage mode hours in a 24-hour period that maximize plant profits earned by selling electricity in the energy market. Ancillary service markets are not considered in this analysis but could be a subject for future work because a system that could transition quickly between storage and regeneration modes could be in a good position to profit from short-lived high prices in ancillary service markets (Chalmers, Chen et al., 2006). In contrast to previous work that used a first-order dispatch model of the ERCOT electric grid, this work is a single plant analysis that uses average diurnal electricity price variations to represent electric grid dynamics.

Since this study does not include a detailed electricity market model, output at the power plant has no influence on electricity prices. The balancing energy market in ERCOT clears every 15 minutes, meaning that electricity prices make discrete jumps at 15-minute intervals, so plant profits are calculated at 15-minute intervals. Only a single price curve is used, so seasonal variations in electricity price are not accounted for.

In the initial formulation of this analysis, storage mode is assumed to turn all necessary stripping, compression, and auxiliary systems to zero-load, and the only available operating points are zero-load storage mode and full-load regeneration mode. Intermediate operating points, as well as partial-load of the power cycle itself, may be incorporated later. Though some auxiliary components of the CO2 capture system remain operational to continue CO2 absorption when stripping and compression is at zero-load, initial analysis will ignore any residual energy penalty, and plant output and efficiency in storage mode is assumed equal to that of the base plant without a CO2 capture system.

In actual operation, there will be a transition time between storage and regeneration modes that depends on the response time of various system components. However, this initial analysis ignores transition performance and allows the plant to choose operating mode at each time interval without regard to system response time. Since profits are always greater in storage

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mode, the model will find the highest price intervals to operate in storage mode and operate in regeneration mode at all other time intervals, and all available solvent storage capacity is used in the day. Future problem development may consider transition performance.

Profit at each time interval accounts for electricity sales, marginal costs, and capital costs scaled by the capital charge factor. Marginal costs account for fuel costs as well as any CO2 costs that would be incurred under a CO2 regulatory policy. In storage mode, output and efficiency are the same as a base plant without CO2 capture, and the CO2 emissions rate is reduced by an amount specified by the CO2 removal rate. In regeneration mode, the energy penalty for CO2 capture scales with storage time by the factor (24/(24-x)), where x is the number of hours in storage mode. For instance, if storage time is 12 hours, there is twice as much CO2 to be stripped and compressed throughout all hours in regeneration mode. In regeneration mode, overall plant efficiency and output decrease, and the CO2 emissions rate increases slightly as a result. CO2 transport and storage costs and other operating and maintenance costs are not included in the current analysis since they are relatively small and would vary little if at all on a cost per unit electricity basis.

Capital charges at each time interval are the same for both modes. The capital cost of solvent inventory increases linearly with total daily solvent storage time and is calculated from the design difference between rich and lean solvent loading, solvent price, plant output and emissions rate, and the desired CO2 removal rate. From the quantity of solvent, the volume and cost of solvent storage tanks is determined from a plant design handbook that describes the cost of large field-erected storage tanks, keeping in mind that there must be storage facilities on both the lean and rich side of the absorber. For all other components whose capital costs increase with storage time, the base capital cost without any solvent storage is assumed to increase with storage time by the factor (24/(24-x)) adjusted for economies of scale. Initially, the base capital cost of all of these components is taken together and scaled as one value, but future development of this problem may include a more detailed representation of the capital costs of each relevant system component. All capital costs are continuous functions of solvent storage time, so the current analytical model does not incorporate discrete changes in component quantity as solvent storage time increases.

An upper bound on the number of hours spent in storage mode is set by the constraint that the energy requirement for CO2 capture in regeneration mode must not exceed the total power plant capacity. In practice, the upper bound on storage time will likely be set by a specific power plant component specification such as the minimum load on the low pressure turbine, but this level of detail has not yet been included.

Formulas have not been included in this report in order to keep document length to a minimum, but I would be happy to furnish them upon request.

Base Case Model Input Parameters The base case electricity prices used in this study were the 2008 annual average ERCOT electricity prices in the balancing energy market1, shown previously in Figure 3.

The base plant output is chosen arbitrarily at 500 MW. Its heat rate of 10.8 MMBTU/MWh2 and CO2 emissions rate of 1.03 tCO2/MWh3 are weighted average values for all coal-fired plants in 1 These prices can be found on the ERCOT website labeled MCPEL for Market Clearing Prices for Energy for Load. 2 Units are million British thermal unts (MMBTU) per megawatt-hour (MWh).

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the ERCOT grid from the Environmental Protection Agency’s (EPA) most recent eGRID database (USEPA, 2007).

Table 1 contains a summary of major parameters used to calculate capital charges. The capital charge factor is chosen from the National Energy Technology Lab (NETL) CCS Systems Analysis Guidelines. A CO2 capture plant analysis performed by Trimeric Corp. is used to determine the base capital costs for stripping and compression equipment that scale with solvent storage time, including requisite pumps and heat exchange equipment. The economy of scale factor is chosen as a relatively high 0.85 because increasing the system size may require a larger number of components rather than simply increasing the size of components, so the average economies of scale may not be very substantial. Storage tank capital costs are calculated using an equation determined from the plot of purchased cost of large field-erected tanks in Plant Design and Economics for Chemical Engineers by Peters, Timmerhaus, and West and are scaled to 2008 dollars using the Chemical Plant Index.

Table 1: These parameters are used to determine capital charges (Peters, Timmerhaus et al., 2002; NETL, 2005; Fisher, 2007; CES, 2009).

Description (units) Value

capital charge factor (%/yr) 17.5 base capital cost of stripping/compression equipment (millions) 4 $192 economy of scale factor for stripping/compression equipment 0.85

The coal price used in this study is the average coal price in Texas’s electricity power sector in 2008 as reported by the Energy Information Administration, MEA price is taken from Chemical Industry News and Intelligence, and the CO2 price is somewhat arbitrarily chosen at a price commonly thought to be near that which is required for economic viability of CO2 capture and sequestration.

Table 2: These prices are assumed in the base case (Rao and Rubni, 2002; ICISpricing, 2008; USEIA, 2009).

Description (units) Value fuel price ($/MMBTU) 1.53

2 2CO Price ($/tCO ) 50 MEA price ($/kgMEA) 1.98

CO2 removal is set to 90%, a commonly cited value since much greater CO2 removal can become prohibitively expensive (Rubin, 2007). MEA mass fraction and capacity to absorb CO2 (the difference between rich and lean loading) is found in Oyenekan Ph.D. dissertation in the University of Texas at Austin Chemical Engineering Department, and the energy requirement for CO2 capture is taken from a recent dynamic analysis of MEA absorption and stripping done by Ziaii.

3 All CO2 quantities are shown in metric tons. 4 All monetary values are in 2008 U.S. dollars.

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Table 3: These parameters are used to define CO2 capture performance (Oyenekan, 2006; Ziaii, Cohen et al., 2008).

Description (units) Value 2fraction of CO removed from flue gas 0.9

mass fraction of solvent in solution 0.3

2solvent capacity - difference between rich and lean loading (molCO /molMEA) 0.12

2 2energy requirement for CO capture (MWh/tCO ) 0.269

Solution and Results Base Case Results: The maximum storage time based on using the plant’s entire output for CO2 stripping and compression in regeneration mode is 18.0 hours, so data are not displayed for longer storage times.

Figure 4 plots the marginal costs of electricity production in storage mode and regeneration mode as a function of solvent storage time. Also plotted is the daily average cost of electricity production for each solvent storage time when the output and amount of time spent in each mode is taken into account. Since storage mode assumes output and costs to return to base plant levels without CO2 capture, marginal cost in storage mode is not a function of solvent storage time. In regeneration mode, increasing solvent storage time requires the plant to process more stored solvent in a shorter amount of time, so marginal costs can increase to very high levels. However, when the output and amount of time spent in each mode is taken into account and costs of electricity production are averaged over a 24-hour period, the daily average cost is not a function of storage time and is equivalent to the marginal cost of an inflexible CO2 capture system.

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inflexible CO2 capture facility. The incremental capital costs of solvent storage are plotted in Figure 5 with regions to represent each of the three major capital cost components. Capital costs increase with storage time largely due to costs of stripping and compression equipment and solvent inventory. The cost of storage

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tanks is relatively small. Costs of stripping and compression equipment do not increase linearly because an economy of scale factor is used in calculations. As a point of reference, a full CO2 capture system without solvent storage for a 500 MW plant is expected to cost around $470 million (USNETL, 2007).

It is also important to note that the MEA inventories required for several hours of solvent storage are on the order of tens of millions of kilograms, so a serious practical concern with large-scale solvent storage is the chemical manufacturing capacity available to supply solvent inventory. The stoichiometry of MEA production requires production of diethanolamine (DEA) and triethanolamine (TEA) in appreciable quantities, so unless there is sufficient demand for all three chemicals, MEA manufacturers may have to charge extra for MEA to account for production of chemicals with lower market demand. In general, physical and economic concerns in solvent manufacturing must also be addressed if flexible CO2 capture with solvent storage is to become a practical system.

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Figure 5: Solvent inventory and stripping and compression equipment constitute the

primary incremental capital costs of flexible CO2 capture with solvent storage.

When all cost components are accounted for along with electricity prices and output, the daily profit vs. solvent storage time can be calculated (Figure 6). Since maximum profits are desired, the base case optimal storage time is actually 0 hours with daily profits of about $237,000; a solvent storage system does not improve profits with base case parameters and this initial optimization formulation.

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Figure 6: With base case parameters, profits are greatest without a solvent storage system.

Sensitivity to Capital Cost Reductions: Since base case results do not support the installation of solvent storage systems, decreasing the capital costs of solvent inventory or stripping and compression equipment may be one way to make solvent storage desirable. Keeping all other parameters constant, a 42% decrease in solvent price is required for a nonzero optimal storage time, and a 50% decrease allows a 2 hour optimal solvent storage time. These MEA price decreases may be unrealistic, but an increase in the design difference between rich and lean loading would accomplish the same effect. In general, a solvent storage system would require a high capacity, low cost solvent.

If MEA price is returned to its base value and a similar sensitivity analysis is performed on the base capital cost of stripping and compression equipment, a 62% cost reduction is required for a nonzero solvent storage time. This level of price reduction would largely have to come in the prices of concrete, steel, and construction materials, and such a steep drop in these commodity prices seems unlikely.

Sensitivity to Changes in Electricity Price: Since the tradeoff in a large solvent storage system is between capital costs and operating profits, another important determinant of optimal solvent storage time is likely to be electricity prices. In order to investigate the effects of changes in electricity prices on solvent storage optimization, the base case prices in each interval are multiplied by a constant factor greater than one. Rather than uniformly shift electricity prices, the price multiplier increases the disparity between high and low electricity prices. For example, if electricity is $30/MWh at 2:00am and $50/MWh at 8:00pm, a price multiplier of 2 would double each price, thereby doubling the difference in the two prices from $20/MWh to $40/MWh.

Figure 7 displays the increase in optimal storage time with electricity price multiplier along with an approximate lifetime profit improvement offered by the solvent storage system. The minimum electricity price multiplier for a nonzero optimal storage time is 1.29, where optimal storage time is 1 hour and daily profits are $408,000. Optimal storage time increases to about 6 hours with a price multiplier of 2.4, remains at about 6 hours up to a 3.8 multiplier, and then jumps considerably to the 12–14-hour range at price multipliers from four to five. Approximate

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lifetime profit improvement is determined by finding the difference in daily profits earned with optimal solvent storage time and without solvent storage, then multiplying this quantity by 365 days of a year and 20 years to approximate additional profits over an economic lifetime. Taxes, capital depreciation, and other detailed economic concerns are not included in this preliminary calculation.

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Figure 7: Optimal storage time and the overall economic benefit of solvent storage

increases with high/low electricity price disparity induced by a price multiplier. To better understand the shape of the optimal storage time curve in Figure 7, the base case electricity prices from Figure 3 are plotted from low to high in Figure 8. As the electricity price multiplier increases from 1 to 2.4, solvent storage becomes economical during about 6 hours of the highest price intervals. Then, because there is a significant drop in price to the next highest price intervals, the price multiplier must increase to above 4 for any additional time intervals to utilize solvent storage. However, since there is a large group of price intervals beyond the 6 hours with the highest prices that have similar electricity prices, all of these price intervals become economical for solvent storage at roughly the same electricity price multiplier, hence the sudden jump from 6 to 14 hours of solvent storage.

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Additional Analysis of Electricity Price Effects: In order to gain an even better understanding of the effects of electricity price distribution on solvent storage time, several hypothetical electricity price curves based on a sinusoidal distribution were created (Figure 9). The base sine curve is means to capture the maximum and minimum actual electricity prices along with their general diurnal variation. From this base sine curve, a shifted curve uniformly raises electricity prices $25/MWh throughout the day, a high amplitude curve systematically increases electricity price disparity throughout the day, and a price spike curve follows the base sine curve except for three 15-minute intervals where electricity prices are set to $500/MWh.

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price distribution on optimal solvent storage time.

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Figure 10 shows the daily profits for each of the four hypothetical price curves as a function of solvent storage time. The base sine curve results are similar to those shown in Figure 6; profits are greatest without solvent storage. A uniform upward shift in electricity prices raises profits across all solvent storage times, but the shape of the profit curve does not change, and solvent storage is still uneconomical. Adding three price spikes, however, makes it optimal to store solvent during these three time intervals, and the profits earned by doing this allow a large range of solvent storage times to be more profitable than a plant without solvent storage. With the systematic increase in price disparity induced by the high amplitude sine curve, the optimal solvent storage time is 9.25 hours.

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Conclusions Flexible CO2 capture using large-scale MEA storage requires tens of millions kgMEA and storage capacities of tens to hundreds m3 (5–50 million gallons) for storage times of 1–12 hours. In this range of storage times, the incremental capital costs of solvent storage are in the tens to hundreds of millions of dollars; storage tank costs are relatively small, as capital costs are dominated by solvent inventory and the need for larger stripping, compression, and auxiliary equipment to process stored solvent in regeneration mode. Solvent manufacturing capacity is a significant limitation to large-scale solvent storage. Low cost and high capacity are desirable solvent characteristics for a solvent storage system.

The need for additional energy in regeneration mode to strip and compress CO2 from stored solvent can result in high marginal costs in regeneration mode, but daily average electricity production costs do not exceed that of a plant using inflexible CO2 capture. In contrast, depending primarily on current fuel and CO2 prices, a flexible capture system that vents additional CO2 may have average production costs greater than, equal to, or less than the cost of operating an inflexible system.

Solvent storage of a few hours or more is attractive when there are large differences between high and low electricity prices over the course of a day, especially if capital or other cost reductions are possible. Even if there are only a few time periods with significantly high

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electricity prices each day, a power plant with flexible CO2 capture using solvent storage can be more profitable than one with an inflexible CO2 capture facility, assuming system response time on the order of 15 minutes. However, base case input parameters and the preliminary modeling methodology do not provide the conditions required for solvent storage to be desirable, and a uniform increase in electricity prices does not change the optimal solvent storage time.

In the absence of a more favorable electricity price distribution, capital cost reductions alone are insufficient to promote solvent storage. However, if base plant stripping and compression equipment was overdesigned and operates at a high rich-lean delta loading, the incremental capital costs of solvent storage would only include storage tanks and solvent inventory. If the comparatively inexpensive storage tanks were built upfront and solvent is slowly stockpiled when prices are lower, the capital cost economics of solvent storage could be much more favorable than is indicated in this analysis.

Other Activities and Future Work M.S. Thesis My M.S. thesis was completed in May 2009 and can be found on the Rochelle Group website at http://www.che.utexas.edu/rochelle_group/Pubs/Cohen_MS_Thesis_2009.pdf.

Long-Term Analysis In order to investigate the performance, environmental impacts, and economics of flexible CO2 capture systems over an investment lifetime, several 20-year fuel and CO2 prices paths have been chosen as input into the first-order ERCOT dispatch model explained in previous work (Cohen, 2009). Data collection and analysis on several of these price paths has been completed, and a full analysis of results is expected to be completed in summer 2009.

Imperial College Collaboration to Compare/Contrast ERCOT and UK Grids Collaboration with Hannah Chalmers of the Imperial College of London is underway to compare and contrast the implications of flexible CO2 capture in the ERCOT and United Kingdom electric grids. Chalmers has completed some preliminary qualitative comparison of the two grids, and I have begun creating model inputs necessary to study flexible CO2 capture in the UK using my electric grid modeling code.

Analyzing Several Replacement Capacity Scenarios and Partial/Zero-Load Points As another extension on previous work with electric grid dispatch modeling, I plan to investigate inflexible CO2 capture scenarios with various types of capacity used to replace output lost to CO2 capture energy and compare these replacement capacity scenarios with flexible CO2 capture cases. Where previous calculations have been limited to a 20% partial-load point for the CO2 capture system (Ziaii, Cohen et al., 2008), I hope to study several other partial-load points such as 0% load with and without a residual energy penalty.

Continuation of Solvent Storage Optimization After completing the other tasks mentioned above, I intend to extend the solvent storage optimization analysis to incorporate electricity price seasonality as well as a finite system response time required to transition from storage mode to regeneration mode. I may also include a more detailed representation of power plant economics as well as consider the possibility of partial-load boiler operation in conjunction with flexible CO2 capture.

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References CES. "CHEMICAL ENGINEERING PLANT COST INDEX (CEPCI)." Chem Eng Sci.

2009:116(3):1/3.

Chalmers H, Chen C et al. “Initial Evaluation of Carbon Capture Plant Flexibility”. 8th International Conference on Greenhouse Gas Control Technologies. Trondheim, Norway, Elsevier. 2006.

Cohen SM. The Implications of Flexible CO2 Capture on the ERCOT Electric Grid. The University of Texas at Austin. M.S. Thesis. 2009:154.

Cohen SM, Rochelle GT et al. “Turning CO2 Capture On & Off in Response to Electric Grid Demand: A Baseline Analysis of Emissions and Economics”. ASME 2nd International Conference on Energy Sustainability. Jacksonville. 2008.

ERCOT. Balancing Energy Services Market Clearing Prices for Energy Annual Report. MCPER_MCPEL_2008.xls. 2008.

Fisher KS, et al. Advanced Amine Solvent Formulations and Process Integration for Near-Term CO2 Capture Success, Trimeric Corp. 2007.

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NETL. Carbon Capture and Sequestration Systems Analysis Guidelines. USDOE. 2005.

Oyenekan B. Modeling of Strippers for CO2 capture by Aqueous Amines. The University of Texas at Austin. Dissertation. 2006:291.

Peters MS, Timmerhaus KD et al. Plant Design and Economics for Chemical Engineers. McGraw-Hill Professional. 2002.

Rao AB, Rubin ES. "A Technical, Economic, and Environmental Assessment of Amine-Based CO2 Capture Technology for Power Plant Greenhouse Gas Control." Environ. Sci. Technol. 2002;36(20):4467–4475.

Rochelle GT et al. ”CO2 Capture by Aqueous Absorption, Second Quarterly Progress Report 2008”. Luminant Carbon Management Program, University of Texas at Austin. 2008.

Rubin ES, Chen C, Rao AB. “Cost and performance of fossil fuel power plants with CO2 capture and storage”. Energy Policy. 2007;35:4444–4454.

Ziaii S, Cohen SM et al. “Dynamic operation of amine scrubbing in response to electricity demand and pricing”. 9th International Conference on Greenhouse Gas Technologies. Washington, DC, Elsevier. 2008.

USEIA (2009). Average Cost of Coal Delivered for Electricity Generation by State, Year-to-Date through March 2009 and 2008 (Dollars per Million Btu). epmxlfile4_10_b.xls, USDOE.

USEPA. Emissions & Generation Resource Integrated Database (eGRID). eGRID2006_Version_2_1. 2007.

USNETL. Cost and Performance Baseline for Fossil Energy Plants. Bituminous Coal and Natural Gas to Electricity. J. M. Klara. 2007;1.

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Modeling Absorber/Stripper Performance with MDEA/PZ


by Peter Frailie


and the




July 1, 2009

Abstract The goal of this study is to evaluate the performance of an absorber/stripper operation that utilizes the MDEA/PZ blended amine. Due to the complexity of this system the model will be developed in several smaller, more manageable parts that can later be combined to form the final model. The first section that will be developed is an MDEA/PZ model based on thermodynamic data, which must initially be developed as separate MDEA and PZ models. Once the MDEA/PZ model has been completed it must be incorporated into separate absorber and stripper models similar to those developed by Van Wagener and Plaza. Those models can then be combined to form the final MDEA/PZ absorber/stripper model. This study is currently in the process of developing the MDEA/PZ model based on thermodynamic data. Separate MDEA and PZ models have already been completed, and the process of combining them has begun. The MDEA/PZ thermodynamic model should be completed early in the next quarter, which will allow work to commence on modeling an absorber/stripper operation.

Introduction The removal of CO2 from process gases using alkanolamine absorption/stripping has been extensively studied for several solvents and solvent blends. An advantage of using blends is that the addition of certain solvents can enhance the overall performance of the CO2 removal system. A disadvantage of using blends is that they are very complex compared to a single solvent, thus making them much more difficult to model.

This study will focus on a blended amine solvent containing piperazine (PZ) and methyldiethanolamine (MDEA). Previous studies have shown that this particular blend has the potential to combine the high capacity of MDEA with the attractive kinetics of PZ (Bishnoi, 2000). These studies have supplied a rudimentary Aspen Plus®-based model for an absorber with MDEA/PZ. The report also makes the recommendation that more kinetic and thermodynamic data must be acquired concerning the MDEA/PZ blend before the model can be significantly improved. Two researchers in the Rochelle lab are currently acquiring this data, but it has not yet been incorporated into an absorber/stripper

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model. One of the major goals of this study will be to improve the supplied Aspen Plus® absorber model with up to date thermodynamic and kinetic data. Another major goal of this study will be to combine absorber and stripper models to evaluate the overall system performance.

Methods and Discussion This quarter’s work dealt primarily with the MDEA model. As mentioned in the previous quarterly report, Aspen Plus® currently has a rate-based MDEA model that uses equilibrium constants to predict speciation. Initially, the approach was to develop a thermodynamic MDEA model that matched the Aspen Plus® model’s predictions, but that proved to be problematic when combining the MDEA and PZ models. Rather than develop the MDEA and PZ models in separate files, it was more effective to develop them in the same file. Most of the data used to regress the thermodynamic parameters was cited by Aspen Plus® in its model.

The first set of parameters regressed concerned the viscosity of the MDEA/H2O/CO2 mixture. The experimental data (Teng, 1994) provided the viscosity of MDEA/H2O mixtures with MDEA mole fraction ranging from 0 to 1 between 25 oC and 80 oC. The parameters selected for this regression were the first terms of the MUKIJ and MULIJ binary parameters for a H2O/MDEA mixture and the first five DIPPR liquid viscosity terms (MULDIP) for pure MDEA. Figure 1 compares the mixture viscosity as a function of CO2 loading at 25 oC for the MDEA model to that of experimental data at 30, 40, 50, and 60 wt % MDEA (Weiland, 1998), and Tables 1 and 2 report the regressed values for the above mentioned parameters.

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4 0.5 0.6

Loading (mol CO2/mol Alkalinity)

Visc

osity

(mPa

.s)

60 wt%

50 wt%

40 wt%

30 wt%

Figure 1: Liquid viscosity as a function of CO2 loading for the MDEA model (lines)

and experimental data (points) at 25 oC

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3

Table 1: Regressed binary parameters for MDEA liquid viscosity

Parameter Value Reference Temperature (K)

MUKIJ/1 -1.71 298.15

MULIJ/1 -12.74 298.15

Table 2: Regressed pure component parameters for MDEA liquid viscosity (Note: temperature units are oC and property units are Pa.s)

Parameter Value

MULDIP/1 -267.1

MULDIP/2 16910

MULDIP/3 36.5

MULDIP/4 -0.038

MULDIP/5 -1.0E5

The next set of parameters concerned the density of liquid MDEA. The parameters regressed in the Aspen Plus® MDEA model were the cation/anion pair parameters for the Clarke liquid density model (VLCLK) for the MDEAH+/HCO3

- and MDEAH+/CO32-

pairs, as well as the interaction parameter VLQKIJ for the MDEA/H2O mixture. Because the Aspen Plus® model was developed in the most recent version of Aspen Plus® and the University of Texas still used Aspen Plus® v2006.5, some of these parameters (i.e. VLQKIJ) have not yet been regressed. This primarily affects the density of the MDEA/H2O/CO2 mixture at low loading. Before the fall semester begins we will be upgrading to the newest version of Aspen Plus®, at which point VLQKIJ will be regressed. Because liquid density does not significantly affect vapor-liquid equilibrium (VLE) predictions, VLQKIJ can be added after the rest of the thermodynamic model has been developed. The experimental data used in the regression (Weiland, 1998) provides the density of MDEA/H2O/CO2 mixtures containing 30, 40, 50, and 60 wt % MDEA between 0 and 0.5 loading at 25 oC. Figure 2 compares the mixture density as a function of CO2 loading at 25 oC for the MDEA model to that of experimental data at 30, 40, 50, and 60 wt % MDEA, and Table 3 reports the regressed values for the above mentioned parameters.

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4

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


Den

sity

(g/m

L)

30 wt%

60 wt%

50 wt%

40 wt%

Figure 2: Liquid density as a function of CO2 loading for the MDEA model (lines)

and experimental data (points) at 25 oC

Table 3: Regressed parameters for MDEA liquid density

Property Cation Anion Value Property Units

VLCLK/1 MDEAH+ HCO3- 0.17 m3/kmol

VLCLK/1 MDEAH+ CO32- 0.18 m3/kmol

The final set of parameters that needed to be determined was the core thermodynamic parameters (Gibbs energies of formation, enthalpies of formation, heat capacities, and ionic interaction parameters). The data used to determine these values concerned MDEA/H2O/CO2 mixture VLE (Jou, 1982) and heat capacity (Weiland, 1997). After initial attempts to regress all of the data simultaneously were unable to converge satisfactorily, a handful of parameters was selected to be set manually to fit the VLE data. The parameters selected were the Gibbs free energy of formation for MDEAH+, the enthalpy of formation for MDEAH+, the first two heat capacity parameters for MDEAH+ (CPAQ0), and the ionic interaction parameter for MDEAH+ and HCO3

- in water (GMELCC). All of the other ionic interaction parameters were set at their regressed values which gave the best fit for the VLE data. Figure 3 compares the partial pressure of CO2 as a function of loading for the MDEA model to that of experimental data at 25, 40, 70, 100, and 120 oC, Figure 4 compares the heat capacity of a MDEA/H2O/CO2 mixture as a function of loading for the MDEA model to that of experimental data at 30, 40, 50,

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5

and 60 wt % MDEA, and Table 4 reports the regressed values for the above mentioned parameters.

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E-03 1.E-02 1.E-01 1.E+00


Part

ial P

ress

ure

CO

2 (kP

a)

25oC

120oC

100oC

70oC

40oC

Figure 3: CO2 partial pressure as a function of CO2 loading for the MDEA model

(lines) and experimental data (points)

2

2.5

3

3.5

4

4.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


Hea

t Cap

acity

(J/g

.K)

30 wt%40 wt%50 wt%

60 wt%

Figure 4: MDEA/H2O/CO2 mixture heat capacity as a function of CO2 loading for

the MDEA model (lines) and experimental data (points)

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6

Table 4: Core thermodynamic parameters for MDEA model

Parameter Species Value Units

DGAQFM MDEAH+ -2.528E8 J/kmol

DHAQFM MDEAH+ -4.92E8 J/kmol

CPAQ0/1 MDEAH+ 920000 J/kmol.K

CPAQ0/2 MDEAH+ -6000 J/kmol.K

GMELCC H2O (MDEAH+/HCO3-) 9.637 N/A

GMELCC (MDEAH+/HCO3-) H2O -4.5 N/A

GMELCC MDEA (MDEAH+/HCO3-) 37.3 N/A

GMELCC (MDEAH+/HCO3-) MDEA -2.47 N/A

GMELCD H2O (MDEAH+/HCO3-) 485.4 oC

GMELCD (MDEAH+/HCO3-) H2O -139.1 oC

GMELCD MDEA (MDEAH+/HCO3-) 2069 oC

GMELCD (MDEAH+/HCO3-) MDEA 360.9 oC

It should be noted that for the heat capacities of the 30, 40, and 50 wt % MDEA mixtures, the percent difference between the literature values and the model values was never greater than 15% over the range of tested values.

Conclusions Overall the conversion of the Aspen Plus® equilibrium constant model to a thermodynamic model went well. The thermodynamic model can adequately predict viscosity and VLE at useful loadings (0–0.25 mol CO2/mol alkalinity), and after the incorporation of the VLQKIJ parameter it should adequately predict molar volume. The biggest concern is the heat capacity predicted by the model, which is particularly important when evaluating absorber and stripper performance.

Future Work The main goal over the next three months will be to finish developing the MDEA/PZ rate-based model using experimentally obtained thermodynamic data. Once the rate-based model has been finalized, it will be applied to an absorber/stripper operation. As in the development of the rate-based model, this model will first be split into two sections (absorber and stripper) for early development, which will be combined later to form the final model. This is similar to the approach used by Jorge Plaza and David Van Wagener, though they did not combine their models into a single operation. Their work and methodology will serve as the basis for the development of the separate absorber and stripper models.

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7

References

Bishnoi S. Carbon Dioxide Absorption and Solution Equilibrium in Piperazine Activated Methyldiethanolamine. The University of Texas at Austin. Ph.D. Dissertation. 2000.

Jou FY, Mather AE, Otto FD. “Solubility of hydrogen sulfide and carbon dioxide in aqueous methyldiethanolamine solutions”. Ind Eng Chem Process Des Dev. 1982;21(4):539–544.

Teng TT, Maham Y, Hepler LG, Mather AE. “Viscosity of Aqueous Solutions of N-Methyldiethanolamine and of Diethanolamine”. J Chem Eng Data. 1994;39:290–293.

Weiland RH, Dingman JC, Cronin DB. “Heat Capacity of Aqueous Monoethanolamine, Diethanolamine, N-Methyldiethanolamine, and N-Methyldiethanolamine-Based Blends with Carbon Dioxide”. J Chem Eng Data. 1997;42:1004–1006.

Weiland RH, Dingman JC, Cronin DB, Browning GJ. “Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine Solutions and Their Blends”. J Chem Eng Data. 1998;43:378–382.

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1

Effective Area and

Mass Transfer Coefficients of Packing


Chao Wang

Supported by the Luminant Carbon Management Program,


and the Process Science and Technology Center



July 3, 2009

Abstract Packings are widely used in distillation, stripping, and scrubbing processes because of their relatively low pressure drop, good mass transfer efficiency, and ease of installation. Packings are also being investigated for the post-combustion carbon capture process for these reasons. Research continues to focus on development of high performance packing, especially on minimizing pressure drop, maximizing mass transfer efficiency, and minimizing costs. The design of packed absorbers for carbon dioxide capture will require the reliable measurement and accurate prediction of the effective area, gas and liquid film mass transfer coefficient. A variety of experimental methods for measuring effective area, gas and liquid film mass transfer coefficient kLa has been reported. Consistent measurements of these important design parameters will begin this summer.

Absorption of CO2 with NaOH is applied to measure the effective area of packings. Atmospheric CO2 in air is used as gas phase and 0.1 M NaOH is used as liquid phase. This is a liquid phase controlled mass transfer system so the liquid phase mass transfer coefficient kl or often referred to as kg

’ can be assumed as the overall mass transfer coefficient KG. In the proposed summer work, the gas flow rate set points are 180, 300, 450 ACFM and liquid flow rate set points are 1, 2.5, 5, 7.5, 10, 15, and 25 gpm/ft2 (same operating conditions explored by Robert Tsai). The effective area can then be calculated by the equation:

RTZkyyu

RTZKyyu

ag

outCO

inCOG

G

outCO

inCOG

e '2

2

2

2 )ln()ln(≈= (1)

Absorption of sulfur dioxide (SO2) with NaOH is applied to measure the gas phase mass transfer coefficient. SO2, blended with ambient air at a composition of approximately 80 ppm, will be absorbed by 1 M NaOH solution. The reaction is instantaneous and the mass transfer process is controlled by the gas phase. Thus the overall mass transfer coefficient KG can be replaced by gas phase mass transfer coefficient kG. This experiment can be combined with the effective area

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2

experiment as long as the gas and liquid flow rates are set at the same level. The gas phase mass transfer coefficient can be calculated by the equation:

e

outSO

inSOG

G ZRTayy

uk

)ln(2

2

= (2)

Desorption of toluene in water with air is applied to measure the liquid phase mass transfer coefficient. Ambient air is used to strip toluene from water. As a result of the high Henry’s constant, the mass transfer resistance is controlled by the liquid phase. The gas flow rates and liquid flow rates are set at the same value with the effective area measurement to make the 3 experiments consistent. The liquid phase mass transfer coefficient can be calculated by the equation:

)/ln( 21 LALAe

LL cc

Zau

k = (3)

Introduction This quarterly report mainly focused on the experimental methods of measurement of effective area, gas-side mass transfer coefficient, liquid-side mass transfer coefficient which will be used in the summer project. Three structured packings will be tested this summer. They are: Raschig RSP 250 wSE, Flexipac 1.6 Y HC, and Mellapak 2X. Limited experiments will be carried out using the Mellapak 252Y.

Raschig RSP 250 wSE is a structured packing developed by RASCHIG-JAEGER. The surface area of this packing is approximately 250 m2/m3. The rows of sinusoidal waves within vertical packing sheets are surface enhanced to encourage greater turbulent radial spread of thin liquid film flows on the front and back of the waves on each sheet within an element. The effect of surface enhancement on the mass transfer efficiency of RSP-250 wSE compared to Raschig Super-Pak 300 without surface enhancement (RSP-300 woSE) is significant with consistently lower HETP values (Schultes, 2006). The pressure drop of RSP-250 wSE is consistently lower by an order of magnitude than the 250 m2/m3 surface area standard structured packings and significantly less than the high capacity types at high flow rates (Olujic, 2001).

Flexipac 1.6 Y HC is a structured packing developed by Koch-Glitsch. The surface area of this packing is 295 m2/m3. An improvement has been made at the base region of the sheets to reduce the liquid holdup at the interface between two sets. The corrugation angle is 90 degrees at the base region of the sheets which is different from the corrugation angle at the bulk region. The effect of this design is to reduce the gas velocity and permit improved draining of liquid at the interface between two stacked elements and increase ultimate usable hydraulic capacity. The capacity increases 10–40% and efficiency increases 10–20% compared to conventional packing of similar geometry (Hausch, 2005).

Mellapak 2X is a structured packing developed by Sulzer Chemtech. The surface area of this packing is 205 m2/m3. The corrugation angle of this packing is 60 degrees which is larger than the conventional packings. This design is to reduce the resistance of the gas flow inside the packing and hence reduce the pressure drop.

The packing properties are as follows:

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3

Table 1: Packing properties

Packing Surface area (m2/m3) Void fraction (%) Channel side (mm) HETP (m)

RSP 250 wSE 250 0.92 5.08

Flexipac 1.6Y HC 295 0.91 9.8 0.2~0.3

Mellapak 2X 205 0.99 21.5 0.63~0.9

Hydraulic data for these packings are as follows:

Hydraulic Performance of RSP 250 wSE

0.0100

0.1000

1.0000

10.0000

0.100 1.000 10.000

F-factor (ft/s) (lb/ft3)0.5

Pres

sure

Dro

p, in

H2O

/ft p

acki

ng

5 gpm/ft2

10 gpm/ft2

15 gpm/ft2

20 gpm/ft2

30 gpm/ft2

DRY DP

300 Dry

300 5

300 10

300 15

Figure 1: Hydraulic performance of RSP 250 wSE (from SRP database)

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4

Figure 2: Hydraulic performance of Flexipac 1.6 Y HC (Hausch, 2005)


Measurement of effective area Absorption of CO2 in air with 0.1 M NaOH solution is used for the measurement of effective area. It is a common method used to measure effective area for packings. Revising my previous literature review (Sep–Dec), a large variety of 2-fluid systems has been used for this purpose. Among these, absorption of CO2 into dilute caustic solution can be considered one of the more attractive options. The Separations Research Program (UT-SRP) used this method to measure effective area for structured packings in air/water column for years. Therefore, we decided to choose this method for our experiment. The reaction

CO2 + 2OH- →CO32-

can be considered irreversible. When CO2 partial pressure is low and hydroxide ion is present in relative excess, the reaction can be treated as pseudo-first order. The reaction rate is:

r=k1[CO2] where

k1=kOH-[OH-].

According to 2-film theory, the overall mass transfer resistance is:

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5

'111

ggG kkK+= (4)

The gas side mass transfer coefficient could be expressed as:

)()(075.1 ,285.0

,2

2

RTdD

hDduk GCO

GCO

Gg = (5)

where uG is the gas velocity, DCO2,G is the diffusivity of CO2 in the gas phase, h is the exposed length of wetted wall column (Bishnoi, 2000).

The gas side resistance was calculated to account for less than 10% of the overall resistance in this situation in the air/water column (Rocha, 1996).

The liquid side mass transfer coefficient could be expressed as:

2,2

2

'

)(][

1 oL

LCOOH

CO

oL

g kDOHk

Hkk

−−

+= (6)

where kLo is the physical mass transfer coefficient in the liquid phase, HCO2 is the Henry’s

constant for CO2 in the liquid, kOH- is the second order reaction rate constant, DCO2,L is the

diffusivity of CO2 in the liquid phase. In the WWC experiments and in our previous air/water

column experiments, 2,2

)(][

oL

LCOOH

kDOHk −

− >>1 (Tsai, 2008), so the “1+” can be ignored. Then the

liquid side mass transfer coefficient could be:

2

,2' ][

CO

LCOOHg H

DOHkk

−−

= (7)

Diffusivity could be calculated by the following equations (Barrett, 1966):

2

5

,21010591.25.7121764.8log

TTD WCO

×−+−= (8)

.)*()*( ,2,2 constDD TWWCOTLCO == μμ (9) Henry’s constant could be calculated by the following equations (Danckwerts, 1970):

252,210 108857.7109044.51229.9)(log TTH WCO

−− ×+×−=− (10)

∑= iiWCO

CO hIHH

,2

210log (11)

gi hhhh ++= −+ (12) kOH

- is calculated by the following equations (Pohorecki, 1988):

Tk

OH

2382895.11log10 −=∞− (13)

210 016.0211.0log II

kk

OH

OH −=∞−

− (14)

With these equations, the overall mass transfer coefficient could be calculated. Then the effective area could be calculated from this equation:

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6

RTZkyyu

RTZKyyu

ag

outCO

inCOG

G

outCO

inCOG

e '2

2

2

2 )ln()ln(≈= (15)

Measurement of gas side mass transfer coefficient Absorption of SO2 with NaOH solution is used for the measurement of gas side mass transfer coefficient. The inlet SO2 concentration is around 80 ppm which is realized by mixing 2% SO2 in a cylinder with air. The initial NaOH concentration is 0.1 M. Large varieties of experimental systems for the measurement of gas side mass transfer coefficient have been reviewed (Rochelle et al., 2009). Among these systems, the SO2/NaOH system has advantages. The property of SO2 is similar to CO2 and they could use the same liquid solution which means these two experiments can be performed at the same time. It is an instantaneous reaction and the equilibrium is easy to reach. The SO2 concentration could be read from an installed SO2 analyzer. The reaction is:

SO2 + 2OH- →SO32-

It is an irreversible instantaneous reaction. The mass transfer resistance at the liquid phase is much smaller compared with the resistance at the gas phase. Thus, the liquid side mass transfer resistance could be ignored. The overall mass transfer coefficient could be assumed equal to the gas side mass transfer coefficient. The volumetric gas side mass transfer coefficient could be calculated by the following equation:

ZRTyyu

ak outSO

inSOG

G

)ln(2

2

= (16)

where uG is the superficial gas velocity, ySO2in and ySO2out is the concentration of SO2 in the gas phase at the inlet and outlet of the column (Sharma, 1966). The SO2 concentration is measured by Thermo 43i SO2 analyzer which has a range of 0–100ppm and can reach as low as 0.5 ppb. The inlet SO2 concentration is adjusted to around 80 ppm by mixing 2% SO2 from the cylinder with air. The outlet SO2 concentration could be around 10 ppb with 10 feet packings. Since from our previous measurement of effective area, the kG value could be calculated by:

e

GG a

akk = (17)

Therefore, we can get the kG value from direct measurement.

These experiments will be combined with the effective area measurements to refine the determination of the effective area and will also be used to determine kg. The inlet air should be spiked with SO2 with air and both the SO2 concentration and the CO2 concentration should be measured. The gas flow rates and liquid flow rates for both experiments are the same. Both experiments use the same NaOH solution, so the concentration of OH- is calculated by

][2][2][][ 23

23

−−−− −−= SOCOOHOH initialremaining (18)

The concentration of carbonate is calculated from the FTIC analysis:

[ ] ( )( )011.12100011000

11

011.121

ln10623

ppmCxLmL

mLg

gCmolC

sogramsgramsCxCO =×××=−

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7

x ppm C could be read from the standard curve.

The concentration of sulfite is calculated from the material balance of S:

tyyGRTPSO outSOinSO *)(*][ 22

23 −=− (19)

where G is the gas flow rate, and t is the time for each run.

Measurement of liquid side mass transfer coefficient Desorption of toluene from water using air is adopted for the measurement of liquid side mass transfer coefficient. According to the 2-film theory:

amkakaK yxx

111+= (20)

where Kx is the overall mass transfer coefficient, kx is the liquid side mass transfer coefficient; ky is the gas side mass transfer coefficient; m is the slope of equilibrium curve (Basmadjian, 2004). If we choose a system with a large m, the liquid side mass transfer resistance will be much greater than the gas side mass transfer resistance. Therefore we can assume the overall liquid phase mass transfer coefficient equals the liquid film mass transfer coefficient. Thus, we can get the value of liquid side mass transfer coefficient from the overall mass transfer coefficient.

Toluene/water system is one such system. The Henry’s constant of toluene in water is 353.1 atm/mol fraction (Chapoy, 2008) and the solubility is 542 ppm (Carl, 1990) at room temperature and atmosphere. m equals Henry’s constant times activity coefficient divided by total pressure. So in this system m is large enough to ignore the gas side resistance. The system used is water saturated with toluene; samples are taken from the inlet and the outlet of the column and analyzed in a GC. The gas flow rate is 180,300 450 CFM and liquid flow rate is 1-25 gpm/ft2. The liquid side mass transfer coefficient could be calculated by the following equation:

)/ln( 21 LALAL

L ccZ

uak = (21)

where uL is the superficial liquid velocity, Z is the packing height and cLA1 and cLA2 is the liquid phase toluene concentration at the inlet and outlet of the column (Linek, 1984). Since we have the value of effective area from our previous experiment, we can calculate the kL by:

e

LL a

akk = (22)

Future Work We will perform these experiments starting around mid-July. Measurements of effective area, kG and kL as a function of liquid and gas load will be performed.

An alternative liquid phase film control system will be investigated which requires the construction of pH control system. This method uses NaOH/CO2 system. According to equation 23:

250

8

2,2

2

'

)(][

1 oL

LCOOH

CO

oL

g kDOHk

Hkk

−−

+= (23)

If the concentration of hydroxyl is controlled at a low level so the chemical term kOH-[OH-]DCO2,L

is much smaller than the physical term kLo, the liquid side mass transfer coefficient measured

should be the physical mass transfer coefficient. Since this system is liquid controlled we can get the liquid side mass transfer coefficient from the overall mass transfer coefficient. The concentration of hydroxyl should not exceed 10-4 mol/L to satisfy this condition.

The key requirement for this system is to control the pH at 10. My proposal is to continuously add 0.1 mol/L NaOH solution to the bulk liquid phase. pH control valve is used to control the flow rate of the buffer solution. The kL

o is estimated to be around 0.8-2*10-4 m/s using the following equation:

2/1)(2SUCDk LEELo

L π= (24)

where DL is the diffusivity of CO2 in liquid phase, CE is correction factor for surface renewal, usually equal to 0.9, ULE is the effective liquid velocity, m/s and S is the side dimension of corrugation (Rocha, 1996).

The rate of CO2 absorbed:

Zackr COoLA

*2= (25)

where kLo is the physical mass transfer coefficient, a is the effective area, cCO2

* is the equilibrium concentration of CO2 in liquid phase and Z is the packing height.

Make the material balance of CO2 in the total system:

Rate of CO2 absorbed = rate of CO2 accumulated in the liquid phase.

)( 0*

2 23

23 =−− −= tCOtCO

LCO

oL cc

tVZack (26)

Where VL is the total volume of liquid in the system, t is the time period, tCO

c −23

and 02

3 =−tCOc is the

concentration of carbonate at t time and t=0 time. So if we take samples every few minutes, we can get the carbon concentration curve with time and the slope is LCO

oL VZack /*

2 . Then we can get the value of kL

oa. Since we have measured the effective area, the liquid side mass transfer coefficient is:

e

oLo

L aakk = (27)

Conclusions For the measurement of effective area, absorption of CO2 in air with 0.1 M NaOH proves to be a reasonable method. The effective area is calculated by the equation:

RTZkyyu

RTZKyyu

ag

outCO

inCOG

G

outCO

inCOG

e '2

2

2

2 )ln()ln(≈= (28)

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9

For the measurement of gas side mass transfer coefficient kG, absorption of SO2 with NaOH solution is adopted. kG is calculated by the equation

e

outSO

inSOG

G ZRTayyu

k)ln(

2

2

= (29)

For the measurement of liquid side mass transfer coefficient kL, desorption of toluene in water with air is adopted. kL is calculated by the equation

)/ln( 21 LALAe

LL cc

Zauk = (30)

References Barrett PVL. Gas absorption on a sieve plate, University of Cambridge, Cambridge, England,

1966.

Basmadjian D. Mass transfer: principles and applications. CRC Press: Boca Raton, FL, 2004.

Bishnoi S. Carbon dioxide absorption and solution equilibrium in piperazine activated methyldiethanolamine, University of Texas at Austin. Ph.D. Dissertation. 2000.

Carl LY, Haur-Chung Y. “Water solubility data for organic compounds”. Poll Engr. 1990;22(10):79–75

Chapoy A, Haghighi H, Tohidi B. “Development of a Henry’s constant correlation and solubility measurements of n-pentane, i-pentane, cyclopentane, n-hexane, and toluene in water”. J Chem Thermod. 2008;40:1030–1037

Danckwerts PV. Gas-Liquid Reactions. McGraw-Hill: New York, 1970.

Hausch G, Nieuwoudt I, Sommerfeldt RA. “Advances in styrene fractionation with InTALOX packed tower systems: Part 2-FLEXIPAC HC Structured Packing”. AIChE Spring Natl Meet Conf Proc. 2005. 1813.

Linek V, Petericek P. “Effective interfacial area and liquid side mass transfer coefficients in aborption columns packed with hydrophilised and untreated plastic packings”. Chem Eng Res Des. 1984;62:13–21.

Olujic Z, Seibert AF, Kaibel B, Jansen H, Rietfort T, Zich E. “Performance of a New High Capacity Structured Packing”. AIChE Spring National Meeting, Houston, TX. 2001.

Pohorecki R, Moniuk W. “Kinetics of Reaction between Carbon Dioxide and Hydroxyl Ions in Aqueous Electrolyte Solutions”. Chem Eng Sci. 1988;43(7):1677.

Rocha JA, Bravo JL, Fair JR. Distillation columns containing structured packings: A comprehensive model for their performance. 2. Mass-Transfer model. Ind Engr Chem Res. 1996;35:1660–1667

Rochelle GT et al. “CO2 Capture by Aqueous Absorption, Fourth Quarterly Progress Report 2008”. Luminant Carbon Management Program. The University of Texas at Austin. 2009.

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Schultes M, Chambers S. “How To Surpass Conventional and High Capacity Structured Packings with Raschig Super-Pak”. AIChE Spring National Meeting, Orlando, FL, 2006.

Tsai R, Schultheiss P et al. “Influence of Surface Tension on Effective Packing Area”. Ind Engr Chem Res. 2008;47:1253–1260

253

1

Pilot Plant Testing of Advanced Process Concepts using Concentrated Piperazine


by Eric Chen


and the




July 12, 2009

Abstract Pilot plant testing of 8 m piperazine in a two-stage heated flash is planned for the Fall of 2009. Substantial modifications to the existing pilot plant at SRP will be needed. Process flow diagrams (PFD) and piping and instrument diagrams (P&ID) have been developed for the new process. Preliminary specifications for the high pressure pump, cross-exchanger, and steam heaters have been developed and are in the process of being formally designed and quoted by various vendors.

Introduction The concept of concentrated (8 m) piperazine with high temperature stripping in a two-stage heated flash is ready for pilot plant testing. The concept is described by Freeman et al. (2009). A successful three-week campaign with a simple stripper was completed in the SRP pilot plant in the Fall of 2008. A campaign is proposed for the SRP pilot plant in the Fall of 2009. This campaign will require substantial equipment modifications for the high temperature stripping, including three heat exchangers, two gas-liquid separators, a high pressure pump. Absorber intercooling will also need to be added and will consist of a pump and a heat exchanger. This system will be operated for three weeks with 8 m PZ to determine mass transfer, heat transfer, and energy performance.

Pilot Plant Modifications

Process Flow Diagram The existing pilot plant at SRP will be modified to evaluate high temperature stripping and absorber intercooling using 8 m piperazine. A process flow diagram has been developed and is shown in Figure 1. The high temperature stripping process will replace the existing simple stripper with a two-stage heated flash. The new process will consist of a high pressure pump, a cross-exchanger, two steam heaters, and two gas-liquid separator vessels and will be installed downstream of the existing cross-exchanger.

254

2

Figure 1: Process flow diagram for the two-stage flash process

255

3

Rich solvent from the existing cross-exchanger will be pressurized by the high pressure pump and fed into the high pressure cross-exchanger. The rich solvent will be further heated to 150 °C in the high pressure steam heater. The first stage of the flash process is designed to operate at 150 °C and 13.5 atm. Flashing will occur in both the cross-exchanger and steam heater. The gas and liquid will be separated in the first separator vessel. Liquid from the bottom of the separator will pass through a throttling valve, dropping the pressure from 13.5 to 8 atm. The solvent will be reheated to 150 °C in the second steam heater, resulting in additional flashing of CO2 and H2O from the liquid.

The CO2 and H2O vapor will exit off the top of both gas-liquid separator vessels and will be combined into a single gas stream. The overhead condenser will condense out the water vapor and pumped to the absorber feed tank. The CO2 vapor will be recycled back to the gas accumulator.

Absorber intercooling will be necessary to obtain the optimal rich loading for the high temperature stripping process. A pump and heat-exchanger will be added. Solvent will be pumped out from the bottom of the chimney tray at the middle of the absorber into a heat exchanger and back into a nozzle above the chimney tray.

Two-Stage Flash Equipment Design The two-stage heated flash process will be designed to be mounted on a portable skid that will enable it to be transported for field testing. Equipment size was specified based on a nominal liquid flow rate of 15 gpm. The skid will consist of a high pressure pump, high pressure cross-exchanger, two steam heaters, and two gas-liquid separators. To accommodate field testing, the skid will be designed to fit inside a standard 20 ft ocean shipping container. The dimensions for the inside length, door width, and door height are 19’4”, 7’8”, and 7’6”, respectively.

Specifications for the two-stage flash process were determined using a spreadsheet model developed by Rochelle and Van Wagener. Both stages of the flash process are designed to operate at a temperature of 150 °C. The pressure of the first and second stage will be 13.5 and 8 atm, respectively. The pressures in the two-stage flash were optimized to vaporize approximately the same amount of CO2 in each stage. The amount of water vaporized in the second flash is about 2.4 times that of the first flash.

The current heat exchanger in the SRP pilot plant has a temperature and pressure rating of 135 °C and 10 atm, respectively. Since the two-stage flash requires higher operating pressures and temperatures, a new high pressure cross-exchanger will be purchased. A high pressure pump will also be purchased and installed downstream on the outlet of the rich stream from the existing cross-exchanger.

Piping and Instrumentation Diagram The piping and instrumentation diagram (P&ID) for the skid is shown in Figure 2. Rich solvent on the cold side of the existing cross-exchanger will first be pumped through a bag filter before being heated in the high pressure cross-exchanger. The high pressure steam heater will further heat the rich solvent to 150 °C. Steam flows to the two steam heaters will be controlled to maintain a temperature of 150 °C in both gas-liquid separator vessels.

256

4

FV-257

TT221B

TT220D

H-114-DI

H-115-DI

P-109

TT221A

TT220C

TT220B

VSC104

V-109

FV-260

PDT260

LT222

TT222

LC222

FE-217

FT217

PC260

FV-226

Steam

Check ValveRelief Valve

Vent to Absorber Feed Tank

TT207

F-108

FT207

DT207

From LP Cross ExchangerCold Side (Rich)

To LP Cross-ExchangerHot-Side (Lean)

Condensate

Flash SepT = 150 C

P = 13.5 atm

Rich Amine StreamQ = 15 gpmT = 120 CP = 15 atm

Rich Amine StreamQ = 15 gpmT = 142 CP = 14 atm

Rich Amine StreamQ = 15 gpmT = 150 C

P = 13.5 atm

Lean Amine StreamQ = 15 gpmT = 150 CP = 8 atm

Lean Amine StreamQ = 15 gpmT = 123 CP = 7 atm

HP Flash Gas StreamQ = 0.08 m3/min

T = 150 CP = 13.5 atm

76% CO2, 24% H2O

TT223B H-116-DI

TT223A

FV-227

Steam

Condensate

FV-258

V-110

PDT261

LT224

TT224

LC224

TT208

FT208

DT208

Flash SepT = 150 CP = 8 atm

FV-261

FE-218

FT218

PC261

Condenser

Check Valve

Bypass to Condenser

Filter

Vent Relief to Absorber Feedtank

TT220A

V-175V-176 V-177 V-178

FE-219

FT219

SteamTrap

Steam Trap

FE-226FT226

TT227A

FE-227FT227

PT227

TT227B

TT226A

PT226

TT226B

PressureRelief

PressureRelief

FV-259

DownstreamPressure Control

PC228

PDT228

F-107

Filter

Figure 2: Piping and instrument diagram for two-stage flash system mounted on skid

257

5

The liquid level in the separators will be controlled separately by a valve downstream of the flow measurement. Control valves in the vapor line downstream of each separator will be used to maintain the pressure at 13.5 and 8 atm in high and low pressure gas-liquid separators. The vapor flow rate will also be measured at the outlet of the each separator and also in the combined vapor stream. The lean solvent exiting the bottom of the low pressure separator flows through a bag filter before being used to preheat the rich solvent in the high pressure cross-exchanger.

The skid will incorporate the latest wireless process instrumentation technology by Emerson Process Management. The benefits of wireless technology will be two-fold. First, it will streamline the installation of the process instruments into the skid. Second, once installed, wireless technology will facilitate incorporation of the skid instrumentation into existing process control systems at a field test site. The wireless technology will eliminate the need to run conduit and wires for all of the process instrumentation.

Wireless level, pressure and temperature transmitters will be used on the two-stage flash skid. Micromotion® flowmeters will be used to measure liquid flow rate and density. Density will be used to indirectly measure CO2 loading and control pilot plant operations. Orifice meters will be used to measure the vapor flow rate leaving the top of each gas-liquid separator. An additional orifice meter will measure the flow rate of the combined vapor stream. Orifice meters will also be used to measure the flow rate of steam for each of the steam heaters.

High Pressure Pump A high pressure pump will be needed to provide the 13.5 atm pressure required for the first stage of the flash. A Grundfos multi-stage centrifugal pump has been identified that will meet the desired specifications. The pump specifications are listed in the table below.

Table 1: High pressure pump specification Model Grundfos CRNE5-24 Flow 20 GPM @ 250 psig Matl. of Const. 316 Stainless Steel Power 7.5 HP / 480V Motor TEFC Controller Variable Speed Mechanical Seal Kalrez

High Pressure Cross-Exchanger The majority of CO2 capture pilot plants are currently designed using MEA as the solvent, which inherently operates at lower temperatures (~120 °C) and pressures to minimize thermal degradation. In contrast, piperazine can be operated at higher temperatures (up to 150 °C) and higher pressures before thermal degradation becomes significant. In order to evaluate the high temperature two-stage flash, a new cross-exchanger will need to be purchased. To reduce capital costs and remain within the practical design limits of cross-exchangers, the new high pressure exchanger will be installed in series with the existing cross-exchanger.

In order to prevent flashing from occurring in the existing low pressure cross-exchanger, the high pressure cross-exchanger will be designed to have an inlet rich solvent (cold-side) temperature of 105 °C. A bypass on the hot-side of the existing cross-exchanger will be used to control and prevent high temperature excursions. The rich solvent is expected to begin flashing at

258

6

approximately 133 °C. The outlet temperature of the rich solvent is expected to be 143 °C if we design the lean solvent (hot-side) to have inlet and outlet temperatures of 150 and 110 °C, respectively. The cross-exchanger will be designed to have a pressure drop of 10 psi. A heat release curve was developed for the high pressure cross-exchanger (Figure 3).

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

40 60 80 100 120 140 160

Temperature (οC)

Cum

ulat

ive

Hea

t Rat

e (B

tu/h

r)

LeanRich

Figure 3: Heat release curve for high pressure cross-exchanger

A preliminary design for the cross-exchanger was developed by Alfa Laval. The original design called for using a single cross-exchanger to heat the rich solvent from 45 to 145 °C. However, the specified temperature range was beyond the practical design limits. Only an outlet temperature of 136 °C was achievable, which was designed without accounting for flashing. Since the high pressure cross-exchanger will be used in series with the existing exchanger, the design should be more than adequate. Table 2 lists the specifications of the high pressure cross-exchanger.

259

7

Table 2: High pressure cross-exchanger specification Vendor. Alfa Laval Model M6-MFD Flow 15 GPM Heat Exchanged 1045 kBTU/hr LMTD 22.1 °F Heat Transfer Area 120.6 ft2 Matl. of Const. 316 Stainless Steel Gasket Matl. EPDMP Design Pressure 250 psi Design Temperature 320 °F

Steam Heaters Two heat exchangers using 120 psia steam will be used to heat the rich solvent to 150 °C, one for each stage in the high temperature stripping process. The first, high pressure steam heater will have a two-phase feed because flashing will have occurred in the high pressure heat exchanger. CO2 and H2O will continue to vaporize as the solvent is heated from 143 to 150 °C. In the second, low pressure steam heater, the feed will again be two-phase because a throttling valve will be used to drop the pressure from 13.5 to 8 atm. Preliminary calculations indicate that the solvent temperature will drop to about 144 °C across the valve. Heat release curves were developed for both steam heaters (Figure 4).

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

142 143 144 145 146 147 148 149 150 151Temperature (oC)

Cum

ulat

ive

Hea

t Rat

e (B

tu/h

r)

HP Steam HeaterLP Steam Heater

Figure 4: Heat release curve for high pressure and low pressure steam heaters

260

8

Absorber Intercooling Design Absorber intercooling will be needed to obtain the optimal rich loading for the two-stage high temperature stripping process. The absorber will need to be operated such that the temperature bulge will be located at the middle of the column. The intercooling system will consist of a pump and heat-exchanger. Liquid will be pumped from the bottom of the chimney tray in the middle of the absorber, cooled in the exchanger, and pumped back above the chimney tray. This design will eliminate the need for level control. An existing pump, P-105, will need to be rebuilt and the Alfa Laval M6-FG heat exchanger used in a previous pilot plant campaign will be used. Cooling water will be used to cool the amine stream.

Pilot Plant Schedule Pilot plant tests using 8 m piperazine and the two-stage high temperature stripping process are planned for Fall 2009. Activities will include the design, layout, and construction of the skid-mounted two-stage flash system and installation of the absorber intercooling system. The pilot plant will be operated for three weeks. The proposed schedule for the planned pilot plant campaign is shown in the table below.

Table 3: Pilot plant schedule for 2009 June Order equipment July Equipment and instrument procurement August-September Begin equipment installation and skid construction October Complete equipment installation November Begin pilot plant campaign December Complete campaign

After the completion of testing at SRP, long term testing of concentrated piperazine with real flue gas is planned. The system will be operated for 3 to 6 months at a coal-fired facility. Some possible locations include the Wilsonville pilot plant constructed by Southern Company and the Tarong pilot unit being constructed by CSIRO in Australia.

Future Work

Additional bids will need to be obtained for the all of the major equipment and then ordered. Designs for the gas-liquid separator will also need to be finalized and then go out for bids. Once the dimensions for the high pressure pump, cross-exchanger, steam heaters and separators have been finalized, layout and construction of the skid can begin.

References

Freeman SA, Dugas R, Van Wagener D, Nguyen T, Rochelle GT. "Carbon dioxide capture with concentrated, aqueous piperazine". IJGGC; in press.

261

Degradation of Concentrated, Aqueous Piperazine (PZ) in CO2

Capture

June 17th, 2009 5th Trondheim Conference on CCS

262

2Degradation of Concentrated PZ for CO2 Capture

Outline

Introduction to Conc. PZWhy Concentrated PZ?

Degradation Methods and MaterialsResults of Degradation Studies

Thermal DegradationOxidation

Conclusions

263


Why Concentrated PZ?

Concentrated PZ = 8 m PZ = 40 wt% PZOxidatively stable with Fe2+, V5+, Ni2+, & Cr3+

Thermally stable up to 150°CStripper: up to 15 atm

1.7X the capacity of 7 m MEASimilar volatility to 7 m MEA2X the kinetic rate of 7 m MEA5-15% less energy intensive than 7 m MEA

264

Degradation Methods and Materials

265


Thermal Deg. – Thermal Cylinders

1/2 inch ID 316 SS tubes with Swagelock® endcaps 10 mL volume

1/2 inch

5 inch

Area : Volume = 315 m2/m3

Placed in forced convection ovensMaintains seal under pressure when heatedEasily conduct multiple experiments simultaneously – high throughput

266


Oxidation – Low Gas Flow Apparatus

0.5 L jacketed reactorAgitation at 1400 rpmOperates continuously for 2 to 5 weeksAccelerates expected oxidationUsed to quickly get degraded samples for analysis

100 mL/min 98%O2-2%CO2

1400 rpm

55°C Water

267


Analytical Methods

PZ conc. – acid titration (total alkalinity)CO2 conc. – total inorganic carbon (TIC)Anion Ion Chromatography (IC)

Formate, acetate, oxalate, glycolate, sulfate, etc.

Cation ICPZ, ethylenediamine (EDA), other amines

Mass Spectrometer (MS) (w/ Cation IC)Atomic Absorption Spectrometry (AA) - metals

268

Results of Degradation Studies

269


Thermal Deg: 8 m PZ, α=0.3, 150°C

PZ Total Formate

Formate

EDA

± 0.03 m

± 4.9 mM

9

270


PZ and MEA Thermal Degradation

23 – 70X more degradation

23 – 70X more degradation

10

271


Thermal Screening (135°C, α=0.4)

[Amine](m) Amine System Amine Loss

(%/week)

10 PZ Piperazine 0.3

7 AMP 2-amino-2-methyl-1-propanol 2.3

7 DGA Diglycolamine® 2.3

7 HEP Hydroxyethyl PZ 3.3

7/2 MDEA/PZ Methyldiethanolamine/PZ 3.7

8 EDA Ethylenediamine 4.0

7 MEA Monoethanolamine 9.3

7 DETA Diethylenetriamine 23.5

(Davis, 2009)

272


Metal Catalyzed PZ Oxidation

Quantify metal catalyzed degradation and inhibition in conc. PZComparison of metals (all mM):

1.0 Fe5.0 CuStainless Steel Metals: 0.7 Cr + 0.3 Fe + 0.3 Ni

Inhibitor “A” (all mM)1 Fe + 100 Inhibitor “A”5 Cu + 0.1 Fe + 100 Inhibitor “A”

273


PZ Resists Oxidation over MEA (1)

Fe

MEA + Fe

(Sexton, 2008)

Reduction in oxidation for concentrated PZ

Reduction in oxidation for concentrated PZ

13

274



Fe

Cu

MEA + Fe + Cu

MEA + Fe

(Sexton, 2008) 14

275



Fe

Cu

MEA + Cr + NiMEA + Fe +

Cu

MEA + Fe

Fe + Cr + Ni

8 m PZ

7 m MEA

(Sexton, 2008) 15

276


Inhibitor “A” Reduces PZ Oxidation

(Sexton, 2008)

Reduction in oxidation due to Inhibitor “A”

Reduction in oxidation due to Inhibitor “A”

16

277


Inhibitor “A” Reduces PZ Oxidation

(Sexton, 2008) 17

278


Conclusion

PZ demonstrates enhanced thermal resistance

Stable up to 150°CUp to 70X less degradation compared to 7 m MEA

PZ oxidizes 3 to 5X slower than 7 m MEAInhibitor “A” is successful in reducing Fe and Cu catalyzed degradation in conc. PZ

279

Thu Nguyen, Marcus Hilliard, Gary T. Rochelle

Luminant Carbon Management Program

The University of Texas at Austin, USA

June 17th, 2009

5th Trondheim Conference on CO2 Capture, Transport, & Storage

Thermodynamics of CO2 Capture: Amine Volatility

281

Outline

Scope Amine Volatility

Background Why is Amine Volatility Important

Apparatus Hot Gas FTIR

Theory Apparent Activity Coefficient

Results 6 ppm – 112 ppm

Conclusion MDEA Solvent is Least Volatile

282

Scope

Screen various amines for volatility

at 40ºC with nominal lean and rich loadings

•7m MEA (monoethanolamine - industry baseline)

•8m PZ (piperazine)

•7m MDEA / 2m PZ (methyldiethanolamine / PZ)

•12m EDA (ethylene diamine)

•5m AMP (2-amino-2-methyl-1-propanol)

283

Why is Amine Volatility Important in CO2Capture?

(1) solvent loss into vapor phase --- capacity reduction, makeup amine cost

(2) cost to recapture lost amine --- capital & operating costs of water wash

(3) venting of residual amine into atmosphere --- environmental risks

Amine Volatility is an Important Screening Criterion for New Amines

Volatility is Most Important at Lean Loading Condition and 40ºC

Background284

Amine Volatility: Stirred Reactor Coupled with FTIR Analyzer

5-10 L/min.

285

Modified Raoult’s Law for VLE

γamine = Pamine / [xamine * Paminesat]

γamine is the apparent activity coefficient (key parameter)

Gibb’s Helmholtz Relations

d (ln Pamine) / d (1/T) = - ∆Hvaporization / R

d (ln γi) / d (1/T) = -∆Hsolution / R

Theory286

Conclusion

Loadings (mol CO2/mol tot alk) shown correspond to PCO2 ~500 Pa at 40ºC

297

0

50

100

150

200

250

300

350

80

90

100

110

120

130

0 0.5 1 1.5 2

t (s

)

F (1

0-3

m2·s

)

[Fe2+] (mM)

Foaming Behavior of Piperazine

Aqueous Solutions for CO2 CaptureXi Chen, Stephanie Freeman and Gary Rochelle

Department of Chemical Engineering, The University of Texas at Austin, USA

Background

Solvent foaming is the #1 operational problem in natural gas processing and refinery sweetening processes. Capacity reduction Premature flooding Solvent losses Downstream process damage

Advantages of Piperazine over MEA as a solvent for CO2 capture

Experimental Apparatus

•Less Packing•Richer Solutions•Lower Regeneration Energy Requirements

•Lower Flow Rates•Lower sensible heat requirements•Smaller Exchangers, Pumps

Results

Conclusions

• Oxidized PZ foamed more than undegraded PZ.• Inhibitor A effectively mitigates foaming.• Formaldehyde significantly increases foaming.• Silicone antifoam at 1ppm greatly reduces foaming.• Fe2+ increases foaming of PZ by 40%.• Fe3+, corrosion & oxidation inhibitors & formic acid have little effects on PZ. foaming.

Acknowledgements

The authors would like to thank the LuminantCarbon Management Program for supporting this project.

NHHN

Piperazine

Flowmeter

To atmosphere

Nitrogen (335 ml/min)

Adapted from Standard Test Method for Foaming Characteristics of Lubricating Oils (ASTM D892)

40°C

400ml test solution

Foaming tendency:Foaminess coefficient

Foam stability: Foam break time: t (sec)

G

V

G

VVF

gt

0

8m PZ, α=0.3, 40 °C

F*=F/F0

t (s)FeSO4 HCHO

Antifoam (Dow corning, Q2-

3183A)0 0 0 1.00 34

1.5mM 0 0 1.32 >3001.5mM 0 1ppm 0.09 <2

0 270mM 0 3.33 N/A0 270mM 1ppm 0.18 200 270mM 2ppm 0.12 <8

Greater CO2 CapacityFaster Rates

Causes of foaming

Contaminants w/feed gas & makeup H2O.• Hydrocarbon• Suspended fine solid particles• Water-soluble surfactants & additives Degradation products of alkanolamine.

• Carboxylic acids• Heat stable salts• Product of formaldehyde & Piperazine

0

5

10

15

20

25

30

35

0

20

40

60

80

100

0 2 4 6 8 10

t (s

)

F (1

0-3

m2·s

)

Amine (molal)

MEA α = 0.4

PZ α = 0.3

Formaldehyde

0

10

20

30

40

50

60

0

50

100

150

200

250

300

350

0 100 200 300

t (s

)

F (

10

-3m

2·s

)

[Formaldehyde] (mM)

8m PZα = 0.340 °C

Antifoam

8m PZ, α=0.3, 40 °C F*=F/F0 t (s)

+CuSO4 (5mM) 0.98 30+CuSO4 (5mM) + A (100mM) 0.85 33

+CuSO4 (5mM) + A (100mM)+ FeSO4 (0.1mM)

0.90 35

+NaVO3 (10mM)+ FeSO4 (0.1mM)

0.77 28

+Formic acid (500mM) 1.06 30

Exp. Solution Additives

Degradation Time (hrs) F (m2/s)

8m PZ None 0 868m PZ 1 mM Fe2+ 70 858m PZ 1 mM Fe2+ 162 >>300

8m PZ1 mM Fe2+ + 100 mM “A” 70 92

8m PZ1 mM Fe2+ + 100 mM “A” 162 68

8m PZα = 0.340 °C Corrosion & Oxidation Inhibitors

Amine Concentration

Degradation Inhibitor “A”

Ferrous Ion

NHNHO + N N

n

Degradation products Analysis

HONH2

Monoethanolamine

25

27

29

31

33

35

37

39

41

72

74

76

78

80

82

84

86

88

0 0.2 0.4 0.6 0.8 1 1.2

t (s

)

F (1

0-3

m2·s

)

[Fe3+] (mM)

8m PZα = 0.340 °C

Ferric Ion

6

6.5

7

7.5

8

8.5

9

0 50 100 150 200

PZ

Co

nc. (m

)

Exp. Time (hr)

1mM Fe2+ +A

1mM Fe2+

298

Jorge M. Plaza, Eric Chen, Gary T. Rochelle

Department of Chemical Engineering, The University of Texas at Austin

MODEL DESCRIPTION

SINGLE INTERCOOLING – K+/PZ

BACKGROUND

Solvent VLE Kinetics

9m MEA Hilliard Regression Aboudheir using Laminar jet model in Aspen

4.5m K+

4.5 m PZ Hilliard Modified Cullinane kinetics to convert into activity based

• Rigorous models in Aspen Plus® RateSepTM

2 MEA + CO2 ↔ MEAH+ + MEACOO-

MEA + CO2 + H2O↔ HCO3- + MEAH+

4.5 m/4.5 m K+/PZ

9.8 m diameter

20 m CMR#2

5% hold upFlue gas

5.49 kmol/s

40oC

12% CO2

Lean flue gas

90% RemovalLean Solvent

Variable loading

Rich Solvent

0.44

0.48

0.52

0.15 0.25 0.35 0.45

Ric

h lo

ad

ing

Lean loading

No Intercooling

Single Intercooling

Critical

L/G

PZ + CO2 + b↔PZCOO-+bH+

PZCOO- + CO2 + b ↔PZ(COO-)2+ bH+

CO2 + OH-↔HCO3-

CO2 +b+H2O ↔bH++HCO3-

0

0.04

0.08

0.12

40

50

60

70

0.0 0.2 0.4 0.6 0.8 1.0

CO

2A

bso

rpti

on

ra

te (

km

ol/s)

T (

oC

)

Z/ZTotal

CO2 rateLiquid T

Vapor T

Intercooling

Top Bottom

0

0.04

0.08

0.12

40

50

60

70

0.0 0.2 0.4 0.6 0.8 1.0

CO

2A

bso

rpti

on

ra

te (

km

ol/s)

Tem

pera

ture

(oC

)

Z/ZTotal

CO2 rate

Liquid T

Vapor T

Top Bottom

• At the critical L/G the T bulge matches themass transfer pinch

• Intercooling reduces absorber T & breaksthe pinch improving performance

CONCLUSIONS

• The (L/G)c is predicted using energybalances around the absorber andbetween the Tbulge assuming:1. The energy due to CO2 absorption

and vaporization of water is includedin the outlet gas enthalpy

2. Tbulge is approximated using the casein which all heat leaves with gas.

3. Water content of exiting gas is inequilibrium with lean solvent

4. Change in liquid flow rate across thecolumn is neglected

5. CO2 content at Tbulge based onresults at various removals

• Intercooling is commonly used in absorbers• Useful with high the heats of absorption

resulting in a T bulge affecting the Pvap ofthe dissolved species.

(L/Gi)c is the critical ratio of liquid to inert gasspecies.

Y is the fraction of H2O and CO2 to inert species inthe gas stream (nCO2/Gi, nH2O/Gi)

Tb is the bulge temperaturehabs│Tb is the CO2 heat of absorption at Tb.hvap│Tb is the heat of vaporization of water at TbCpG

out is the gas heat capacity at outlet conditions.CpLin is the liquid heat capacity at inlet conditionsR is the desired removal.

• Removal at the bulge (Rb) calculatedfrom the following empirical correlation:

Rb= 1.47 R – 0.70

Pack

H

(m)

R

(%)(L/G)c Tb (oC) yout

H2O ToutG

(oC)

Aspen Appx Aspen Appx Aspen Appx Aspen Appx

5 90 3.9 4.0 69 68 0.09 0.06 46 4010 90 4.1 4.0 72 68 0.10 0.06 46 4020 90 4.1 4.0 72 68 0.09 0.06 46 4010 80 3.8 4.1 69 66 0.10 0.06 48 4010 60 3.5 4.5 61 62 0.09 0.06 47 40

• Optimized loadings and flowdefined by stripper

11.4 m diameter

Flexipac 1Y

80% flooding

Variable heightFlue gas

6.1 kmol/s

40oC

13.3% CO2

Lean flue gas

90% Removal

Lean Solvent

40oC

(57.6 kmol/s)

0.4 loading

Rich Solvent

0.495 loading

0

0.04

0.08

0.12

0.16

40

45

50

55

0 0.2 0.4 0.6 0.8 1

CO

2A

bso

rpti

on

ra

te (

km

ol/s)

T (

oC

)

Z/ZTotal

Vapor T

Liquid T

CO2 rate

Top Bottom

15 m packing

84.7% CO2 removal

Rich ldg = 0.489

0

0.04

0.08

0.12

0.16

40

45

50

55

0 0.2 0.4 0.6 0.8 1

CO

2A

bso

rpti

on

ra

te (

km

ol/s)

T (

oC

)

Z/ZTotal

Liquid T

Vapor T

CO2 rate

BottomTop

Intercooling5.16 m packing

90% CO2 removal

Rich ldg = 0.495

Removal IntercoolingPacking

Height (m)

90%None Infeasible

Mid column 6.07Optimized 5.16

• At (L/G)c intercooling improvessolvent capacity up to 45%

• The model (L/G)c is within 10%for removal >80% and for Tbwithin 4oC for K+/PZ

• For 9m MEA Intercoolingincreased performance allowing90% removal.

• Optimum placement of theintercooled stage can reducepacking height by 13%.

C.W.

C.W.

SINGLE INTERCOOLING

MEA

b = PZ, PZCOO- ,CO3- ,H2O, OH-

299

Rochelle Q2 Report 2009 (1)

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Transcript of Rochelle Q2 Report 2009 (1)