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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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
Case 47 Regenerator Temperature Profile
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
Case 32 Regenerator Temperature Profile
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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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
• Process Description
• Model development
• Steady-State Validation
• Dynamic Validation
• Observations & Conclusions
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.
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