Supercritical Carbon Dioxide Extraction and Fractionation ...Supercritical Carbon Dioxide Extraction...

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

WAHAB MAQBOOL

Master of Engineering in Chemical Engineering

Submitted in fulfilment of the requirement for the degree of

D o c t o r o f P h i l o s o p h y

School of Chemistry, Physics and Mechanical Engineering

Science & Engineering Faculty

Q u e en sl an d U n iv er s i t y of T e ch no lo gy

2019

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil i

KEYWORDS

Aspen Plus®

Carbon dioxide

Chemical separation

Data regression

Equation of state

Fractionation

Green solvent

Internal rate of return

Net present value

Peng-Robinson

Phase equilibrium

Pilot plant

Process optimization

Process simulation

Process utilities

Renewable chemicals

Supercritical fluid extraction

Techno-economics

Vapour-liquid

ii Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

ABSTRACT

Bio-oil produced by the thermochemical treatment of lignocellulosic biomass is a

complex liquid mixture of compounds which in its crude state has a relatively low value.

Its typically large aqueous component has further cemented the reputation of bio-oil as a

challenging candidate source of renewable chemicals. In this PhD study a supercritical

fluid extraction (SFE) process using carbon dioxide as a solvent was developed and

investigated as a potentially energy efficient, cost effective alternative to conventional

distillation for the extraction and subsequent fractionation of high value target

compounds from bio-crude. A bio-oil is more commonly known as bio-crude when it is

produced from hydrothermal liquefaction (HTL) process.

To date, SFE has been used commercially for some niche applications such as

decaffeination or the recovery of essential oils and bioactive compounds from plant

derived material. The use of SFE for extraction of bio-oils has been the subject of a limited

number of experimental studies. Although basic vapour-liquid equilibrium (VLE) data

was available prior to the current PhD study for some potential target bio-oil compounds,

no previous attempt had been made to develop and implement the necessary VLE models

required for rigorous process investigation, optimisation and design. There have been no

reports in the literature on the techno-economics of SFE as a means of extracting high

value compounds from bio-oil.

Solubility data for a key (exemplar) target compound was experimentally determined

using both synthetic (no-sampling) and analytic-gravimetric (sampling) solubility cell

methods to appropriately extend the pressure and temperature ranges of data previously

reported in the literature and to develop the phase equilibrium models necessary for

process simulation. The model developed for binary VLE data from the literature

(validated against the original sources of data and bench scale measurements from the

current PhD study) was implemented on the Aspen Plus® process simulation platform

using a Peng-Robinson-Boston-Mathias (PR-BM) property method. The model

predictions for stage-wise pressure reduction fractionation of bio-crude components

using supercritical carbon dioxide as the solvent were successfully validated with a series

of pilot plant trials. A raw bio-crude produced by the HTL of bagasse derived black liquor

was used as feedstock in the SFE pilot plant trials. The pilot plant validation trials also

established the accuracy of utilising multiple binary VLE models to predict the

fractionation of real bio-crude solubilised in a scCO2 column extract stream of known

composition (a key simplifying assumption necessary in the development of the larger

process model).

The validated Aspen Plus® model was subsequently used to undertake a techno-

economic study of SFE using carbon dioxide. Solvent/ bio-oil (S/B) ratio is one of the key

determinants in the economics of any SFE process. Although extraction and fractionation

efficiencies increase with increasing S/B ratios, so too do the associated costs. The

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil iii

techno-economic study established that increasing the S/B ratio from 6.2 to 12.4 and

20.2, will decrease the corresponding Internal Rate of Return (IRR) from 15% to 12.3%

and 9.5% respectively. The corresponding increase in operational costs were 9.1%

(S/B = 12.4) and 17.4% (S/B = 20.2) relative to that of for the base case (S/B = 6).

The economics of SFE and conventional distillation processes for the recovery of target

compounds from bio-crude, were compared. For a base case plant capacity of 22.8

tonne/hr of biocrude, an IRR value of approximately 15% was achieved for SFE two-stage

(P-1), SFE single stage (P-2) and distillation combined with multistage evaporation (P-4)

scenarios. For the distillation alone scenario (P-3) the IRR value at the base case plant

capacity was -2.1%. To achieve the minimum assumed company hurdle rate (IRR = 10%),

a plant capacity of about 8 tonne/hr of biocrude was needed for both SFE scenarios (P-1,

P-2) and for distillation combined with multistage evaporation (P-4). For distillation

alone (P-3) the needed capacity is huge, at least 820 tonne/hr. Similarly a 20% IRR is

possible for P-1, P-2 and P-4 up to plant capacity of about 50 tonne/hr, while for P-3 the

capacity should be ridiculously higher, more than 5000 tonne/hr.

For the double and single stage SFE scenarios (P-1 and P-2), the IRRs drop to 11.7% and

11.9% respectively with a doubling of the price of imported electricity used. For P-3 and

P-4 distillation processes the corresponding IRR drop will be just to -2.2% and 15.1%

respectively. Similarly upon doubling the steam price, the IRR for P-1, P-2 and P-4 will

decrease to about 13.7%, while the corresponding decrease in IRR will be up to -6.5% for

P-3 process. The IRRs drop from 15% to about 5%, for P-1, P-2 and P-4, upon 25%

decrease in product sale prices, the corresponding increase in IRRs will be up to 39%

when product sale prices increased by 75%. For P-3, the IRR will reach 10% and 20%

with at least 75% and 165% respectively increase in product sale prices.

iv Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

TABLE OF CONTENTS

Keywords .................................................................................................................................. i

Abstract .................................................................................................................................... ii

table of contents ...................................................................................................................... iv

List of Figures ........................................................................................................................ vii

List of Tables ......................................................................................................................... xii

List of Publications ............................................................................................................... xiv

Statement of Original Authorship ...........................................................................................xv

Acknowledgements ............................................................................................................... xvi

Chapter 1: Introduction .......................................................................................1

1.1 Research problem ...........................................................................................................1

1.2 Novelty of this work .......................................................................................................2

1.3 Research aims and objectives .........................................................................................3

1.4 Research outcomes .........................................................................................................3

1.5 Summary of chapters ......................................................................................................5

1.6 Reference ........................................................................................................................7

Chapter 2: Literature Review ..............................................................................9

2.1 Title: Supercritical carbon dioxide separation of carboxylic acids and phenolics from bio-oil of

lignocellulosic origin: understanding bio-oil compositions, compounds solubilities and their

fractionation ..............................................................................................................................9

2.2 Abstract ...........................................................................................................................9

2.3 Introduction ..................................................................................................................12

2.4 Composition of bio-oil from thermochemical conversion of biomass .........................14 2.4.1 Monophenols and low molecular weight acid contents of bio-oil ......................15

2.5 Binary system solubility data of bio-oil compounds ....................................................17

2.6 Supercritical CO2 extraction and fractionation of bio-oil .............................................19

2.7 Discussion .....................................................................................................................26 2.7.1 Solubility data .....................................................................................................26 2.7.2 Modelling Binary solubility data ........................................................................32 2.7.3 Use of binary data in preliminary assessment and design of fractionation ........35 2.7.4 Solubility data consistency and accuracy ...........................................................36

2.8 Conclusion ....................................................................................................................37

2.9 Supporting Information ................................................................................................38

2.10 References ....................................................................................................................41

Chapter 3: Fundamental Experimental Data and Equation of State Model 49

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil v

3.1 Title: Comparison of literature data, thermodynamic modelling and simulation of supercritical

fluid extraction of benzyl alcohol ...........................................................................................49

3.2 Abstract .........................................................................................................................49

3.3 Introduction ..................................................................................................................52

3.4 Experimental methodology ...........................................................................................54 3.4.1 Materials .............................................................................................................54 3.4.2 Apparatus and procedures ..................................................................................54

3.5 Thermodynamic modelling ...........................................................................................57

3.6 Results and discussion ..................................................................................................59 3.6.1 Solubility data .....................................................................................................59

3.7 Process design and techno-economic evaluation using Aspen Plus® to extract bio-oil from the

aqueous hydrothermally liquefied product .............................................................................66 3.7.1 Simulation results and SFE techno-economics ..................................................70

3.8 Conclusions ..................................................................................................................71

3.9 Glossary and Nomenclature ..........................................................................................72

3.10 Appendix ......................................................................................................................73

3.11 Supporting Information ................................................................................................74

3.12 References ....................................................................................................................77

Chapter 4: Bio-oil Mixture Model, Pilot Plant Validation, Aspen Plus® Simulation

and Techno-economics..............................................................................................79

4.1 Title: Extraction and purification of renewable chemicals from hydrothermal liquefaction bio-

oil using supercritical carbon dioxide: A techno-economic evaluation ..................................79

4.2 Abstract .........................................................................................................................79

4.3 Introduction ..................................................................................................................82

4.4 Experimental methodology ...........................................................................................83 4.4.1 Materials .............................................................................................................83 4.4.2 Bio-crude preparation and its characteristics......................................................83 4.4.3 The SFE pilot plant setup ...................................................................................84 4.4.4 Extraction and Fractionation Procedure .............................................................84 4.4.5 Gas chromatography mass spectrometry (GC-MS) analysis ..............................86 4.4.6 Nuclear magnetic resonance (NMR) spectroscopy ............................................86

4.5 Thermodynamic modelling ...........................................................................................86

4.6 Process design and techno-economic evaluation using Aspen Plus® ..........................90 4.6.1 First separator .....................................................................................................92 4.6.2 Second separator .................................................................................................94 4.6.3 Recycling ............................................................................................................95 4.6.4 Product purification ............................................................................................95

4.7 A techno-economic assessment of process scenarios ...................................................98

4.8 Results and Discussion .................................................................................................99 4.8.1 Sensitivity analysis ...........................................................................................108 4.8.1.1 Capital cost ....................................................................................................108 4.8.1.2 Electricity .......................................................................................................110 4.8.1.3 Steam .............................................................................................................113 4.8.1.4 Product sale price ...........................................................................................115

4.9 Conclusions ................................................................................................................118

vi Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

4.10 Glossary and Nomenclature ........................................................................................119

4.11 Supporting Information ..............................................................................................120

4.12 References ..................................................................................................................126

Chapter 5: Conclusions and Recommendations ............................................129

5.1 Conclusions ................................................................................................................129

5.2 Recommendations for future work .............................................................................131

Appendix ...............................................................................................................................133

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil vii

LIST OF FIGURES

Figure 1-1 Schematic showing the relationship of the current PhD study to the

broader AISRF project ....................................................................................2

Figure 1-2 Flow of chapters according to research aims of this work ..........................5

Figure 2-1 General experimental setup of supercritical CO2 extraction system with optional co-solvent addition. T: temperature measurement and control, BPR: back pressure regulator, MV: micrometering valve. .......................20

Figure 2-2 Extract yields and concentration of single ring phenols in extract from supercritical fluid rectification of softwood Kraft lignin microwave-pyrolysis oil for varying solvent to bio-oil ratio. (inherent and experimental random errors were not reported in the original source) [25] ...............................................................................................................21

Figure 2-3 Effect of adsorbent on typical selective enrichment of phenols and acids in scCO2 extraction of corn stalk pyrolysis oil (original pyrolysis oil contained 10.74 % phenols and 28.15% acids). (inherent errors related to extract yields and compositions and experimental random errors were not reported in the original source) [30] ..................................................23

Figure 2-4 Ratio of total benzenoids extracted to total acids extracted as a function of different solvent/bio-oil ratios used in scCO2 extraction of wheat-wood sawdust [29] and wheat-hemlock [26] pyrolysis oils ..............................24

Figure 2-5 Effect of increasing pressure on solubilities of different bio-oil compounds in supercritical carbon dioxide at 333 K temperature. Random or ultimate error were not reported for eugenol in the original source [89]. For vanillin the maximum reported uncertainty of + 16.4% is shown [83]. ..............................................................................................................26

Figure 2-6 Solubility isotherms showing crossover pressure regions for vanillin-CO2 (left) and phenol-CO2 (right) binary systems. The maximum reported uncertainty for vanillin [83] of ±16.4% is shown. ....................................27

Figure 2-7 CO2 densities calculated at 40 oC with PR-EOS [115] and Span and Wagner EOS [111] .......................................................................................28

Figure 2-8 Solubility data (see supporting information, Table 2.8S) plots of different monophenols and acetic acid. CO2 density is calculated here using the Span and Wagner [111] method. The maximum reported uncertainty for vanillin [83] of 16.4% is shown. Random or ultimate error were not reported in the original source for eugenol [89]. .....................29

Figure 2-9 CO2 density variation as a function of temperature and pressure, Left: 3-D surface plot of temperature-pressure and CO2 density, Right: 2-D plane plot of CO2 density curves against pressure axis at different temperatures ...............................................................................................30

Figure 2-10 Effect of CO2 induced acidity (in terms of final solution pH of 3, 3.4 & 4.2 corresponding to initial pH of 3, 5 and 8 respectively) on percent

viii Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

recoveries of phenol and 2,4,6-trichlorophenol solutes from aqueous matrices at 150 atm pressure during supercritical extraction with pure CO2. Inherent and experimental random errors were not reported in the original source [116]. ..................................................................................31

Figure 2-11 Parity plots of experimental vs predicted solubility of different bio-oil compounds on natural log scale. Dots of one colour correspond to one data source. ..................................................................................................34

Figure 2-12 Solubilities of different monophenols in scCO2 predicted by fitted model at 308 K temperature ......................................................................35

Figure 2-13 Extraction trends of different monophenols with scCO2 from bio-oil mixtures of softwood Kraft lignin [25] and beech wood [23] pyrolysis oils ......................................................................................................................36

Figure 2-14 Parity plots of experimental versus predicted solubilities using data and parameters based on [84] (plot A) and using the same correlation parameters to predict solubility data presented in [86] (plot B) ...........36

Figure 3-1 High-pressure phase equilibrium apparatus used in this study to determine

benzyl alcohol solubility in scCO2. Labels: 1: CO2 cylinder; 2: CO2 pump; 3:

connections for chiller; 4: micrometering valve; 5: safety relief valve; 6: vent

micrometering valve; 7: analogue pressure gauge; 8: water heater connections;

9: mixer; 10: view cell; 11: pressure transducer; 12: pressure indicator; 13:

thermocouple; 14: temperature indicator; 15: syringe; 16: two-way valve; 17:

distributor; 18: rupture disc. .........................................................................55

Figure 3-2 Configuration of view cell assembly used in this study to measure solute

solubility in scCO2 by continuous flow sampling method. ..........................56

Figure 3-3 Comparison of benzyl alcohol solubility in scCO2, determined in this study

by visual and sampling methods of solubility determination. Horizontal error

bars represent the average uncertainty in measured precipitation pressure;

vertical error bars are standard deviation in the measured mole fraction

solubility. ......................................................................................................62

Figure 3-4 Predicted (PR-EOS) and experimental (Walther et al.[12]) composition -

pressure phase diagram for a benzyl alcohol-CO2 binary system ................63

Figure 3-5 Comparison of experimental solubility data of benzyl alcohol determined in

this study, with that of PR-EOS model predictions. The model was first

optimized with the help of experimental VLE data of Walther et al. [12] ...64

Figure 3-6 Comparison of solubility data of benzyl alcohol in CO2 vapour phase from

literature [10, 11] and the regressed model of this work based on Walther et al.

[12] data ........................................................................................................65

Figure 3-7 Comparison of solubility data of benzyl alcohol in CO2 liquid phase from

literature [10, 11] and the regressed model of this work based on Walther et al.

[12] data ........................................................................................................66

Figure 3-8 Aspen Plus® process diagram for SFE and subsequent distillation processes

used in the recovery of benzyl alcohol from an aqueous mixture. ...............68

Figure 3-9 Techno-economic summary of SFE of benzyl alcohol from binary aqueous

mixture, for different solvent/bio-oil ratios. .................................................70

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil ix

Figure 4-1 Pilot plant setup used in this work for supercritical extraction and

fractionation of bio-crude (T: temperature control, Sep: separator, MV:

micrometering valve). Sep-1 and Sep-2 were wrapped in trace heaters to

compensate for the cooling effects resulting from depressurisation of the

extract streams. .............................................................................................84

Figure 4-2 Aspen Plus® process flowsheet for supercritical extraction of bio-crude

followed by two-stage fractionation of column extract (part of P-1). ..........92

Figure 4-3 Effect of temperature on distribution coefficients of components to be

fractionated by stage-wise pressure reduction. P = 90 bar ...........................93

Figure 4-4 Separation factors of components tend to decrease and approach unity at

higher pressures. T = 43.1 oC........................................................................93

Figure 4-5 Distribution coefficients of components will decrease with decrease in

pressure. T = 43.1 oC ....................................................................................94

Figure 4-6 Operating cost of CO2 compression from ambient to 60 bar (liquid state)

pressure vs liquid CO2 make-up cost. ...........................................................95

Figure 4-7 Aspen Plus® flowsheet for the multi-stage evaporation and distillation

processes used in the recovery of products following scCO2 extraction and

fractionation (Scenario P-1) ..........................................................................97

Figure 4-8 Black liquor bio-crude before (A) and after (B) acidification. ...............100

Figure 4-9 Relative concentrations of compounds in Separator-2 samples of

supercritical extract, collected at a temperature of 18.4 oC and a pressure of

46.8 bar. Legend numerical values correspond to first separator pressure

conditions (in bar abs). Concentration measurements were determined by GC-

MS; Aspen Plus® model PR-BM was used in the simulations. .................101

Figure 4-10 Comparison of experimental scCO2 fractionation of extracted bio-crude

with Aspen Plus® model of this work. (A) Data of phenol (GC-MS) and acetic

acid (NMR) for fraction-2. (B) Catechol relative concentration in fraction-1

relative to p-cresol in the same fraction. Legend numerical values in both

figures (A) and (B) correspond to first separator pressure conditions. Fraction-

2 was collected at 18.4 oC temperature and 46.8 bar pressure. ..................102

Figure 4-11 Mass ratios of compounds in second fraction of supercritical extract,

collected at 18.4 oC temperature and 46.8 bar pressure. Legend numerical

values correspond to first separator pressure conditions. Amounts determined

by GC-MS method. Aspen Plus® model PR-BM was used in simulation.103

Figure 4-12 Compound recoveries of bio-crude into pure chemical products. ........105

Figure 4-13 Techno-economic summary of four process simulations to compare

basically supercritical separation of bio-crude with that of distillation. .....106

Figure 4-14 Effect of solvent/bio-oil ratio on annualized operating costs and profits of

SFE of bio-oil .............................................................................................107

Figure 4-15 Investment analysis for bio-oil separation technologies of SFE and

conventional distillation ..............................................................................107

Figure 4-16 Investment analysis for different solvent/bio-oil ratios in SFE of bio-oil (P-

1) .................................................................................................................108

x Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

Figure 4-17 Effect of plant capacity (capital costs) on techno-economics of SFE two-

stage (P-1), SFE single stage (P-2), distillation (P-3) and distillation combined

with multistage evaporation (P-4) processes of bio-crude separation into pure

chemical compounds ..................................................................................110

Figure 4-18 Comparison of profitability of SFE and distillation scenarios, for separation

of bio-crude into pure chemical compounds, with change in plant capacity110

Figure 4-19 Effect of electricity price on IRR and NPV of SFE two-stage (P-1)

separation of bio-crude ...............................................................................111

Figure 4-20 Effect of electricity price on IRR and NPV of SFE single stage (P-2)


Figure 4-21 Effect of electricity price on IRR and NPV of distillation (P-3) separation

of bio-crude .................................................................................................112

Figure 4-22 Effect of electricity price on IRR and NPV of distillation combined with

multistage evaporation (P-4) separation of bio-crude ................................112


of bio-crude into pure chemical compounds, with increase in electricity

purchase price .............................................................................................113

Figure 4-24 Effect of steam price on IRR and NPV of SFE two-stage (P-1) separation

of bio-crude .................................................................................................113

Figure 4-25 Effect of steam price on IRR and NPV of SFE single stage (P-2) separation

of bio-crude .................................................................................................114

Figure 4-26 Effect of steam price on IRR and NPV of distillation (P-3) separation of

bio-crude .....................................................................................................114

Figure 4-27 Effect of steam price on IRR and NPV of distillation combined with



of bio-crude into pure chemical compounds, with increase in steam price 115

Figure 4-29 Effect of product sale price on IRR and NPV of SFE two-stage (P-1)


Figure 4-30 Effect of product sale price on IRR and NPV of SFE single-stage (P-2)


Figure 4-31 Effect of product sale price on IRR and NPV of distillation (P-3) separation

of bio-crude .................................................................................................117

Figure 4-32 Effect of product sale price on IRR and NPV of distillation combined with



of bio-crude into pure chemical compounds, with change in product sale

prices. ..........................................................................................................118

Figure 4-34S Aspen Plus® process flowsheet for supercritical extraction of bio-crude

followed by single-stage collection of column extract (part of P-2). .........122

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil xi

Figure 4-35S Aspen Plus® process flowsheet for distillation of products from single-

stage collection of supercritical extract (P-2). Extraction column bottom

(raffinate) is treated with evaporation process. ...........................................123

Figure 4-36S Aspen Plus® process flowsheet for distillation of bio-crude itself, without

any upstream extraction done on it (P-3). ...................................................124

Figure 4-37S Aspen Plus® process flowsheet for distillation of bio-crude itself, without

any upstream extraction done on it. Aqueous stream off first distillation

column (D1) contains catechol, and is evaporated off to recover catechol (P-4).

....................................................................................................................125

xii Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

LIST OF TABLES

Table 2.1 Experimental techniques and conditions used in literature studies for measurement of solute solubilities in supercritical carbon dioxide ......19

Table 2.2 Effect of pressure and temperature on extract yield and product concentration in extract during supercritical CO2 extraction of sugarcane bagasse and cashew nut shell pyrolysis oils. (inherent and experimental random errors were not reported in the original source) [27] ..............22

Table 2.3 Yields and acid-phenol contents of extracts obtained at 333.15 K temperature and 150 bar pressure during scCO2 extraction of beech wood pyrolysis oil (inherent and experimental random errors were not reported in the original source) [23] ........................................................25

Table 2.4 Chrastil correlation parameters for the solubility of several bio-oil compounds in supercritical CO2.................................................................33

Table 2.5S Single ring phenolics and low molecular weight carboxylic acid contents in bio-oils ......................................................................................39

Table 2.6S Major chemical compounds in low molecular weight carboxylic acid

fraction of bio-oils ........................................................................................39

Table 2.7S Major chemical compounds in single ring phenolic fraction of bio-oils .40

Table 2.8S Solubility data of single ring phenolics and acetic acid with supercritical

carbon dioxide in binary systems .................................................................40

Table 3.1 Aspen Plus® pure component properties used in modelling of this work .59

Table 3.2 Benzyl alcohol solubility in scCO2 data determined using the visual method

......................................................................................................................60

Table 3.3 Benzyl alcohol solubility in scCO2 data determined using the sampling

method ..........................................................................................................61

Table 3.4 Benzyl alcohol - CO2 binary interaction parameter values for a PR-EOS

derived from the VLE data of Walther et al. [12]. ........................................63

Table 3.5 Aspen Plus® process scenarios simulated in this study, for recovery of benzyl

alcohol from binary water mixture ...............................................................67

Table 3.6S Stream specifications of Aspen Plus® simulation for SFE of benzyl alcohol

aqueous mixture. CO2/aqueous mixture ratio = 10 .......................................74





Table 3.9S Utilities summary of Aspen Plus® simulation for SFE of benzyl alcohol

aqueous mixture ............................................................................................76

Table 3.10S Economic evaluation summary of Aspen Plus® simulation for SFE of

benzyl alcohol aqueous mixture ...................................................................77

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil xiii

Table 4.1 Parameters used in this work for the supercritical CO2 pilot plant extraction

and fractionation of bio-crude produced from HTL of sugarcane bagasse black

liquor. Extraction was performed at 55oC temperature and 206.4 bar pressure,

and Sep-2 was maintained at 18.4oC temperature and 46.8 bar pressure. ....85

Table 4.2 Critical properties of pure compounds used in the Aspen Plus® modelling of

the binary systems .........................................................................................88

Table 4.3 Percent AARD between predicted and experimental VLE data for different

solute-CO2 binary systems using the default regression coefficients for the PR-

BM property method model available in Aspen Plus® ................................89

Table 4.4 Numerical values of binary interaction parameters obtained after regressing

the experimental VLE data (Table 4.3) of different solute-CO2 binary systems,

with the EOS model of PR-BM property method within Aspen Plus® data

regression system ..........................................................................................89

Table 4.5 Description of Aspen Plus® simulation scenarios simulated in this work, for

recovery of compounds from bio-crude. ......................................................90

Table 4.6 Composition of bio-crude used in Aspen Plus® simulations of this work .98

Table 4.7 Raw material cost and product prices used in techno-economic evaluations of

this work .......................................................................................................99

Table 4.8 Utilities prices used in this work for Aspen Plus® simulations .................99

Table 4.9S Summary of economic evaluation for different separation and purification

processes of bio-crude (P-1 to P-4) ............................................................121

Table 4.10S Summary of product concentrations in extract fractions of pilot plant SFE

trials ............................................................................................................121

xiv Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

LIST OF PUBLICATIONS

Journal articles

1] Wahab Maqbool, Philip Hobson, Kameron Dunn, William Doherty; Supercritical Carbon

Dioxide Separation of Carboxylic Acids and Phenolics from Bio-Oil of Lignocellulosic

Origin: Understanding Bio-Oil Compositions, Compound Solubilities, and Their

Fractionation, Industrial & Engineering Chemistry Research, 56 (12), 3129-3144, 2017.

2] Wahab Maqbool, Kameron Dunn, William Doherty, Neil McKenzie, Philip Hobson;

Comparison of literature data, thermodynamic modelling and simulation of supercritical

fluid extraction of benzyl alcohol, Chemical Engineering & Processing: Process

Intensification, submitted.

3] Wahab Maqbool, Kameron Dunn, William Doherty, Neil McKenzie, Dylan Cronin, Philip

Hobson; Extraction and purification of renewable chemicals from hydrothermal

liquefaction bio-oil using supercritical carbon dioxide: A techno-economic evaluation,

Industrial & Engineering Chemistry Research, 58 (13), 5202-5214, 2019.

Poster paper

1] Wahab Maqbool, Philip Hobson, Kameron Dunn, William Doherty; Positive sealing material for supercritical carbon dioxide, Supergreen Conference, December 1-3, 2017, Nagoya, Japan.

Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil xv

STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

Signature:

Date: 5th November, 2019

QUT Verified Signature

xvi Supercritical Carbon Dioxide Extraction and Fractionation of Bio-oil

ACKNOWLEDGEMENTS

I thankfully acknowledge Queensland University of Technology (QUT) to assist me with

my Doctor of Philosophy (IF49) candidature with QUT Postgraduate Research Award,

Australia-India Strategic Research Fund Top Up Scholarship and QUT HDR Tuition Fee

Sponsorship.

I would like to thank my supervisory team Philip Hobson, William Doherty and Kameron

Dunn for accepting me as a PhD student under their supervision, and for providing

valuable guidance and practical research expertise in the field of Energy and Process

Engineering.

I am thankful also to Neil Mckenzie, Kameron Dunn and Barry Hume for their generous

experimental and facilities maintenance support.

Lalehvash Moghaddam, Dylan Cronin, Adrian Baker, Wanda Stolz and Daniela Tikel

provided me with a lot of research support while I was working in analytical chemistry

laboratory. I am thankful to all of them.

I am fully aware of the sacrifices made by my parents and my brothers and sisters in

preparing me and allowing me to take on such a lengthy endeavour, and for making me

able to do it.

I will be under the burden of favours made by my Pakistani Fellows here at QUT, Fawad

Shah (mastermind), Aziz Pawar and Imran and Company. These Pakistani fellows helped

me a lot to keep going towards the end of my PhD.

My wife Sehar held me together and going through the final year of my PhD.

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 Research problem

Thermochemically produced bio-oil from lignocellulosic biomass is a complex

mixture of oxygenated hydrocarbons, pyrolytic lignin and water. Bio-oil is

potentially an abundant renewable source of fuels and high value chemicals [1-

3]. The complex nature of bio-oil presents many technological obstacles in

exploring its potential as a renewable feedstock for chemicals production.

Conventional chemical separation processes such as distillation and solvent

extraction often require high thermal energy inputs [4]; solvents may be

hazardous, expensive and difficult to recover [5]. Although supercritical

extraction has been widely studied with food, pharmaceutical and other niche

production systems it has been used industrially for only a few niche applications

such as the decaffeination of beverages as well as the extraction of essential oils

and bioactive compounds [6, 7].

Interest in the application of SFE to bio-oil fractionation has emerged in recent

years. Previous investigations into the use of SFE as a lower cost, environmentally

friendly alternative to conventional bio-crude separation processes have been

carried out [2, 3, 8-16] although these studies have been limited to bench scale

investigations.

This PhD study was part of larger project entitled integrated technologies for

economically sustainable bio-based energy run under the Australia-India Strategic

Research Fund (AISRF). This larger collaborative research project addressed

major gaps in knowledge and understanding around the production of biofuels

from surplus non-food non-fodder agriculture and forest residues in Australia

and India and was aimed at developing and demonstrating scalable and

sustainable technology platforms for commercial deployment. The PhD study is

a part of Sub-project 4 in the broader AISRF project (see Figure 1-1) and was to

establish the technical and financial feasibility of using SFE as an upgrading and

value adding process for bio-crude by addressing the following research

questions:

How does SFE as a process for the separation of bio-crude compounds compare

to other more conventional separation techniques in terms of the degree of

separation and energy efficiency?

What is the capability of thermodynamic model to accurately describe SFE and

fractionation of bio-crude?

2 Chapter 1: Introduction

What are the prospects of process integration and optimization to make SFE of

renewable chemicals from bio-crude a technically and economically viable option

in a bio-refinery?

1.2 Novelty of this work

SFE has been flagged previously as a potentially low cost, energy efficient process

for the recovery of renewable chemicals from bio-oil. This PhD study provides the

first comprehensive experimental and theoretical analysis to confirm the

extraction conditions, process configuration and financial outcomes required to

establish SFE as a potential technology for recovering high value renewable

chemicals from bio-oil. To this end this study has made notable contributions to

knowledge in related areas including:

Figure 1-1 Schematic showing the relationship of the current PhD

study to the broader AISRF project

Sub-project 1:

Biomass supply economics and logistics

Sub-project 4: Thermochemical

conversion (hydrothermal

liquefaction) of lignin-rich

streams to bio-oil

PhD Study:

Supercritical CO2

extraction and

fractionation of high

value chemicals from

bio-oil

Sub-project 3:

Biochemical

conversion of

cellulose-rich streams

Sub-project 2:

Biomass analysis and deconstruction

Techno-economics of integrated project technologies


• The proposed and demonstrated effectiveness of binary VLE data and

models as an accurate and relatively simple means of determining

supercritical fractionation conditions associated with the recovery of

individual chemicals from complex mixtures.

• The experimental measurement and use of existing VLE data to determine

the equations of state models required to accurately simulate the process

of SFE extraction of renewable chemicals from bio-oil.

• The implementation and use of the above EOS models within detailed

process simulation scenarios to determine preferred configurations and

establish optimum process conditions for chemical extraction and

purification using SFE and conventional technologies.

• The first published continuous pilot plant scale demonstration trials of the

recovery of chemicals from bio-oil using supercritical CO2.

• The provision of a detailed techno-economic assessment of SFE for

recovering chemicals from bio-oil and a comparison of the capital and

operating (including energy) costs of an equivalent plant utilising

conventional distillation and multi-stage evaporation technologies.

1.3 Research aims and objectives

This work was aimed at evaluating and optimising the efficacy of SFE utilising

supercritical CO2 for the recovery of chemicals from bio-crude produced from the

hydrothermal liquefaction (HTL) of black liquor (a by-product of sugarcane

bagasse pulping process).

The above aim was achieved so far by defining the objectives:

• Determine the experimental vapour-liquid equilibrium (VLE) data for key

bio-crude compounds (where this data was not available from the

literature);

• Investigate the thermodynamic modelling of a bio-crude mixture as a

series of binary VLE systems;

• Analyse the developed model in a process simulation environment for

validation of the QUT SFE pilot plant results;

• Study and compare the optimized techno-economics of SFE with

conventional distillation as a means of recovering high value compounds

from bio-crude

1.4 Research outcomes

Research outcomes associated with this study include:


1. Equation-of-state (EOS) based thermodynamic model development for SFE of

bio-crude with use of only solute-solvent binary interaction parameters

thereby neglecting the solute-solute interactions. The developed model

successfully predicted the fractionation conditions for stage-wise pressure

reduction separation of a multicomponent supercritical extract stream in our

experimental pilot plant SFE trials. The model was able to predict that for our

specific bio-crude system, catechol was the least soluble compound among

acetic acid, phenol, 4-ethylphenol and p-cresol, and could be selectively

separated into first separator when set at a relatively higher pressure.

2. The developed model was used in the construction of SFE process simulations

in Aspen Plus®, in which it was shown through evaluation of techno-

economics that increasing the S/B ratio from 6.2 up to 20.2 will decrease the

IRR from 15% to 9.5%.

3. A comprehensive techno-economic evaluation and comparison was made

between Aspen Plus® simulation scenarios for SFE with and without

fractionation and conventional distillation of bio-crude. Distillation of bio-

crude when combined with multistage evaporation, incurs slightly lower

annualized operating costs than SFE processes, yet the IRR value of about

15% is achieved for both SFE and distillation combined with multistage

evaporation processes. Distillation alone did not prove economical for bio-oil

separation with an IRR value of -2.1%. In terms of IRR, two-stage SFE was

shown to be marginally better (by 0.3%) than a single stage SFE process. For

the double and single stage SFE scenarios (P-1 and P-2), the IRRs reduced to

11.7% and 11.9% respectively with a doubling of the price for imported

electricity used. For the P-3 and P-4 distillation processes doubling imported

electricity costs reduced the IRR’s to -2.2% and 15.1% respectively.

Similarly doubling the steam price, the IRR’s for P-1, P-2 and P-4 will decrease

to about 13.7%, whilst the P-3 process IRR is reduced to -6.5%.

For P-1, P-2 and P-4, the IRRs drop from 15% to about 5% with a 25%

decrease in product sale prices, whilst the corresponding increase in IRRs will

be up to 39% when product sale prices increased by 75%. For P-3, the IRR

will reach 10% and 20% with at least 75% and 165% respectively increase in

product sale prices.


1.5 Summary of chapters

Figure 1-2 Flow of chapters according to research aims of this work


This chapter (Chapter 1) describes the research problem investigated, reasoning

for and scope of research work investigated. It articulates the aims of this work

and the associated target outcomes.

Chapter 2 is a critical review (review paper) of experimental studies to date on

SFE of compounds from bio-oil (pyrolysis oil, HTL oil) and of the availability of

relevant solubility data on binary systems associated with bio-oil compounds.

Binary data from the literature are correlated by empirical models as a means of

evaluating the quality of and comparing data from different sources. The focus on

VLE data of binary systems at this stage was in anticipation of implementing the

associated models within Aspen Plus® as a process optimisation and design tool

for commercial SFE systems (see Chapter 3 and 4).

Previous experimental SFE studies are critically reviewed to establish at an early

stage, the relative importance of process parameters such as temperature,

pressure, solvent density, pH etc., on SFE of bio-oil. Knowledge gaps are identified

and used to further refine the proposed experimental program associated with

the current study.

Chapter 3 is a submitted research article reporting on the phase equilibrium

experiments undertaken as part of this study to determine the solubility of benzyl

alcohol (as an exemplar of a bio-oil compound) in scCO2. Solubility data points

were determined for this binary system encompassing the full range of

temperatures and pressures relevant to the extraction and fractionation of target

bio-oil compounds. Data was determined using both synthetic (no-sampling) and

analytic-gravimetric (sampling) methods. Data measured by these means were

compared where possible to that reported in the literature.

This chapter describes a validation process by which it was shown that

thermodynamic correlations (developed in this study) based on data from the

literature could be used to accurately predict solubility characteristics measured

at the higher temperatures and pressures measured in the current study. The

purpose of determining experimental solubility data in this work is to compare

VLE data sets from literature, for use in process modeling and simulation.

Chapter 4, also a published research article, reports on the pilot scale trials

undertaken in this study. The trials were used to establish the accuracy of

utilising multiple binary VLE models to predict the fractionation of real bio-crude

solubilised in a scCO2 column extract stream of known composition.

This chapter also summarises the results of implementing binary VLE models in

the Aspen Plus® simulation code as the basis for the design and thermodynamic

optimisation of a practical, commercial scale SFE plant for the extraction and

recovery of target compounds from bio-crude. A financial overlay was developed

to determine the associated capital and operating costs of the plant in Aspen

Plus® based process scenarios. These models enabled a detailed techno-


economic comparison to be made between the proposed SFE and distillation

technologies.

A more complete description of the Aspen Plus® model and outputs (not

included in the above publication) is provided in Chapter 5.

Chapter 5 draws together the findings of this PhD study to provide a series of

conclusions regarding the viability of SFE compared with conventional

distillation technology. Recommendations for future work required to advance

SFE technology to the point where its full credentials as an economically and

environmentally sustainable means of extracting high value compounds from

bio-oil, can be realised.

An appendix at the end of this document provides details of the Aspen Plus®

simulation data and flowsheets associated with the summary results provided in

Chapter 4. It includes predicted stream conditions (temperature, pressure, flow

etc.) and a more complete reporting of utility requirements and costs.

1.6 Reference

[1] https://www.btg-btl.com/en/applications/oilproperties, accessed: 11 Sep 2018.

[2] T. Cheng, Y. Han, Y. Zhang, C. Xu, Molecular composition of oxygenated

compounds in fast pyrolysis bio-oil and its supercritical fluid extracts, Fuel, 172 (2016)

49-57.

[3] Y. Feng, D. Meier, Extraction of value-added chemicals from pyrolysis liquids

with supercritical carbon dioxide, Journal of Analytical and Applied Pyrolysis, 113

(2015) 174-185.

[4] V. Balan, Current challenges in commercially producing biofuels from

lignocellulosic biomass, ISRN biotechnology, 2014 (2014) 1-31.

[5] A.R. Boyd, P. Champagne, P.J. McGinn, K.M. MacDougall, J.E. Melanson, P.G.

Jessop, Switchable hydrophilicity solvents for lipid extraction from microalgae for

biofuel production, Bioresource Technology, 118 (2012) 628-632.

[6] J.L. Martinez, Supercritical fluid extraction of nutraceuticals and bioactive

compounds, CRC Press, Boca Raton, FL, 2008.

[7] M.A. McHugh, V.J. Krukonis, Supercritical fluid extraction principles and

practice, 2nd ed., Butterworth-Heinemann, Boston, 1994.

[8] Y.H. Chan, S. Yusup, A.T. Quitain, Y.H. Chai, Y. Uemura, S.K. Loh, Extraction

of palm kernel shell derived pyrolysis oil by supercritical carbon dioxide: Evaluation

and modeling of phenol solubility, Biomass and Bioenergy, 116 (2018) 106-112.

[9] Y.H. Chan, S. Yusup, A.T. Quitain, Y. Uemura, S.K. Loh, Fractionation of

pyrolysis oil via supercritical carbon dioxide extraction: optimization study using

response surface methodology (RSM), Biomass and Bioenergy, 107 (2017) 155-163.

[10] Y. Feng, D. Meier, Comparison of supercritical CO2, liquid CO2, and solvent

extraction of chemicals from a commercial slow pyrolysis liquid of beech wood,

Biomass and Bioenergy, 85 (2016) 346-354.

[11] B.P. Mudraboyina, D. Fu, P.G. Jessop, Supercritical fluid rectification of lignin

microwave-pyrolysis oil, Green Chemistry, 17 (2015) 169-172.

[12] S. Naik, V.V. Goud, P.K. Rout, A.K. Dalai, Supercritical CO2 fractionation of

bio-oil produced from wheat-hemlock biomass, Bioresource Technology, 101 (2010)

7605-7613.

http://www.btg-btl.com/en/applications/oilproperties


[13] R.N. Patel, S. Bandyopadhyay, A. Ganesh, Extraction of cardanol and phenol

from bio-oils obtained through vacuum pyrolysis of biomass using supercritical fluid

extraction, Energy, 36 (2011) 1535-1542.

[14] E. Perez, C.O. Tuck, M. Poliakoff, Valorisation of lignin by depolymerisation and

fractionation using supercritical fluids and conventional solvents, The Journal of

Supercritical Fluids, 133 (2018) 690-695.

[15] P.K. Rout, M.K. Naik, A.K. Dalai, S.N. Naik, V.V. Goud, L.M. Das, Supercritical

CO2 fractionation of bio-oil produced from mixed biomass of wheat and wood

sawdust, Energy & Fuels, 23 (2009) 6181-6188.

[16] J. Wang, H. Cui, S. Wei, S. Zhuo, L. Wang, Z. Li, W. Yi, Separation of Biomass

Pyrolysis Oil by Supercritical CO2 Extraction, Smart Grid and Renewable Energy, 01

(2010) 98-107.

Chapter 2: Literature Review 9

Chapter 2: Literature Review

This chapter is a critical review of the literature associated with SFE as a means

of extracting and fractionating target compounds from bio-oil. Like any other

chemical separation process, design of a continuous SFE process requires

fundamental phase equilibrium data (aka VLE data) of the mixture system under

investigation. Application of SFE for mixtures other than bio-oils have been

extensively reported in the literature, e.g., for palm fatty acid distillates [1, 2],

limonene and blackcurrant seed oil [3], pepper’s liquid extract [4], fish by-

products [5-7], soybean oil [8], terpene oils [9], lemon essential oil [10], plant

matrices [11, 12], rapeseed oil [13] and olive oil [14] etc. Use of modern process

simulation software (Aspen Plus®) is also reported [15-18] in modelling the

phase behaviour of fatty acid/ scCO2 systems.

There are relatively few experimental studies in the literature [19-30] describing

the SFE of compounds from bio-oil. Unlike other SFE studies (i.e. for mixtures

other than bio-oil) detailed EOS modelling associated with bio-oil (including bio-

crude from HTL derived black liquor) is, to the best of our knowledge, entirely

absent from the literature.

This chapter also describes the use in this study of Chrastil type models [31] of

binary mixtures of individual bio-oil compounds and scCO2 in order to provide:

a) a means of comparing and elucidating any discrepancies between

reported data, and

b) an understanding of factors impacting the relative solubility trends of

selected bio-oil compounds in scCO2

2.1 Title: Supercritical carbon dioxide separation of carboxylic acids and phenolics from bio-oil of lignocellulosic origin: understanding bio-oil compositions, compounds solubilities and their fractionation

Wahab Maqbool, Philip Hobson*, Kameron Dunn, William Doherty

Queensland University of Technology (QUT), 2 George St, Gardens Point 4000 Brisbane,

Australia

2.2 Abstract

Bio-oil produced from the thermochemical treatment of lignocellulosic biomass

is increasingly recognised as a potentially abundant source of renewable

10 Chapter 2: Literature Review

chemicals and fuels. Single ring phenolics and low molecular weight carboxylic

acids are significant constituent compound groups found in bio-oil and are

important end product or intermediate commodity chemicals. Fractionation of

bio-oil using supercritical fluids (usually with CO2 as a solvent) is a relatively new

process being investigated worldwide at both laboratory and pilot scales.

Solubility data associated with supercritical carbon dioxide (scCO2) and the many

chemical compounds in the complex bio-oil mixture are required to predict the

extraction behaviour of different bio-oil compounds.

This article starts with a review of the composition of bio-oil in terms of the

phenolic and low molecular weight carboxylic acid fractions which are

potentially of commercial interest. Binary solubility data of major compounds in

these bio-oil fractions with supercritical CO2 are summarized and discussed.

Results from previously reported studies in which scCO2 is used as a solvent to

recover bio-oil fractions are reviewed and collated. Density and temperature

based Chrastil type models are developed using available data for the solubility

in scCO2 of some of the major bio-oil compounds. Finally, extraction of

compounds from the complex bio-oil mixture is discussed in terms of the trends

predicted by the respective individual binary solubility models.


2.3 Introduction

Thermochemical conversion processes have the potential to provide a highly

effective means of biomass valorisation through the production of a range of high

value fuels and chemicals. Among these technologies, fast pyrolysis and

hydrothermal liquefaction have in recent years attracted significant interest due

to the relative simplicity of the associated processes, high value products and the

potential to target a range of compounds of special interest through judicious

control of process conditions [32-34]. Both technologies produce an intermediate

bio-oil product which is a complex mixture of compounds forming a micro-

emulsion in which holocellulose (cellulose + hemicellulose) decomposition

products are stabilizing the lignin macro-molecules through hydrogen bonding

[35]. Bio-oil typically has a high water and pyrolytic lignin content together with

a number of other chemical classes including acids, sugars, esters, aldehydes,

ketones, phenol and phenol derivatives [36-38]. Bio-oil in its original state has

high acidity (pH 2.0-2.5), high viscosity, is thermally unstable and largely

immiscible with conventional liquid fossil fuels. Fractionation into thermally

stable and concentrated compounds is critical if the full potential of bio-oil as a

source of fuels and chemicals is to be realised [39, 40].

Bio-oil can be fractionated by standard process separation techniques. Liquid-

liquid extraction [41] may require large solvent volume [42] and separation of

the solvent itself from the fractionated products as an additional step.

Conventional distillation methods like steam distillation [43] and fractional

distillation [44] can also be used but they are generally energy intensive

processes and can cause thermal degradation of the products [42]. In a review by

Kim et al., [45] supercritical fluid extraction (SFE) using CO2 as a solvent and a

limited number of other techniques such as switchable hydrophilicity solvents

(SHS) and molecular distillation [46] were endorsed as appropriate means of

fractionating phenolic rich bio-oils. SHS are solvents which show change in

properties (such as polarity) in response to the addition of a trigger component

(usually CO2) in the system [47]. With the use of SHS, extract yields may increase

because of the enhanced dissolving power of the solvent but the recovered

solvent is more contaminated with products [48] when compared with the use of

scCO2 alone as a solvent. Molecular distillation can require the use of excessive

temperatures (up to 130 ⁰C) [49] and has limited scope for tuning selectivity

based on vapour pressure differences of compounds. Supercritical carbon

dioxide fractionation (SCF) by contrast permits a high degree of selectivity

through control of both density and temperature where the temperatures

employed do not cause product degradation.

SCF has been the focus of a number of major collaborative research programs to

explore its potential in fractionation and stabilization of bio-oil [24, 50, 51].

Historically, supercritical extraction and fractionation techniques have been used


in the food and nutraceutical industries for the recovery of plant, animal and food

extracts [52, 53]. Supercritical solvents are favoured in these and other

applications due to their relatively high densities and diffusivities [54]. CO2 is a

commonly used supercritical solvent because of its non-toxicity, non-

flammability, low cost and abundant availability [54, 55]. In addition CO2 has

advantages over other commonly used solvents like ethanol, methanol, acetone,

ethane and propane due to its near ambient critical conditions [29]. Although

solvents such as ethane and propane have lower critical pressures than carbon

dioxide they are highly flammable [56].

Some of the advantages of using SCF for bio-oil separation are the high level of

control of solvent density (and therefore solubility) that can be achieved through

relatively small variations in temperature and pressures, [57] its suitability for

thermally labile natural substances [58] and selective extraction of low polarity

compounds (aldehydes, ketones, phenols etc.) [59]. The scCO2 extraction is not

without its disadvantages. For example: a) it is a weakly polar solvent and

therefore limited to the selective extraction of non-polar to weakly polar

compounds; b) the use of high pressures and densities in this process to enhance

total extract yields may result in poor separation of feed mixture components

and; c) although the extract yield and selectivity associated with scCO2 can be

modified with the use of a polar co-solvent, some of these solvents may be

problematic particularly for pharmaceutical and food applications.

The wide spectrum of chemical compound classes present in bio-oil provides

significant challenges for extraction using scCO2. For this reason, the number of

experimental studies reporting on SFE of actual bio-oil (rather bio-oil synthesised

from model compounds) are relatively few. One of the main challenges in the

extraction of compounds from such complex systems is the non-availability of

appropriate vapour-liquid equilibrium (VLE) data for the design of multistep

fractionation processes. In addition to reviewing available data, this work will lay

down some simple procedures to estimate the extraction behaviour of bio-oil

compounds using simple binary VLE data.

Bio-oil is a complex mixture of compounds. The presence of water in bio-oil

requires special attention and has been challenging in the past for extensive

experimental studies aimed at designing effective fractionation processes for

aqueous mixtures. For future more detailed design purposes, complete phase

equilibrium data including distribution coefficients of components between the

scCO2 and aqueous phases need to be determined. This study explores the use of

a simple methodology in which binary solubility data alone is used to understand

fractionation of the solutes-rich scCO2 phase typical of that produced by a

relatively high temperature and high pressure scCO2 counter flow water

stripping column. Components extracted by the scCO2 water stripping stage will

have minimal water content and therefore the assumption of negligible solute-

solute interactions (in relation to water) may be invoked.


In this paper the most prevalent low molecular weight carboxylic acids and single

ring phenol (monophenol) components typically found in bio-oil will be

identified and the availability and accuracy of the corresponding binary VLE data

summarized. Binary solubility data of these major compounds will be discussed

and modelled to compare their solubility trends. Experimental bio-oil extraction

studies from the literature will be critically reviewed and the reported extraction

behaviour will be discussed in the light of binary VLE data.

2.4 Composition of bio-oil from thermochemical conversion of biomass

Composition data for thermochemical conversion of biomass is abundantly

available in the literature for a wide range of operational conditions, process and

rector designs. Experimental studies have used a range of temperature and

pressure conditions for collection and condensation of and subsequent analysis

of hydrothermal liquefaction and pyrolysis products. The role played by water

both as a reactant and product as well as the way in which bio-oil water content

is reported provides further complications in interpreting reported data. Water

is produced in large quantities during pyrolysis and is considered a part of

pyrolysis oil; in hydrothermal liquefaction water (or a hydrocarbon or a

combination of both) is added to the biomass as a reactant [60-62].

In a study by Doassans-Carrere et al. [36] fast pyrolysis and direct liquefaction of

identical biomass feedstocks (beech sawdust) is compared in terms of bio-oil

compositions. Here, differences in chemical compositions of pyrolysis and

liquefaction oils may be explained by different fraction collection and fraction

designation procedures. In this study, [36] the removal of water from the

liquefaction oil also caused removal of acetic acid, phenol and two other

unidentified compounds. Differences in chemical compositions of the pyrolysis

and liquefaction oil samples may also be explained through the prevalence of

hydrolysis reactions in the liquefaction process in which opening of the

levoglucosan ring structure occurs resulting in the production of sugars. By

contrast levoglucosan was reported as a significant component of pyrolysis oil.

Acetic acid, acetone, furans, phenols, oxalic acid and levoglucosane were largely

present in pyrolysis oil while liquefaction oil contained ketones, phenols

(guaiacol, syringol), furans, levulinic acid and etheric compounds.

Castellvi Barnes et al. [63] also compared the pyrolysis and liquefaction of pine

wood feedstock. Liquefaction studies were carried out with 10 wt % pine wood

using a reaction time and temperature of 30 min and 300 ⁰C respectively in three

solvents: guaiacol (GL), a guaiacol-water (GWL) mixture and water (WL)

separately. Pyrolysis oil was obtained by treating the pine wood at 500 oC for a

reaction time of 20-25 min for solid particles and below 2 seconds for the oil. Gel

permeation chromatography (GPC) was used to isolate solvents and different

fractions based on apparent molecular weight. The apparent molecular weight


distribution through GPC showed a significantly greater proportion of heavy

molecules in liquefaction compared to pyrolysis oils. In terms of deoxygenation,

between 35-45% oxygen is lost in liquefaction while 20% is removed in pyrolysis

compared to the original oxygen contents in the wood. In both types of bio-oil,

carbohydrates and lignin are believed to be contributing to the production of

aromatic and aliphatic compounds the relative proportions of which are

dependent on the type of process and in the case of liquefaction oils, the nature

of the solvent. Generally, in liquefaction, the yield of aromatics (typically 40% to

60%) was greater than the lignin content of untreated wood (25%) which

suggests that carbohydrates are converted to aromatics in bio-oil along with

lignin. The aromatic contents of pyrolysis oil were consistent with those present

in the original untreated wood. Furans, phenols, acetic acid and other aromatic

and aliphatic compounds were usually present in both types of bio-oils. For the

three liquefaction solvents and pyrolysis trials the extent of deoxygenation

appeared to occur in the order of: WL > GWL > GL > pyrolysis. A qualitative

parameter of reaction severity (extent of decrease in residual carbohydrates and

oxygen content both in oil and solid residues) was defined to compare the

liquefaction bio-oils and it was proposed in the order of: WL > GWL > GL. This

indicates that reaction severity in effect increases with increasing water

concentration as it will cause a decrease in both residual carbohydrates and

oxygen content.

Ponomarev et al. [64] reviewed thermochemical methods for biomass conversion

including hydrothermal liquefaction, liquefaction in organic solvents and

pyrolysis. Use of different liquefaction solvents such as low molecular weight

acids, phenols, alcohols or different combinations of these compounds with or

without water were reported as the cause of large differences in bio-oil

composition. Bio-oil composition from fast pyrolysis is strongly dependent upon

operating temperature and residence time and as with liquefaction the resulting

bio-oils contained many chemical classes such as acids, phenols, alcohols and

other lignin and carbohydrate degradation products.

In summary, separation techniques using polarity or any other property related

to intermolecular interactions can be used for fractionation of both pyrolysis and

liquefaction oils owing to significant similarities in their compositions.

2.4.1 Monophenols and low molecular weight acid contents of bio-oil

Phenolics form the largest group of chemical compounds within bio-oil (up to 50 wt%)

[65] and are present in the form of monomeric units (monophenols) and oligomers

(pyrolytic lignin, weight up to ~ 5000 amu) [66]. Monophenols and low molecular

weight carboxylic acids are always present in lignocellulosic derived bio-oils.


Table 2.5S (supporting information) summarizes the phenolic and acid contents of

bio-oils from pyrolysis and liquefaction of different biomass feedstocks. Major

chemical compounds of both bio-oil fractions are listed in supporting information

Table 2.6S and Table 2.7S on wt% dry biomass basis.

Monophenols are of special interest to the chemical industry as intermediates for a

wide range of products such as paints, resins and adhesives.

Table 2.5S (supporting information) shows a collation of bio-oil composition data

reported as wt% of dry biomass (where the appropriate mass balance has been

reported) and area% of the spectra produced by gas chromatography–mass

spectrometry (GC-MS) analysis of the bio-oil to determine monophenols and

carboxylic acid contents. Where data is reported on an area% basis amounts are seen

to vary over a wide range; when reported on dry biomass basis monophenols and acids

yields are in the range of 6-10 wt% each. The yield values calculated in our work using

composition data reported in the literature match those quoted more generally (i.e.

without reference to specific biomass sources) in the literature where yields of both

acids and monophenols are in the range of 5-10 wt% each on dry biomass basis [67,

68].

It is evident from Table 2.6S (supporting information) that acetic acid is the most

abundant of all the low molecular weight acids. Acetic acid derives from the

cellulose component of biomass via the production and subsequent

decomposition of 2-Furancarboxaldehyde and 5-methyl-2-Furancaboxaldehyde

[61]. Acetic and formic acids may also originate from the rupture of lignin

aliphatic chains [69].

Phenolics are formed from the lignin in biomass and it is believed that

degradation of lignin produces mainly 2-methoxyphenol (guaiacol) [61] and

syringol [70] depending upon the nature of the wood (softwood or hardwood)

feedstock. Further decomposition of guaiacols at higher temperatures (> 350 oC)

produces mainly phenol, catechols, cresols and vanillin [61, 71]. Besides low

molecular weight carboxylic acids, significantly greater amounts of longer chain

acids (fatty acids) and cyclic acids (containing benzene rings) may also be present

in bio-oils. A study by Gao et al. [72] indicated the presence of much greater

amounts of n-hexadecanoic acid (7.21 area %) compared to acetic acid (1.61 area

%) in liquefaction oil of rice straw. Zhu et al. [73] reported 10.38 % and 30.82 %

bio-oil chromatograph areas associated with 3-hydroxybenzoic acid (Salicylic

acid) and total acids respectively.

Acetic acid and the major monophenols identified above are of significant

importance to industry. Global acetic acid production capacity exceeds 12 x 106

t/ annum and is used mainly in manufacture of vinyl acetate and acetic anhydride

[74]. Acetate esters are produced by reactions of acetic acid with olefins or


alcohols [75]. Methanol carbonylation is currently the preferred route for the

industrial scale production of acetic acid [76].

All above mentioned monophenols are important building blocks and

intermediates for chemical, food, pesticide and pharmaceutical industries [77,

78]. Syringol and guaiacol are naturally occurring phenols used mainly by the

food industry for smoke flavouring [79]. Syringols are produced from hardwood

that contains a higher amount of methoxy-substituted lignin while softwoods

contain a lignin unit called guaiacylpropane which yield guaiacols upon

thermochemical conversion to bio-oil [79]. Guaiacols, at higher temperatures, are

eventually converted to catechols and alkyl phenols [80]. Vanillin is a phenolic

aldehyde and used primarily in the food, beverage and pharmaceutical industries.

2.5 Binary system solubility data of bio-oil compounds

Earlier sections of this article have identified acetic acid, syringol, phenol, cresol,

guaiacol and catechol as major chemical compounds present in bio-oil derived

from lignocellulosic biomass. Binary system solubility studies of these bio-oil

compounds with scCO2 will be summarized in this section. Binary solubility data

of different bio-oil compounds with scCO2 is not only an indication of comparative

solubilities and trends but also an indispensable source of data for more rigorous

and accurate predictive thermodynamic modelling of multicomponent mixtures.

Experimental VLE data of binary systems can be used to derive solute-solvent

interaction parameters for use in an equation of state (EOS) along with an

appropriate mixing rule to account for degrees of non-idealness in a

multicomponent mixture. In a study by Gironi et al. [7] to investigate supercritical

carbon dioxide extraction of fish oil ethyl esters, experimental data was modelled

to Peng-Robinson (PR) EOS with van der Waals mixing rules and the interaction

parameters were determined from binary data. Similarly, in a lemon essential oil

deterpenation study [10], PR-EOS was used for modelling the extraction process

together with van der Waals mixing rules. In this study [10], binary data was used

to calculate three solute-solvent and one solute-solute pair of interaction

parameters. In a fish oil extract study [7] only solute-solvent binary interaction

parameters were used. Both fish oil and lemon essential oil are multicomponent

mixtures and were modelled with a reasonable approximation. Comparison of

model predictions and experimental data gave percentage average absolute

relative deviation values of 1.63 for fish oil [7] and 11.67 for lemon oil [10]

system.

Solubility is typically defined as mole fraction or weight fraction of a solute in

supercritical fluid. Different configurations of experimental techniques are in use

for phase equilibria study and solubility data determination. These techniques

together with critical reviews on their applications and limitations are

extensively reported in the literature [56, 81, 82]. Generally, these experimental

techniques are categorized as either static or dynamic (flow) techniques.


In the static technique, the solute is immersed in the supercritical fluid for an

extended period of time in order to reach equilibrium. This technique has

variations which can be described as analytical, synthetic or gravimetric

depending upon the method adopted for solubility measurement. In the

analytical method a constant volume cell is used from which small samples can

be withdrawn for compositional analysis after equilibration is reached. In the

synthetic method, a variable volume view cell is used to adjust cell volume and

pressure. Known amounts of solute and solvent are brought together in the

equilibrium cell and conditions such as pressure and temperature are varied until

the cloud point (beginning of precipitation) is observed. There is no need for

sampling; solubility is calculated [81] from Eq. (2.1),

𝑦2 =𝑛2

𝑛1+𝑛2 (2.1)

Where y2 is mole fraction solubility and n1 and n2 are moles of carbon dioxide and

solute loaded in the equilibrium cell. Gravimetric methods are least adopted due

to low experimental accuracy. In this method [81], solute is placed in a vial and

the vial is then placed in a pressure vessel. Perforations between vial and

pressure vessel allows the solute to saturate the supercritical fluid present in

both the vessel and vial. At the end of the experiment the system is depressurized

and the remaining solute in the vial is gravimetrically measured to calculate

solubility through solute mass difference and system volume.

In dynamic or flow methods [81], supercritical fluid is continuously flowing

through the solute packed equilibrium cell and is being analysed at the cell exit

by chromatographic, spectroscopic, gravimetric or other techniques. Solubility is

calculated as (Eq. (2.2)):

𝑦2 =𝑛2

𝑛1+𝑛2=

𝑛2

𝑄1𝜌1𝑡+𝑛2 (2.2)

Where n1 and n2 are moles of carbon dioxide and solute respectively collected

over time t; Q1 and ρ1 are the volumetric flow rate and molar density of CO2

respectively at the same conditions.

Common limitations of dynamic methods are the possibility of mass transfer

restrictions between solute and solvent due to short residence time or larger

solvent flow rates, solute clogging of the restrictor and large compositional

variations when a multicomponent system is being investigated. Using the static

method, a number of solubility data points may be collected at different

conditions with a single small loading of solute. However, in the static-synthetic

method, care must be taken in order to get accurate and consistent data by visual

observations.

Table 2.1 summarizes experimental methods and conditions used in different

studies reported in the literature for binary system solubility measurement of

different monophenols and acids. Corresponding mole fraction solubility data of


these studies can be found in supporting information (Table 2.8S). Temperature,

pressure and mole fraction solubility uncertainties associated with the studies

reported in Table 2.1 were ranging from ±0.1 K to ±1 K, ±0.1 bar to ±2 bar and

2% to 5% respectively. Only Skerget et al. [83] study showed uncertainty in mole

fraction solubility varying over a wide range of ±0.4 % to ±16.4 %.

Table 2.1 Experimental techniques and conditions used in literature studies for

measurement of solute solubilities in supercritical carbon dioxide

Solute Experimental

technique

Temperature

(K)

Pressure

(bar)

Equilibration

time (min)

Sample

volume

(mL)

Ref.

Phenol,

Catechol

Static-

recirculating 333.15 - 363.15 100 - 350 30 0.10 [84]

Phenol Flow 309.15 - 333.15 79.3 – 249.4 - - [85]

Catechol Static 308.15 - 338.15 122 - 405 45 0.122 [86]

Guaiacol,

P-cresol Flow 323.15 – 423.15 20 - 200 - - [87]

O-cresol,

P-cresol Static 323.15 – 473.15 99 - 348 840 - [88]

Eugenol Flow 313.15 - 333.15 60 - 160 - - [89]

Vanillin Static 313.2 - 353.2 80 - 276.5 60 0.20 [83]

Vanillin Flow 308.15 - 318.15 83 - 195 - - [90]

Eugenol Flow 308.15 - 328.15 14.8 – 125.1 - - [91]

Acetic

acid Flow 313.2 - 353.2 11 - 111 - - [92]

2,5-DMP Flow 308.15 74 - 267 - - [93]

3,4-DMP Flow 308.15 82 - 262 - - [94]

2,5 and

2,3-DMP Flow 308.0 101 - 280 - 0.120 [95]

2.6 Supercritical CO2 extraction and fractionation of bio-oil

Experimental studies reporting on the use of supercritical CO2 for the extraction

and fractionation of bio-oil components will be summarized in this section.

Experimental schemes, process conditions and yields and recoveries of low

molecular weight acids and monophenols will be critically analysed.

Elements of a typical experimental process scheme for supercritical extraction

[96-98] are shown in Figure 2-1. Such an experimental setup, besides extraction,

can also be used for the dynamic measurement of solubility.


Figure 2-1 General experimental setup of supercritical CO2 extraction system

with optional co-solvent addition. T: temperature measurement and control,

BPR: back pressure regulator, MV: micrometering valve.

Referring to Figure 2-1, CO2 is supplied from a reservoir in pressurized liquid

form. A high pressure pump and pre-heater deliver CO2 to the extraction vessel

at the target temperature and pressure. Where required a co-solvent may be

added prior to the pre-heater to fine tune selectivity for particular compounds

and fractions. The extraction vessel is a stainless steel container which has either

been loaded with the sample from which compounds are to be extracted (semi-

continuous operation) or the liquid sample is fed continuously from the middle

or top of the vessel (counter-current operation). A heating bath / oven or any

other temperature element controls and maintains the extraction vessel

temperature. Solvents and solutes leave the extraction vessel through a restrictor

or a back pressure regulating valve to maintain the required pressure in the

extraction vessel. Extraction times of between 20 and 90 minutes are typical and

vary depending on the nature of the system of solvents and solutes and the extent

of extraction required. Solute (extract) precipitates out of the gaseous CO2/ co-

solvent mixture after depressurisation across the restrictor; the solute product is

then recovered from collection vessel. Solute-free gaseous CO2 is either vented or

recycled back to the CO2 reservoir for reuse. Left-over mixture is collected as

raffinate from the bottom of the extraction vessel.

In a variation on the system shown in Figure 2-1 Mudraboyina et al. [25] included

a rectification column to separate single ring phenolics from bio-oil derived from

the microwave pyrolysis of softwood Kraft lignin. Extraction was performed at

35 oC and 80 bar pressure on a 4 g bio-oil sample using a CO2 flow rate of 10

mL/min. Extraction was reported for varying amounts of total CO2 used by

varying the extraction time. The extract was found to be selectively enriched with


all major single ring phenols except catechol. Catechol has a low partial vapour

pressure and therefore low solubility relative to many other single ring phenols

at any given temperature and pressure. Single ring phenols which were

concentrated in the extract included creosol, guaiacol, cresol, phenol and its

derivatives. Figure 2-2 shows the variation with solvent use in extract yield and

concentration where the solvent use is presented here as the ratio (S/B) of the

total mass of CO2 to initial mass of pyrolysis bio-oil. A maximum mass transfer

rate at a CO2 usage of 49.4 g/g is indicated by the maximum concentration of

single ring phenols in the extract at this point.

Figure 2-2 Extract yields and concentration of single ring phenols in extract from

supercritical fluid rectification of softwood Kraft lignin microwave-pyrolysis oil

for varying solvent to bio-oil ratio. (inherent and experimental random errors

were not reported in the original source) [25]

Patel et al. [27] studied scCO2 extraction of cardanol (a type of phenolic lipid) and

phenols from pyrolysis oils derived from cashew nut shells and sugarcane

bagasse respectively. Pyrolysis oils were mixed with sawdust (1:1 by weight) to

provide surface support in the extractor. Table 2.2 compares extraction results of

both oils at different operating pressures and temperatures.


Table 2.2 Effect of pressure and temperature on extract yield and product

concentration in extract during supercritical CO2 extraction of sugarcane bagasse

and cashew nut shell pyrolysis oils. (inherent and experimental random errors

were not reported in the original source) [27]

Results Sugarcane bagasse pyrolysis oil

(extraction of phenol)

Cashew nut shells pyrolysis oil

(extraction of cardanol) – at 333

(K)

Pressure (bar) 120 300 200 250 300

Yield % 9 (at 333 K) 15 (at 333 K) 43 54 63

Concentration % 36.85 (at 300 K) 71.22

(at 300 K) 50.89 64.90 85.50

Conditions associated with high concentration phenol extract (300 bar, 300 K)

resulted in relatively higher concentrations of cresols (19.72 %) and 4-

ethylphenol (26.79 %) in bagasse pyrolysis oil extract. Similarly, the conditions

associated with high cardanol extract concentration (300 bar, 333 K) also caused

4.89 % 2-ethylphenol in cashew nut shells pyrolysis oil extract.

Wang et al. [30] studied scCO2 extraction of pyrolysis oil obtained from

pulverized corn stalk. Adsorbents in the form of silica gel crystals or a 5Å

molecular sieve were used as surface support for bio-oil in order to investigate

the effect of intermolecular forces between adsorbent and bio-oil. Equilibration

time for extraction was up to 3 hours and 1 hour respectively for the trials with

and without the use of adsorbents. Extractions were carried out at 45 ⁰C and 260

bar pressure. A 12 g bio-oil sample was spiked on 30 g adsorbents; 50 normal

litre of CO2 was used for 12 g of bio-oil extraction. Figure 2-3 shows the effect of

adsorbents on enrichment (ER) associated with the extraction of phenols and

acids; in all trials the extract yield was 20 %. Uncertainties in both temperature

and pressure measurements were ±1 K and ±1 bar respectively.


Figure 2-3 Effect of adsorbent on typical selective enrichment of phenols and

acids in scCO2 extraction of corn stalk pyrolysis oil (original pyrolysis oil

contained 10.74 % phenols and 28.15% acids). (inherent errors related to extract

yields and compositions and experimental random errors were not reported in

the original source) [30]

Inspection of Figure 2-3 indicates that the use of scCO2 results in a high level of

selective enrichment of phenols relative to acids for all the adsorbent options

used. This result is primarily because of the high polarity and resulting hydrogen

bonding of acids with water and other polar molecules. Use of support materials

(adsorbents) provided a mixed response in which silica gel was slightly more

effective than the 5A molecular sieve in the selective enrichment of phenols.

Rout et al. [29] and Naik et al. [26] studied scCO2 extraction of pyrolysis oils

produced from wheat-wood sawdust and wheat-hemlock respectively. In both

studies, bio-oil samples were mixed with 2 mm glass beads before being placed

in the extractor. Rout et al. [29] studied fractionation at 45 ⁰C with a CO2 flow rate

of 30 g/min. Three fractions were collected at a pressure of 250 bar with an

extraction interval of 2 hours each and a fourth fraction was collected at 300 bar.

Extract yields and compositions were determined with uncertainties of ± 0.4 %

and ± 2.8 % respectively. Naik et al. [26] studied fractionation at 40 oC with CO2

flow rate of 40 g/min. Fractions were collected at 100, 250 and 300 bar with an

extraction interval of 2 hours for each collection. Uncertainties of ± 0.8 % and ± 1

% were associated with extract yields and compositions respectively. In both

studies the extraction of carboxylic acids and benzenoids (containing phenolics)

were reported. There is sufficient similarity in the conditions associated with

these two studies to draw some comparisons in terms of the impact of pressures

and temperatures on the preferential extraction of benzenoids using scCO2.

Figure 2-4 shows the ratio of total benzenoids extracted to total acids extracted

as a function of scCO2 solvent use.


Figure 2-4 Ratio of total benzenoids extracted to total acids extracted as a

function of different solvent/bio-oil ratios used in scCO2 extraction of wheat-

wood sawdust [29] and wheat-hemlock [26] pyrolysis oils

Inspection of Figure 2-4 indicates that for the wheat-hemlock pyrolysis oil the

regime of progressively increasing extraction pressure resulted in an enrichment

of benzenoids in the scCO2 solvent stream [26]. By contrast there was little

improvement in enrichment of benzenoids in the scCO2 solvent stream beyond a

S/B ratio of 100 for the near-constant pressure regime implemented in the

wheat-wood pyrolysis oil extractions [29]. Other underlying factors which may

be influencing the differences in enrichment reported in these two studies are

initial concentration of the target solutes in the bio-oil samples (which were not

reported in either study) and extraction temperature.

Effect of initial bio-oil water content on extract yield and composition was shown

in a supercritical extraction study on beech wood pyrolysis oil [23]. The effects of

operating pressure and initial bio-oil water content were investigated in

extraction experiments carried out on slow pyrolysis oil (SP), fast pyrolysis oil

upper/aqueous phase (FPU), fast pyrolysis oil lower/hydrocarbon phase (FPL)

with water contents of 1.13, 43.44 and 18.98 wt % respectively. The experimental

setup was similar to previous studies reported in this section. In each case 80 g

of the bio-oil sample was adsorbed on silica gel and extraction carried out using

a constant flow of scCO2 to achieve a S/B ratio of 45. Experiments were performed

at 333.15 K temperature and pressures of 150, 200 and 250 bar. The resulting

extract yields and compositions for trials carried out at 150 bar are summarized

in Table 2.3.


Table 2.3 Yields and acid-phenol contents of extracts obtained at 333.15 K

temperature and 150 bar pressure during scCO2 extraction of beech wood

pyrolysis oil (inherent and experimental random errors were not reported in the

original source) [23]

Parameter SP FPU FPL

Initial bio-oil water content (wt%) 1.13 43.44 18.98

Extract yield (wt%) 40.5 7.4 8.0

Extract weight (g) 32.4 5.9 6.4

Extract

composition

Acids (wt%) 9.5 23.8 15.2

Phenols (wt%) 15.9 9.1 13

Acids (wt% of initial bio-oil acids) 58.3 10.2 13.6

Phenols (wt% of initial bio-oil

phenols)

49.2 25.7 27.9

SP: slow pyrolysis oil, FPU: fast pyrolysis oil upper phase, FPL: fast pyrolysis oil

lower phase

Although similarly detailed data for the extractions carried out at 200 and 250

bar were not reported, it was noted [23] that the resulting variation in extraction

yield between oil samples was large at all extraction pressures. This result was

attributed to the large variation in water contents of the three oil samples.

In summary, the experimental studies reviewed in this section on the

supercritical fractionation of bio-oil indicate a number of fundamental process

trends:

i) Higher S/B ratios favour higher fractional yields at the expense of a

slight decline in wt% concentration of desired products in extract [25].

ii) Increase in solvent density (a function of temperature and pressure)

gives higher extract yield as is obvious from Patel et al. [27] although

this enhanced solvation power may change the selectivity of products

in the extract [99].

iii) Supercritical extractions with pure CO2 solvent show a tendency

towards solvating non-polar and slightly polar compounds like

phenols, aldehydes and ketones while strong polar compounds like

acids, sugars and alcohols will tend to remain in the raffinate.

Compounds which initially remain in the raffinate may be extracted

with scCO2 when dilution of the preferentially solvated compounds in

the raffinate has occurred [23].

iv) Small molecular weight compounds are more likely to be extracted

than higher molecular weight compounds.

v) Bio-oil water content has a significant effect on extraction and

selectivity of strong polar compounds primarily due to the formation

of strong intermolecular forces such as hydrogen bonding. In


supercritical extraction of chemically synthesized bio-oil [30] polarity

was shown to play a dominant role; propanoic acid showed a higher

tendency to be extracted over acetic acid in spite of higher partial

vapour pressure of acetic acid.

2.7 Discussion

2.7.1 Solubility data

The extent of solute solubility in supercritical fluids is mainly dependent on the

vapour pressure of the solute and the solute-solvent intermolecular interactions

[100]. It has been previously documented in the literature that solute vapour

pressure (volatility) or melting point may be directly linked to its solubility [101,

102]. As a general rule of thumb, the higher the melting point of a solute, the lower

its solubility. However, this contribution of solute vapour pressure to solubility

should not be over-simplified; Lou et al. [103] showed that an increase in

measured solubility can be a result of increased vapours transport from mixture

to vapour phase due to increasing vapour pressure rather than solute-solvent

interactions. However, in supercritical fluids this phenomenon of increased

solubility due to higher vapour pressures is widely accepted as a part of overall

increase in solubility. Figure 2-5 compares solubilities of some bio-oil compounds

(see supporting information, Table 2.8S) from the monophenol group at identical

temperature and pressure conditions.

Figure 2-5 Effect of increasing pressure on solubilities of different bio-oil

compounds in supercritical carbon dioxide at 333 K temperature. Random or

ultimate error were not reported for eugenol in the original source [89]. For

vanillin the maximum reported uncertainty of + 16.4% is shown [83].


It is evident from Figure 2-5 that at 333 K temperature, catechol which has a

melting point of 105 oC [104] will show much lower solubility in scCO2 than

phenol with a melting point of 40.9 oC [104]. In other words, at 333 K

temperature, phenol will have much higher vapour pressure than catechol. In

Figure 2-5, melting points of compounds are in the order of: catechol (105 oC) >

vanillin (81.5 oC) > phenol (40.9 oC) > eugenol (-9.1 oC), so their solubility in

supercritical CO2 at 333 K temperature will show the trend: catechol < vanillin <

phenol < eugenol. It can be seen (Figure 2-5) that isothermal increase in pressure

causes increase in solubility of solutes in scCO2. It is a well described fact in

literature that isothermal increase in pressure increases solvent density which

results in higher solvation powers [105-107]. Increase in solvent density will not

only show enhanced solubility effect but also an opportunity to increase the

solubility differences of compounds.

Another important phenomenon frequently encountered in supercritical

solubility studies is the occurrence of retrograde (crossover) region [108-110].

This is a pressure region where solubility isotherms meet and divide solubility

data in to two sections. In the low pressure section (before the crossover region),

solubility decreases with increasing temperature due to the corresponding

reduction in density of the supercritical fluid solvent. In the higher pressure

regions (after the crossover region), solvent density is only slightly affected by

temperature so in this region solute solubility increases with increasing

temperature as the effect of the corresponding increase in solute vapour pressure

starts to dominate. Figure 2-6 illustrates this crossover region for phenol and

vanillin solubility data (see supporting information, Table 2.8S).

Figure 2-6 Solubility isotherms showing crossover pressure regions for vanillin-

CO2 (left) and phenol-CO2 (right) binary systems. The maximum reported

uncertainty for vanillin [83] of ±16.4% is shown.

Solubility isotherms of phenol intersect around 280 bar while for the vanillin-CO2

binary system the crossover region occurs near 160 bar. Crossover region is an

important phenomenon in design of a separation process for concentration or

dilution of a particular compound as it allows increased or decreased solubility

of a compound by variation in temperature [110].


For supercritical solubility data generation, it is of utmost importance to correctly

calculate fluid densities with a reliable EOS. A number of thermodynamic

equations have been proposed for the calculation of solvent densities at different

temperature and pressure conditions. Span and Wagner [111] proposed an EOS

in the form of Helmholtz energy which can be used up to temperatures of 1100 K

and at pressures up to 8000 bar. Equation by Span and Wagner [111] is

considered very accurate and reliable and is also featured in REFPROP database

by NIST [112] and the ThermoFluids [113] computer program. Tabulated CO2

properties calculated with Span and Wagner [111] equation of state are also

available in literature as a published book [114].

An example of the extent of variation between predictions using two different

EOSs is shown in Figure 2-7 which gives CO2 density variation with pressure at

40 oC as calculated by the PR-EOS [115] and the Span and Wagner EOS [111].

Inspection of Figure 2-7 indicates significant variation in predicted CO2 density

values at around 110 bar.

Figure 2-7 CO2 densities calculated at 40 oC with PR-EOS [115] and Span and

Wagner EOS [111]

Figure 2-8 gives a complete illustration of solubility isotherms of different

monophenols and acids (see supporting information, Table 2.8S) against CO2

densities. Density rather than pressure of CO2 is mostly used to plot solubility

data of different compounds as the solvation power (density) of the solvent is

directly linked with pressure as well as temperature. Inspection of Figure 2-8

indicates that there is a general trend of increase in compound solubility with

increase in solvent density. It also shows that solubilities of catechol, vanillin and

dimethylphenol (DMP) isomers are far less than other compounds in the figure.


Figure 2-8 Solubility data (see supporting information, Table 2.8S) plots of

different monophenols and acetic acid. CO2 density is calculated here using the

Span and Wagner [111] method. The maximum reported uncertainty for vanillin

[83] of 16.4% is shown. Random or ultimate error were not reported in the

original source for eugenol [89].


However, there is potential opportunity for fractionation of these compounds by

exploiting a) crossover phenomena and b) different partial vapour pressures of

compounds at different temperatures. This exploitation is possible by changes in

temperature and pressure whereby same density values at different

temperatures can be achieved as is shown in Figure 2-9 or different temperature-

density combinations may also be produced as per process design requirement.

Figure 2-9 CO2 density variation as a function of temperature and pressure, Left:

3-D surface plot of temperature-pressure and CO2 density, Right: 2-D plane plot

of CO2 density curves against pressure axis at different temperatures

Another important system parameter affecting extraction yields and product

recoveries is pH [116]. In supercritical extraction with CO2, there would be a

combined contribution to solution pH from both the crude bio-oil and the induced

acidity of CO2 (by the formation of carbonic acid in solution). The acidic

compounds will be preferentially extracted relative to basic compounds due to a

shift of the ionization equilibrium towards the formation of their associated non-

ionic form. Combs et al. [116] studied the effect of pH on supercritical extractions

of phenol and 2,4,6-trichlorophenol (TCP) from aqueous matrices. Buffered and

non-buffered assays were subjected to supercritical extractions with initial

sample pHs of 3.0, 5.0 and 8.0. Extractions were studied at a single temperature

and two different pressures of 150 atm and 300 atm. It was found that, for non-

buffered solutions, extractions with supercritical CO2 lowered the final pH of the

system to a minimum of 3.0 and maximum of 4.2 for an initial pH of 3.0 and 8.0

respectively. At the lower extraction pressure (150 atm) this lowering of final pH

caused an increase in the percent recovery associated with both of the individual

solute (phenol and TCP) components; at 300 atm pressure the reverse effect on

percent recovery was observed. For the buffered solution, the minimum final pH

was 3.0 corresponding to an initial pH of 3.0 prior to extraction; the maximum

final pH was 5.8 corresponding to an initial pH of 8.0. In the case of the buffered

solution an increase in the percent recovery associated with the individual solute

components was observed for a decrease of final pH; this was found to occur at

both 150 atm and 300 atm extraction pressures. Phenol and TCP are both acidic


compounds with pKa values of 9.9 and 6.0 respectively. In solution, ionization

occurs, which for the case of phenol is according to,

𝑃ℎ𝑒𝑛𝑜𝑙 ↔ 𝐻+ + 𝑝ℎ𝑒𝑛𝑜𝑙𝑎𝑡𝑒− (2.3)

Lowering of the solution pH by the introduction of CO2 and subsequent formation

of carbonic acid will cause the equilibrium of this ionization process to shift to

the left in Eq. (2.3) resulting in increased formation of the more readily extracted

neutral form of the compound. As TCP is more acidic than phenol a greater change

in its ionization equilibrium shift and therefore a greater improvement in TCP

percent recovery relative to phenol percent recovery was observed [116] upon

lowering of solution pH (Figure 2-10).

Figure 2-10 Effect of CO2 induced acidity (in terms of final solution pH of 3, 3.4 &

4.2 corresponding to initial pH of 3, 5 and 8 respectively) on percent recoveries

of phenol and 2,4,6-trichlorophenol solutes from aqueous matrices at 150 atm

pressure during supercritical extraction with pure CO2. Inherent and

experimental random errors were not reported in the original source [116].

In a multi-component system, solubility of a solute in scCO2 is largely affected by

parameters such as temperature, solvent density (dependent upon temperature

and pressure) and solute-solvent properties (pH, solute-solute intermolecular

forces and solute-solvent intermolecular forces). In a simple binary system,

intermolecular forces are homologous due to the presence of only single solute

type molecules and this makes predictive modelling of such system relatively

reliable and accurate. However, in multi-component systems, such predictive

modelling is a challenging task due to presence of different unaccounted solute-

solute and solute-solvent intermolecular forces.

Although, some binary data (CO2 + bio-oil compound, see supporting information,

Table 2.8S) and ternary data (CO2 + two bio-oil compounds) are available in the

literature and have been successfully modelled with simple empirical or rigorous


thermodynamic models, complete predictive modelling of multi-component

phase behaviour of bio-oil in supercritical CO2 is not yet realized. The reasons

behind the scarcity of complete predictive modelling of multi-component

systems like bio-oils are:

• the complex nature of the system due to the presence of multi-

components;

• varying compositions (whereby composition is changing over time in a

continuous fractionation process) and;

• non-realization of complete intermolecular interactions due to limitations

imposed by non-homologous feedstock availability and tedious research

work requirements

In the absence of multi-component data, the determination of solubility data for

binary systems remains of fundamental importance. Such data provides guidance

on the degree or extent of separation possible between two or more components

at different temperatures and pressures. Binary solubility data also allows us to

visualize solubility trends of pure components in supercritical CO2 e.g.;

identification of retrograde and non-retrograde regions.

2.7.2 Modelling Binary solubility data

To quantitatively compare and use the binary system solubility data (see

supporting information, Table 2.8S) modelling was performed on it with a density

and temperature based Chrastil model [31]. It is a semi-empirical model and is

mathematically described in Eq. (2.4) and Eq. (2.5):

𝑆 = 𝜌𝑘exp (𝑎

𝑇+ 𝑏) (2.4)

Eq. (4) can be represented in logarithmic form as:

𝑙𝑛𝑆 = 𝑘𝑙𝑛𝜌 + (𝑎

𝑇+ 𝑏) (2.5)

Where S (g/L) is the solute solubility, ρ (g/L) is solvent density, T is temperature

in Kelvin and k, a and b are empirically determined constants. Constant k is an

association number representing average number of CO2 molecules in formed

solvato complex. The constant a depends upon heats of solvation and

vaporization of solute and constant b depends upon molecular weights of solute

and CO2 and as well as on the value of k. It is important to note here that typical

phase equilibrium data in literature do not report density of co-existing phases.

That is why an assumption is made here, to convert mole fraction solubility data

into g/L units, stating that the density of gas phase (CO2 + solute) is

approximately equal to the density of pure CO2 at same temperature and pressure

conditions. Eq. (2.5) was correlated with the experimental data (see supporting

information, Table 2.8S) giving a set of co-efficient values for each compound

summarized in Table 2.4. Where data for individual compounds were available


from multiple sources, this data was combined to produce the parameters in

Table 2.4. Goodness of fit (Table 2.4) is determined in terms of adjusted co-

efficient of determination (R2), sum of squares due to error (SSE) and root mean

square error (RMSE).

Table 2.4 Chrastil correlation parameters for the solubility of several bio-oil

compounds in supercritical CO2

Compound Parameter Goodness of fit

Obs.1 k a b R2 SSE RMSE

Phenol 3.999 -3241 -12.81 0.9489 4.418 0.2915 55

Catechol 3.644 -3525 -12.33 0.9685 2.033 0.1958 56

P-cresol 3.201 -3240 -7.627 0.9254 4.577 0.4561 25

O-cresol 3.075 -2526 -7.862 0.7893 2.571 0.606 10

Guaiacol 3.916 -3251 -11.35 0.9137 2.717 0.5495 12

Eugenol 4.187 -635.5 -20.85 0.9430 1.937 0.3860 16

Vanillin 3.916 -4863 -8.747 0.9715 2.20 0.2164 50

2,5-DMP 3.373 -18630 40.87 0.9937 0.008679 0.02809 14 1 number of observations

The correlation fit was good for most of the compounds with the exception of o-

cresol, p-cresol and guaiacol. These three compounds did not show good linear

relationship between density and solubility on a natural log scale plot. This non-

linearity was caused by some of the experimental data points recorded at

relatively high temperatures. Upper temperature in solubility measurements of

o-cresol and p-cresol was 473.15 K and 393.15 K in the guaiacol study. Parity

plots (Figure 2-11) were generated to show the correlation between predicted

solubility (by fitted Chrastil model [31]) and experimental solubility of different

bio-oil compounds.


Figure 2-11 Parity plots of experimental vs predicted solubility of different bio-

oil compounds on natural log scale. Dots of one colour correspond to one data

source.


2.7.3 Use of binary data in preliminary assessment and design of fractionation

The Chrastil model [31] correlation of solubility data gives a unique characteristic

equation for each compound. These equations provide a means of ready

comparison of solubilities for different compounds and also to visualize solubility

trends. Figure 2-12 gives such a comparison of solubilities at 308 K temperature

predicted by the fitted model. The model predictions suggest that at 308 K

solubilities of different monophenols are in the order: guaiacols > phenols >

catechols. This solubility trend of pure compounds was also observed in actual

experimental extraction of bio-oil mixtures (Figure 2-13). According to Figure

2-12, solubility of guaiacol, phenol and catechol will increase with increasing

solvent densities, however selectivity of guaiacol in the extract will drastically

improve compared to the phenol and catechol selectivities. To choose optimum

conditions for the recovery of guaiacol (which has a relatively high solubility),

solvent temperature as well as density should also be considered in the

separation design process. Guaiacol exhibits crossover behaviour at around 180

bar which is significantly lower than the crossover pressures of phenol (280 bar)

and catechol (270 bar). This would indicate that extracting between 180 and 270

bar at higher temperatures will favour the selectivity of guaiacol over phenol and

catechol. At 318 K temperature rather than 308 K, a favourable solvent density of

850 g/L (according to Figure 2-12) can be achieved with 240 bar pressure. This

pressure is in the optimal range for the separation of guaiacol. Inspection of

Figure 2-12 and 13 provides a qualitative insight into the relationship between

the trends exhibited by actual solubility and apparent solubility. According to

Figure 2-12, the actual solubility of different phenols is in the order of guaiacols

> phenols > catechols. When we compare this with experimental extraction

results (Figure 2-13), we observe a similar trend in apparent solubility.

Figure 2-12 Solubilities of different monophenols in scCO2 predicted by fitted

model at 308 K temperature


Figure 2-13 Extraction trends of different monophenols with scCO2 from bio-oil

mixtures of softwood Kraft lignin [25] and beech wood [23] pyrolysis oils

2.7.4 Solubility data consistency and accuracy

Getting consistent and accurate high pressure experimental solubility data has

been a challenging task for experimenters. Discrepancies do exist in data of

different phase equilibria and solubility data measurement studies. For example

when the Chrastil model [31] was correlated with the experimental solubility

data of catechol reported by Garcia-Gonzalez et al. [84] it provided a good fit (R2

= 0.9777); use of the same correlation parameters provided a poor fit (R2 =

0.8495) to a different catechol solubility data set [86] (Figure 2-14).

Figure 2-14 Parity plots of experimental versus predicted solubilities using data

and parameters based on [84] (plot A) and using the same correlation parameters

to predict solubility data presented in [86] (plot B)

Such differences in solubilities and non-consistency of data are generally caused

by impurities in solute and/or solvent [117], improper calibrations of pressure,

temperature and analytical equipment, use of sampling techniques with different

precisions [118, 119] and technical variations in experimental setups [82].

Minimization of potential source of errors and standardization of experimental


and sampling procedures will greatly help in generating consistent, accurate and

reliable solubility and phase equilibrium data.

2.8 Conclusion

A review of the available literature indicates that there are similarities between

pyrolysis oil and bio-crude from lignocellulosic biomass in terms of chemical

composition. Major and industrially important compounds are highlighted from

the monophenol and low molecular weight acid fractions of bio-oil. Few

experimental studies are reported in the literature on supercritical extraction of

bio-oil. For the experimental studies reported results were encouraging in terms

of extract yields and percent recoveries of phenol and acid fractions.

Design of an efficient supercritical extraction process for bio-oil necessarily

requires extensive solubility data determination of different bio-oil compounds

and rigorous thermodynamic modelling of complex bio-oil phase equilibria.

Binary system experimental solubility data for some of the compounds present

in bio-oil are available in the literature, however there are still many other

important compounds for which binary system (bio-oil compound + CO2)

solubility data are required. Moreover, discrepancies exist in some of the

solubility data available in the literature. Accurate and consistent solubility data

generation at relevant supercritical conditions (308-353 K and 80-350 bar) are

required for efficient supercritical CO2 extraction process design for bio-oil

fractionation. Solubility data is also of utmost importance for acquiring

experimental binary interaction parameters for use in complex phase behaviour

modelling. Semi-empirical or thermodynamic correlation of solubility data are

useful tools with which to compare solubility trends of different compounds and

to provide a method of estimating temperature and pressure conditions for

optimum solubility and selectivity of a compound out of a complex mixture.

The measurement of extensive (but currently unavailable) binary, ternary,

quaternary and multi-component phase equilibria data will provide the basis for

rigorous thermodynamic modelling, optimisation and process design of practical

bio-oil fractionation plants using supercritical CO2.

Bio-oil is a mixture of behemoth number of components [22], many of which are

in minor amounts and can be neglected in specifying the more generalized

composition of a given bio-oil, but can’t be neglected in truly predictive modelling

of the multi-component bio-oil mixture with scCO2. This makes mathematical

correlation of the phase behaviour immensely cumbersome and non-practical.

For this very reason, none of the literature studies [19-30] on SFE of bio-oil

employs EOS based thermodynamic models to understand and design the bio-oil

SFE process. For a comprehensive study of SFE of bio-oil, and to find out optimum

extract yield and composition conditions, there will be a need for a

multicomponent phase behaviour correlation [19] of the whole process. Binary


VLE data of many compounds of our interest is available in literature, and in the

upcoming chapters of this work binary VLE data will be primarily used to predict

the stage-wise pressure reduction fractionation of our supercritical bio-oil

extract. This work postulates that binary VLE data alone is sufficient in describing

the fractionation part of SFE, and it is a reasonable assumption too as in

supercritical CO2 extract, bio-oil components will be absorbed in gaseous state

whereby the solute-solute interactions will be at their minimum.

For some major bio-oil components, identified in this work, no experimental VLE

data was found for them e.g., for 4-ethylphenol and formic acid. It is important

that the VLE data for such important components be made available for future

process design studies of SFE of bio-oil. This work, in upcoming chapters, will

study EOS based modelling of black liquor bio-crude, but primarily for predicting

the fractionation of supercritical extract. Model predictions for extract

fractionation will be compared with experimental pilot plant extraction and

fractionation trials. In simulating the SFE process in Aspen Plus®, solute-solute

interactions will not be taken into account. Effect of fractionation on techno-

economics of bio-crude SFE will be evaluated. In Aspen Plus® simulations, SFE

process will also be techno-economically compared to conventional distillation

process.

2.9 Supporting Information

Tables listing contents of single ring phenolics and low molecular weight

carboxylic acids in bio-oils; solubilities of different compounds in supercritical

carbon dioxide

This material is available free of charge via the Internet at http://pubs.acs.org.


Table 2.5S Single ring phenolics and low molecular weight carboxylic acid

contents in bio-oils

Biomass Bio-oil

type

Phenolics1

(GC area %) Ref. Biomass

Bio-oil

type

Acids2

(GC area %) Ref.

Rice husk PO3 15.9 [120] Rice husk PO 31.2 [120]

Bagasse

BC4

29.1 [60] Corn Stover

BC

21.4 [121]

Cypress 36.6 [122] Aspen 17.6 [121]

Corn Stover 59.3 [121] Rice straw 1.6 [72]

Aspen 55.2 [121] Barley straw 7.6 [73]

P. Indicus PO 51.0 [40] - - - -

Rice straw BC

14.5 [72] - - - -

Barley straw 39.3 [73] - - - -

Biomass Bio-oil

type

Phenolics

(wt % dry

biomass)

Ref. Biomass Bio-oil

type

Acids

(wt % dry

biomass)

Ref.

Rice husk

PO

2.9 [123] Rice husk

PO

6.1 [123]

Beech wood 6.7 [124] Beech wood 6.9 [124]

Spruce wood 6.4 [124] Spruce wood 8.0 [124]

Hazelnut shell 8.5 [124] Hazelnut shell 8.7 [124]

Olive husk 8.4 [124] Olive husk 7.1 [124]

Corn stalk 11.2 [125] Pine 5.6 [126]

Pine 4.0 [126]

1 phenolics include phenols, syringols, catechols, guaiacols, cresols, vanillin and

other substituted phenols, 2 low molecular weight carboxylic acids include formic

acid, acetic acid, propionic acid, butyric acid and their simple derivatives, 3

pyrolysis oil, 4 bio-crude,

Table 2.6S Major chemical compounds in low molecular weight carboxylic acid

fraction of bio-oils

Carboxylic acids

Compound Formic acid Acetic acid Propanoic acid Butyric

acid Ref.

wt%

(dry

basis)

PO 4.7 - - 1.4 [123]

PO1 0.5 5.3 0.3 0.4 [124]

PO2 0.6 6.3 0.4 0.4 [124]

PO3 0.6 6.5 0.5 0.6 [124]

PO4 0.5 5.1 0.4 0.5 [124]

1 pyrolysis oil from beech wood, 2 pyrolysis oil from spruce wood, 3 pyrolysis oil

from hazelnut shell, 4 pyrolysis oil from olive husk


Table 2.7S Major chemical compounds in single ring phenolic fraction of bio-oils

Phenolic monomers

Compound Guaiacol Catechol Phenol Cresol Syringol Vanillin Ref.

wt%

(dry

basis)

PO1 0.9 - 0.7 0.9 3.3 - [124]

PO2 0.8 - 0.7 0.8 3.1 - [124]

PO3 0.7 - 1.9 1.2 3.6 - [124]

PO4 0.7 - 1.8 1.2 3.6 - [124]

PO 1.3 3.5 2.2 2.0 0.3 0.8 [125]

1 pyrolysis oil from beech wood, 2 pyrolysis oil from spruce wood, 3 pyrolysis oil

from hazelnut shell, 4 pyrolysis oil from olive husk

Table 2.8S Solubility data of single ring phenolics and acetic acid with supercritical

carbon dioxide in binary systems

System Temp.

(K) Mole fraction solubility (y × 106)

Pressure (bar) 100 125 150 175 200 225 250 275 300 325 350

Phenol

[84]

333.15 1140 5340 1360

0

2360

0

3600

0

4065

0

4462

0

5438

0

5990

0 67820 77300

348.15 1840 3460 7350 1631

0

2792

0

3627

0

4020

0

5565

0

6210

0 70500 80910

363.15 2040 3250 7490 1153

0

1979

0

2799

0

3866

0

5171

0

6539

0 77490 90640

Catechol

[84]

333.15 122 335 1078 1625 1990 2273 2608 2738 2957 2998 3054

348.15 148 345 569 1031 1524 1917 2358 2708 3096 3494 3847

363.15 210 283 453 906 1463 1963 2400 2900 3656 4065 4583

Pressure (bar) 80.9 96.8 111 123.6 146.3 161.5 183 193 207.6 221.7 249.4

Phenol

[85] 309.15 7314

1127

0

1280

0

1390

0

1502

0

1604

0

1651

0

1670

0

1755

0 18220 18160

Pressure (bar) 127 146.1 160.3 177.5 186.8 192.5 204 211.1 221.8 232.9 241.8

Phenol

[85] 333.15

1233

0

1872

0

2195

0

2830

0

3065

0

3158

0

3146

0

3890

0

3644

0 44600 46600

Pressure (bar) 122 162 203 243 284 324 365 405 - - -

Catechol

[86]

308.15 690 970 1010 1100 1150 1220 1310 1340 - - -

318.15 800 1060 1260 1470 1570 1700 1810 1920 - - -

328.15 750 1280 1630 1880 2210 2390 2490 2560 - - -

338.15 660 1560 2180 2580 2990 3270 3610 3940 - - -

Pressure (bar) 20 30 40 50 60 80 100 130 160 185 200

p-cresol

[87]

353.15 625 506 546 578 688 1050 1660 4080 1020

0 17800 22600

393.15 4570 3680 3210 3120 3120 3490 4270 6040 9460 - 17300

423.15 1360

0

1040

0 8810 8660 8140 8290 8960

1120

0

1480

0 - 20800

Pressure (bar) 20 30 40 50 60 80 100 130 160 180 200

Guaiacol

[87]

323.15 125 115 159 205 311 838 5330 4100

0 -

11100

0 -

353.15 799 718 676 735 762 1120 2090 5950 1840

0 - 56500

393.15 4550 3850 3380 3300 3470 3810 4760 6890 1160

0 - 23100

Pressure (bar) 102 142 201 250 268 -

o-cresol

[88] 323.15 7200 35500 70300 118400 210000 -

Pressure (bar) 99 152 202 250 300 -

o-cresol

[88] 473.15 36400 41800 56100 85900 261000 -

Pressure (bar) 100 150 200 250 301 348

p-cresol

[88] 323.15 3100 17800 27100 35500 44300 52000

Pressure (bar) 101 151 200 250 299 336

p-cresol

[88] 473.15 28300 32600 42700 60800 102800

31500

0

Pressure (bar) 60 80 100 120 140 160

Eugenol

[89]

313.15 330 2100 29290 38190 52530 58020

323.15 280 1360 7360 26850 40440 50210


a Dimethylphenol

Author Information

Corresponding Author

*Email: [email protected]

Notes

The authors declare no competing financial interest.

Acknowledgments

This work was undertaken with Australian Federal Government and Queensland

University of Technology support under the Australia-India Strategic Research

Fund program.

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333.15 230 970 3400 11700 25320 37640

Pressure (bar) 87 116.5 149 174 186.5 197.8 204.5 227 235 259.3 273.3

Vanillin

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Pressure (bar) 84.3 108.7 127.3 148.3 161.5 172.5 178 200.1 232.9 276.5 -

Vanillin

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Chapter 3: Fundamental Experimental Data and Equation of State Model

This chapter will determine high-pressure solubility data of an exemplar bio-oil aromatic compound (benzyl alcohol) in scCO2. The data is determined in a variable volume full-view cell by two different techniques of solubility determination for the sake of comparison and validation with literature. The purpose of determining experimental solubility data in this work is to compare VLE data sets from literature, for use in process modeling and simulation. Aspen Plus® is used for modelling the phase behaviour of benzyl alcohol and CO2 binary system with the help of PR-BM property method. This property method will then also be used in Chapter 4 for different other compounds of our bio-crude mixture.

3.1 Title: Comparison of literature data, thermodynamic modelling and simulation of supercritical fluid extraction of benzyl alcohol

Wahab Maqbool, Kameron Dunn*, William Doherty, Neil McKenzie, Philip Hobson

Queensland University of Technology (QUT), 2 George Street, Gardens Point, 4000 Brisbane,

Australia

3.2 Abstract

Benzyl alcohols are important class of aromatic alcohols used in the fine chemical

and pharmaceutical industries which can be found in extracted bio-oils produced

from the thermochemical liquefaction of lignocellulosic biomass.

Equation-of-state (EOS) models can be used to describe the vapour-liquid

equilibrium (VLE) to support supercritical fluid extraction (SFE) studies of key

compounds from bio-oils, but ideally require experimentally determined binary

VLE data at appropriate conditions of temperature and pressure.

In this study, high-pressure binary solubility data is reported for benzyl alcohol

and a supercritical carbon dioxide (scCO2) mixture. Data has been determined

experimentally at temperatures of 313 K, 333 K and 353 K and at pressures up to

284 bar. Data was determined with both continuous flow analytic (sampling) and

static-synthetic (visual) methods, and used to validate and support existing

published solubility data which was subsequently used in process modelling and

simulation.

50 Chapter 3: Fundamental Experimental Data and Equation of State Model

It was shown that the literature VLE data regression on Peng-Robinson-Boston-

Mathias (PR-BM) model was good in predicting benzyl alcohol solubility data

determined both in this study and in previous literature. The regressed model

was incorporated into Aspen Plus® process simulations for the SFE of benzyl

alcohols from an aqueous mixture (representing bio-oil). Techno-economics of

different SFE process scenarios are determined and compared with by

solvent/bio-oil (S/B) ratios of 10, 15 and 20.

Keywords

Aspen Plus; Benzyl alcohol; Bio-oil; Supercritical fluid extraction; Carbon dioxide;

Techno-economics


3.3 Introduction

Literature on enriched bio-oils containing monomeric alkylated benzyl alcohol

structures produced following a high yielding and selective eucalyptus

organosolv lignin depolymerisation process, operating under both

thermochemical and solvolysis conditions was recently published [1]. Benzyl

alcohols are used for higher-value applications in the fine chemical and

pharmaceutical industries and thus represent a significant value adding

opportunity if recovered and purified. The recovered bio-oil components were

produced following laboratory diethyl ether solvent extraction of the aqueous

reaction mixture. To assist in scaling-up the technology in obtaining crude

extracted bio-oils enriched in alkylated benzyl alcohol structures, SFE using CO2

was considered as a potential technological pathway forward.

Bio-oil can be fractionated by standard process separation techniques such as

liquid-liquid extraction and distillation. Liquid-liquid extraction may necessitate

large solvent volumes and subsequent separation of the solvent itself from the

fractionated products with often requires thermal energy. Conventional

distillation methods like steam distillation and fractional distillation can also be

used but they are generally energy intensive processes and can cause thermal

degradation of the products [2].

To design a SFE process to separate target components out of a mixture requires

experimentally determined high-pressure VLE data from which working

correlations can be developed. These correlations can be used to predict the

phase behaviour of a system at any temperature and pressure within the range

of the original experimental data [3, 4].

Bio-oils are a complex liquid mixture of many chemical compounds [5]. It is

therefore not feasible to experimentally determine the VLE data for all

compounds and associated interactions within the bio-oil and CO2 system.

However, by working with a highly diluted mixture of bio-oil compounds in

scCO2, the approximation of minimal inter-compounds or solute-solute

interactions can be assumed for the vapour phase (though not for the liquid bio-

oil phase). This simplifying assumption has been reported previously in the

literature for modelling the SFE of various complex mixtures such as palm oil [6]

and soybean oil deodorizer distillate [7].

The motivation for the solubility data measurements made in the current study

was for the comparison of literature VLE data and then utilisation of this binary

VLE data from literature in the design of a process for the recovery of aromatic

alcohols from bio-oil by means of a SFE process. This study has determined the

dew point data for the benzyl alcohol and CO2 binary system. Dew point data is a

temperature and pressure condition at which dissolved solute in the supercritical

CO2 phase starts to condense as dew or precipitates.


Benzyl alcohol has a hydroxymethyl group attached to a single benzene ring and

thus seemed an appropriate model compound candidate to represent both alkyl

benzyl alcohols structures and more broadly aromatic alcohol compounds

commonly found in more generic bio-oils [8, 9] also produced from the

thermochemical conversion of lignocellulosic biomass.

The aim of this study was to firstly validate an appropriate binary VLE data set

from literature through acquiring additional binary VLE data of benzyl alcohol

from both EOS modelling and experimental solubility studies, and secondly

develop simulations of an industrial scale process for a pseudo bio-oil mixture to

understand the SFE techno-economics. In the simulation work, benzyl alcohol

and a water mixture are used as a pseudo bio-oil mixture for modelling purposes.

VLE data found in the literature for benzyl alcohol and carbon dioxide binary

systems [10-13] is available up to a maximum pressure of 232 bar (but only for

318.2K isotherm [13]) and temperatures of 308.2K-453.15K (though more

typically limited to pressures up to 200 bar). The first aim of the current study

was to determine benzyl alcohol solubility data within a range of pressures up to

284 bar and at temperatures 313 K, 333 K and 353 K, and then compare this to

the literature VLE data sets and the solubility data of this work with an EOS

model. Experimental data are always valuable when designing and optimizing

relevant processes and in developing and testing fitted thermodynamic models.

As a second aim, simulation of SFE has been carried out with benzyl alcohol-CO2

binary VLE data. Hence two assumptions are necessary to be mentioned here: a)

solute-solute (benzyl alcohol-water) interactions are not included; b) and water-

CO2 interactions are also not included. It is also to be noted that there is yet no

experimental data available for ternary system of benzyl alcohol + water + CO2 in

literature.

Aspen Plus® process simulations were constructed to determine and compare

the techno-economics of three process scenarios for S/B ratios of 10, 15 and 20.

As the mole fraction solubility of solute (benzyl alcohol) is typically in the range

of 0.004-0.03, it is always desirable to conduct supercritical extractions at S/B

ratios greater than 1. But it should also be noted that the SFE process will become

excessively uneconomical at very large S/B ratios. For this reason this study has

carried out SFE simulations at an intermediate range of S/B ratios of 10-20 [2] to

provide the reader a reference to relate the SFE economics to a desired plant

scale. Simulation results provided a comprehensive outlook of the effects

observed on SFE process economics when raising S/B ratio from 10 to 20. This

first-time study presents the techno-economics of SFE of a pseudo bio-oil mixture

comprising only benzyl alcohol and water components.


3.4 Experimental methodology

3.4.1 Materials

Carbon dioxide (purity ≥ 99.9 wt%) was purchased from Supagas (Australia).

Benzyl alcohol (purity ≥ 99.0 wt%) was purchased from Sigma-Aldrich

(Australia). All chemicals were used as supplied.

3.4.2 Apparatus and procedures

The high-pressure phase equilibrium apparatus used in this study was supplied

by Separex (France). This apparatus (see Figure 3-1) consisted of a manual

capstan pump for CO2 pressurization and delivery and a full view cell assembly

with an additional integrated capstan pump to vary cell volume. View cell volume

could be varied from 15 mL to 66 mL. The view cell was a sapphire tube mounted

between two stainless steel flanges. A sample loop and a feed injection port were

also attached to the cell assembly. A single analogue pressure gauge (Model

233.50, WIKA Germany) was used to monitor CO2 pump pressure, while one

analogue (Model 232.50, WIKA Germany) and one digital pressure transducer

(GS4200, ESI Technology UK) were used to monitor cell pressure. A

thermocouple (Sonde PT100, TC S.A. France) was used to monitor the

temperature of the cell contents. A recirculating water heater (DC30, Thermo

Haake Germany) was used to run hot water in a glass jacket surrounding the

sapphire cell. A recirculating water chiller (LTD-6, Grant Instruments UK) was

used to cool the CO2 pump chamber by running cold water in a surrounding

stainless-steel jacket. Cell contents were mixed with a magnetically coupled

stirrer bar (set at 400 rpm) driven via an electromagnetic plate placed beneath

the flange at the base of the view cell. Cell temperature and digital pressure

transducer were accurate up to ± 0.3-0.8 K and ±0.25% respectively. Though the

dials of the analogue pressure gauges were marked with 10 bar intervals, these

gauges were used only for a qualitative check. The high-pressure phase

equilibrium apparatus was rated for a maximum temperature and pressure of

120 oC and 350 bar respectively.


Figure 3-1 High-pressure phase equilibrium apparatus used in this study to

determine benzyl alcohol solubility in scCO2. Labels: 1: CO2 cylinder; 2: CO2 pump;

3: connections for chiller; 4: micrometering valve; 5: safety relief valve; 6: vent

micrometering valve; 7: analogue pressure gauge; 8: water heater connections;

9: mixer; 10: view cell; 11: pressure transducer; 12: pressure indicator; 13:

thermocouple; 14: temperature indicator; 15: syringe; 16: two-way valve; 17:

distributor; 18: rupture disc.

Solubility was determined by both static-synthetic (visual) and continuous-flow

analytic (sampling) methods. The purpose of acquiring data via two distinct

solubility determination methods was to provide a means of cross-checking new

data, and to assist in comparing data of this work to data from other sources

potentially using different methods and procedures. A known amount of solute

was injected into the cell for each method via a syringe that was connected to the

bottom flange of the cell with a valve. Liquid CO2 from a gas cylinder (having an

internal dip tube) was fed to a manual capstan pump where it was cooled to 12 oC and subsequently further pressurised and delivered by the capstan pump at

the required pressure. The amount of liquid CO2 injected into or otherwise passed

through the cell to the sample loop outlet, was determined by reading (from a

Vernier scale) the number of calibrated manual turns applied to capstan pump. It

is preferential to use a gas-meter to calculate the amount of gas in an

experimental setup, but due to non-availability of a gas-meter, it was found

through literature data comparison of our data that the amount of CO2 calculation

by counting the number of pump capstan turns was also an accurate technique.

Once the desired temperature inside the view cell was achieved, the chosen

procedure to determine the solubility data was followed.

For the visual method, solute was first completely solubilized by increasing the

pressure within the view cell via a second capstan piston attached directly to the


view cell. The pressure was then slowly reduced until the solute started

precipitating out. The corresponding pressure was noted and the solubility at

that point was simply calculated from the already known amounts of solute and

solvent that had entered the view cell. Subsequent solubility points were

determined by adding progressively more solvent and determining the

corresponding (lower) precipitation pressure. In the visual method the pressure

was reduced in small increments of about 0.5- 2.0 bar that was subsequently

followed by a waiting period of approximately 5 seconds between each

increment.

In visual method, the amount of solute injected into the view cell was usually up

to a few grams to minimize the uncertainty in determined solubility. It is

important to note that using small quantities of solute are more likely to

introduce larger uncertainties in the determined solubility data. Also, charging of

solute into the view-cell was followed by pumping some air to completely carry

the solute into the view-cell. The view cell was then purged with gaseous CO2 to

remove air.

Figure 3-2 Configuration of view cell assembly used in this study to measure

solute solubility in scCO2 by continuous flow sampling method.

For the sampling method (Figure 3-2), cell connections were rearranged in such

a way that CO2 was continuously fed in through the bottom flange and left the

sample cell via the sampling loop outlet. In the sampling method of solubility


determination, both valves across the sample loop were open to allow the flow of

CO2 out of the view cell. A small sampling vial was attached to the loop outlet

where depressurisation of the solute laden sample occurred and the previously

solubilised solute collected. Each sample was collected over a period of 30

minutes to 1 hour. Mass flow rates of liquid CO2 from the pump into the cell

ranged between 0.15 to 0.32 g.min-1. The amount of solute sample collected in

the experimental trials ranged between 0.15 – 0.40 g. The variation in CO2 mass

flow rates had no effect on determined solubility indicating that saturated

equilibrium conditions were prevalent.

3.5 Thermodynamic modelling

Modelling was performed in Aspen Plus® software using the Peng-Robinson-

Boston-Mathias (PR-BM) property method. The Peng-Robinson EOS is the basis

of PR-BM property method [14]. The Boston-Mathias alpha function and

asymmetric mixing rules [15] are used in conjunction with EOS to enable

modelling of polar, non-ideal chemical systems [16]. Eqs. (1-14) are

mathematical expressions of the PR-BM model with asymmetric mixing rules.

𝑃 =𝑅𝑇

𝑉𝑚−𝑏−

𝑎

𝑉𝑚(𝑉𝑚+𝑏)+𝑏(𝑉𝑚−𝑏) (1)

𝑏 = ∑ 𝑥𝑖𝑏𝑖𝑖 (2)

𝑎 = 𝑎0 + 𝑎1 (3)

𝑎0 = ∑ ∑ 𝑥𝑖𝑥𝑗(𝑎𝑖𝑎𝑗)0.5

(1 − 𝑘𝑖𝑗)𝑗𝑖 (4)

Eq. (4) is the standard quadratic mixing term, where 𝑘𝑖𝑗 has been made

temperature dependent.

𝑘𝑖𝑗 = 𝑘𝑖𝑗(1)

+ 𝑘𝑖𝑗(2)

𝑇 + 𝑘𝑖𝑗(3)

𝑇⁄ (5)

Where 𝑘𝑖𝑗 = 𝑘𝑗𝑖 and superscripts (1), (2) and (3) are numbered terms in Eq. (5)

𝑎1 = ∑ 𝑥𝑖[∑ 𝑥𝑗((𝑎𝑖𝑎𝑗)1 2⁄ 𝑙𝑖,𝑗)1 3⁄𝑛𝑗=1 ]

3𝑛𝑖=1 (6)

Eq. (6) is an additional asymmetric term used to model highly non-linear systems

𝑙𝑖𝑗 = 𝑙𝑖𝑗(1)

+ 𝑙𝑖𝑗(2)

𝑇 + 𝑙𝑖𝑗(3)

𝑇⁄ (7)

Where 𝑙𝑖𝑗 ≠ 𝑙𝑗𝑖 and superscripts (1), (2) and (3) are numbered terms in Eq. (7)

The pure component parameters for PR-EOS are calculated as follows:

𝑎𝑖 = 𝛺𝑎

𝑅2𝑇𝑐𝑖,𝑒𝑥𝑝2

𝑃𝑐𝑖,𝑒𝑥𝑝𝛼𝑖 (8)

𝛺𝑎 =8(5𝜂𝑐+1)

49−37𝜂𝑐≈ 0.45724 (8a)


𝑏𝑖 = 𝛺𝑏𝑅𝑇𝑐𝑖,𝑒𝑥𝑝

𝑃𝑐𝑖,𝑒𝑥𝑝 (9)

𝛺𝑏 =𝜂𝑐

𝜂𝑐+3≈ 0.07780 (9a)

𝜂𝑐 = [1 + √4 − 2√23

+ √4 + 2√23

]−1

≈ 0.25308 (9b)

The parameter 𝛼 is a temperature function, and is meant to improve the

correlation of the pure component vapour pressure. In standard PR-EOS, this

parameter is expressed with Eqs. (10-11).

𝛼𝑖(𝑇) = [1 + 𝑚𝑖(1 − 𝑇𝑟𝑖1 2⁄

)]2 (10)

𝑚𝑖 = 0.37464 + 1.54226𝜔𝑖 − 0.26992𝜔𝑖2 (11)

𝛼, defined in Eqs. (10-11) is used when 𝑇𝑟 < 1 (aka subcritical temperature),

otherwise the Aspen BM alpha function (Eqs. (12-14)) is used.

𝛼𝑖(𝑇) = [𝑒𝑥𝑝[𝐶𝑖(1 − 𝑇𝑟𝑖𝑑)]]

2

(12)

𝑑𝑖 = 1 + 𝑚𝑖 2⁄ (13)

𝐶𝑖 = 1 − 1 𝑑𝑖⁄ (14)

Such an α-function like BM does not pass the consistency test recently developed

by Le Guennec et al. [17-19]. In our case we did not notice any special loss of

accuracy when regressing Walther et al. [12] VLE data to PR-BM model in Aspen

Plus® Data Regression system.

Binary interaction parameters (𝑘𝑖𝑗 , 𝑙𝑖𝑗) must be determined from regression of

phase equilibrium data. The optimized values of these binary interaction

parameters were obtained by the maximum-likelihood objective function (Eq.

(15)), defined within the Aspen Plus® data regression system.

𝑄 = ∑ 𝑤𝑛 ∑ [(𝑇𝑒,𝑖−𝑇𝑚,𝑖

𝜎𝑇,𝑖)

2

+ (𝑃𝑒,𝑖−𝑃𝑚,𝑖

𝜎𝑃,𝑖)

2

+ ∑ (𝑥𝑒,𝑖,𝑗−𝑥𝑚,𝑖,𝑗

𝜎𝑥,𝑖,𝑗)

2𝑁𝐶−1𝑗=1 +𝑁𝑃

𝑖=1𝑁𝐷𝐺𝑛=1

∑ (𝑦𝑒,𝑖,𝑗−𝑦𝑚,𝑖,𝑗

𝜎𝑦,𝑖,𝑗)

2𝑁𝐶−1𝑗=1 ] (15)

In phase equilibrium measurements, there can be errors in measurement of

temperature, pressure and in the compositions of both vapour and liquid phases.

During data regression in this study, the standard deviations specified for

measured variables were as follows: 0.1 oC in temperature, 0.1% in pressure

(bar), 0.1% in liquid mole fraction and 1% in vapour mole fraction. The weighting

factor (𝑤𝑛) value of 1 was specified for all involved data groups in our regression

case. The objective function (Eq. (15)) was minimized using Britt-Luecke

algorithm [20].

Pure component properties of critical temperature (Tc), critical pressure (Pc) and

acentric factor (ω) used in the EOS modelling of this work are given in Table 3.1.


Table 3.1 Aspen Plus® pure component properties used in modelling of this work

Component Tc (oC) Pc (bar) ω

Carbon dioxide 31.06 73.83 0.2236

Water 373.9 220.6 0.3449

Benzyl alcohol 447 43.74 0.3631

3.6 Results and discussion

3.6.1 Solubility data

Temperature, pressure and solute amount were determined with an uncertainty

of ±0.2K, ±1.5 bar and ±0.0005 g respectively.

Benzyl alcohol solubility was determined by both visual and sampling methods

at temperatures of 313.15K, 333.15K and 353.15K. The pressure range

investigated was 93-284 bar. This study extends the availability of existing

solubility data to above 200 bar. At least duplicate measurements were taken in

each case. Table 3.2 and Table 3.3 report average solubility measurements for

benzyl alcohol in scCO2 determined by visual and sampling methods respectively.


Table 3.2 Benzyl alcohol solubility in scCO2 data determined using the visual

method

P

(bar)

CO2 density a (g/L)

Mole fraction

solubility, y x

103

P

(bar)

CO2 density

(g/L)

Mole fraction

solubility, y x

103

313.15 K 313.15 K

284 901 21.4 93.7 562 5.6

214.2 852 18.3 93.2 554 5.0

198.4 838 17.3 333.15 K

169.7 808 16.2 168.6 661 13.8

160.7 796 14.9 163 646 12.3

151.4 782 13.9 155.8 624 11.0

140.8 765 13.0 150 604 10.0

132 747 12.2 147 592 9.7

119 715 11.5 145.8 588 9.8

117.5 710 10.9 142.5 573 8.7

116 706 10.6 138 551 7.7

112.8 694 10.0 131 512 6.3

110.1 684 9.4 126 479 5.2

106 665 8.8 353.15 K

103.2 650 8.2 149 423 7.7

101 636 7.8 146.8 413 7.0

98.7 618 7.1 143.8 400 5.9

97.3 606 6.8 137 370 4.9

94.4 572 6.1 131 344 4.5

a Calculated by Span and Wagner equation of state [21].


Table 3.3 Benzyl alcohol solubility in scCO2 data determined using the sampling

method

T (K) P

(bar)

CO2 density

(g/L)

Mole fraction

solubility, y x

103

313.15 100 629 6.9

313.15 135 754 12.0

313.15 200 840 16.8

313.15 250 879 18.8

333.15 200 724 17.4

333.15 280 814 27.8

353.15 280 724 29.8

Reproducibility of the results in both methods was considered acceptable. For the

visual method the precipitation pressure was determined with a maximum

uncertainty of ±2.5 bar; in the sampling method the maximum standard deviation

(see Appendix) between solubility measurement replicates was ±4.8% but

typically ±2%. Ultimate inherent uncertainty in mole fraction solubility resulting

from the propagation of individual system errors was estimated to be within

±1%.

A comparison of solubility data determined in this study at 313.15K showed good

agreement between the two methods (see Figure 3-3) with an average absolute

relative deviation (AARD1) of 5.9% between the two sets of data obtained from

the visual and the sampling method. A possible explanation for the slightly lower

values obtained from the sampling method is that some benzyl alcohol vapours

escape with the vapourised CO2 solvent leaving the collection vial (see Figure

3-2). When CO2 depressurizes in the collection vial, it releases the absorbed

benzyl alcohol in the vial and then CO2 vents out of the vial. As the collection vial

is not sub-cooled, some benzyl alcohol vapours might not condense and hence

escape with the CO2 being vented out.

1 See Appendix for the definition of AARD


Figure 3-3 Comparison of benzyl alcohol solubility in scCO2, determined in this

study by visual and sampling methods of solubility determination. Horizontal

error bars represent the average uncertainty in measured precipitation pressure;

vertical error bars are standard deviation in the measured mole fraction

solubility.

Through our work on both solubility experimental methods, it was noted that the

visual method was quicker than the sampling method, and required

comparatively very small amounts of solute for solubility determination. On the

other hand, the sampling method was evidently more accurate in terms of

dividing the experimental error across a larger amount of samples collected over

extended time periods. It is also inherently difficult for the visual method to

determine solubility for different mixture compositions at exactly the same

pressure values, whilst for the sampling method this would not be an issue. Often

during the visual method, it was evident that small condensed or precipitated

droplets were hard to observe upon pressurization-depressurization cycles for

phase equilibrium measurements, thereby compromising the accuracy of data.

In this study, the Walther et al. [12] data was modelled within Aspen Plus® using

our selected model of PR-EOS. From our preliminary modelling works, it was

found that PR-EOS with BM mixing rules was quite good in representing binary

phase equilibria when at least one compound (benzyl alcohol) was of a polar

nature. Regressions were performed on 313.2K, 353.2K and 393.1K isotherms,

over a pressure range of 80.9 to 200.8 bar. The resulting optimized binary

interaction parameter values are given in Table 3.4. The parameters given in


Table 3.4 are for only those terms of Eq. (5) and Eq. (7) which resulted in

statistically significant values.

Table 3.4 Benzyl alcohol - CO2 binary interaction parameter values for a PR-EOS

derived from the VLE data of Walther et al. [12].

Parameter 𝑘𝑖𝑗(1)a 𝑙𝑖𝑗

(1) 𝑙𝑗𝑖

(1) 𝑙𝑖𝑗

(2) 𝑙𝑗𝑖

(2)

Value b 0.1321 -0.1882 -0.4729 0.00054 0.0012

a component i is solute and component j is CO2, b in SI units

The PR-EOS model utilising the interaction parameters given in Table 3.4

provided a good fit (see Figure 3-4) for the regressed data [12]. The deviations of

the PR-EOS model predictions relative to the experimental VLE data [12] were

about 15% AARD for vapour phase, and less than 1% AARD for liquid phase

compositions.

Figure 3-4 Predicted (PR-EOS) and experimental (Walther et al.[12]) composition

- pressure phase diagram for a benzyl alcohol-CO2 binary system


The model was effective in predicting the mole fraction composition of both

phases over the regressed temperature range. At pressures typically ≥ 100 bar,

vapour phase average AARD (14.5%) is acceptable, given the difficulty, generally,

encountered in determining the experimental vapour phase data. When data

below 100 bar is also included in the comparison, the vapour phase composition

varied by 45.8 %AARD. Slightly higher vapour phase AARD in solubility data is a

result of making the binary interactions parameter independent of temperature,

which gives same parameter values over the whole temperature range used in

regression. Regressed model predicted the liquid phase composition of Walther

et al. [12] very well, with maximum AARD less than 1%.

The regressed model was then used to predict the solubility of benzyl alcohol at

temperature, pressure conditions other than that used in regressing the binary

interaction parameters and used to validate solubility data in this study and from

other literature. This study has determined extended experimental solubility

data of benzyl alcohol at conditions of pressures from 93 bar up to 284 bar. Figure

3-5 is a graphic comparison between the regressed model’s predictions and the

actual experimental solubility data determined in this study.

Figure 3-5 Comparison of experimental solubility data of benzyl alcohol

determined in this study, with that of PR-EOS model predictions. The model was

first optimized with the help of experimental VLE data of Walther et al. [12]

Model predictions were in good agreement with the laboratory solubility data

also determined from this study (Figure 3-5). Relative discrepancies between the

model and experimental data produced in this study were found to be within 4.8

– 12.1 %AARD. It was also found in this study that our model regression based on


the 313.2K and 353.2K isotherms reported by Walther et al. [12] provided an

accurate prediction (1.8 %AARD) of the liquid phase composition of the 393.1K

isotherm of the same study [12]. Hence, the regressed model proved capable of

correctly predicting both the liquid phase and vapour phase (solubility data of

this study) compositions.

Comparison of the regressed model with other literature studies [10, 11]

revealed that vapour phase compositions of benzyl alcohol and CO2 system varied

by 42 % - 58 % AARD (Figure 3-6).

Figure 3-6 Comparison of solubility data of benzyl alcohol in CO2 vapour phase

from literature [10, 11] and the regressed model of this work based on Walther

et al. [12] data

However, the regressed model was quite successful in predicting the liquid phase

compositions of the same binary system in literature [10, 11], with maximum

AARD less than 5% (Figure 3-7).


Figure 3-7 Comparison of solubility data of benzyl alcohol in CO2 liquid phase

from literature [10, 11] and the regressed model of this work based on Walther

et al. [12] data

3.7 Process design and techno-economic evaluation using Aspen Plus® to extract bio-oil from the aqueous hydrothermally liquefied product

The regressed model was then incorporated into simulating process scenarios in

Aspen Plus® process simulation software. A pseudo binary bio-oil (the crude

aqueous mixture product following liquefaction) was assumed to be the feed to a

designated 20 tonne/hr capacity SFE plant. Benzyl alcohol constituted 30%

(wt/wt) or 6.7% (mol/mol) of the pseudo feed aqueous mixture, and represented

the monomeric bio-oil components that required recovery. It is worthy of note

that for an actual 30 wt% benzyl alcohol aqueous mixture, the solubility of pure

benzyl alcohol in water is exceeded, however given the intent of the simulation

work to extract a crude bio-oil product from an aqueous fraction, it has been

modelled as a single-phase liquid to mimic an approximate typical bio-oil

composition where two distinct phases don’t often exist following liquefaction.

For this reason decanting of the enriched “benzyl alcohol” phase was not

incorporated into the process modelling scenario. Three process scenarios were

however considered and modelled in Aspen Plus® as shown in Table 3.5.


Table 3.5 Aspen Plus® process scenarios simulated in this study, for recovery of

benzyl alcohol from binary water mixture

Scenario Symbol Solvent/bio-oil

(mass basis)

Process-1 (P-1) 10

Process-2 (P-2) 15

Process-3 (P-3) 20

Figure 3-8 presents the process diagram for SFE of benzyl alcohol out of a water

mixture, as simulated by Aspen Plus®. Bio-oil feed is pressurized to 200 bar and

preheated to 55 oC.


Figure 3-8 Aspen Plus® process diagram for SFE and subsequent distillation

processes used in the recovery of benzyl alcohol from an aqueous mixture.


For the process model CO2 is recycled from an upstream separator, pressurized

and then also preheated along with the bio-oil feed to achieve the extraction

conditions of 200 bar pressure and 55 oC temperature. We have chosen an

intermediate extraction conditions for column based on our review [2] of

literature on SFE studies. Solvent-bio-oil mixture is then equilibrium separated

in an extraction column. The exit stream at the top of the column (column top) is

comprised of a supercritical solvent laden with extracted components from the

bio-oil feed stream. The exit stream at the base of the column is the raffinate

stream. Column top is depressurized into a separator (Sep) at 60 bar pressure

and 32 oC temperature, to precipitate/separate out the extracted benzyl alcohol

in the base of the separator and the solvent now gaseous CO2 is then recycled at

60 bar. This gaseous CO2 in then cooled and condensed into a liquid CO2 phase.

Liquid CO2 is then pumped and recycled back to preheater.

Dissolved solvent in liquid bottoms of extraction column and separator are

recovered in collectors (Col-1 and Col-2 respectively), at ambient pressure of 1

bar. It was found through calculations that it is more economical to undertake

multistage compression and cooling of the gaseous CO2 solvent back to the

extraction pressure, rather than via external pressurised CO2 make-up. According

to our calculations, the cross-over point between compression related

operational cost and external make-up cost for solvent was about at 28 kmol/hr

of solvent, where above this value it was more economical to compress and reuse

CO2. In all our three process scenarios, ambient pressure CO2 mole rates were

always greater than 59 kmol/hr and therefore recycled to preheater following

compression and cooling cycles.

The liquid extract recovered from the bottom of the collector (Col-2) is pumped

into an atmospheric distillation column (Dist.). Water is distilled from the top of

distillation column, and liquid benzyl alcohol concentrated in the bottom. The

product from the top of the distillation column is passed through a heat

exchanger to preheat the feed stream to the distillation column. Distillation

column bottoms are feed through a steam generator (SG3), and then through a

heat exchanger (HEX2) to recover remaining energy for preheating the feed

stream to the distillation column. The cooled benzyl alcohol enriched stream is

then crystallised via cooling to produce a market-ready benzyl alcohol product.

Stream specifications and temperature and pressure conditions of Aspen Plus

simulation are provided as Supporting Information.

Bio-oil was the only raw material added and its value ($136.4 USD/tonne) was

assumed to be defined by its heating value (11.93 MJ/kg) given actual bio-oil

content and current typical crude oil price [22]. Benzyl alcohol sale price of

$1,100 USD/tonne [23] was assumed for a product purity of at least 99%. In

evaluating the techno-economics, a total plant life of 20 years and a company

hurdle rate of 20% were assumed. A plant commissioning time of 1.5 years and a

plant availability of 95% during the year was also assumed.


3.7.1 Simulation results and SFE techno-economics

According to Aspen Plus® simulation results, benzyl alcohol extraction yields in

the column were 99.99 wt% for all three P-1, P-2 and P-3 process scenarios (see

Table S1 to S3 of supplementary information). Whilst the water contents of the

supercritical extracts corresponded to 3.8%, 4.9% and 5.9% of the feed bio-oil

water contents in P-1, P-2 and P-3 respectively. The extracted benzyl alcohol

product after being recovered from collector (Col-2) was then concentrated in an

upstream distillation unit to at least a 99% purity. Techno-economic evaluation

of the three process scenarios are summarized in Figure 3-9.

Figure 3-9 Techno-economic summary of SFE of benzyl alcohol from binary

aqueous mixture, for different solvent/bio-oil ratios.


When S/B ratio of 10 in P-1 was increased to 15 and 20, the capital costs of the

plant increased by 5.6% in P-2 and 12.3% in P-3 respectively. Annual utilities

costs of P-2 and P-3 were 41.5% and 75.6% greater than P-1 respectively.

Summary of different utilities rates in each simulated process scenario of

different S/B ratio is provided in supplementary information (Table S4).

Summary of economic evaluation for each simulated case is also provided in

supplementary information (Table S5). Similar trend was also seen in annual

operating costs, which were 3.2% and 5.9% greater than P-1 respectively. The

bulk of the operating cost is driven by raw material cost, utility cost and capital

depreciation. Operating cost also include site development cost, labour etc.

components. Profit was calculated by subtracting operating cost from product

sales. In all three process scenarios, benzyl alcohol product recovery of about

96% or more was observed. The effect of increasing S/B ratio from 10 to 15 and

20 was eventually seen by a decrease in total annual profits by 5.3% and 9.9%

respectively.

In summary:

• the regressed model not only predicted the solute solubility data of this

work, but also the complete VLE data isotherms from literature too;

• the use of the regressed model in process simulation is an important step

forward towards techno-economic evaluation of application of SFE

technology in bio-oil separation; and

• the techno-economic comparison of SFE process scenarios made in this

study can be used in relating the effect of different S/B usage on plant

economics.

3.8 Conclusions

Benzyl alcohol solubility in scCO2 was determined in this study using both visual

and sampling techniques; solubility data from both techniques were shown to be

in good agreement.

The Peng-Robinson-Boston-Mathias (PR-BM) correlation developed from the

VLE data previously reported in the literature was found to be good in predicting

the solute solubility data determined in this study. This regressed correlation has

also been shown to provide a good model for predicting binary VLE data of

literature studies, over the pressure and temperature conditions relevant to the

recovery of benzyl alcohol from lignocellulosic derived bio-oils. Simulation

results suggested that for a 30 wt% benzyl alcohol aqueous mixture, SFE on a 20

tonne/hr plant will generate about $25 million USD profit annually, with

solvent/bio-oil usage ratio of 10. It was also shown that in application of SFE to

bio-oil separations, increasing the solvent/bio-oil ratio from 10 to 20 will

decrease the annual total profits of the plant by just 9.9%. In future studies it is


recommended that in order to determine the actual product recovery and its

effect on SFE techno-economics, experimental VLE data for multicomponent

system should be determined and used in process simulation. Optimization of the

column extraction conditions should also be considered in future studies

involving bio-oil SFE simulations.

3.9 Glossary and Nomenclature

Model = Aspen Plus® PR-BM property method

𝑎𝑖, 𝑏𝑖 = model parameters for pure components

𝑎, 𝑏 = model parameters for mixture

e = estimated data

i = data for data point i (eq 15)

j = fraction data for component j (eq 15)

𝑘𝑖𝑗 , 𝑙𝑖𝑗 = binary interaction parameters in model

m = measured data

NDG = the number of data groups in the regression case

NC = the number of components present in the data group

NP = the number of points in data group n

P = pressure

𝑃𝑐 = critical pressure of a component

Q = maximum-likelihood objective function to be minimized

R = gas constant

T = temperature

𝑇𝑐 = critical temperature of a component

𝑇𝑟 = reduced temperature

Wn = the weight of data group n

x, y = liquid and vapour mole fractions respectively

𝛼 = temperature function in eq 8

σ = standard deviation of the indicated data

𝜔 = acentric factor of a component


3.10 Appendix

Standard deviation

Standard deviation was calculated with the MS Excel® function ‘STDEV.P’, and is

defined in Eq. (A.1).

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 = √∑(𝑥−��)2

𝑛 (A.1)

𝑥 is a value in the data set, �� is the mean of the data set, and 𝑛 is the number of

data points.

Relative standard deviation

𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 = (𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛

𝑠𝑎𝑚𝑝𝑙𝑒 𝑚𝑒𝑎𝑛) × 100 (A.2)

AARD

𝐴𝐴𝑅𝐷% =100

𝑁∑

|𝑦1−𝑦2|

𝑦1

𝑁𝑖=1 (A.3)

𝑦1 is reference data point, 𝑦2 is data point to be compared with reference data,

and 𝑁 is the number of data points compared.



Table 3.6S Stream specifications of Aspen Plus® simulation for SFE of benzyl

alcohol aqueous mixture. CO2/aqueous mixture ratio = 10

Benzyl

Alcohol CO2 WATER

3 COOL1 SEP 0.6 4553.0 15.8 4569.4 200725.0 32.0 60 -94602

3-1 PUMP3 COOL1 0.6 4553.0 15.8 4569.4 200725.0 18.5 60 -96479

4 COL-2 SEP 55.5 41.3 29.9 126.6 8355.4 32.0 60 -61488

5 MIX1 COL-1 0.0 17.7 0.0 17.7 779.1 54.3 1 -93731

9 MIX1 COL-2 0.0 41.3 0.0 41.3 1817.4 30.0 1 -93953

10 PUMP2 COL-2 55.5 0.0 29.9 85.4 6538.0 30.0 1 -45132

10-1 HEX1 PUMP2 55.5 0.0 29.9 85.4 6538.0 30.0 1.2 -45131

10-2 HEX2 HEX1 55.5 0.0 29.9 85.4 6538.0 34.4 1.2 -44993

10-3 DIST HEX2 55.5 0.0 29.9 85.4 6538.0 75.1 1.2 -43660

11 COMP1 MIX1 0.0 59.0 0.0 59.0 2596.5 37.4 1 -93887

11-1 SG1 COMP1 0.0 59.0 0.0 59.0 2596.5 314.3 13.5 -91072

11-2 COOL2 SG1 0.0 59.0 0.0 59.0 2596.5 140.0 13.5 -92973

11-3 COMP2 COOL2 0.0 59.0 0.0 59.0 2596.5 50.0 13.5 -93884

11-4 SG2 COMP2 0.0 59.0 0.0 59.0 2596.5 357.6 200 -90921

11-5 COOL3 SG2 0.0 59.0 0.0 59.0 2596.5 140.0 200 -94058

14 HEX1 DIST 1.5 0.0 29.9 31.4 699.6 73.2 1 -65379

15 SG3 DIST 54.0 0.0 0.0 54.0 5838.4 202.9 1 -26052

15-1 HEX2 SG3 54.0 0.0 0.0 54.0 5838.4 140.0 1 -29328

15-2 CRYST HEX2 54.0 0.0 0.0 54.0 5838.4 95.1 1 -31435

AQUEOUS1 COL-1 COLUMN 0.0 17.7 747.2 765.0 14241.1 55.0 200 -68704

AQUEOUS2 COL-1 0.0 0.0 747.2 747.3 13462.0 54.3 1 -68115

AQUEOUS3 HEX1 1.5 0.0 29.9 31.4 699.6 54.4 1 -65756

CO2RECY1 PREHEAT COOL3 0.0 59.0 0.0 59.0 2596.5 55.0 200 -96039

EXTRACT SEP COLUMN 56.1 4594.3 45.7 4696.0 209081.0 55.0 200 -94934

FEED-1 PUMP1 55.5 0.0 777.1 832.6 20000.0 22.0 1 -65551

FEED-2 PREHEAT PUMP1 55.5 0.0 777.1 832.6 20000.0 25.6 200 -65374

FEED-3 COLUMN PREHEAT 56.1 4612.0 792.9 5461.0 223322.0 55.0 200 -91279

PRODUCT CRYST 54.0 0.0 0.0 54.0 5838.4 30.0 1 -34156

SEP2REC1 PREHEAT PUMP3 0.6 4553.0 15.8 4569.4 200725.0 29.1 200 -96246

Enthalpy

(cal/mol)To Unit From UnitStream Name

Pressure

(bar)

Temperature

(oC)

Total Flow

(kg/hr)

Total Flow

(kmol/hr)

Flowrate (kmol/hr)




Benzyl

Alcohol CO2 WATER

3 COOL1 SEP 0.8 6765.4 26.1 6792.3 298302.0 32.0 60.0 -94590

3-1 PUMP3 COOL1 0.8 6765.4 26.1 6792.3 298302.0 18.5 60.0 -96468

4 COL-2 SEP 55.5 44.6 37.9 138.1 8648.0 32.0 60.0 -62586

5 MIX1 COL-1 0.0 17.6 0.0 17.6 773.3 54.3 1.0 -93731

9 MIX1 COL-2 0.0 44.6 0.0 44.6 1964.3 30.0 1.0 -93953

10 PUMP2 COL-2 55.5 0.0 37.9 93.4 6683.6 30.0 1.0 -46923

10-1 HEX1 PUMP2 55.5 0.0 37.9 93.4 6683.6 30.0 1.2 -46923

10-2 HEX2 HEX1 55.5 0.0 37.9 93.4 6683.6 35.1 1.2 -46768

10-3 DIST HEX2 55.5 0.0 37.9 93.4 6683.6 74.2 1.2 -45538

11 COMP1 MIX1 0.0 62.2 0.0 62.2 2737.7 36.9 1.0 -93890

11-1 SG1 COMP1 0.0 62.2 0.0 62.2 2737.7 313.6 13.5 -91080

11-2 COOL2 SG1 0.0 62.2 0.0 62.2 2737.7 140.0 13.5 -92973

11-3 COMP2 COOL2 0.0 62.2 0.0 62.2 2737.7 50.0 13.5 -93884

11-4 SG2 COMP2 0.0 62.2 0.0 62.2 2737.7 357.6 200.0 -90921

11-5 COOL3 SG2 0.0 62.2 0.0 62.2 2737.7 140.0 200.0 -94058

14 HEX1 DIST 1.9 0.0 37.9 39.8 888.9 73.2 1.0 -65379

15 SG3 DIST 53.6 0.0 0.0 53.6 5794.8 202.9 1.0 -26052

15-1 HEX2 SG3 53.6 0.0 0.0 53.6 5794.8 140.0 1.0 -29328

15-2 CRYST HEX2 53.6 0.0 0.0 53.6 5794.8 94.2 1.0 -31473

AQUEOUS1 COL-1 COLUMN 0.0 17.6 739.2 756.7 14089.7 55.0 200.0 -68708

AQUEOUS2 COL-1 0.0 0.0 739.2 739.2 13316.4 54.3 1.0 -68115

AQUEOUS3 HEX1 1.9 0.0 37.9 39.8 888.9 55.1 1.0 -65742

CO2RECY1 PREHEAT COOL3 0.0 62.2 0.0 62.2 2737.7 55.0 200.0 -96039

EXTRACT SEP COLUMN 56.3 6810.0 64.0 6930.3 306950.0 55.0 200.0 -95201

FEED-1 PUMP1 55.5 0.0 777.1 832.6 20000.0 22.0 1.0 -65551

FEED-2 PREHEAT PUMP1 55.5 0.0 777.1 832.6 20000.0 25.6 200.0 -65374

FEED-3 COLUMN PREHEAT 56.3 6827.6 803.2 7687.1 321040.0 55.0 200.0 -92609

PRODUCT CRYST 53.6 0.0 0.0 53.6 5794.8 30.0 1.0 -34156

SEP2REC1 PREHEAT PUMP3 0.8 6765.4 26.1 6792.3 298302.0 25.3 200.0 -96242

Temperature

(oC)

Pressure

(bar)

Enthalpy

(cal/mol)Stream Name To Unit From Unit

Flowrate (kmol/hr)Total Flow

(kmol/hr)

Total Flow

(kg/hr)




Table 3.9S Utilities summary of Aspen Plus® simulation for SFE of benzyl alcohol

aqueous mixture

Benzyl

Alcohol CO2 WATER

3 COOL1 SEP 1.1 8995.7 36.8 9033.5 396676.0 32.0 60.0 -94582

3-1 PUMP3 COOL1 1.1 8995.7 36.8 9033.5 396676.0 18.5 60.0 -96461

4 COL-2 SEP 55.5 47.6 45.8 148.8 8917.4 32.0 60.0 -63425

5 MIX1 COL-1 0.0 17.4 0.0 17.4 766.6 54.3 1.0 -93731

9 MIX1 COL-2 0.0 47.6 0.0 47.6 2092.7 30.0 1.0 -93953

10 PUMP2 COL-2 55.5 0.0 45.8 101.3 6824.7 30.0 1.0 -48396

10-1 HEX1 PUMP2 55.5 0.0 45.8 101.3 6824.7 30.0 1.2 -48396

10-2 HEX2 HEX1 55.5 0.0 45.8 101.3 6824.7 35.7 1.2 -48229

10-3 DIST HEX2 55.5 0.0 45.8 101.3 6824.7 73.4 1.2 -47083

11 COMP1 MIX1 0.0 65.0 0.0 65.0 2859.3 36.6 1.0 -93894

11-1 SG1 COMP1 0.0 65.0 0.0 65.0 2859.3 313.1 13.5 -91086

11-2 COOL2 SG1 0.0 65.0 0.0 65.0 2859.3 140.0 13.5 -92973

11-3 COMP2 COOL2 0.0 65.0 0.0 65.0 2859.3 50.0 13.5 -93884

11-4 SG2 COMP2 0.0 65.0 0.0 65.0 2859.3 357.6 200.0 -90921

11-5 COOL3 SG2 0.0 65.0 0.0 65.0 2859.3 140.0 200.0 -94058

14 HEX1 DIST 2.3 0.0 45.8 48.1 1072.3 73.2 1.0 -65379

15 SG3 DIST 53.2 0.0 0.0 53.2 5752.4 202.9 1.0 -26052

15-1 HEX2 SG3 53.2 0.0 0.0 53.2 5752.4 140.0 1.0 -29328

15-2 CRYST HEX2 53.2 0.0 0.0 53.2 5752.4 93.4 1.0 -31510

AQUEOUS1 COL-1 COLUMN 0.0 17.4 731.3 748.8 13941.9 55.0 200.0 -68710

AQUEOUS2 COL-1 0.0 0.0 731.3 731.3 13175.3 54.3 1.0 -68115

AQUEOUS3 HEX1 2.3 0.0 45.8 48.1 1072.3 55.7 1.0 -65731

CO2RECY1 PREHEAT COOL3 0.0 65.0 0.0 65.0 2859.3 55.0 200.0 -96039

EXTRACT SEP COLUMN 56.5 9043.3 82.5 9182.3 405594.0 55.0 200.0 -95339

FEED-1 PUMP1 55.5 0.0 777.1 832.6 20000.0 22.0 1.0 -65551

FEED-2 PREHEAT PUMP1 55.5 0.0 777.1 832.6 20000.0 25.6 200.0 -65374

FEED-3 COLUMN PREHEAT 56.5 9060.7 813.9 9931.1 419535.0 55.0 200.0 -93345

PRODUCT CRYST 53.2 0.0 0.0 53.2 5752.4 30.0 1.0 -34156

SEP2REC1 PREHEAT PUMP3 1.1 8995.7 36.8 9033.5 396676.0 25.0 200.0 -96236

Temperature

(oC)

Pressure

(bar)

Enthalpy

(cal/mol)Stream Name To Unit From Unit

Flowrate (kmol/hr)Total Flow

(kmol/hr)

Total Flow

(kg/hr)

S/B = 10 S/B = 15 S/B = 20

Name Fluid Rate Rate UnitsCost/Hour

[USD/hr]

Cost/Hour

[USD/hr]

Cost/Hour

[USD/hr]

Electricity 2496 KW 193.43 267.93 327.64

Chilled Water Water 34034500 BTU/H 7.62 11.34 15.09

Cooling Water Water 5034803 BTU/H 1.13 1.36 1.59

Low Pressure Steam Steam 11254130 BTU/H 22.51 38.70 52.36

Low Pressure Steam Generation Steam 1881414 BTU/H -3.74 -3.86 -3.95

High Pressure Steam Steam 4844389 BTU/H 12.79 15.32 17.78


Table 3.10S Economic evaluation summary of Aspen Plus® simulation for SFE of

benzyl alcohol aqueous mixture

Author information

Corresponding author

*E-mail: [email protected]

Notes


Acknowledgements



Fund program.

3.12 References

[1] W. Wanmolee, J.N. Beltramini, L. Atanda, J.P. Bartley, N. Laosiripojana, W.O.

Doherty, Effect of HCOOK/Ethanol on Fe/HUSY, Ni/HUSY, and Ni–Fe/HUSY

Catalysts on Lignin Depolymerization to Benzyl Alcohols and Bioaromatics, ACS

omega, 16 (2019) 16980-16993.

[2] W. Maqbool, P. Hobson, K. Dunn, W. Doherty, Supercritical carbon dioxide

separation of carboxylic acids and phenolics from bio-oil of lignocellulosic origin:

Understanding bio-oil compositions, compound solubilities, and their fractionation,

Industrial & Engineering Chemistry Research, 56 (2017) 3129-3144.

[3] E.A. Brignole, S. Pereda, Phase equilibrium engineering, Elsevier, Amsterdam,

2013.

[4] H. Orbey, S.I. Sandler, Modeling vapor-liquid equilibria : cubic equations of state

and their mixing rules, Cambridge University Press, New York, 1998.



(2015) 174-185.

S/B Ratio 20 15 10Total Capital Cost [USD] 28166200 26477900 25086700

Total Operating Cost [USD/Year] 30295400 29532900 28616200

Total Raw Materials Cost [USD/Year] 22709500 22709500 22709500

Total Product Sales [USD/Year] 376937000 379711000 382573000

Total Utilities Cost [USD/Year] 3418610 2754750 1946510

Desired Rate of Return [Percent/'Year] 20 20 20

P.O. Period [Year] 2.49 2.47 2.44

Equipment Cost [USD] 7434100 6925800 6448400

Total Installed Cost [USD] 13214600 12244000 11484200

Total Profit [USD/Year] 346641600 350178100 353956800


[6] C.S. Lim, Z.A. Manan, M.R. Sarmidi, Simulation modeling of the phase behavior

of palm oil-supercritical carbon dioxide, Journal of the American Oil Chemists'

Society, 80 (2003) 1147-1156.

[7] M.E. Araujo, N.T. Machado, M.A.A. Meireles, Modeling the phase equilibrium of

soybean oil deodorizer distillates+ supercritical carbon dioxide using the Peng−

Robinson EOS, Industrial & engineering chemistry research, 40 (2001) 1239-1243.

[8] S. Wang, Z. Luo, Pyrolysis of biomass, Beijing De Gruyter, Science Press, Berlin,

2017.




[10] J.-T. Chen, M.-J. Lee, Vapor-liquid equilibria for benzyl alcohol with carbon

dioxide, ethane, or nitrogen at elevated pressures, Fluid phase equilibria, 130 (1997)

231-242.

[11] S. Liao, Y. Hou, S. Li, X. Chen, W. Wu, High-pressure phase equilibria for the

binary system carbon dioxide+ benzyl alcohol, The Journal of Supercritical Fluids, 55

(2010) 32-36.

[12] D. Walther, G. Maurer, High-pressure vapor-liquid equilibria for carbon dioxide+

benzonitrile, CO2+ benzyl alcohol, CO2+ 2-tert-butylphenol, CO2+ methoxybenzene,

and CO2+ 1, 2, 3, 4-tetrahydronaphthalene at temperatures between 313 and 393 K

and pressures up to 20 MPa, Journal of Chemical and Engineering Data, 38 (1993)

247-249.

[13] H. Chen, S. Zhang, Y. Su, Experimental measurement of supercritical CO2-low

volatility liquid phase equilibria, Chinese Journal of Chemical Engineering, 3 (1993)

52-60.

[14] D.-Y. Peng, D.B. Robinson, A new two-constant equation of state, Industrial &

Engineering Chemistry Fundamentals, 15 (1976) 59-64.

[15] J.F. Boston, P.M. Mathias, Phase equilibria in a third-generation process

simulator, in: Proceedings of the 2nd International Conference on Phase Equilibria

and Fluid Properties in the Chemical Process Industries, West Berlin, 1980, pp. 823-

849.

[16] Peng-Robinson: Aspen Plus Help, in, Aspentech.

[17] Y. Le Guennec, S. Lasala, R. Privat, J.-N. Jaubert, A consistency test for α-

functions of cubic equations of state, Fluid Phase Equilibria, 427 (2016) 513-538.

[18] Y. Le Guennec, R. Privat, J.-N. Jaubert, Development of the translated-consistent

tc-PR and tc-RK cubic equations of state for a safe and accurate prediction of

volumetric, energetic and saturation properties of pure compounds in the sub-and

super-critical domains, Fluid Phase Equilibria, 429 (2016) 301-312.

[19] Y. Le Guennec, R. Privat, S. Lasala, J.-N. Jaubert, On the imperative need to use

a consistent α-function for the prediction of pure-compound supercritical properties

with a cubic equation of state, Fluid Phase Equilibria, 445 (2017) 45-53.

[20] H.I. Britt, R.H. Luecke, The estimation of parameters in nonlinear, implicit

models, Technometrics, 15 (1973) 233-247.

[21] R. Span, W. Wagner, A new equation of state for carbon dioxide covering the

fluid region from the triple-point temperature to 1100 K at pressures up to 800 MPa,

Journal of Physical and Chemical Reference Data, 25 (1996) 1509-1596.

[22] https://www.eia.gov/todayinenergy/prices.php, accessed: 02 Jan 2019.

[23] https://www.made-in-china.com/products-search/hot-china-

products/Benzyl_Alcohol.html, accessed: 05 Jan 2019.

http://www.eia.gov/todayinenergy/prices.php

http://www.made-in-china.com/products-search/hot-china-products/Benzyl_Alcohol.html

http://www.made-in-china.com/products-search/hot-china-products/Benzyl_Alcohol.html

Chapter 4: Bio-oil Mixture Model, Pilot Plant Validation, Aspen Plus® Simulation and Techno-economics 79

Chapter 4: Bio-oil Mixture Model, Pilot Plant Validation, Aspen Plus® Simulation and Techno-economics

This chapter will use the PR-BM property method, successfully implemented

previously in Chapter 3, to model phase behaviour of our bio-crude compounds

with CO2. The model is then successfully validated on pilot plant trials of SFE of

bio-crude and subsequent two-stage fractionation of extract stream. Aspen

Plus® simulation scenarios are then constructed to evaluate the techno-

economics of SFE of our bio-crude mixture for varying solvent/bio-crude ratios.

The SFE process economics were also compared with a conventional distillation

process for bio-crude.

4.1 Title: Extraction and purification of renewable chemicals from hydrothermal liquefaction bio-oil using supercritical carbon dioxide: A techno-economic evaluation

Wahab Maqbool, Kameron Dunn, William Doherty, Neil Mckenzie, Dylan Cronin, Philip

Hobson*

Queensland University of Technology (QUT), 2 George Street, Gardens Point, 4000 Brisbane,

Australia

4.2 Abstract

Supercritical fluid extraction (SFE) and fractionation of products from a complex

mixture such as bio-oil, where many compounds are present in low

concentrations, is a difficult process to model. This difficulty arises from the

uncertainty associated with those interactions between mixture components for

which fundamental vapour-liquid equilibrium (VLE) data is not available. In this

work a novel extraction and purification concept is investigated using a

predictive model developed from VLE data of binary solute-solvent systems;

solute-solute interactions in the supercritical carbon dioxide (scCO2) phase are

neglected. The predictive component of the work employs an equation of state

(EOS) model to achieve the above task. The results of pilot plant trials utilising a

80 Chapter 4: Bio-oil Mixture Model, Pilot Plant Validation, Aspen Plus® Simulation and Techno-economics

bio-crude feedstock were shown to be in good agreement with the model

predictions. Aspen Plus® process simulations were developed for the extraction

process which comprised of supercritical extraction and subsequent purification

steps utilising distillation and multistage evaporation. A techno-economic

analysis of different process designs were evaluated for comparison. In

particular, distillation as the primary separation process with and without

multistage evaporation were simulated to compare the economics of

supercritical extraction to distillation. It was found from simulation results that

distillation is a very energy intensive process, and total operating costs for it are

always greater than supercritical extraction counterparts. Combining multistage

evaporation with distillation will reduce the total operating cost to a slightly

lower value than that required for a supercritical extraction processes. However,

the internal rate of return (IRR) value was similar for both SFE and distillation

combined with multistage evaporation processes. Whilst the solvent/bio-oil

(S/B) ratio will have a considerable impact on total profits of SFE process in

relation to distillation.


4.3 Introduction

Supercritical fluid extraction is currently in use for a number of niche applications

[1, 2] such as the decaffeination of coffee or the recovery of essential oils and

bioactive compounds from plant materials. The use of SFE for the extraction of

compounds from bio-oil has been the subject of a limited number experimental

studies [3-13]. The lack of fundamental investigations into SFE of bio-oil can be

attributed to the highly complex nature of bio-oil and the difficulty this presents

in describing this process in terms of phase equilibria. Bio-oils are made up of

large portions of water and many other individual chemical compounds, but the

latter only in small quantities [4, 6, 7, 13].

A fundamental modelling approach based on an equation of state was adopted in

the current study to investigate the novel SFE and subsequent purification of bio-

oil compounds. The developed model for multicomponent mixture was used to

determine the subsequent staged depressurization conditions required for the

recovery of individual compounds or groups of compounds from the supercritical

extract phase. In this work, in-house produced bio-oil from hydrothermal

liquefaction (HTL) of black liquor, also known more commonly as bio-crude, was

first extracted with scCO2 and subsequently fractionated in to two product

fractions with the use of stage-wise pressure reduction techniques.

In the currently proposed extraction process the highly dilute bio-crude in water

feedstock is first fed into the top of a SFE extraction column. Literature review

and preliminary experiments helped to determine the conditions of temperature,

pressure and bio-crude pH at which the SFE of our bio-crude from the aqueous

phase will produce equilibrated extract samples in the pilot plant trials. The

supercritical extract stream emerging from the top of the extraction column will

be loaded with different bio-crude compounds. As the bio-crude compounds are

absorbed in scCO2 medium, solute-solute interaction effects will be negligible in

this phase as compared to the liquid bio-crude phase. Exclusion of solute-solute

interactions will simplify the system such that only solute-solvent binary

interaction effects will now play the determining role in the phase behaviour

description of supercritical extract phase. The application of stage-wise pressure

reduction techniques for the purification of bio-compounds have been reported

in the literature[2, 14] but for mixtures other than bio-oils.

A Peng-Robinson equation of state[15] (PR-EOS) model was developed to

investigate the phase behaviour of the solutes-rich supercritical phase. This

model was subsequently validated against pilot plant scale trials for predicting

the stage-wise pressure reduction fractionation. This study is aimed at validating

the model predictions at preferably saturated (low S/B) conditions. That is why

a range of S/B ratios will be investigated in subsequent simulation work to give

reader some information on variation of SFE plant economics with change in

solvent usage. Another aim of this study is to, for the first time, compare the


techno-economics of scCO2 separation of bio-crude with that of a conventional

distillation process. This has been achieved by simulating both the SFE and

distillation separation processes in Aspen Plus® and then evaluating the

respective process economics.

4.4 Experimental methodology

4.4.1 Materials

Carbon dioxide was purchased from Supagas (Australia), with purity ≥ 99.9 wt%.

Bio-crude was produced in-house from the HTL of black liquor, where the black

liquor was a lignin-rich by-product of a bagasse pulping process. Phenol, p-cresol,

catechol, 4-ethylphenol, acetic acid, docosane, sulphuric acid and acetone were

purchased from Sigma-Aldrich (Australia), each with purity ≥ 99.0 wt% except

for sulphuric acid and 4-ethylphenol which were ≥ 98.0 wt% and ≥ 97.0 wt% pure

respectively.

4.4.2 Bio-crude preparation and its characteristics

About 50 litres of bio-crude was produced from black liquor using the HTL

continuous reactor facility at QUT. HTL liquefaction of the black liquor was

performed at a temperature and pressure of 290oC and 220 bar respectively. The

HTL reactor residence time was 60 minutes. The bio-crude product was stored at

2oC in a closed container prior to the SFE pilot plant extraction trials. The

produced bio-crude was homogenous, had a dense blackish appearance and a

viscosity similar to water. When physical settling and separation is possible, the

oil fraction should be separated from aqueous fraction of bio-oil as a first option.

Doing so will not only reduce the SFE plant footprint but will also be very helpful

in reducing the operating costs of SFE separation as a result of working with

relatively small volumes.

The bio-crude produced in this work was quite thin and didn’t show any signs of

phase separation upon settling and weeks long storage. However, in relevant

future works, it is advised to look for any possible scenario of physical settling

and separation as a first resort for bio-oil separation. The native HTL bio-crude

had a pH of 9.0 but preliminary SFE pilot plant trials revealed that the extraction

at such a high pH was problematic as it caused foaming, clogging and carry-over

of water from the extraction column. To lower the pH of the bio-crude, sulphuric

acid was incrementally added and then vigorously agitated with an electric mixer

until a final pH of 4.4 was achieved. This pH-lowered bio-crude (pH=4.4) was

centrifuged at 3300 rpm (Beckman GS-6R centrifuge, Marshall Scientific, USA) for

5 minutes, to remove precipitates and suspended solids.


4.4.3 The SFE pilot plant setup

The SFE pilot plant used to determine the initial extraction and verify the

predicted stage-wise fractionation of bio-crude components is shown

schematically in Figure 4-1. The pilot plant was purchased from Applied

Separations (USA) and installed and commissioned at the QUT Pilot Plant

Precinct. It consisted of a CO2 reservoir, bio-crude feed tank, CO2 pre-heater,

temperature-regulated extraction column and two separators in series. Pressure

in the separators was controlled with back pressure regulating valves. The shut-

off valve in-between extraction column and first separator was used as a back-

pressure regulator to control the pressure in extraction column. Extraction

column was a 4 Litre vessel. Column ID is about 50 mm, whilst column height is

2.2 m. Separator 1 and 2 had a volume of 300 mL and 4 L respectively.

Figure 4-1 Pilot plant setup used in this work for supercritical extraction and

fractionation of bio-crude (T: temperature control, Sep: separator, MV:

micrometering valve). Sep-1 and Sep-2 were wrapped in trace heaters to

compensate for the cooling effects resulting from depressurisation of the extract

streams.

4.4.4 Extraction and Fractionation Procedure

Carbon dioxide from the reservoir cylinder is supplied at a set flow rate by a high

pressure pneumatic pump (Haskel, USA). This high-pressure CO2 is then passed

through a 1250 watts pre-heater to bring the CO2 up to the desired extraction

temperature, before entering into the extraction column. The extraction column

is a 4 litre stainless steel tubular vessel in which CO2 enters from bottom and bio-

crude from the top. The CO2 and bio-crude streams flow in counter current over

a densely packed bed made up of small tubular elements. The CO2 stream absorbs

the majority of non-aqueous bio-crude and then leaves from the top of the

extraction column where it is fed into the two separators in series. The remaining

bio-crude and the majority of water is continuously drained from the bottom of


the extraction column as raffinate. Micrometering and back pressure regulating

valves are positioned so as to produce the required pressures in column and

separators respectively. Once the steady state operation has been reached and no

more fluctuations in temperatures, pressures and flowrates are observed,

sampling procedures are initiated. Separator fractions and column raffinate

samples were collected every 15-30 minutes once continuous operation was

achieved.

From the bio-oil solubility in scCO2 reported in the literature[3, 16] and

preliminary trials on a lab scale solubility cell, it was determined that minimum

mass flow ratios of CO2 to bio-oil of under 10 could be used, in the pilot plant

trials, to ensure getting saturated extractions and consistent solubility data for

analysis. Normally S/B should be greater than 10 to maximise yields but was

limited in the pilot plant trials to less than this value because of pump cavitation

issues. Run conditions used for the pilot plant extraction and fractionation trials

are summarized in Table 4.1.

Table 4.1 Parameters used in this work for the supercritical CO2 pilot plant

extraction and fractionation of bio-crude produced from HTL of sugarcane

bagasse black liquor. Extraction was performed at 55oC temperature and 206.4

bar pressure, and Sep-2 was maintained at 18.4oC temperature and 46.8 bar

pressure.

No.

Sep-1 CO2 flow1 (mL/min)

Bio-crude flow

(mL/min)

S/B ratio2 (mass basis)

Extract Yield (%)

Press. (bar)

Temp. (oC)

1

137.6 49

217 89 2.5 0.4

2 202 88 2.3 0.7

3 203 89 2.3 0.6

4

116.3 47

307 68 4.5 1.1

5 284 50 5.7 0.9

6 299 52 5.7 1.0

7

91.5 43

260 41 6.3 1.2

8 291 41 7.1 1.2

9 303 42 7.2 1.7

10 300 42 7.1 1.7

1 Flow rate is given for CO2 at extraction column inlet. Corresponding CO2 inlet

temperature and pressure conditions were 48.6oC and 206.4 bar respectively. 2

Bio-crude density was 1.09 g/mL.

After reviewing the temperature and pressure conditions commonly found in the

literature[4, 16] for such an extraction process and to ensure the density


difference between the two phases inside the extraction column was at least 150

g/L[17] to avoid flooding, the conditions in Table 4.1 were chosen in this work to

make a comparison between our experimental fractionation results and the

model predictions. Maximum CO2 density used in the pilot plant trials was 763

g/L.

4.4.5 Gas chromatography mass spectrometry (GC-MS) analysis

The quantities of several key compounds present in the bio-crude and extraction

products were determined by GC-MS analysis. This process was performed on an

Agilent (US) 6890 Series Gas Chromatograph and a HP 5975 mass spectrometer

detector, employing helium as the carrier gas. The installed column was a

dimethyl polysiloxane Agilent DB 5-MS, 30 m x 0.32 mm x 0.25 μm. A split-less

injection of 2 μL was delivered to the injection port set at 250 °C. The

temperature program commenced at 70 °C and was heated at a rate of 5 °C.min-1

to a temperature of 320 °C. Compounds were identified from the spectra by

means of the Wiley library-HP G1035A and NIST mass spectra libraries and

subsets-HP G1033A (a criteria quality value >90% was used). Analytical samples

were prepared in acetone at a concentration of 0.05 mg/mL. Standard solutions

of pure chemicals were also prepared in acetone, in order to produce a 5-point

calibration curve over a concentration range of 0.025 to 0.3 mg/mL. All standards

and analytical samples were spiked with Docosane at a concentration of 0.06

mg/mL, to act as an internal standard.

4.4.6 Nuclear magnetic resonance (NMR) spectroscopy

Each sample (100 mg) of the collected oil fraction was dissolved in 0.9 mL of

deuterated water (D2O)) and filtered. The 1H spectra were then recorded at 25

°C on a Bruker AVANCE III HD 600 MHz NMR spectrometer (Agilent, US)

equipped with a cooled 5 mm TCI Cryoprobe. A total of 8 transients having an

acquisition time of 1.7 seconds and a spectral width of 9 kHz were recorded using

the Bruker pulse sequence noesygppr1d which features water suppression. The

triplet phenol reference peak was used as an internal chemical shift reference

point (δH = 7.25). Processing used shifted squared sine bell Gaussian apodization

in 1H. Data processing and plots were carried out using ACD/NMR processing

software, with automatic phase and baseline correction.

4.5 Thermodynamic modelling

Modelling was implemented in Aspen Plus® software, using the Peng-Robinson-

Boston-Mathias (PR-BM) property method [18]. The Peng-Robinson Equation of

State (PR-EOS)[15] forms the basis of the PR-BM property method, and BM alpha

function and asymmetric mixing rules are used in conjunction with the EOS to

make it suitable for modelling polar, non-ideal chemical systems. Eqs 1-14 are

mathematical expression of PR-BM model with asymmetric mixing rules.


𝑃 =𝑅𝑇

𝑉𝑚−𝑏−

𝑎

𝑉𝑚(𝑉𝑚+𝑏)+𝑏(𝑉𝑚−𝑏) (1)

𝑏 = ∑ 𝑥𝑖𝑏𝑖𝑖 (2)

𝑎 = 𝑎0 + 𝑎1 (3)

𝑎0 = ∑ ∑ 𝑥𝑖𝑥𝑗(𝑎𝑖𝑎𝑗)0.5

(1 − 𝑘𝑖𝑗)𝑗𝑖 (4)

Eq 4 is the standard quadratic mixing term, where 𝑘𝑖𝑗 has been made

temperature-dependent

𝑘𝑖𝑗 = 𝑘𝑖𝑗(1)

+ 𝑘𝑖𝑗(2)

𝑇 + 𝑘𝑖𝑗(3)

𝑇⁄ (5)

Where 𝑘𝑖𝑗 = 𝑘𝑗𝑖 and superscripts (1), (2) and (3) are numbered

terms in eq 5

𝑎1 = ∑ 𝑥𝑖[∑ 𝑥𝑗((𝑎𝑖𝑎𝑗)1 2⁄ 𝑙𝑖,𝑗)1 3⁄𝑛𝑗=1 ]

3𝑛𝑖=1 (6)

Eq 6 is an additional asymmetric term used to model highly non-linear systems

𝑙𝑖𝑗 = 𝑙𝑖𝑗(1)

+ 𝑙𝑖𝑗(2)

𝑇 + 𝑙𝑖𝑗(3)

𝑇⁄ (7)

Where 𝑙𝑖𝑗 ≠ 𝑙𝑗𝑖 and superscripts (1), (2) and (3) are numbered

terms in eq 7

The pure component parameters for PR-EOS are calculated as follows:

𝑎𝑖 = 𝛼𝑖0.45724𝑅2𝑇𝑐𝑖

2

𝑃𝑐𝑖 (8)

𝑏𝑖 = 0.07780𝑅𝑇𝑐𝑖

𝑃𝑐𝑖 (9)

The parameter 𝛼𝑖 in Eq. 8 is used to improve the accuracy of predicted

temperature response of the pure component vapour pressure. In standard PR-

EOS, this parameter is expressed with eqs 10-11.

𝛼𝑖(𝑇) = [1 + 𝑚𝑖(1 − 𝑇𝑟𝑖1 2⁄

)]2 (10)

𝑚𝑖 = 0.37464 + 1.54226𝜔𝑖 − 0.26992𝜔𝑖2 (11)

𝛼𝑖 defined in eq 10 is used when 𝑇𝑟 < 1 (subcritical temperature), otherwise

Aspen BM alpha function (eqs 12-14) is used.

𝛼𝑖(𝑇) = [𝑒𝑥𝑝[𝐶𝑖(1 − 𝑇𝑟𝑖𝑑)]]

2

(12)

𝑑𝑖 = 1 + 𝑚𝑖 2⁄ (13)

𝐶𝑖 = 1 − 1 𝑑𝑖⁄ (14)

Binary interaction parameters (𝑘𝑖𝑗 , 𝑙𝑖𝑗) must be determined from regression of

phase equilibrium data. The optimized values of these binary interaction

parameters were obtained by maximum-likelihood algorithm (eq 15), defined

within the Aspen Plus® data regression system.


𝑄 = ∑ 𝑤𝑛 ∑ [(𝑇𝑒,𝑖−𝑇𝑚,𝑖

𝜎𝑇,𝑖)

2

+ (𝑃𝑒,𝑖−𝑃𝑚,𝑖

𝜎𝑃,𝑖)

2

+ ∑ (𝑥𝑒,𝑖,𝑗−𝑥𝑚,𝑖,𝑗

𝜎𝑥,𝑖,𝑗)

2𝑁𝐶−1𝑗=1 +𝑁𝑃

𝑖=1𝑁𝐷𝐺𝑛=1

∑ (𝑦𝑒,𝑖,𝑗−𝑦𝑚,𝑖,𝑗

𝜎𝑦,𝑖,𝑗)

2𝑁𝐶−1𝑗=1 ] (15)

Table 4.2 provides the standard pure component properties of critical

temperature (Tc), critical pressure (Pc) and acentric factor (ω), used in the Aspen

Plus® modelling of the binary systems.

Table 4.2 Critical properties of pure compounds used in the Aspen Plus®

modelling of the binary systems

Component Tc (oC) Pc (bar) ω

Carbon dioxide

31.06 73.83 0.2236

p-Cresol 431.5 51.5 0.5072

4-Ethylphenol 443.3 42.9 0.5154

Phenol 421.1 61.3 0.4435

Catechol 490.85 74.9 0.6937

Acetic acid 318.8 57.9 0.4665

Water 373.9 220.6 0.3449

The default binary interaction parameters available in in Aspen Plus® were

adjusted in this study such that the PR-BM property method used in the analysis

produced predictions which agreed more closely with experimental solubility

data published in the open literature. Table 4.3 shows the deviations between the

default Aspen Plus® predictions and experimental vapour-liquid equilibrium

(VLE) data from literature, for all our binary systems. Regressed values of binary

interaction parameters for all our binary systems are given in Table 4.4. Acetic

acid in Aspen Plus® showed relatively poor agreement with experimental vapour

phase solubility data giving an average absolute relative deviation (AARD) of

about 30% when compared to Bamberger et al. (2000)[19] and about 35% to

Jonasson et al. (1998)[20] data. On the other hand, liquid phase composition data

of this system was reasonably represented with the same model, where the AARD

between model predictions and both experimental studies[19, 20] was within

10%. Bamberger et al. (2000)[19] also pointed out towards difficulty in

modelling the VLE data of acetic acid, whence his selected model represented the

vapour phase composition with yet 18% deviation to experimental data, but only

when more sophisticated modelling approach of taking into account the

dimerization of acetic acid was adopted. Yet, the model predictions of Bamberger

et al. (2000)[19] were 50% smaller than reported by Jonasson et al. (1998)[20].

This means the model chosen in this work, and which represents all our other

binary systems very well, can be reasonably extended to acetic acid and CO2


binary system too, as the average deviation between our model predictions and

experimental data of different sources[19, 20] is on average 25-35% AARD. For

catechol experimental VLE data was not available, as catechol will be present in

solid phase and will exhibit solid-fluid equilibrium at our interested supercritical

extraction conditions. For this binary system no regression was done, and it was

found that the default model predictions were in reasonable agreement to

experimental solid-fluid data of Garcia et al. (2001)[21], with average deviation

of less than 20% AARD for data determined under 200 bar pressure. Similarly, no

experimental VLE data was available for 4-ethylphenol and CO2 binary system, so

no regression could be performed on this system as well, rendering the model

description of this system totally predictive in nature based upon critical

properties of pure components listed in Table 4.2 above.

Table 4.3 Percent AARD between predicted and experimental VLE data for

different solute-CO2 binary systems using the default regression coefficients for

the PR-BM property method model available in Aspen Plus®

Binary system Experimental data Isotherms (Temp. in K) Model deviation

(% AARD)

Phenol Pfohl et al. (1997)[22] 373.15 4.8

Yau et al. (1992)[23] 348, 373, 398 15.5, 8.4, 4.9

Catechol Garcia et al. (2001)[21] 333.15, 348.15, 363.15 15.5, 21.2, 21.8

Acetic acid Bamberger et al. (2000)[19] 353.2, 313.2, 333.2 28.0, 34.8, 31.8

Jonasson et al. (1998)[20] 323, 348 45.5, 24.5

p-Cresol Lee et al. (1999)[24] 353.15, 393.15, 423.15 4.2, 2.4, 1.9

Pfohl et al. (1997)[22] 373.15 9.1

Water

Bamberger et al. (2000)[19] 323.2, 333.2, 353.1 0.9, 0.6, 0.4

Dohrn et al. (1993)[25] 323.1 1.0

Briones et al. (1987)[26] 323.14 1.5

Table 4.4 Numerical values of binary interaction parameters obtained after

regressing the experimental VLE data (Table 4.3) of different solute-CO2 binary

systems, with the EOS model of PR-BM property method within Aspen Plus® data

regression system

Binary system 𝑘𝑖𝑗(1)1 𝑘𝑖𝑗

(2) 𝑙𝑖𝑗

(1) 𝑙𝑗𝑖

(1) 𝑙𝑖𝑗

(2) 𝑙𝑗𝑖

(2)

Phenol 0.08882 - 0.11836 0.02185 - -

Acetic acid 0.05469 - 0.18117 0.06455 - -

p-Cresol 0.29673 -0.00057 0.32347 0.21729 -0.00065 -0.00065

Water -0.32147 0.001 -0.32052 0.19947 - -

1 component i is solute and component j is CO2


The purpose of selecting a few model compounds from bio-oil in this project is

only for developing a methodology. The developed methodology is supposed to

be extended to whole bio-oil (mostly including compounds present in large

quantities) in a real scenario. Of course, after extraction and fractionation, the

rest of the bio-oil is not meant to be thrown away, unless it is very diluted or

contains residual compounds in very small quantities. Residue bio-oils would

likely find use as fuel.

4.6 Process design and techno-economic evaluation using Aspen Plus®

After modelling the individual binary phase behaviour of each selected chemical

compound with scCO2, simulations were run in Aspen Plus® to determine the

potential for fractionation of the column extract stream. The solute-solute

interaction parameters were set to zero. Only solute-solvent binary interaction

parameters were employed to determine if the binary interaction parameters

alone were sufficient to describe the phase behaviour and predict the

fractionation characteristics for a defined column extract stream composition. In

total, four process scenarios were simulated and economically evaluated. These

scenarios are summarised in Table 4.5.

Table 4.5 Description of Aspen Plus® simulation scenarios simulated in this

work, for recovery of compounds from bio-crude.

Scenario

Process-1 (P-1)

• Initial scCO2 extraction of bio-crude from aqueous component;

• two-stage scCO2 fractionation of biocrude extract;

• further purification of fractionated components using conventional distillation and;

• catechol recovery from aqueous extraction column raffinate using multi-stage evaporation.

Process-2 (P-2) • As for P-1 but with single-stage scCO2 fractionation to

recover column extract

Process-3 (P-3)

• Atmospheric distillation of bio-crude;

• distillation includes the recovery and separation of the catechol and water components.

Process-4 (P-4) • As for P-3 but with multi-stage evaporation to recover

catechol from the bottom stream of the first distillation column

A FLASH2 separator unit operation was employed in the Aspen Plus® simulation

model to produce the solutes-rich stream representative of supercritical extract

stream leaving our pilot plant extraction column. The governing relationships


used in the FLASH2 model are not reported here as they are readily available in

the open literature [27-29]. Upon depressurization of the extract stream the

predicted equilibrium composition of both the liquid and vapour phases were

dictated by the thermodynamic models described in Section 4.5. Two additional

FLASH2 units downstream of the solute rich extraction stream were used in the

Aspen Plus® flowsheet to simulate the pilot plant separators. In the simulation

the extraction column and separators were operated at the same pressure and

temperature conditions maintained in the pilot plant trials.

In the simulation it was assumed that downstream distillation of the extracted

products would be used to recover individual bio-crude compounds. The

RadFrac® unit available in Aspen Plus® was used to simulate the additional

distillation columns. For comparison purposes Aspen Plus® simulation was also

developed in which all bio-crude products were recovered through conventional

distillation.

By way of example Figure 4-2 is the process flowsheet of the SFE and

fractionation sections of the P-1 scenario. Process flow sheets for the remaining

scenarios are provided as Supporting Information (Figure 4-34S, Figure 4-35S,

Figure 4-36S, Figure 4-37S). Referring to Figure 4-2, bio-crude is pumped from

ambient conditions (22oC, 1 bar) to 206.4 bar pressure while the CO2 is recycled

from downstream units at 206.4 bar pressure and then preheated along with bio-

crude in a preheater to 55oC.

Both are then flashed separated in an extraction column at 55oC temperature and

206.4 bar pressure. Temperature and pressure conditions for fractionation are

selected by the model for maximum separation between catechol and the

remaining compounds.


Figure 4-2 Aspen Plus® process flowsheet for supercritical extraction of bio-

crude followed by two-stage fractionation of column extract (part of P-1).

4.6.1 First separator

Figure 4-3 shows the effect of temperature on distribution coefficients (K) of

different components in a typical SFE fractionation process. The distribution

coefficient is the mole fraction of a component in supercritical CO2 phase divided

by the mole fraction of that component in liquid phase. It is evident from Figure

4-3 that the effect of temperature on the extent of separation between mixture

components is significant for all components except catechol and water. Of the

selected compounds used in the current study therefore only catechol and water

will be retained in the first separator upon depressurization. Figure 4-3 also

indicates that the distribution coefficients for phenol, p-cresol, 4-ethylphenol and

acetic acid rise rapidly as the temperature drops below 45oC.

In our first separator the cooling effect of depressurization was compensated for

to some extent by external heat provision resulting in the temperature dropping

to 43.1oC. Without any external heat supply in the first separator, the

temperature will drop to 39 oC and eventually the mixture will revert to a liquid

phase with no feed going into second separator. At 43.1 oC, all components, except

catechol and water, will have K values greater than 5, corresponding to

favourable separation process design conditions. Lowering the temperature

further to 40oC will further increase the catechol K value by almost two-fold (1.8

times).


Figure 4-3 Effect of temperature on distribution coefficients of components to be

fractionated by stage-wise pressure reduction. P = 90 bar

It is a well-reported phenomenon for supercritical extraction technologies that as

the pressure increases, separation among components start to diminish,[1, 30] as

can be observed in Figure 4-4 where the separation factor (α) is the ratio of the

distribution coefficient of one solute component relative to another.

Figure 4-4 Separation factors of components tend to decrease and approach unity

at higher pressures. T = 43.1 oC

By contrast, the lower the fractionation pressure, the greater the possible

separation among components. However, there is a trade-off; inspection of the

distribution coefficients in Figure 4-5 shows that as pressure decreases, so too do

the distribution coefficients of components. For example, at 75 bar the α values


are very attractive (Figure 4-4), but this is achieved at the expense of extremely

low distribution coefficients (Figure 4-5).

Figure 4-5 Distribution coefficients of components will decrease with decrease in

pressure. T = 43.1 oC

A suitable compromise pressure condition can be found at 90 bar, where α values

of all our product compounds relative to catechol, are acceptably high as are their

respective distribution coefficients. The next least soluble compound after

catechol and water is phenol, and at 90 bar its K value of 5.2 will drop to just 0.55

at 75 bar. For practical separation, an α value of at least 2 is necessary [30-32]. In

our extract mixture, though all compounds show quite higher α values in

reference to catechol, even at pressures as high as 180 bar, but at such pressure

the catechol K value is about 11 times greater than at 90 bar.

4.6.2 Second separator

The second separator was operated at 60 bar and 32oC, to allow pressurized

recycling of lean CO2 coming off it. It is important to keep the temperature in the

second separator slightly above the saturation temperature of CO2 (22 oC) at 60

bar to keep most of CO2 in vapour form to be recycled. So basically what has been

done in this work is provision of external heat supply in both separators so as to

ultimately keep the second separator temperature at 32 oC. Through iteration on

second separator, it was found that raising the temperature of the second

separator from 22 oC to 32 oC will increase the pressurized recycling of CO2 from

11.8% to 93.9% of total CO2 in use.


4.6.3 Recycling

Vapour CO2 leaving the second separator is cooled down to convert it to liquid

form and then pumped to 206.4 bar again to introduce it at the preheater inlet for

reuse in the extraction column. Liquid product fractions are collected from

separators 1 and 2 (SEP-1, SEP-2) in collectors 2 and 3 (COL-2, COL-3)

respectively. Extraction column raffinate is collected in collector 1 (COL-1). A

small amount of CO2, about 6% of total in use, is released from the liquid products

recovered at ambient pressure from the column and both separators. It was

calculated in this work that compression and recycling of this residual CO2 is

more economical (Figure 4-6) than to make it up from an external supply of CO2.

In Scenario P-2, single stage collection is performed at 60 bar and 32oC. For this

scenario the amount of residual CO2 being recycled at ambient pressure is 3% of

total CO2 in use. In the P-1 and P-2 scenarios, the amount of CO2 being

recompressed from ambient conditions are 196 kmol/hr and 102.5 kmol/hr

respectively.

Figure 4-6 Operating cost of CO2 compression from ambient to 60 bar (liquid

state) pressure vs liquid CO2 make-up cost.

4.6.4 Product purification

Liquid products from the double and single separators in P-1 and P-2 respectively

are sent to distillation columns where further separation and purification takes

place. For both of these scenarios the extraction column raffinate is fed to a multi-

stage evaporator set for single product (catechol) recovery (Figure 4-7).

In P-1 and P-2, raffinate from the extraction column contains predominantly

unrecovered catechol (86 wt% of catechol initially in the bio-crude feed) and

water. This catechol/ water mixture is assumed in these scenarios to be passed

through a multi-stage evaporation process to recover the catechol. A four stage

pressure reduction and vapour heat recovery regime was implemented in the


multi-stage evaporator set with the final (predominantly water) component

being condensed at 0.065 bar (abs) and 40.3oC. The multi-stage evaporator set

was predicted to remove 88 wt% of water in the extraction column raffinate. A

final distillation step is performed post-evaporation to remove the remaining

water and recover commercial purity catechol.


Figure 4-7 Aspen Plus® flowsheet for the multi-stage evaporation and distillation

processes used in the recovery of products following scCO2 extraction and

fractionation (Scenario P-1)


Modelling conventional distillation of the biocrude component of the scCO2

extraction products in P-1 and P-2 predicted separation and recovery of (in order

of ascending boiling point temperature) water, acetic acid, phenol, p-cresol, 4-

ethylphenol and catechol. The bottoms stream of the first distillation columns in

P-3 and P-4 each contain a dilute solution of catechol in water. Simulating the

recovery of catechol from the bottoms stream by subsequent conventional

distillation (P-3) and multi-stage evaporation (P-4) enables a direct techno-

economic comparison to be made between these two options for catechol

recovery.

Steam was assumed as the heating medium in the extraction, fractionation,

distillation and evaporation stages of the proposed process scenarios. Wherever

the distillation top temperature was more than 140oC, heat recovery was used for

steam generation. Heat recovered from streams less than 140oC was used for pre-

heating the bio-crude feed stream prior to distillation. Where appropriate

distillate fractions were sent for further cooling and crystallisation to get the final

products in market-ready form.

4.7 A techno-economic assessment of process scenarios

The proprietary Aspen Process Economic Analyzer® was used with Aspen Plus®

process simulation software to undertake a techno-economics assessment of the

four process design scenarios. The compositional analysis of the bio-crude used

in the simulation was based on an analysis of bio-crude produced during HTL

continuous reactor pilot plant trials at QUT. The HTL feedstock used in these

trials was a lignin-rich black liquor produced from the bio-refining of bagasse.

The identity and relative concentrations of five main chemicals in the bio-crude

were determined using GC-MS and NMR analysis. A normalised relative bio-crude

composition based on these five chemicals (Table 4.6) were used as inputs in the

simulation model. A value of 90wt% water contents in bio-oil was assumed for

the Aspen Plus® simulation scheme. An S/B mass ratio of 6.2 was used in our

simulation work. The effects of higher S/B ratios (of up to 20.2) are discussed in

the techno-economic assessment of the P-1 scenario.

Table 4.6 Composition of bio-crude used in Aspen Plus® simulations of this work

Component Phenol p-

Cresol 4-Ethyl phenol

Catechol Acetic Acid

Water

Composition (wt%,

normalized) 0.88 0.03 0.03 0.44 8.61 90

Bio-crude was the primary raw material input to all the simulated process

scenarios and its value was assumed to be defined by its heating value (3717

kJ/kg) relative to crude oil and current crude oil prices [33, 34]. Unit bio-crude


production costs, CO2 price [35] and end-product sales prices [36] in this work

are listed in Table 4.7.

Table 4.7 Raw material cost and product prices used in techno-economic

evaluations of this work

Material Price (USD/tonne) Material Price (USD/tonne)

Bio-crude 42 4-Ethylphenol 1,200

Phenol 1,500 Acetic Acid 1,200

Catechol 2,400 Aqueous1 (AA) 360

p-Cresol 1,100 Carbon Dioxide 176.5

1 Here aqueous (AA) is a weak acetic acid solution, and its price is derived from pure acetic acid price on content basis.

All product prices are based on 99% purity, except for 4-ethylphenol and aqueous

(AA) which are 98% and 30% pure respectively. Carbon dioxide was completely

recycled in this simulation work and its purchase price was treated as a capital.

In our Aspen Plus simulations, the ultimate fuel source for electricity and steam

utilities (Table 4.8) is natural gas. The utilities prices are calculated based on their

energy contents, and are current [37].

Table 4.8 shows the utilities prices used in this work for evaluating techno-

economics of different process scenarios.

Table 4.8 Utilities prices used in this work for Aspen Plus® simulations

Utility Price/unit Utility Price/unit

Electricity 0.0775 USD/kWhr LP steam 1 1.90E-06 USD/kJ

Cooling water 2.12E-07 USD/kJ HP steam 2 2.50E-06 USD/kJ

Chilled water 2.12E-07 USD/kJ LPSG 3 -1.89E-06 USD/kJ

1 low-pressure steam, 2 high-pressure steam, 3 low-pressure steam generation

Economics were evaluated assuming a total plant life of 20 years and for a

company hurdle rate of 10%. The plant start-up time was given as 18 months and

the plant availability to be 95% (8327 hr). Capital costs in this work include

equipment costs (see Appendix), installation costs and site development costs.

Stream specifications and summary of utilities costs are also provided in the

Appendix. Results are presented in terms of costs associated with total capital,

raw materials (feedstock), utilities and operating, as well as annual product sales

and profit.

4.8 Results and Discussion

The bio-crude pH was lowered from 9.0 to 4.4 by the addition of 2% (vol/vol) of

98 wt% pure sulphuric acid resulting in approximately 0.9 wt% of initial bio-

crude dropping out of solution. The bio-crude was then centrifuged to remove


any suspended solids (Figure 4-8) prior to extraction and fractionation in the

scCO2 extraction pilot plant.

Figure 4-8 Black liquor bio-crude before (A) and after (B) acidification.

Sampling of extraction and fractionation pilot plant products were performed

under steady state operating conditions. Throughout the pilot plant trials

maximum standard deviations in temperature and pressure of the extraction

column, separator-1 and separator-2 were ±0.5 oC, ±0.8 oC and ±1.1 oC, and ±2.9

bar, ±1.7 bar and ±1.4 bar respectively. Average extract yield was 1.0 wt% of bio-

crude feed rate, and it varied over 0.4 wt% to 1.7 wt% for an S/B range of 2.3 to

7.2. The average mass fraction solubility of bio-crude in scCO2 was 0.00213 with

a maximum relative standard deviation of 23.3%. This level of deviation in bio-

crude solubility in scCO2 was deemed to be a reasonable indicator that

equilibrium conditions had been achieved within the extraction column. Product

concentrations in extract fractions of pilot plant are given in Table 4.10S of

Supporting Information.

The relative concentrations of phenol, p-cresol, catechol and 4-ethylphenol were

quantified using GC-MS and acetic acid concentration was measured using NMR.

The volume of sample collected from Separator-2 during trials was sufficient to

obtain triplicate GC-MS results although only sufficient to produce duplicate NMR

measurements for acetic acid. Both the experimental and simulated results

indicated that only catechol would be recovered from Separator-1. The

concentration of catechol (0.142 mg/mL) in our bio-crude was almost half of the

most abundant compound phenol (0.288 mg/mL); the polar nature catechol

would mitigate against its extraction with scCO2. The concentration of other

selected compounds in our bio-crude was 0.011 mg/mL for p-Cresol, 2.803

mg/mL for acetic acid and 4-ethylphenol was less than the detection limit of GC.

In Chapter 4, we used a bio-crude produced from HTL of black liquor, and it didn’t

happen to have all those typical bio-oil compounds previously identified in

Chapter 2. Compounds which were there in our in-house produced bio-crude

were used for experimental comparison and simulation purposes. Though 4-


ethylphenol was found in our bio-crude in small quantity, it was not either

identified as an abundant compound in most of the bio-oil composition studies

reviewed in Chapter 2. We could quantitate a few model compounds in our bio-

crude, that’s why the pilot plant experimental results and simulation scheme used

those compounds as a model system in this work.

The sample volumes collected at Separator-1 samples were small compared to

those collected at Separator-2. Also, the concentration of catechol in Separator-1

samples was not much, so the GC-MS results for separator-1 samples should be

regarded here more of a qualitative nature.

Analysis of Separator-2 samples indicated that the relative standard deviation in

concentration measurements for phenol, p-cresol and 4-ethylphenol ranged from

14.4% to 24.8%, while for catechol and acetic acid the maximum deviation was

12.1%. Figure 4-9 shows a comparison between the pilot plant experimental

relative concentrations determined by a GC-MS method compared with model

predictions for Separator-2 extracts. Figure 4-10a is a comparison of model and

experimental results determined both by GC-MS (phenol) and NMR (acetic acid),

also for Separator-2 samples. Figure 4-10b shows a comparison of the predicted

and measured (GC-MS) relative concentrations of catechol and p-cresol for

Separator-1 samples.

Figure 4-9 Relative concentrations of compounds in Separator-2 samples of

supercritical extract, collected at a temperature of 18.4 oC and a pressure of 46.8

bar. Legend numerical values correspond to first separator pressure conditions

(in bar abs). Concentration measurements were determined by GC-MS; Aspen

Plus® model PR-BM was used in the simulations.


Figure 4-10 Comparison of experimental scCO2 fractionation of extracted bio-

crude with Aspen Plus® model of this work. (A) Data of phenol (GC-MS) and

acetic acid (NMR) for fraction-2. (B) Catechol relative concentration in fraction-1

relative to p-cresol in the same fraction. Legend numerical values in both figures

(A) and (B) correspond to first separator pressure conditions. Fraction-2 was

collected at 18.4 oC temperature and 46.8 bar pressure.

Phenol and acetic acid were relatively abundant compounds of bio-crude; other

compounds were found to be present in comparatively small quantities. Figure

4-9 and Figure 4-10 indicate reasonable qualitative agreement between

experimental and model data for all compounds. However quantitative

comparison in terms of relative concentrations was good for phenol and acetic

acid only due to the relative abundance (and therefore reduced experimental

uncertainty) associated with these two compounds. Absolute deviation between

experimental and model data for phenol ranged from 17.6% to 20.4%, and for

acetic acid it was 31.0%.

By comparing the measured and modelled component mass ratios some of the

experimental uncertainty associated with measuring the small quantities of p-

cresol, 4-ethylphenol and catechol present in the bio-crude fractions, can be

circumvented (Figure 4-11).


Figure 4-11 Mass ratios of compounds in second fraction of supercritical extract,

collected at 18.4 oC temperature and 46.8 bar pressure. Legend numerical values

correspond to first separator pressure conditions. Amounts determined by GC-

MS method. Aspen Plus® model PR-BM was used in simulation.

Inspection of Figure 4-11 indicates reasonable agreement in terms of the mass

ratios between experimental and model data for these more minor components.

Maximum absolute deviation between model and experimental data for

catechol/p-cresol and catechol/4-ethylphenol was under 20% (ranging from

1.21% to 19.61%) except for samples collected at 91.5 bar separator-1 pressure

for which absolute deviation reached 44.81% and 48.63% respectively. This

discrepancy was most probably caused by catechol precipitation during the

fractionation process, as inspection of Figure 4-11 indicates that the ratio of 4-

ethylphenol/p-cresol (where catechol and the potential for crystallisation is

absent) showed a maximum absolute deviation of just 9.98% to the model, at all

studied conditions.

Figure 4-5 indicates that extent of fractionation of the bio-crude extract into two

fractions, by means of stage-wise pressure reduction is limited by the respective

phase equilibrium characteristics of the bio-crude components. Extraction can be

more effective into distinct fractions in the first place in column where K values

are more favourable, and should be above 1 for a practical separation [31, 32].

Such higher K values, for some compounds, in an extraction column become

possible due to involvement of solute-solute interactions and tendency of being

selectively extracted into vapour phase in comparison to other compounds. For

example, in Figure 4-5, acetic acid is showing higher K values than many others

in a supercritical extract stream, and comes out potentially as a good candidate

when to be fractionated into a lower pressure separator, but when it is seen in

the context of supercritical extraction itself in a column it is well-known that

acetic acid shows very small K value, about 0.03 (weight basis),[2] and largely

remains in liquid (water) phase. It means, though, a compound like acetic acid


might be a bad choice when it comes to extracting it , but once extracted out of

bio-crude, this compound shows better tendency to be further fractionated by

stage-wise pressure reduction. Our initial pilot plant runs on a different kind of

bio-crude also endorsed the possibility of such a stage-wise fractionation in

which acetic acid was being collected in the last separator, just like the simulation

results of this work suggested.

The only difference between simulations of P-1 and P-2 processes was that in

former two-stage fractionation was done based on optimal conditions of our

model, while in later only single stage fractionation was performed. The effect of

this fractionation will be seen translated into overall techno-economics of

process, presented later in this document. Column extract yields in P-1 and P-2

were 12.95 wt% and 12.98 wt% respectively, whereby 9.15 wt% and 9.39 wt%

respectively of extracted products were recycled back from separator-2 to the

extraction column. Recycle stream from separator-2 contained primarily water

along with small amounts of acetic acid, therefore it was deemed not economical

in this work to remove these two components from recycled CO2 before putting

it back into the extraction column. The final collected product yields of both these

P-1 and P-2 processes were 9.59 wt% each (dry basis), with 2.03 wt% and 1.87

wt% respectively of feed bio-crude water contents in them. More than 94% of

CO2 being used in both processes was recycled off sepearator-2 at 60 bar, the rest

being recycled at ambient pressure after depressurization of liquid products from

the extraction column and both separators.

In all four process designs, three products including acetic acid, catechol and

phenol were produced with at least 99% purity. On the other hand, 4-

ethylphenol, p-cresol and weak acetic acidic solutions in water (aqueous AA)

were produced in purity ranges of 80%-85.5%, 78.3%-85% and 21%-26%

respectively. Water/acetic acid and p-cresol/4-ethylphenol could not be

separated from each other beyond the above mentioned ranges. Multiple stage

evaporation recovered 97.5% of catechol in the P-1, P-2 and P-4 scenarios and

was able to remove 87.9% of the water entering the evaporator station. The

performance of the multiple stage evaporation unit was evaluated in terms of

tonnes of water evaporated per tonne of steam supplied. This ratio was 3.01 in

both in P-1 and P-2, and 4.58 in P-4. In P-4, the ratio was greater than 4 (ideal)

because the feed stream into the evaporation station was at a higher temperature

of 101oC than in P-1 and P-2 scenarios where it was 54.3oC. Final predicted

recoveries of each product is shown in Figure 4-12 for all four process scenarios

simulated. With the exception of acetic acid, the product recovery is similar for

all four process scenarios. In the case of acetic acid P-1 shows improved recovery

relative to the other process scenarios. This improved performance for the P-1

process scenario is attributed to 2-stage scCO2 fractionation where residual

water removed at the first fractionation stage reduces the loss of acetic acid in


the subsequent distillation stage (water and acetic acid have similar boiling

points).

Figure 4-12 Compound recoveries of bio-crude into pure chemical products.

The results of the economic analyses carried out for all our four process scenarios

are summarized in Figure 4-13, Figure 4-14, Figure 4-15 and Figure 4-16. Some

points of note are:

• Total Raw Material Costs are dominated by those associated with the

purchase of the bio-crude feedstock (and are therefore identical for all

scenarios);

• process P-3 is the most capital intensive scenario due to the high

distillation capacity required;

• P-1 has a marginally higher Total Capital Cost than P-2 to accommodate a

separate distillation unit to separate catechol from fraction-1;

• exceptionally high Total Utilities and Total Operating Costs are incurred

in the P-3 scenario due to the high steam required for distillation;

• P-4 has the lowest Total Utilities and Operating Costs due to the energy

efficiency of water removal by the multiple stage evaporation unit;

• P-1 indicates the highest Total Product Sales due to the efficiency of

product recovery of this scenario and the highest overall profitability

generating annual Total Profits that are 16.7% and 11.8% greater than P-

2 and P-4 respectively. P-3 did not produce any profit, rather gave a

negative value for annual Total Profits of about 6.3 million USD.

• Inclusion of costs incurred by the addition of sulphuric acid in bio-crude

to lower pH in SFE process will not have a significant effect on the annual

Total Profits of P-1 and P-2 scenarios as it will be an increase of just 23.8%

in Raw Material Costs relative to those scenarios in which there had been

no pH adjustment.


• When S/B ratios of 12.4 and 20.2 were used in the P-1 scenario, the Total

Operating Cost increased by 9.1% and 17.4% respectively, while the

annual Total Profits decreased by 22.7% and 47.8% respectively relative

to that produced for the base case condition of S/B = 6.2 (see Figure 4-14).

Figure 4-13 Techno-economic summary of four process simulations to compare

basically supercritical separation of bio-crude with that of distillation.


Figure 4-14 Effect of solvent/bio-oil ratio on annualized operating costs and

profits of SFE of bio-oil

Discounted cash flow (DCF) analysis were also performed for the above

mentioned simulation scenarios. The calculated internal rate of return (IRR) and

net present value (NPV) for a company hurdle rate of 10 % over a plant life of 20

years are summarized in Figure 4-15 and Figure 4-16.

At base plant capacity of 22.8 tonne/hr, IRR values of 15.0%, 14.7%, -2.1% and

15.3% were obtained for P-1, P-2, P-3 and P-4 respectively.

Figure 4-15 Investment analysis for bio-oil separation technologies of SFE and

conventional distillation

For P-1, when S/B ratio of 6.2 was increased to 12.4 and 20.2, the corresponding

IRR were 12.3% and 9.5% respectively.


Figure 4-16 Investment analysis for different solvent/bio-oil ratios in SFE of bio-

oil (P-1)

One of the biggest influences on the economics will be the actual moisture content

of the biocrude. Currently a value of 90% is being used i.e. this is actually the

aqueous fraction, when HTL is optimised the real biocrude can be decanted from

the aqueous fraction and will have a significantly lower moisture content of as

low as 10% moisture.

4.8.1 Sensitivity analysis

Variation in capital costs (plant capacity), and utilities costs are also investigated

in this work, for their effect on IRR and NPV of both the SFE and distillation

technologies.

4.8.1.1 Capital cost

For same product/utilities ratio, plant capacity was variated, and the subsequent

effect on IRR and NPV of all four process scenarios was determined. Capital costs

for plant capacities other than the base case (for which the plant capacity was

22.8 tonne/hr) were determined through capacity scale-up rule (Eq. 16) with

exponent value of 0.7.

𝐶𝑜𝑠𝑡𝐵 = 𝐶𝑜𝑠𝑡𝐴 (𝑆𝑖𝑧𝑒𝐵

𝑆𝑖𝑧𝑒𝐴)

0.7

(16)

To achieve the minimum assumed company hurdle rate (IRR = 10%), a plant

capacity of about 8 tonne/hr of biocrude was needed for both SFE scenarios (P-

1, P-2) and for distillation combined with multistage evaporation (P-4). For

distillation alone (P-3) the needed capacity is huge, at least 820 tonne/hr.

Similarly a 20% IRR is possible for P-1, P-2 and P-4 up to plant capacity of about

50 tonne/hr, while for P-3 the capacity should be ridiculously higher, more than

5000 tonne/hr. (Figure 4-17 and Figure 4-18).


Figure 4-17 Effect of plant capacity (capital costs) on techno-economics of SFE

two-stage (P-1), SFE single stage (P-2), distillation (P-3) and distillation

combined with multistage evaporation (P-4) processes of bio-crude separation

into pure chemical compounds

Figure 4-18 Comparison of profitability of SFE and distillation scenarios, for

separation of bio-crude into pure chemical compounds, with change in plant

capacity

4.8.1.2 Electricity

In case of SFE (P-1), when electricity base case purchase price of 0.0775

USD/kWhr was raised up to 100% in value, the IRR decreased from 15.0% to

11.7%, and the NPV decreased by 67.2% (Figure 4-19).


Figure 4-19 Effect of electricity price on IRR and NPV of SFE two-stage (P-1)

separation of bio-crude

For SFE single stage (P-2) process, increasing the base electricity purchase price

up to 100% will decrease the IRR from 14.7% to 11.9%, and NPV by 59.7%

(Figure 4-20).

Figure 4-20 Effect of electricity price on IRR and NPV of SFE single stage (P-2)


For distillation of bio-crude (P-3), 100% increase in electricity purchase price will

decrease the IRR from -2.1% to just -2.2%, and NPV by just 0.4% (Figure 4-21).


Figure 4-21 Effect of electricity price on IRR and NPV of distillation (P-3)


In case of distillation combined with multistage evaporation (P-4) of bio-crude,

100% increase in electricity purchase price will decrease the IRR from 15.3% to

15.1%, and NPV by just 2.0% (Figure 4-22).

Figure 4-22 Effect of electricity price on IRR and NPV of distillation combined

with multistage evaporation (P-4) separation of bio-crude

For the double and single stage SFE scenarios (P-1 and P-2), the IRRs drop to

11.7% and 11.9% respectively with a doubling of the price of imported electricity

used. For P-3 and P-4 distillation processes the corresponding IRR drop will be

just to -2.2% and 15.1% respectively (Figure 4-23).



separation of bio-crude into pure chemical compounds, with increase in

electricity purchase price

4.8.1.3 Steam

In the case of SFE (P-1), when steam base case prices of 2.50E-06 USD/kJ for HP

steam and 1.90E-06 USD/kJ for LP steam were raised up to 100%, the IRR

decreased from 15.0% to 13.9%, and NPV by 21.8% (Figure 4-24).

Figure 4-24 Effect of steam price on IRR and NPV of SFE two-stage (P-1)


For SFE single stage (P-2) process, increasing the base steam price up to 100%

will decrease the IRR from 14.7% to 13.5%, and NPV by 27.3% (Figure 4-25).


Figure 4-25 Effect of steam price on IRR and NPV of SFE single stage (P-2)


For distillation of bio-crude (P-3), 100% increase in steam price will decrease the

IRR from -2.1% to -6.5%, and NPV by 21.8% respectively (Figure 4-26).

Figure 4-26 Effect of steam price on IRR and NPV of distillation (P-3) separation

of bio-crude

In case of distillation combined with multistage evaporation (P-4) of bio-crude,

100% increase in steam price will decrease the IRR from 15.3% to 13.7%, and

NPV by 29.7% (Figure 4-27).


Figure 4-27 Effect of steam price on IRR and NPV of distillation combined with

multistage evaporation (P-4) separation of bio-crude

Upon doubling the steam price, the IRR for P-1, P-2 and P-4 will decrease to about

13.7%, while the corresponding decrease in IRR will be up to -6.5% for P-3

process (Figure 4-28).


separation of bio-crude into pure chemical compounds, with increase in steam

price

4.8.1.4 Product sale price

In case of SFE (P-1), when product sale prices were raised up to 75% in value, the

IRR increased from 15.0% to 38.9%, and NPV from 23.5M USD to 151.8M USD

(Figure 4-29). At 25% decrease of product sale prices, the IRR decreased to 5.4%,

and NPV to -19.3M USD (Figure 4-29).


Figure 4-29 Effect of product sale price on IRR and NPV of SFE two-stage (P-1)


In case of SFE (P-2), when product sale prices were raised up to 75% in value, the

IRR increased from 14.7% to 39.4%, and NPV from 19.5M USD to 136.2M USD

(Figure 4-30). At 25% decrease of product sale prices, the IRR decreased to 4.7%,

and NPV to -19.4M USD (Figure 4-30).

Figure 4-30 Effect of product sale price on IRR and NPV of SFE single-stage (P-2)


In case of distillation (P-3), when product sale prices were raised up to 75% in

value, the IRR increased from -2.1% to 9.7%, and NPV from -113.9M USD to -3.7M

USD (Figure 4-31). For this case, the IRR value of 20%, and the corresponding

NPV value of 128.5M USD will be achieved at 165% increase of product sale prices

(Figure 4-31).


Figure 4-31 Effect of product sale price on IRR and NPV of distillation (P-3)


In case of distillation combined with multistage evaporation process (P-4), when

product sale prices were raised up to 75% in value, the IRR increased from 15.3%

to 38.8%, and NPV from 21.6M USD to 131.7M USD (Figure 4-32). At 25%

decrease of product sale prices, the IRR decreased to 5.9%, and NPV to -15.1M

USD (Figure 4-32).

Figure 4-32 Effect of product sale price on IRR and NPV of distillation combined

with multistage evaporation (P-4) separation of bio-crude

The IRRs drop from 15% to about 5%, for P-1, P-2 and P-4, upon 25% decrease

in product sale prices, the corresponding increase in IRRs will be up to 39% when

product sale prices increased by 75% (Figure 4-33). For P-3, the IRR will reach

10% and 20% with at least 75% and 165% respectively increase in product sale

prices (Figure 4-33).



separation of bio-crude into pure chemical compounds, with change in product

sale prices.

4.9 Conclusions

This study has used pilot plant data and process modelling to investigate the

industrial scale use of scCO2 extraction and fractionation of bio-crude for the

recovery of renewable chemicals from a lignin-rich HTL bio-crude.

It was confirmed through pilot plant extraction and fractionation trials that it is

possible to effectively model the extraction characteristics of a multi-component

bio-crude by a series of individual solute-solvent binary interaction parameters

regressed from experimental VLE data.

Aspen Plus® process and economic models of four design scenarios were

developed to compare supercritical extraction with conventional distillation of

bio-crude. These models indicated that two stage scCO2 extraction of bio-crude

combined with multiple stage evaporation to remove water and recover catechol

(process scenario P-1) generates 16.7% and 11.8% more Total Profit annually

than single stage SFE process (P-2) and distillation with multiple stage

evaporation (P-4) process scenarios respectively. Distillation alone (P-3) of

whole bio-crude did not prove to be a profitable scenario, rather gave a negative

value for annual total profits of about 6.3 million USD. Solvent/biocrude ratio will

have considerable impact on total profits of SFE process in relation to distillation

combined with multistage evaporation.

The techno-economic study established that increasing the S/B ratio from 6.2 to

12.4 and 20.2, will decrease the corresponding IRR from 15% to 12.3% and 9.5%

respectively. The corresponding increase in operational costs were 9.1%

(S/B = 12.4%) and 17.4% (S/B = 20.2%) relative to that of for the base case

(S/B = 6).


The economics of SFE and conventional distillation processes for the recovery of

target compounds from bio-crude, were compared. For a base case plant capacity

of 22.8 tonne/hr of biocrude, an IRR value of approximately 15% was achieved

for P-1, P-2 and P-4 scenarios. For P-3 the IRR value at the base case plant capacity

was -2.1%. To achieve the minimum assumed company hurdle rate (IRR = 10%),

a plant capacity of about 8 tonne/hr of biocrude was needed for both SFE

scenarios (P-1, P-2) and for distillation combined with multistage evaporation (P-

4). For distillation alone (P-3) the needed capacity is huge, at least 820 tonne/hr.

Similarly a 20% IRR is possible for P-1, P-2 and P-4 up to plant capacity of about

50 tonne/hr, while for P-3 the capacity should be ridiculously higher, more than

5000 tonne/hr.

For the double and single stage SFE scenarios (P-1 and P-2), the IRRs drop to

11.7% and 11.9% respectively with a doubling of the price of imported electricity

used. For P-3 and P-4 distillation processes the corresponding IRR drop will be

just to -2.2% and 15.1% respectively. Similarly upon doubling the steam price,

the IRR for P-1, P-2 and P-4 will decrease to about 13.7%, while the

corresponding decrease in IRR will be up to -6.5% for P-3 process.

The IRRs drop from 15% to about 5%, for P-1, P-2 and P-4, upon 25% decrease

in product sale prices, the corresponding increase in IRRs will be up to 39% when

product sale prices increased by 75%. For P-3, the IRR will reach 10% and 20%

with at least 75% and 165% respectively increase in product sale prices.

These results suggest two main areas of future investigation to further improve

the profitability of industrial scale scCO2 recovery of chemicals from bio-crude:

1. HTL production of bio-crude should be tailored to produce fewer

compounds but in large amounts rather than more compounds in small

amounts. This will simplify the post-extraction treatment for further

purification of products; and

2. modelling of the scCO2 extraction (i.e. pre-fractionation) process itself is

needed to identify extraction conditions with improved yields and

product composition profiles.

4.10 Glossary and Nomenclature

Model = Aspen Plus® PR-BM property method

𝑎𝑖, 𝑏𝑖 = model parameters for pure components

𝑎, 𝑏 = model parameters for mixture

e = estimated data

i = data for data point i, (eq 15)

j = fraction data for component j (eq 15)


𝑘𝑖𝑗 , 𝑙𝑖𝑗 = binary interaction parameters in model

m = measured data

NDG = the number of data groups in the regression case

NC = the number of components present in the data group

NP = the number of points in data group n

P = pressure

𝑃𝑐 = critical pressure of a component

Q = maximum-likelihood objective function to be minimized

R = gas constant

T = temperature

𝑇𝑐 = critical temperature of a component

𝑇𝑟 = reduced temperature

Wn = the weight of data group n

x, y = liquid and vapor mole fractions respectively

𝛼 = temperature function in eq 8

σ = standard deviation of the indicated data

𝜔 = acentric factor of a component


Aspen Plus® process flowsheets for supercritical extraction and distillation

processes (P-2, P-3 and P-4), table listing summary of economic evaluation for

different separation and purification processes of bio-crude (P-1 to P-4)


Table 4.9S Summary of economic evaluation for different separation and

purification processes of bio-crude (P-1 to P-4)

Summary P-1 P-2 P-3 P-4

Total Capital Cost

(USD) 74,769,400 65,861,700 195,929,000 65,031,600

Total Operating

Cost (USD/Year) 16,311,591 15,174,001 25,892,468 13,745,081

Total Raw

Materials Cost

(USD/Year)

8,077,180 8,077,180 8,077,180 8,077,180

Total Product

Sales (USD/Year) 22,848,019 20,775,373 19,610,328 19,591,220

Total Utilities Cost

(USD/Year) 2,813,630 2,321,840 3,610,440 953,110

Desired Rate of

Return

(Percent/Year)

10 10 10 10

P.O. Period (Year) 2.84 2.63 4.58 2.85

Equipment Cost

(USD) 16,880,066 15,746,566 56,214,331 13,400,231

Total Installed

Cost (USD) 34,912,600 30,652,300 98,996,000 30,514,700

Total Profit

(USD/Year) 6,536,428 5,601,372 -6,282,140 5,846,138

Table 4.10S Summary of product concentrations in extract fractions of pilot plant

SFE trials

Run

Phenol p-Cresol 4-ethylphenol catechol Phenol p-Cresol 4-ethylphenol catechol

1 2.134 0.190 0.180 0.270 0.120 ND ND 0.290

2 0.674 0.070 0.060 0.160 ND ND ND ND

3 1.188 0.120 0.110 0.170 - - - -

4 2.446 0.250 0.280 0.220 0.186 ND ND ND

5 2.530 0.300 0.280 0.430 0.100 0.010 ND 0.160

6 2.419 0.220 0.260 0.400 0.152 0.010 ND 0.160

7 1.672 0.130 0.110 0.210 0.173 ND ND 0.160

8 1.899 0.160 0.140 0.370 0.425 0.010 ND 0.310

9 2.099 0.160 0.160 0.340 0.218 0.010 ND 0.160

10 0.279 0.040 0.040 0.160 0.243 0.010 ND 0.200

Fraction- 2 Concentration (mg/mL) Fraction- 1 Concentration (mg/mL)


Figure 4-34S Aspen Plus® process flowsheet for supercritical extraction of bio-

crude followed by single-stage collection of column extract (part of P-2).


Figure 4-35S Aspen Plus® process flowsheet for distillation of products from

single-stage collection of supercritical extract (P-2). Extraction column bottom

(raffinate) is treated with evaporation process.


Figure 4-36S Aspen Plus® process flowsheet for distillation of bio-crude itself,

without any upstream extraction done on it (P-3).


Figure 4-37S Aspen Plus® process flowsheet for distillation of bio-crude itself,

without any upstream extraction done on it. Aqueous stream off first distillation

column (D1) contains catechol, and is evaporated off to recover catechol (P-4).


Author Information

Corresponding Author

*E-mail: [email protected]

Notes


Acknowledgements



Fund program.

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Chapter 5: Conclusions and Recommendations 129

Chapter 5: Conclusions and Recommendations

5.1 Conclusions

1. Supercritical CO2 has the potential to extract chemical compounds from

aqueous mixtures like bio-oil. The extraction of target compounds into the

vapour phase is dependent on their chemical nature and equilibrium

distributions in either phase, i.e., vapour and liquid phases. Extract yields

also depend upon feed composition and extraction conditions such as

temperature, pressure, solvent density (cumulative of temperature and

pressure), mixture pH etc. From available experimental data so far, it is

evident that increasing the temperature and solvent density will increase

the extract yields.

2. The main challenge in modelling the extraction part of SFE process is the

presence of innumerable components in bio-oil, which makes the

predictive modelling non-practical as the required experimental VLE data

for solute-solute interactions is enormous and scarce as well. A reliable

thermodynamic model has not yet been applied to find out ideal extract

yield and composition conditions.

3. This work has established the accuracy of an assumption, we made earlier

in this work, that bio-oil components in dissolved form in scCO2 show

minimal solute-solute interactions. The phase behaviour description of a

stage-wise pressure reduction fractionation solely based on binary solute-

solvent interaction parameters was shown possible in this work. This

means, you can find out equilibrium compositions of fractionation

products, with the help of a model like PR-EOS and with experimental VLE

data of solute-solvent systems only.

4. Experimental binary VLE data of some major bio-oil compounds such as

formic acid, 4-ethylphenol etc., is not yet available in literature. With those

compounds for which binary VLE data exists there are discrepancies

between reported measurements and it is important for the user to

identify such variations. Given the often different process conditions

reported, the best way to identify such discrepancies is to correlate all

comparable data sets with empirical models such as Chrastil or EOS (e.g.

PR-EOS) based models and then calculate deviations of that model using

data sets from different sources. Binary VLE data of an exemplary bio-oil

compound (benzyl alcohol) was extended in this work through

130 Chapter 5: Conclusions and Recommendations

experimental determination. The modelling on this binary mixture of

benzyl alcohol and CO2 was successfully performed through use of PR-BM

property method in Aspen Plus®.

5. EOS model was successfully extended to our bio-crude mixture containing

acetic acid, phenol, catechol, p-cresol, 4-ethylphenol and water, using only

binary VLE data of respective compounds with CO2.

6. Using the developed model for multicomponent bio-crude mixture, Aspen

Plus® simulation scenarios were constructed for SFE of our bio-crude

mixture with stage-wise pressure reduction separation of column extract

stream. For comparison purpose, conventional distillation scenarios were

also constructed within Aspen Plus.

7. The developed model for our bio-crude mixture was then successfully

validated on pilot plant SFE trials. Model predictions were in close

agreement with experimental compositions of product fractions obtained

with stage-wise pressure reduction separation of extracted stream.

8. Thus validated model was used in optimizing the simulation of SFE of our

bio-crude. Energy integration was implemented to recover the excess

heat, and CO2 recycling was also proposed to minimize the operational

costs. Separation of extracted compounds into stage-wise pressure

reduction separators was optimized using distributions coefficients and

separation factors of the involved components.

9. This study provides the first comprehensive and systematic techno-

economic comparison between SFE and conventional distillation of bio-

crude. For a base case bio-crude plant capacity of 22.8 tonne/hr, IRR value

of about 15% was achieved for SFE two-stage (P-1), SFE single stage (P-2)

and distillation combined with multistage evaporation (P-4) processes,

while for distillation alone (P-3) the IRR value at the said plant capacity

was -2.1%. All processes were simulated in Aspen Plus to recover bio-

crude products at purities consistent with market requirements.

Distillation and/or multi-stage evaporation had to be employed in both

SFE and distillation based processes to achieve the necessary commercial

product standards. To achieve the minimum assumed company hurdle

rate (IRR = 10%), a plant capacity of about 8 tonne/hr of biocrude was

needed for both SFE scenarios (P-1, P-2) and for distillation combined

with multistage evaporation (P-4). For distillation alone (P-3) the needed

capacity is huge, at least 820 tonne/hr. Similarly a 20% IRR is possible for

P-1, P-2 and P-4 up to plant capacity of about 50 tonne/hr, while for P-3

the capacity should be ridiculously higher, more than 5000 tonne/hr.

10. The costs of sulphuric acid required to lower the pH of the bio-crude

feedstock prior to the SFE process (an increase in 23.8% in Raw Material

Costs relative to those scenarios in which there had been no pH


adjustment) does not significantly impact the Total Profits of scenarios P-

1 and P-2. Acid pre-treatment of bio-crude prevented foaming, clogging

and carry-over of water from the extraction column. Column carry over

could also be avoided by lowering pH through addition of CO2 only, but

before the mixture enters column.

11. In SFE two-stage (P-1) process, when base case S/B ratio of 6.2 was

increased to 12.4 and 20.2, the corresponding IRR dropped from 15% to

12.3% and 9.5% respectively, and the corresponding increase in

operational costs were 9.1% and 17.4% respectively.

12. For the double and single stage SFE scenarios (P-1 and P-2), the IRRs drop

to 11.7% and 11.9% respectively with a doubling of the price of imported

electricity used. For P-3 and P-4 distillation processes the corresponding

IRR drop will be just to -2.2% and 15.1% respectively.

13. Similarly upon doubling the steam price, the IRR for P-1, P-2 and P-4 will

decrease to about 13.7%, while the corresponding decrease in IRR will be

up to -6.5% for P-3 process.

14. The IRRs drop from 15% to about 5%, for P-1, P-2 and P-4, upon 25%

decrease in product sale prices, the corresponding increase in IRRs will be

up to 39% when product sale prices increased by 75%. For P-3, the IRR

will reach 10% and 20% with at least 75% and 165% respectively

increase in product sale prices.

5.2 Recommendations for future work

1. Methods for controlling bio-oil composition should be optimized in the

upstream thermo-chemical (HTL or pyrolysis) steps, as bio-oil

composition significantly impacts downstream extraction and

fractionation operations. Besides selecting a suitable thermochemical

conversion process, type of biomass feedstock should also be investigated

to produce a bio-oil of simple desired composition. Typically a bio-oil

mixture of relatively fewer target compounds, but of similar aqueous

fraction, will generate more profit than a bio-oil of many compounds in

small quantities. Additionally, the predictive modelling of a simpler

composition bio-oil (fewer target compounds) though not a trivial task is

significantly simpler than that for more complex SFE feedstocks.

Complete predictive modelling of a bio-oil of more than four or five target

components will require an excessive amount of experimental work to

work out solute-solute interaction data although it is recognised that

producing such a ‘simple’ bio-oil is in itself a difficult task to accomplish.

2. Once an optimum bio-oil composition has been selected the next step of

any future study should focus on modelling the extraction column (as


opposed to fractionation) processes using an EOS based model. A

recommended way of achieving this is to assume bio-oil is a pseudo-

binary mixture, in which one component is water and the second

component should represent all other compounds in bio-oil. Optimal

conditions for extract yields could subsequently be achieved by

performing extractions at desired temperature and pressure ranges, and

then correlating the VLE data of representative compound (of all bio-oil

compounds except water) with an EOS based model. Following

optimisation of the extraction process, subsequent fractionation

conditions can then be explored using the simpler binary (solute-solvent)

interaction parameters and neglecting solute-solute interactions as

reported in Chapter 4.

3. Future works on SFE of bio-oil should try to find ways for minimizing

water contents of bio-oil before putting it through supercritical extraction

column through use of several ways including but not limited to physical

settling and phase separation, solvent-solvent extraction etc.

4. In the current study, a fixed S/B ratio was used in the extraction stage of

the pilot plant. With the development of improved extraction process

models (Recommendation 2) the techno-economic impact of alternative

S/B ratios should be investigated and optimised for the complete SFE

process. Inclusion of thermochemical treatment process and water

treatment sections of the plant in over-all techno-economics of a future

SFE study on bio-oil will also be of great value.


APPENDIX

Stream Specifications

Table A- 1 Stream molar flows and temperature, pressure conditions in P-1

Stream

NameFrom To CO2 Phenol Catechol p-Cresol Acetic Acid Water

4-

Ethylphenol

Mass Flow

(Total)Temp. Press.

kg/hr oC Bar

1 Sep-01 Sep-02 3189.21 2.16 0.07 0.07 36.25 15.02 0.06 143031.0 43.1 90.0

10 COL-3 P1F2 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 30.0 1.0

10-1 P1F2 HEX3 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 30.0 1.2

10-2 HEX3 HEX4 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 36.2 1.2

10-3 HEX4 HEX5 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 70.2 1.2

10-4 HEX5 HEX6 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 74.7 1.2

10-5 HEX6 HEX7 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 74.8 1.2

10-6 HEX7 HEX8 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 75.0 1.2

10-7 HEX8 D1F2 0.00 2.10 0.07 0.07 32.67 7.80 0.06 2323.3 75.1 1.2

11 MIX1 COMP1 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 33.4 1.0

11-1 COMP1 SG1 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 308.2 13.5

11-2 SG1 COOL2 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 140.0 13.5

11-3 COOL2 COMP2 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 50.0 13.5

11-4 COMP2 SG2 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 361.9 206.4

11-5 SG2 COOL3 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 140.0 206.4

12 D1F1 HEX1 0.00 0.00 0.00 0.00 0.00 15.31 0.00 276.1 100.6 1.0

13 D1F1 SG1F1 0.00 0.00 0.05 0.00 0.00 0.00 0.00 6.0 241.1 1.0

13-1 SG1F1 HEX2 0.00 0.00 0.05 0.00 0.00 0.00 0.00 6.0 140.0 1.0

13-2 HEX2 CRYST1F1 0.00 0.00 0.05 0.00 0.00 0.00 0.00 6.0 106.1 1.0

14 D1F2 HEX3 0.00 0.00 0.00 0.00 2.73 7.79 0.00 304.6 87.9 1.0

14-1 HEX3 CRYST0 0.00 0.00 0.00 0.00 2.73 7.79 0.00 304.6 56.2 1.0

15 D1F2 P2F2 0.00 2.10 0.07 0.07 29.93 0.01 0.06 2018.8 120.4 1.0

15-1 P2F2 D2F2 0.00 2.10 0.07 0.07 29.93 0.01 0.06 2018.8 120.5 1.2

16 D2F2 HEX4 0.00 0.00 0.00 0.00 29.92 0.01 0.00 1797.3 118.4 1.0

16-1 HEX4 CRYST1F2 0.00 0.00 0.00 0.00 29.92 0.01 0.00 1797.3 75.2 1.0

17 D2F2 P3F2 0.00 2.10 0.07 0.07 0.01 0.00 0.06 221.5 182.7 1.0

17-1 P3F2 D3F2 0.00 2.10 0.07 0.07 0.01 0.00 0.06 221.5 182.7 1.2

18 D3F2 SG1F2 0.00 2.08 0.00 0.00 0.01 0.00 0.00 197.1 180.8 1.0

18-1 SG1F2 HEX5 0.00 2.08 0.00 0.00 0.01 0.00 0.00 197.1 140.0 1.0

18-2 HEX5 CRYST2F2 0.00 2.08 0.00 0.00 0.01 0.00 0.00 197.1 79.7 1.0

19 D3F2 P4F2 0.00 0.02 0.07 0.07 0.00 0.00 0.06 24.4 207.8 1.0

19-1 P4F2 D4F2 0.00 0.02 0.07 0.07 0.00 0.00 0.06 24.4 207.8 1.2

2 Sep-01 COL-2 0.34 0.00 0.05 0.00 0.00 15.31 0.00 297.0 43.1 90.0

20 D4F2 SG2F2 0.00 0.02 0.00 0.05 0.00 0.00 0.00 7.4 196.2 1.0

20-1 SG2F2 HEX6 0.00 0.02 0.00 0.05 0.00 0.00 0.00 7.4 140.0 1.0

20-2 HEX6 CRYST3F2 0.00 0.02 0.00 0.05 0.00 0.00 0.00 7.4 79.8 1.0

21 D4F2 P5F2 0.00 0.00 0.07 0.02 0.00 0.00 0.06 17.0 220.9 1.0

21-1 P5F2 D5F2 0.00 0.00 0.07 0.02 0.00 0.00 0.06 17.0 221.0 1.2

22 D5F2 SG3F2 0.00 0.00 0.00 0.02 0.00 0.00 0.06 9.1 212.9 1.0

22-1 SG3F2 HEX7 0.00 0.00 0.00 0.02 0.00 0.00 0.06 9.1 140.0 1.0

22-2 HEX7 CRYST4F2 0.00 0.00 0.00 0.02 0.00 0.00 0.06 9.1 80.0 1.0

23 D5F2 SG4F2 0.00 0.00 0.07 0.00 0.00 0.00 0.00 7.9 242.3 1.0

23-1 SG4F2 HEX8 0.00 0.00 0.07 0.00 0.00 0.00 0.00 7.9 140.0 1.0

23-2 HEX8 CRYST5F2 0.00 0.00 0.07 0.00 0.00 0.00 0.00 7.9 106.1 1.0

3 Sep-02 COOL1 3020.76 0.06 0.00 0.00 3.58 7.22 0.00 133294.0 32.0 60.0

3-1 COOL1 PUMP3 3020.76 0.06 0.00 0.00 3.58 7.22 0.00 133294.0 19.2 60.0

4 Sep-02 COL-3 168.45 2.10 0.07 0.07 32.67 7.80 0.06 9737.0 32.0 60.0

4EPHENOL CRYST4F2 0.00 0.00 0.00 0.02 0.00 0.00 0.06 9.1 30.0 1.0

kmol/hr


5 COL-1 MIX1 26.90 0.00 0.00 0.00 0.00 0.00 0.00 1183.9 54.3 1.0

7 COL-2 MIX1 0.34 0.00 0.00 0.00 0.00 0.00 0.00 15.0 30.0 1.0

8 COL-2 P1F1 0.00 0.00 0.05 0.00 0.00 15.31 0.00 282.1 30.0 1.0

8-1 P1F1 HEX1 0.00 0.00 0.05 0.00 0.00 15.31 0.00 282.1 30.0 1.2

8-2 HEX1 HEX2 0.00 0.00 0.05 0.00 0.00 15.31 0.00 282.1 55.3 1.2

8-3 HEX2 D1F1 0.00 0.00 0.05 0.00 0.00 15.31 0.00 282.1 55.7 1.2

9 COL-3 MIX1 168.45 0.00 0.00 0.00 0.00 0.00 0.00 7413.6 30.0 1.0

ACETICAC CRYST1F2 0.00 0.00 0.00 0.00 29.92 0.01 0.00 1797.3 30.0 1.0

AQ13-1 P1D3 HEX9 0.00 0.00 0.76 0.00 0.00 135.79 0.00 2530.2 40.3 1.0

AQ13-2 HEX9 HEX10 0.00 0.00 0.76 0.00 0.00 135.79 0.00 2530.2 60.7 1.0

AQ13-3 HEX10 D7 0.00 0.00 0.76 0.00 0.00 135.79 0.00 2530.2 61.2 1.0

AQ14 D7 HEX9 0.00 0.00 0.00 0.00 0.00 135.79 0.00 2446.3 101.1 1.0

AQ15 D7 SGD7 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 244.6 1.0

AQ15-1 SGD7 HEX10 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 140.0 1.0

AQ15-2 HEX10 CRYST1D7 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 106.2 1.0

AQ2 EVA1 HEXE1 0.00 0.03 0.00 0.00 0.06 201.21 0.00 3631.4 101.1 1.0

AQ2-1 HEXE1 PEVA1 0.00 0.03 0.00 0.00 0.06 201.21 0.00 3631.4 86.1 1.0

AQ3 EVA1 V1 0.00 0.02 0.78 0.00 0.00 916.26 0.00 16594.2 101.1 1.0

AQ3-1 V1 HEXE1 0.00 0.02 0.78 0.00 0.00 916.26 0.00 16594.2 83.1 0.5

AQ3-2 HEXE1 EVA2 0.00 0.02 0.78 0.00 0.00 916.26 0.00 16594.2 83.2 0.5

AQ4 EVA2 HEXE2 0.00 0.01 0.00 0.00 0.00 234.40 0.00 4224.4 83.2 0.5

AQ4-1 HEXE2 PEVA2 0.00 0.01 0.00 0.00 0.00 234.40 0.00 4224.4 65.3 0.5

AQ5 EVA2 V2 0.00 0.00 0.78 0.00 0.00 681.87 0.00 12369.7 83.2 0.5

AQ5-1 V2 HEXE2 0.00 0.00 0.78 0.00 0.00 681.87 0.00 12369.7 62.3 0.2

AQ5-2 HEXE2 EVA3 0.00 0.00 0.78 0.00 0.00 681.87 0.00 12369.7 62.3 0.2

AQ6 EVA3 HEXE3 0.00 0.00 0.00 0.00 0.00 263.26 0.00 4743.6 62.3 0.2

AQ6-1 HEXE3 PEVA3 0.00 0.00 0.00 0.00 0.00 263.26 0.00 4743.6 43.3 0.2

AQ7 EVA3 V3 0.00 0.00 0.77 0.00 0.00 418.60 0.00 7626.2 62.3 0.2

AQ7-1 V3 HEXE3 0.00 0.00 0.77 0.00 0.00 418.60 0.00 7626.2 40.3 0.1

AQ7-2 HEXE3 EVA4 0.00 0.00 0.77 0.00 0.00 418.60 0.00 7626.2 40.3 0.1

AQ8 EVA4 HEXE4 0.00 0.00 0.01 0.00 0.00 282.81 0.00 5096.0 40.3 0.1

AQ8-1 HEXE4 PEVA4 0.00 0.00 0.01 0.00 0.00 282.81 0.00 5096.0 40.2 0.1

AQ9 EVA4 P1D3 0.00 0.00 0.76 0.00 0.00 135.79 0.00 2530.2 40.3 0.1

AQUEOUS COLUMN COL-1 26.90 0.04 0.78 0.00 0.07 1117.48 0.00 21409.5 55.0 206.4

AQUEOUS1 COL-1 EVA1 0.00 0.04 0.78 0.00 0.07 1117.48 0.00 20225.6 54.3 1.0

AQUEOUS2 CRYST0 0.00 0.00 0.00 0.00 2.73 7.79 0.00 304.6 30.0 1.0

BIOOIL1 PUMP1 0.00 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.0

BIOOIL2 PUMP1 PREHEAT 0.00 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 25.0 206.4

CATECHO1 CRYST1F1 0.00 0.00 0.05 0.00 0.00 0.00 0.00 6.0 30.0 1.0

CATECHO2 CRYST5F2 0.00 0.00 0.07 0.00 0.00 0.00 0.00 7.9 30.0 1.0

CATECHO3 CRYST1D7 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 30.0 1.0

CO2RECY1 COOL3 195.69 0.00 0.00 0.00 0.00 0.00 0.00 8612.4 55.0 206.4

EXTRACT COLUMN Sep-01 3189.55 2.16 0.12 0.07 36.26 30.33 0.06 143328.0 55.0 206.4

FEED PREHEAT COLUMN 3216.45 2.20 0.91 0.07 36.32 1147.81 0.06 164737.0 55.0 206.4

P-CRESOL CRYST3F2 0.00 0.02 0.00 0.05 0.00 0.00 0.00 7.4 30.0 1.0

PHENOL CRYST2F2 0.00 2.08 0.00 0.00 0.01 0.00 0.00 197.1 30.0 1.0

SEP2REC1 PUMP3 3020.76 0.06 0.00 0.00 3.58 7.22 0.00 133294.0 30.2 206.4

WATER1 PEVA1 0.00 0.03 0.00 0.00 0.06 201.21 0.00 3631.4 86.1 1.0

WATER2 PEVA2 0.00 0.01 0.00 0.00 0.00 234.40 0.00 4224.4 65.3 1.0

WATER3 PEVA3 0.00 0.00 0.00 0.00 0.00 263.26 0.00 4743.6 43.3 1.0

WATER4 PEVA4 0.00 0.00 0.01 0.00 0.00 282.81 0.00 5096.0 40.2 1.0

WATER5 HEX9 0.00 0.00 0.00 0.00 0.00 135.79 0.00 2446.3 80.7 1.0

WATER6 HEX1 0.00 0.00 0.00 0.00 0.00 15.31 0.00 276.1 75.3 1.0



Stream

NameFrom To CO2 Phenol Catechol p-Cresol Acetic Acid Water

4-

Ethylphenol

Mass Flow

(Total)Temp. Press.

kg/hr oC Bar

1 Sep-01 COOL1 3113.97 0.03 0.00 0.00 3.72 8.97 0.00 137434.0 32.0 60.0

10 D2 HEX2 0.00 0.00 0.00 0.00 25.19 0.00 0.00 1512.7 118.4 1.0

10-1 HEX2 CRYST1 0.00 0.00 0.00 0.00 25.19 0.00 0.00 1512.7 74.8 1.0

11 D2 P5 0.00 2.10 0.12 0.07 0.01 0.00 0.06 227.3 183.7 1.0

1-1 COOL1 P2 3113.97 0.03 0.00 0.00 3.72 8.97 0.00 137434.0 18.3 60.0

11-1 P5 D3 0.00 2.10 0.12 0.07 0.01 0.00 0.06 227.3 183.7 1.2

12 D3 SGD3 0.00 2.09 0.00 0.00 0.01 0.00 0.00 197.4 181.2 1.0

12-1 SGD3 HEX3 0.00 2.09 0.00 0.00 0.01 0.00 0.00 197.4 140.0 1.0

12-2 HEX3 CRYST2 0.00 2.09 0.00 0.00 0.01 0.00 0.00 197.4 78.6 1.0

13 D3 P6 0.00 0.01 0.12 0.07 0.00 0.00 0.06 29.9 212.2 1.0

13-1 P6 D4 0.00 0.01 0.12 0.07 0.00 0.00 0.06 29.9 212.2 1.2

14 D4 SGD4 0.00 0.01 0.00 0.06 0.00 0.00 0.00 7.4 197.6 1.0

14-1 SGD4 HEX4 0.00 0.01 0.00 0.06 0.00 0.00 0.00 7.4 140.0 1.0

14-2 HEX4 CRYST3 0.00 0.01 0.00 0.06 0.00 0.00 0.00 7.4 78.7 1.0

15 D4 P7 0.00 0.00 0.12 0.01 0.00 0.00 0.06 22.4 224.9 1.0

15-1 P7 D5 0.00 0.00 0.12 0.01 0.00 0.00 0.06 22.4 224.9 1.2

16 D5 SGD5 0.00 0.00 0.00 0.01 0.00 0.00 0.06 8.8 213.7 1.0

16-1 SGD5 HEX5 0.00 0.00 0.00 0.01 0.00 0.00 0.06 8.8 140.0 1.0

16-2 HEX5 CRYST4 0.00 0.00 0.00 0.01 0.00 0.00 0.06 8.8 78.9 1.0

17 D5 SGD5-2 0.00 0.00 0.12 0.00 0.00 0.00 0.00 13.7 244.2 1.0

17-1 SGD5-2 HEX6 0.00 0.00 0.12 0.00 0.00 0.00 0.00 13.7 140.0 1.0

17-2 HEX6 CRYST5 0.00 0.00 0.12 0.00 0.00 0.00 0.00 13.7 106.0 1.0

2 Sep-01 COL-2 75.52 2.10 0.12 0.07 32.67 21.37 0.06 5898.0 32.0 60.0

3 COL-1 MIX1 26.94 0.00 0.00 0.00 0.00 0.00 0.00 1185.7 54.3 1.0

4EPHENOL CRYST4 0.00 0.00 0.00 0.01 0.00 0.00 0.06 8.8 30.0 1.0

5 COL-2 MIX1 75.52 0.00 0.00 0.00 0.00 0.00 0.00 3323.8 30.0 1.0

6 COL-2 P3 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 30.0 1.0

6-1 P3 HEX1 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 30.0 1.2

6-2 HEX1 HEX2 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 46.1 1.2

6-3 HEX2 HEX3 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 69.8 1.2

6-4 HEX3 HEX4 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 73.6 1.2

6-5 HEX4 HEX5 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 73.7 1.2

6-6 HEX5 HEX6 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 73.9 1.2

6-7 HEX6 D1 0.00 2.10 0.12 0.07 32.67 21.37 0.06 2574.1 74.1 1.2

7 MIX1 COMP1 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 36.5 1.0

7-1 COMP1 SG1 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 312.9 13.5

7-2 SG1 COOL2 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 140.0 13.5

7-3 COOL2 COMP2 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 50.0 13.5

7-4 COMP2 SG2 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 361.9 206.4

7-5 SG2 COOL3 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 140.0 206.4

8 D1 HEX1 0.00 0.00 0.00 0.00 7.48 21.37 0.00 834.1 87.9 1.0

8-1 HEX1 CRYST0 0.00 0.00 0.00 0.00 7.48 21.37 0.00 834.1 51.1 1.0

9 D1 P4 0.00 2.10 0.12 0.07 25.19 0.00 0.06 1740.0 120.9 1.0

9-1 P4 D2 0.00 2.10 0.12 0.07 25.19 0.00 0.06 1740.0 120.9 1.2

ACETICAC CRYST1 0.00 0.00 0.00 0.00 25.19 0.00 0.00 1512.7 30.0 1.0

AQ13-1 P1D3 HEXD6-1 0.00 0.00 0.76 0.00 0.00 136.00 0.00 2534.1 40.3 1.0

AQ13-2 HEXD6-1 HEXD6-2 0.00 0.00 0.76 0.00 0.00 136.00 0.00 2534.1 68.1 1.0

AQ13-3 HEXD6-2 D6 0.00 0.00 0.76 0.00 0.00 136.00 0.00 2534.1 68.7 1.0

AQ14 D6 HEXD6-1 0.00 0.00 0.00 0.00 0.00 136.00 0.00 2450.1 101.1 1.0

AQ15 D6 SGD6 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 244.6 1.0

AQ15-1 SGD6 HEXD6-2 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 140.0 1.0

AQ15-2 HEXD6-2 CRYST1D6 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 106.2 1.0

AQ2 EVA1 HEXE1 0.00 0.02 0.00 0.00 0.06 201.53 0.00 3637.1 101.1 1.0

AQ2-1 HEXE1 PEVA1 0.00 0.02 0.00 0.00 0.06 201.53 0.00 3637.1 86.1 1.0

kmol/hr


AQ3 EVA1 V1 0.00 0.02 0.78 0.00 0.00 917.69 0.00 16619.8 101.1 1.0

AQ3-1 V1 HEXE1 0.00 0.02 0.78 0.00 0.00 917.69 0.00 16619.8 83.1 0.5

AQ3-2 HEXE1 EVA2 0.00 0.02 0.78 0.00 0.00 917.69 0.00 16619.8 83.2 0.5

AQ4 EVA2 HEXE2 0.00 0.01 0.00 0.00 0.00 234.76 0.00 4231.0 83.2 0.5

AQ4-1 HEXE2 PEVA2 0.00 0.01 0.00 0.00 0.00 234.76 0.00 4231.0 65.3 0.5

AQ5 EVA2 V2 0.00 0.00 0.78 0.00 0.00 682.93 0.00 12388.9 83.2 0.5

AQ5-1 V2 HEXE2 0.00 0.00 0.78 0.00 0.00 682.93 0.00 12388.9 62.3 0.2

AQ5-2 HEXE2 EVA3 0.00 0.00 0.78 0.00 0.00 682.93 0.00 12388.9 62.3 0.2

AQ6 EVA3 HEXE3 0.00 0.00 0.00 0.00 0.00 263.67 0.00 4750.9 62.3 0.2

AQ6-1 HEXE3 PEVA3 0.00 0.00 0.00 0.00 0.00 263.67 0.00 4750.9 43.3 0.2

AQ7 EVA3 V3 0.00 0.00 0.77 0.00 0.00 419.26 0.00 7637.9 62.3 0.2

AQ7-1 V3 HEXE3 0.00 0.00 0.77 0.00 0.00 419.26 0.00 7637.9 40.3 0.1

AQ7-2 HEXE3 EVA4 0.00 0.00 0.77 0.00 0.00 419.26 0.00 7637.9 40.3 0.1

AQ8 EVA4 HEXE4 0.00 0.00 0.01 0.00 0.00 283.25 0.00 5103.9 40.3 0.1

AQ8-1 HEXE4 PEVA4 0.00 0.00 0.01 0.00 0.00 283.25 0.00 5103.9 40.2 0.1

AQ9 EVA4 P1D3 0.00 0.00 0.76 0.00 0.00 136.00 0.00 2534.1 40.3 0.1

AQUEOUS COLUMN COL-1 26.94 0.04 0.78 0.00 0.07 1119.22 0.00 21442.6 55.0 206.4

AQUEOUS1 COL-1 EVA1 0.00 0.04 0.78 0.00 0.07 1119.22 0.00 20256.9 54.3 1.0

AQUEOUS2 CRYST0 0.00 0.00 0.00 0.00 7.48 21.37 0.00 834.1 30.0 1.0

BIOOIL1 P1 0.00 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.0

BIOOIL2 P1 PREHEAT 0.00 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 25.0 206.4

CATECHO2 CRYST5 0.00 0.00 0.12 0.00 0.00 0.00 0.00 13.7 30.0 1.0

CATECHO3 CRYST1D6 0.00 0.00 0.76 0.00 0.00 0.00 0.00 83.9 30.0 1.0

CO2RECY1 COOL3 102.47 0.00 0.00 0.00 0.00 0.00 0.00 4509.5 55.0 206.4

EXTRACT COLUMN Sep-01 3189.49 2.14 0.12 0.07 36.40 30.34 0.06 143332.0 55.0 206.4

FEED PREHEAT COLUMN 3216.43 2.18 0.91 0.07 36.46 1149.56 0.06 164774.0 55.0 206.4

P-CRESOL CRYST3 0.00 0.01 0.00 0.06 0.00 0.00 0.00 7.4 30.0 1.0

PHENOL CRYST2 0.00 2.09 0.00 0.00 0.01 0.00 0.00 197.4 30.0 1.0

SEP2REC1 P2 3113.97 0.03 0.00 0.00 3.72 8.97 0.00 137434.0 21.6 206.4

WATER1 PEVA1 0.00 0.02 0.00 0.00 0.06 201.53 0.00 3637.1 86.1 1.0

WATER2 PEVA2 0.00 0.01 0.00 0.00 0.00 234.76 0.00 4231.0 65.3 1.0

WATER3 PEVA3 0.00 0.00 0.00 0.00 0.00 263.67 0.00 4750.9 43.3 1.0

WATER4 PEVA4 0.00 0.00 0.01 0.00 0.00 283.25 0.00 5103.9 40.2 1.0

WATER5 HEXD6-1 0.00 0.00 0.00 0.00 0.00 136.00 0.00 2450.1 73.1 1.0



Stream

NameFrom To Phenol Catechol p-Cresol Acetic Acid Water

4-

Ethylphenol

Mass Flow

(Total)Temp. Press.

kg/hr oC Bar

1 P1 HEX1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.2

10 D4 SG1 2.11 0.00 0.00 0.01 0.00 0.00 198.7 181.2 1.0

10-1 SG1 HEX6 2.11 0.00 0.00 0.01 0.00 0.00 198.7 140.0 1.0

10-2 HEX6 CRYST2 2.11 0.00 0.00 0.01 0.00 0.00 198.7 70.8 1.0

11 D4 P6 0.01 0.00 0.07 0.00 0.00 0.06 16.2 205.3 1.0

1-1 HEX1 HEX2 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 39.8 1.2

1-10 HEX10 HEX11 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.9 1.2

11-1 P6 D5 0.01 0.00 0.07 0.00 0.00 0.06 16.2 205.3 1.2

1-11 HEX11 D1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.9 1.2

12 D5 SG2 0.01 0.00 0.06 0.00 0.00 0.00 7.5 198.2 1.0

1-2 HEX2 HEX3 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 53.4 1.2

12-1 SG2 HEX7 0.01 0.00 0.06 0.00 0.00 0.00 7.5 140.0 1.0

12-2 HEX7 CRYST3 0.01 0.00 0.06 0.00 0.00 0.00 7.5 70.8 1.0

13 D5 SG3 0.00 0.00 0.01 0.00 0.00 0.06 8.8 214.2 1.0

1-3 HEX3 HEX4 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 63.2 1.2

13-1 SG3 HEX8 0.00 0.00 0.01 0.00 0.00 0.06 8.8 140.0 1.0

13-2 HEX8 CRYST4 0.00 0.00 0.01 0.00 0.00 0.06 8.8 70.8 1.0

14 D7 HEX2 0.01 0.00 0.00 0.00 375.26 0.00 6761.2 101.1 1.0

1-4 HEX4 HEX5 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 64.0 1.2

15 D7 SG5 0.00 0.31 0.00 0.00 0.01 0.00 34.1 212.3 1.0

1-5 HEX5 HEX6 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.5 1.2

15-1 SG5 HEX10 0.00 0.31 0.00 0.00 0.01 0.00 34.1 140.0 1.0

15-2 HEX10 CRYST6 0.00 0.31 0.00 0.00 0.01 0.00 34.1 106.2 1.0

16 D8 HEX3 0.01 0.00 0.00 0.00 353.18 0.00 6363.5 101.1 1.0

1-6 HEX6 HEX7 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.8 1.2

17 D8 SG6 0.00 0.29 0.00 0.00 0.01 0.00 32.1 212.3 1.0

1-7 HEX7 HEX8 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.8 1.2

17-1 SG6 HEX9 0.00 0.29 0.00 0.00 0.01 0.00 32.1 150.0 1.0

17-2 HEX9 CRYST7 0.00 0.29 0.00 0.00 0.01 0.00 32.1 106.2 1.0

1-8 HEX8 HEX9 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.8 1.2

1-9 HEX9 HEX10 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 65.9 1.2

2 D1 P2 2.12 0.00 0.07 32.74 36.87 0.06 2845.0 88.1 1.0

2-1 P2 D2 2.12 0.00 0.07 32.74 36.87 0.06 2845.0 88.1 1.2

3 D1 P3 0.03 0.91 0.00 0.00 1103.72 0.00 19986.1 101.1 1.0

3-1 P3 SPLIT 0.03 0.91 0.00 0.00 1103.72 0.00 19986.1 101.1 1.2

3-2 SPLIT D6 0.01 0.31 0.00 0.00 375.26 0.00 6795.3 101.1 1.2

3-3 SPLIT D7 0.01 0.31 0.00 0.00 375.26 0.00 6795.3 101.1 1.2

3-5 SPLIT D8 0.01 0.29 0.00 0.00 353.19 0.00 6395.5 101.1 1.2

4 D2 HEX4 0.00 0.00 0.00 9.70 36.87 0.00 1246.4 87.6 1.0

4-1 HEX4 CRYST8 0.00 0.00 0.00 9.70 36.87 0.00 1246.4 69.0 1.0

4EPHENOL CRYST4 0.00 0.00 0.01 0.00 0.00 0.06 8.8 30.0 1.0

5 D2 P4 2.12 0.00 0.07 23.04 0.00 0.06 1598.6 121.0 1.0

5-1 P4 D3 2.12 0.00 0.07 23.04 0.00 0.06 1598.6 121.0 1.2

6 D6 HEX1 0.01 0.00 0.00 0.00 375.26 0.00 6761.2 101.1 1.0

7 D6 SG4 0.00 0.31 0.00 0.00 0.01 0.00 34.1 212.3 1.0

7-1 SG4 HEX11 0.00 0.31 0.00 0.00 0.01 0.00 34.1 140.0 1.0

7-2 HEX11 CRYST5 0.00 0.31 0.00 0.00 0.01 0.00 34.1 106.1 1.0

8 D3 HEX5 0.00 0.00 0.00 23.04 0.00 0.00 1383.6 118.4 1.0

8-1 HEX5 CRYST1 0.00 0.00 0.00 23.04 0.00 0.00 1383.6 70.5 1.0

9 D3 P5 2.12 0.00 0.07 0.01 0.00 0.06 214.9 182.3 1.0

9-1 P5 D4 2.12 0.00 0.07 0.01 0.00 0.06 214.9 182.4 1.2

ACETICAC CRYST1 0.00 0.00 0.00 23.04 0.00 0.00 1383.6 30.0 1.0

AQUEOUS CRYST8 0.00 0.00 0.00 9.70 36.87 0.00 1246.4 30.0 1.0

BIOOIL1 P1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.0

CATECHO1 CRYST5 0.00 0.31 0.00 0.00 0.01 0.00 34.1 30.0 1.0

CATECHO2 CRYST6 0.00 0.31 0.00 0.00 0.01 0.00 34.1 30.0 1.0

CATECHO3 CRYST7 0.00 0.29 0.00 0.00 0.01 0.00 32.1 30.0 1.0

P-CRESOL CRYST3 0.01 0.00 0.06 0.00 0.00 0.00 7.5 30.0 1.0

PHENOL CRYST2 2.11 0.00 0.00 0.01 0.00 0.00 198.7 30.0 1.0

WATER1 HEX1 0.01 0.00 0.00 0.00 375.26 0.00 6761.2 44.8 1.0

WATER2 HEX2 0.01 0.00 0.00 0.00 375.26 0.00 6761.2 58.4 1.0

WATER3 HEX3 0.01 0.00 0.00 0.00 353.18 0.00 6363.5 68.2 1.0

kmol/hr



Stream

NameFrom To Phenol Catechol p-Cresol Acetic Acid Water

4-

Ethylphenol

Mass Flow

(Total)Temp. Press.

kg/hr oC Bar

10 D4 SG1 2.13 0.00 0.00 0.01 0.00 0.00 201.0 181.2 1.0

10-1 SG1 HEX3 2.13 0.00 0.00 0.01 0.00 0.00 201.0 140.0 1.0

10-2 HEX3 CRYST2 2.13 0.00 0.00 0.01 0.00 0.00 201.0 32.6 1.0

11 D4 P6 0.01 0.00 0.07 0.00 0.00 0.06 16.2 205.3 1.0

1-1 P1 HEX1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.2

11-1 P6 D5 0.01 0.00 0.07 0.00 0.00 0.06 16.2 205.3 1.2

12 D5 SG2 0.01 0.00 0.06 0.00 0.00 0.00 7.5 198.2 1.0

1-2 HEX1 HEX2 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 24.5 1.2

12-1 SG2 HEX4 0.01 0.00 0.06 0.00 0.00 0.00 7.5 140.0 1.0

12-2 HEX4 CRYST3 0.01 0.00 0.06 0.00 0.00 0.00 7.5 32.7 1.0

13 D5 SG3 0.00 0.00 0.01 0.00 0.00 0.06 8.8 214.2 1.0

1-3 HEX2 HEX3 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 27.2 1.2

13-1 SG3 HEX5 0.00 0.00 0.01 0.00 0.00 0.06 8.8 140.0 1.0

13-2 HEX5 CRYST4 0.00 0.00 0.01 0.00 0.00 0.06 8.8 32.7 1.0

1-4 HEX3 HEX4 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 27.6 1.2

1-5 HEX4 HEX5 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 27.7 1.2

1-6 HEX5 HEX6 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 27.7 1.2

1-7 HEX6 D1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 27.8 1.2

2 D1 P2 2.14 0.00 0.07 32.74 36.84 0.06 2846.8 88.1 1.0

2-1 P2 D2 2.14 0.00 0.07 32.74 36.84 0.06 2846.8 88.1 1.2

3 D1 P3 0.00 0.91 0.00 0.00 1103.74 0.00 19984.2 101.1 1.0

3-1 P3 EVA1 0.00 0.91 0.00 0.00 1103.74 0.00 19984.2 101.1 1.2

4 D2 HEX1 0.00 0.00 0.00 9.69 36.84 0.00 1245.6 87.6 1.0

4-1 HEX1 CRYST-0 0.00 0.00 0.00 9.69 36.84 0.00 1245.6 29.5 1.0

4EPHENOL CRYST4 0.00 0.00 0.01 0.00 0.00 0.06 8.8 30.0 1.0

5 D2 P4 2.14 0.00 0.07 23.05 0.00 0.06 1601.2 121.0 1.0

5-1 P4 D3 2.14 0.00 0.07 23.05 0.00 0.06 1601.2 121.0 1.2

8 D3 HEX2 0.00 0.00 0.00 23.04 0.00 0.00 1384.0 118.4 1.0

8-1 HEX2 CRYST1 0.00 0.00 0.00 23.04 0.00 0.00 1384.0 32.5 1.0

9 D3 P5 2.14 0.00 0.07 0.01 0.00 0.06 217.2 182.3 1.0

9-1 P5 D4 2.14 0.00 0.07 0.01 0.00 0.06 217.2 182.3 1.2

ACETICAC CRYST1 0.00 0.00 0.00 23.04 0.00 0.00 1384.0 30.0 1.0

AQ13-1 P1D3 HEX7 0.00 0.88 0.00 0.00 133.71 0.00 2506.1 40.3 1.0

AQ13-2 HEX7 D6 0.00 0.88 0.00 0.00 133.71 0.00 2506.1 68.1 1.0

AQ14 D6 HEX7 0.00 0.00 0.00 0.00 133.71 0.00 2408.8 101.1 1.0

AQ15 D6 SGD6 0.00 0.88 0.00 0.00 0.00 0.00 97.3 244.6 1.0

AQ15-1 SGD6 HEX6 0.00 0.88 0.00 0.00 0.00 0.00 97.3 140.0 1.0

AQ15-2 HEX6 CRYST5 0.00 0.88 0.00 0.00 0.00 0.00 97.3 106.2 1.0

AQ2 EVA1 HEXE1 0.00 0.00 0.00 0.00 198.83 0.00 3582.5 101.1 1.0

AQ2-1 HEXE1 PEVA1 0.00 0.00 0.00 0.00 198.83 0.00 3582.5 86.1 1.0

AQ3 EVA1 V1 0.00 0.90 0.00 0.00 904.91 0.00 16401.7 101.1 1.0

AQ3-1 V1 HEXE1 0.00 0.90 0.00 0.00 904.91 0.00 16401.7 83.2 0.5

AQ3-2 HEXE1 EVA2 0.00 0.90 0.00 0.00 904.91 0.00 16401.7 83.2 0.5

AQ4 EVA2 HEXE2 0.00 0.00 0.00 0.00 231.62 0.00 4173.2 83.2 0.5

AQ4-1 HEXE2 PEVA2 0.00 0.00 0.00 0.00 231.62 0.00 4173.2 65.3 0.5

AQ5 EVA2 V2 0.00 0.90 0.00 0.00 673.29 0.00 12228.5 83.2 0.5

AQ5-1 V2 HEXE2 0.00 0.90 0.00 0.00 673.29 0.00 12228.5 62.3 0.2

AQ5-2 HEXE2 EVA3 0.00 0.90 0.00 0.00 673.29 0.00 12228.5 62.3 0.2

AQ6 EVA3 HEXE3 0.00 0.01 0.00 0.00 260.14 0.00 4687.0 62.3 0.2

AQ6-1 HEXE3 PEVA3 0.00 0.01 0.00 0.00 260.14 0.00 4687.0 43.3 0.2

AQ7 EVA3 V3 0.00 0.89 0.00 0.00 413.16 0.00 7541.5 62.3 0.2

AQ7-1 V3 HEXE3 0.00 0.89 0.00 0.00 413.16 0.00 7541.5 40.3 0.1

AQ7-2 HEXE3 EVA4 0.00 0.89 0.00 0.00 413.16 0.00 7541.5 40.3 0.1

AQ8 EVA4 HEXE4 0.00 0.01 0.00 0.00 279.45 0.00 5035.4 40.3 0.1

AQ8-1 HEXE4 PEVA4 0.00 0.01 0.00 0.00 279.45 0.00 5035.4 40.2 0.1

kmol/hr


Utilities Costs

Table A- 5 Utilities cost P-1, LP: low pressure, HP: high pressure




AQ9 EVA4 P1D3 0.00 0.88 0.00 0.00 133.71 0.00 2506.1 40.3 0.1

AQUEOUS CRYST-0 0.00 0.00 0.00 9.69 36.84 0.00 1245.6 30.0 1.0

BIOOIL1 P1 2.14 0.91 0.07 32.74 1140.58 0.06 22831.1 22.0 1.0

CATECHO3 CRYST5 0.00 0.88 0.00 0.00 0.00 0.00 97.3 30.0 1.0

P-CRESOL CRYST3 0.01 0.00 0.06 0.00 0.00 0.00 7.5 30.0 1.0

PHENOL CRYST2 2.13 0.00 0.00 0.01 0.00 0.00 201.0 30.0 1.0

WATER1 PEVA1 0.00 0.00 0.00 0.00 198.83 0.00 3582.5 86.1 1.0

WATER2 PEVA2 0.00 0.00 0.00 0.00 231.62 0.00 4173.2 65.3 1.0

WATER3 PEVA3 0.00 0.01 0.00 0.00 260.14 0.00 4687.0 43.3 1.0

WATER4 PEVA4 0.00 0.01 0.00 0.00 279.45 0.00 5035.4 40.2 1.0

WATER5 HEX7 0.00 0.00 0.00 0.00 133.71 0.00 2408.8 73.1 1.0

Utility NameChilled

WaterElectricity

Cooling

WaterLP Steam

LP Steam

GenerationHP Steam

Utility type Water Electricity Water Steam Steam Steam

Costing rate [USD/hr] 5.0 219.1 8.3 77.2 -10.2 64.5

Utility NameChilled

WaterElectricity

Cooling

WaterLP Steam

LP Steam

GenerationHP Steam

Utility type Water Electricity Water Steam Steam Steam

Costing rate [USD/hr] 5.3 152.4 8.0 81.6 -6.5 63.2

Utility Name ElectricityCooling

Water

LP Steam

GenerationHP Steam

Utility type Electricity Water Steam Steam

Costing rate [USD/hr] 0.03 33.0 -2.2 398.4

Utility Name ElectricityCooling

Water LP Steam

LP Steam

GenerationHP Steam

Utility type Electricity Water STEAM Steam Steam

Costing rate [USD/hr] 0.08 9.7 15.9 -2.3 102.8


Equipment Cost

Table A- 9 Equipment costs P-1, RP: reflux pump


NameEquipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]

HEX10 7600 D1F1-reb 17100 D5F2-cond 8400 P1F2 3800

HEX6 7600 D1F1-RP 4300 D5F2-cond acc 15100 P2F2 3800

HEX5 7600 D1F1-tower 1048100 D5F2-reb 12300 P3F2 3800

HEX7 7600 D1F2-cond 9800 D5F2-RP 4300 P4F2 3800

Sep-02 48800 D1F2-cond acc 15100 D5F2-tower 183100 P5F2 3800

CRYST0 8200 D1F2-reb 11600 D7-cond 17600 PEVA1 4300

COL-1 124400 D1F2-RP 4300 D7-cond acc 13300 PEVA2 4500

COL-2 32100 D1F2-tower 403300 D7-reb 162600 PEVA3 4500

COL-3 24600 D2F2-cond 10900 D7-RP 5300 PEVA4 4500

COLUMN 124400 D2F2-cond acc 12300 D7-tower 6821100 PREHEAT 30300

COMP1 1381800 D2F2-reb 17100 EVA1 22700 PUMP1 786500

COMP2 1927500 D2F2-RP 5200 EVA2 19000 PUMP3 617300

COOL1 152900 D2F2-tower 935700 EVA3 19000 Sep-01 118900

COOL2 10600 D3F2-cond 17700 EVA4 30300 SG1 26000

COOL3 21700 D3F2-cond acc 15100 HEX1 7600 SG1F1 7600

CRYST1D7 30000 D3F2-reb 12600 HEX3 7600 SG1F2 8200

CRYST1F1 30000 D3F2-RP 4300 HEX9 8300 SG2 166100

CRYST1F2 8500 D3F2-tower 532100 HEXE1 40700 SG2F2 7600

CRYST2F2 30000 D4F2-cond 8500 HEXE2 40800 SG3F2 7600

CRYST3F2 30000 D4F2-cond acc 15100 HEXE3 42900 SG4F2 7600

CRYST4F2 30000 D4F2-reb 12200 HEXE4 62600 SGD7 8100

CRYST5F2 30000 D4F2-RP 4300 P1D3 3800 HEX2 7600

D1F1-cond 9700 D4F2-tower 104800 P1F1 3800 HEX4 8200

D1F1-cond acc 15100 HEX8 7600

NameEquipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]

HEX5 7600 COMP2 1816200 D3-cond 17600 P1 786500

D6-cond 17600 COOL1 172600 D3-cond acc 15100 P2 595300

D6-cond acc 16700 COOL2 9600 D3-reb 12700 P3 3800

D6-reb 162400 COOL3 16100 D3-RP 4300 P4 3800

D6-RP 5300 CRYST0 9600 D3-tower 533700 P5 3800

D6-tower 6821100 CRYST1 8500 D4-cond 8400 P6 3800

PEVA3 4500 CRYST2 30000 D4-cond acc 15100 P7 3800

EVA3 19000 CRYST3 30000 D4-reb 12200 PREHEAT 30500

HEXE4 62600 CRYST4 30000 D4-RP 4300 Sep-01 79100

EVA4 30300 CRYST5 30000 D4-tower 104800 SG1 18300

HEXE2 40800 D1-cond 12400 D5-cond 8400 SG2 93000

SGD6 8100 D1-cond acc 10500 D5-cond acc 15100 SGD3 8200

PEVA4 4500 D1-reb 15300 D5-reb 12300 SGD4 7600

CRYST1D6 30000 D1-RP 5000 D5-RP 4300 SGD5 7600

PEVA1 4300 D1-tower 774900 D5-tower 183100 SGD5-2 7600

HEXD6-2 7600 D2-cond 10800 HEX1 8100 PEVA2 4500

EVA2 19000 D2-cond acc 11900 HEX2 8200 P1D3 3800

COL-1 124400 D2-reb 17000 HEX3 7600 HEX6 7600

COL-2 24600 D2-RP 5100 HEX4 7600 HEXE1 40700

COLUMN 124400 D2-tower 834100 HEXD6-1 8500 HEXE3 42900

COMP1 1442200 EVA1 22700




NameEquipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]

HEX9 7600 D2-cond 15985 D5-RP 4300 HEX2 11000

HEX6 7600 D2-cond acc 11100 D5-tower 117900 HEX3 10900

HEX7 7600 D2-reb 17000 D6-cond 33400 HEX4 8300

HEX8 7600 D2-RP 5000 D6-cond acc 23900 HEX5 8400

HEX10 7600 D2-tower 1188800 D6-reb 81900 P1 5000

CRYST1 8400 D3-cond 10700 D6-RP 6600 P2 3800

CRYST2 30000 D3-cond acc 13200 D6-tower 17002300 P3 4900

CRYST3 30000 D3-reb 15500 D7-cond 33400 P4 3800

CRYST4 30000 D3-RP 5000 D7-cond acc 23900 P5 3800

CRYST5 30000 D3-tower 775100 D7-reb 81900 P6 3800

CRYST6 30000 D4-cond 17600 D7-RP 6600 SG1 8200

CRYST7 30000 D4-cond acc 15100 D7-tower 17002300 SG2 7600

CRYST8 9500 D4-reb 15200 D8-cond 32700 SG3 7600

D1-cond 20646 D4-RP 4300 D8-cond acc 23700 SG4 7600

D1-cond acc 17000 D4-tower 553500 D8-reb 79300 SG5 7600

D1-reb 17500 D5-cond 8400 D8-RP 6600 SG6 7600

D1-RP 5500 D5-cond acc 15100 D8-tower 15968200 HEX11 7600

D1-tower 2561600 D5-reb 12200 HEX1 11000

NameEquipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]Name

Equipment

Cost [USD]

CRYST1 7600 CRYST5 30000 D3-cond acc 13200 P2 3800

HEXE3 42700 CRYST-0 7600 D3-reb 15500 P3 4900

P1D3 3800 CRYST2 30000 D3-RP 5000 P4 3800

HEXE4 61900 CRYST3 30000 D3-tower 775100 P5 3800

HEX4 7600 CRYST4 30000 D4-cond 19200 P6 3800

SGD6 8200 D1-cond 20646 D4-cond acc 15100 SG1 8200

HEX7 8500 D1-cond acc 17000 D4-reb 15200 SG2 7600

EVA4 30300 D1-reb 21300 D4-RP 4300 SG3 7600

HEX3 7600 D1-RP 5500 D4-tower 553500 HEXE1 39200

HEX6 7600 D1-tower 3237800 D5-cond 8400 PEVA2 4500

HEXE2 40600 D2-cond 15985 D5-cond acc 15100 EVA2 19000

PEVA4 4500 D2-cond acc 11100 D5-reb 12200 EVA3 19000

HEX5 7600 D2-reb 17000 D5-RP 4300 D6-cond 17500

EVA1 21900 D2-RP 5000 D5-tower 117900 D6-cond acc 13300

PEVA1 4300 D2-tower 1188800 HEX2 8400 D6-reb 160800

HEX1 8500 D3-cond 10700 P1 5000 D6-RP 5300

PEVA3 4500 D6-tower 6534600

Supercritical Carbon Dioxide Extraction and Fractionation ...Supercritical Carbon Dioxide Extraction...

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Transcript of Supercritical Carbon Dioxide Extraction and Fractionation ...Supercritical Carbon Dioxide Extraction...