CS Biliyok ESCAPE22 Presentation

Dynamic Validation of Model for Post-Combustion Chemical Absorption CO2Capture Plant

Chet Biliyok

Meihong WangSchool of Engineering, Cranfield University, Bedfordshire

Adekola LawalProcess Systems Enterprise Ltd, London

Frank SeibertSeparation Research Program, University of Texas at Austin

19th June 2012

Outline

• Background

• Motivation

• Process Description

• Model development

• Steady-State Validation

• Dynamic Validation

• Observations & Conclusions

Global Warming / Climate Change

Source: Berkeley Earth Surface Temperature Group - Nov, 2011Source: Shakun et al (2012),”Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation”, Nature, doi:10.1038/nature10915

“With high confidence…heat waves in Texas and

Moscow…caused by human induced climate change” –

James E. Hansen, New York Times, May 2012

“Scientists see climate change link to Australian floods” –

Reuters, Jan 2011

• CO2 concentration is currently 394 ppm and is increasing by

2-3 ppm every year.

• Atmospheric CO2 must not exceed 450 ppm to ensure that

temperature rise stays below 2oC.

• IPCC recommends that CO2 emissions be cut by 50% by

2050 compared 1990 levels.

• Emissions from the 50,000 power plants around the world

account for about 25% of global level of CO2.

• Energy demand expected to rise with increasing population

and the emergence of the BRICS countries.

Challenges

Source: U.S. Energy Information Administration, 2010 International Energy Outlook

Projected World Marketed Energy Use

Power Generation Solutions

Source: IEA (2010) Energy Technology Perspectives, Scenarios and Strategies to 2050

IEA’s BLUE Map Scenario

CO2 Capture & Sequestration Systems

Source: IPCC (2005) Special Report on Carbon Dioxide Capture and Storage

• Extensive deployment is

critical: 100 large‐scale CCS

projects are needed by

2020, and 3400 by 2050.

• Global CCS identified 75

active large-scale integrated

CCS projects in 2012.

CO2 Capture Technologiesa

Post Combustion – Pulverized Coal Oxyfuel Combustion – Pulverized Coal

Pre combustion – Gasification

a Ciferno, J. P., Litynski, J. L. and Plasynski, S. I. (2010), DOE/NETL Carbon Dioxide

Capture and Storage RD&D Roadmap

Outline

• Background

• Motivation






Motivations

• Current CCS technologies add up to 80% to the cost of electricity

for a new power plant.

• Energy penalty introduced to power plant reduces output by up to

30%.

• Costs of capture account for nearly 80% of an integrated CCS

project.

• Before CCS commercialization, operational characteristics of

integrated plant need to be fully understood.

• High cost of full scale demonstration plants (about $1 Billion)

makes modelling & simulation a viable option.

• Dynamic validation required to ensure model predicts plant

dynamic response accurately.

Outline

• Background

• Motivation






aGlobal CCS Institute (2012), CO2 Capture Technologies – Post Combustion

Capture, Canberra, Australia.

Fluor Daniel’s EconamineChemical Absorption Processa

Large-Scale Integrated Post-Combustion Capture Plants

• Statoil Mongstad, Norway (2012)

• Teneska Trailblazer Project, Texas, US (2014)

• Boundary Dam Station, Saskatchewan, Canada (2014)

• SSE Ferrybridge Station, West Yorkshire UK (2015)

• E.ON ROAD Project, Rotterdam, Netherlands (2015)

• PGE Belchatow Station, Lodz, Poland (2015)

• GETICA Project, Gorj, Romania (2016)

• Bow City Power, Alberta, Canada (2017)

Outline

• Background

• Motivation






Reactive Absorption Modelling Approaches a

Rate-based

Approach

Equilibrium-based

Approach

a Kenig, E. Y., Schneider, R. and Górak, A. (2001), "Reactive absorption: Optimal process design

via optimal modelling", Chemical Engineering Science, vol. 56, no. 2, pp. 343-350.

Modified Gas-Liquid Contactor Model

Model Formulation

• Chemical Equilibrium is defined by ElecNRTL Activity

Coefficient Model in Aspen Properties.

• Maxwell-Stefan Formulation used to determine fluxes across

films.

• Vapour diffusivity calculated by the Fuller method.

• Liquid diffusivity determined by a method provided by

Veersteeg and van Swaaij.

• Onda correlation used to determine the mass transfer

coefficients in the films and the wetted area.

• Heat of Absorption determined via equations derived from

tests at the University of Texas at Austin.

Outline

• Background

• Motivation






Lean MEA

Rich MEA

Steady-State Validation of Capture Plant Model

Case L/G ratio CO2 removal (%)

32 6.5 95

47 4.6 69

aDugas, R.E. (2006). Pilot Plant Study of Carbon Dioxide Capture by Aqueous Monoethanolamine. Master thesis, Chemical Engineering, University of Texas at Austin.

Two casesa chosen to

represent two

extremes of operation

i.e. High and low

levels of CO2 capture.

Results – standalone and linked columns a

18

Case 32 Regenerator Temperature Profile

340

350

360

370

380

390

400

-2 0 2 4 6 8 10

Height from the bottom (m)

Tem

per

atu

re (

K)

Pilot Plant

measurements

Rate-Based model


340

345

350

355

360

365

370

-2 0 2 4 6 8 10

Height from the bottom (m)

Te

mp

era

ture

(K

)

Pilot Plant

Measurements

Rate-Based Model

Case 32 Absorber Temperature Profile

300

305

310

315

320

325

330

335

340

345

350

-2 0 2 4 6 8 10

Height from bottom of packing (m)

Tem

pera

ture

(K

)

Pilot plant

Measurements

Rate-based

model

Equilibrium-

based model


340

350

360

370

380

390

400

-2 0 2 4 6 8 10

Height from the bottom of packing (m)

Te

mp

era

ture

(K

)

Pilot Plant Measurements

Rate-Based IntegratedModel

Rate-Based Stand-aloneModel

Case 32 Absorber Temperature Profile

300

305

310

315

320

325

330

335

340

345

-2 0 2 4 6 8 10

Height from bottom of packing (m)

Te

mp

era

ture

(K

) Pilot plant

Measurements

Rate-based Integrated

model

Rate-based Stand-

alone model

a Lawal, A. et al (2010), Dynamic modelling and analysis of post-combustion CO2 chemical

absorption process for coal-fired power plants, Fuel, vol. 89, no. 10, pp. 2791-2801.

Outline

• Background

• Motivation






Dynamic Validation of CO2 Capture Plant Model

SRP Pilot plant, Univ. Texas at Austin

• First successful attempt

at dynamic validation of

a CO2 Capture model.

• Pilot plant data from the

Separation Research

Program at the

University of Texas at

Austin.

Dynamic Validation Assumptions

• Cases selected based on the following criteria:

o Significant change in the plant input that would affect CO2 capture

performance.

o Negligible variation in regenerator reboiler temperature.

o Minimal number of additional disturbances in other inputs.

• Moisture content assumed constant and is a function of

the ambient air relative humidity.

• Reboiler temperature reading in the pilot plant used as set

point of temperature controller in reboiler model.

Case 1 – Conventional process a

a Lawal, A. (2010), Study of a Post-Combustion CO2 Capture Plant for Coal-Fired Power Plant through

Modelling and Simulation (PhD thesis), Cranfield University, Bedford.

Case 1 - Isolated process inputs and disturbances

0 1 2 3 4 5 6 7 8 9

1.4

1.6

1.8

2

Time (hours)

Mass f

low

rate

(kg/s

)

(a) Lean MEA mass flow rate to the absorber

0 1 2 3 4 5 6 7 8 9

0.16

0.18

0.2

0.22

0.24

Time (hours)

CO

2 m

ass f

raction

(b) CO2 composition in inlet gas to absorber

0 1 2 3 4 5 6 7 8 9310

315

320

325

330

335

Time (hours)

Tem

pera

ture

(K

)

(c) Temperature of inlet gas to absorber(K)

CO2

• Slow decrease in

lean solvent flow

rate into the

absorber.

• Fluctuating CO2

Composition of

flue gas into the

absorber.

• Increase in the

temperature of

flue gas into the

absorber.

Case 1 – Plant and model response comparison

0 1 2 3 4 5 6 7 8 9320

330

340

350

Time (hours)

Tem

pera

ture

(K

)

(a) Temperature at 6.77m above the bottom of absorber packing

0 1 2 3 4 5 6 7 8 9310

320

330

340

Time (hours)

Tem

pera

ture

(K

)

(b) Temperature at 4.48m above the bottom of absorber packing

0 1 2 3 4 5 6 7 8 9310

315

320

325

Time (hours)

Tem

pera

ture

(K

)

(c) Temperature at 2.19m above the bottom of absorber packing

0 1 2 3 4 5 6 7 8 90

0.02

0.04

0.06

Time (hours)

Mass f

raction

(d) CO2 mass fraction in treated gas stream

0 1 2 3 4 5 6 7 8 90.1

0.2

0.3

Time (hours)

Heat

Duty

(M

W)

(e) Reboiler heat duty

CO2

Logged pilot plantmeasurement

Dynamic model predictions

TOP

MIDDLE

BOTTOM

Case 2 – Intercooled Absorber

RTD

4071

RTD

4077

RTD

4076

RTD

4073

RTD

4075

RTD

4079

RTD

4078

RTD

4074

RTD

4072

RTD

40710

Reference

Body Flange

Body Flange

Body Flange

Body Flange

3.59

0.46

4.27

1.871.41

2.65

3.05

3.74

4.23

5.15

6.34

8.65

8.95

9.98

Note : All Measurements in metres

Case 2 - Intercooled Absorber

312

314

316

318

320

322

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time

(a) Intercooled Solvent Return Temperature

0.166

0.171

0.176

0.181

0.186

0 0.5 1 1.5 2

Mas

s Fr

acti

on

Time

(b)Inlet CO2 Mass Fraction to absorber

313

314

315

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time

(c)Inlet Lean Amine Temperature

306

307

308

309

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time

(d) Inlet Flue Gas Temperature

• Step decrease in the

intercooled solvent

return temperature

• Fluctuating CO2

composition in the

flue gas

• Falling lean amine

inlet temperature

• Falling flue gas inlet

temperature

Case 2 – Plant and model response comparison

312

314

316

318

320

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time (hrs)

RTD4078a)Temperature at 8.65m above the reference point

310

312

314

316

318

320

322

324

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time (hrs)

RTD4077

b)Temperature at 6.34m above the reference point

312

314

316

318

320

322

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time (hrs)

RTD4076 Pilot Plant Data LogsDynamic Model Predictions

c)Temperature at 5.15m above the reference point

318

320

322

324

326

328

330

0 0.5 1 1.5 2

Tem

per

atu

re (

K)

Time (hrs)

RTD4072

d)Temperature at 2.65m above the reference point

0

0.01

0.02

0.03

0.04

0.05

0 0.5 1 1.5 2

Mas

s Fr

acti

on

Time (hrs)

e) CO2 Mass Fraction of treated gas

Outline

• Background

• Motivation






Observations and Conclusions

• Model prediction for the absorber temperature profile

tracks very well with the pilot plant measurements.

• Model effectively handles a number of process inputs and

disturbances at the same time.

• For the conventional process, model consistently

underestimates treated gas CO2 concentration and

overestimates reboiler duty.

• For the intercooled process, model prediction is very close

but slightly overestimates treated gas CO2 concentration.

• Onda wetted area estimate and chemical equilibrium

assumption are the likely causes of model discrepancy.

THANK YOU FOR YOUR

ATTENTION

QUESTIONS

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