The 22nd European Symposium on Computer Aided Process Engineering - Conference presentation of Chet Biliyok

1. Dynamic Validation of
Model for Post-Combustion
Chemical Absorption CO2
Capture Plant
Chet Biliyok
Meihong Wang
School of Engineering, Cranfield University, Bedfordshire
Adekola Lawal
Process Systems Enterprise Ltd, London
Frank Seibert
Separation Research Program, University of Texas at Austin
19th June 2012

2. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

3. Global Warming /
Climate Change “Scientists see climate change link to Australian floods” –
Reuters, Jan 2011
“With high confidence…heat waves in Texas and
Moscow…caused by human induced climate change” –
James E. Hansen, New York Times, May 2012
Source: Shakun et al (2012),”Global warming preceded by increasing
Source: Berkeley Earth Surface Temperature Group - Nov, 2011
carbon dioxide concentrations during the last
deglaciation”, Nature, doi:10.1038/nature10915

4. Challenges
Projected World Marketed Energy Use
• 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.
Source: U.S. Energy Information Administration, 2010 International Energy Outlook

5. Power Generation
Solutions
IEA’s BLUE Map Scenario
CO2 Capture & Sequestration Systems
• Extensive deployment is
critical: 100 large‐scale CCS
projects are needed by
Source: IEA (2010) Energy Technology Perspectives, Scenarios and Strategies to 2050 2020, and 3400 by 2050.
• Global CCS identified 75
active large-scale integrated
CCS projects in 2012.
Source: IPCC (2005) Special Report on Carbon Dioxide Capture and Storage

6. 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

7. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

8. 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.

9. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

10. Fluor Daniel’s Econamine
Chemical Absorption
Processa
aGlobalCCS Institute (2012), CO2 Capture Technologies – Post Combustion
Capture, Canberra, Australia.

11. 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)

12. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

13. 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.

14. Modified Gas-Liquid
Contactor Model

15. 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.

16. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

17. Steady-State Validation
of Capture Plant Model
Two casesa chosen to
represent two Lean MEA
extremes of operation
i.e. High and low
levels of CO2 capture.
Rich MEA
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.

18. Results – standalone and linked columns a
Case 32 Absorber Temperature Profile
Case 32 Absorber Temperature Profile 345
Case 47 Regenerator Temperature Profile
340
350
370
345 335
340 365 330
Pilot plant
Temperature (K)
335 Measurements
Temperature (K)
360 325
Temperature (K)
330 Pilot plant Rate-based Integrated
320 Pilot Plant
model
325
Measurements 355 Measurements
Rate-based 315 Rate-Based Model Rate-based Stand-
320 model alone model
350 310
315 Equilibrium-
310
based model 305
345
305 300
340 -2 0 2 4 6 8 10
300
-2 0 2 4 6 8 10 -2 0 2 4 6 Height from bottom of packing (m)
8 10
Height from bottom of packing (m) Height from the bottom (m)
Case 32 Regenerator Temperature Profile
Case 32 Regenerator Temperature Profile
400
400
Pilot Plant Measurements
390
390 Rate-Based Integrated
Model
380
Temperature (K)
Rate-Based Stand-alone
380 Model
Temperature (K)
370 Pilot Plant
measurements
370
Rate-Based model
360
360
350
350
340
340 -2 0 2 4 6 8 10
-2 0 2 4 6 8 10
Height from the bottom of packing (m)
Height from the bottom (m)
a Lawal,A. et al (2010), Dynamic modelling and analysis of post-combustion CO2 chemical
18
absorption process for coal-fired power plants, Fuel, vol. 89, no. 10, pp. 2791-2801.

19. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

20. Dynamic Validation of
CO2 Capture Plant
Model
• 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.
SRP Pilot plant, Univ. Texas at Austin

21. 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.

22. 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.

23. Case 1 - Isolated process
inputs and disturbances
(a) Lean MEA mass flow rate to the absorber
2
1.8
Mass flow rate (kg/s)
1.6
• Slow decrease in 1.4
lean solvent flow 0 1 2 3 4 5 6 7 8 9
Time (hours)
rate into the 0.24
CO2
(b) CO2 composition in inlet gas to absorber
absorber. 0.22
CO2 mass fraction
• Fluctuating CO2 0.2
0.18
Composition of 0.16
flue gas into the 0 1 2 3 4 5 6 7 8 9
absorber. Time (hours)
(c) Temperature of inlet gas to absorber(K)
335
• Increase in the 330
Temperature (K)
temperature of 325
320
flue gas into the 315
absorber. 310
0 1 2 3 4 5 6 7 8 9
Time (hours)

24. Case 1 – Plant and model response comparison (a) Temperature at 6.77m above the bottom of absorber packing
350
Temperature (K)
TOP
340
330
320
0 1 2 3 4 5 6 7 8 9
Time (hours)
(b) Temperature at 4.48m above the bottom of absorber packing
340
Temperature (K)
MIDDLE
330
320
310
0 1 2 3 4 5 6 7 8 9
Time (hours)
(c) Temperature at 2.19m above the bottom of absorber packing
325 Logged pilot plant
Temperature (K)
BOTTOM
320 measurement
315 Dynamic model
310 predictions
0 1 2 3 4 5 6 7 8 9
Time (hours)
CO2 (d) CO2 mass fraction in treated gas stream
0.06
Mass fraction
0.04
0.02
0
0 1 2 3 4 5 6 7 8 9
Time (hours)
(e) Reboiler heat duty
0.3
Heat Duty (MW)
0.2
0.1
0 1 2 3 4 5 6 7 8 9
Time (hours)

25. Case 2 – Intercooled
Absorber
Note : All Measurements in metres
Body Flange
9.98 RTD
40710
8.95 RTD
4079
RTD
8.65 3.59
4078
Body Flange
6.34 RTD
4077 0.46
Body Flange
5.15 RTD
4076
4.23 RTD
4075
3.74 RTD
4074
4.27
3.05 RTD
4073
2.65 RTD
4072
Body Flange
1.41 RTD 1.87
4071
Reference

26. Case 2 - Intercooled
Absorber
(a) Intercooled Solvent Return Temperature
322
320
Temperature (K)
318
316
314
312
0 0.5 1 1.5 2 Time
• Step decrease in the 0.186
(b)Inlet CO2 Mass Fraction to absorber
intercooled solvent 0.181
Mass Fraction
return temperature 0.176
0.171
• Fluctuating CO2 0.166
composition in the 0 0.5 1
(c)Inlet Lean Amine Temperature
1.5 2 Time
315
flue gas
Temperature (K)
• Falling lean amine 314
inlet temperature
313
• Falling flue gas inlet 0 0.5 1
(d) Inlet Flue Gas Temperature
1.5 2 Time
309
temperature
Temperature (K)
308
307
306
0 0.5 1 1.5 2 Time

27. Case 2 – Plant and model response comparison a)Temperature at 8.65m above the reference point
320
RTD4078
Temperature (K)
318
316
314
312
0 0.5 1 1.5 2 Time (hrs)
b)Temperature at 6.34m above the reference point
324
322 RTD4077
Temperature (K)
320
318
316
314
312
310
0 0.5 1 1.5 2 Time (hrs)
c)Temperature at 5.15m above the reference point
322
RTD4076 Pilot Plant Data
Temperature (K)
320
318 Logs
316 Dynamic Model
314 Predictions
312
Time (hrs)
0 0.5 1 1.5 2
d)Temperature at 2.65m above the reference point
330
328 RTD4072
Temperature (K)
326
324
322
320
318
0 0.5 1 1.5 2 Time (hrs)
e) CO2 Mass Fraction of treated gas
0.05
0.04
Mass Fraction
0.03
0.02
0.01
0
0 0.5 1 1.5 2 Time (hrs)

28. Outline
• Background
• Motivation
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions

29. 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.

30. THANK YOU FOR YOUR
ATTENTION
QUESTIONS