AbstractThis paper presents steady state and dynamic modelling of post combustion CO2 capture using 30 wt% MEA integrated with models of CO2 compression and the steam power cycle. It uses multivariable optimization tools to maximize hourly profit of a 100 MWe coal-fired power plant. Steady state optimization for design provided optimum lean loading and CO2 removal as a function of price ratio (CO2 price/electricity price). The results indicated that for price ratio between 2.1 and 7, the plant should be designed at removal between 70% and 98% and lean loading in the range of 0.22–0.25. Dynamic optimization determined the operation of the capture system in response to two partial load scenarios (reboiler steam load reduction and power plant boiler load reduction) and provided optimum set points for steam rate, solvent circulation rate and stripper pressure control loops. Maximum profit is maintained by allowing the stripper pressure to drop and implementing a ratio control between solvent and steam rate (and flue gas rate for partial boiler load operation)

Ziaii, Sepideh

Rochelle, Gary T.

Edgar, Thomas F.

Botero, Fernando (escultor)Pla general lateral de l'obra. De singular 
iconografia, d'hImatge i Producció Editorialrtròfia sense mida i 
generosos volums. De bronze , mesura 
2,30 x 7 x 2,30 metres. Del 1990, en 
l'actual emplaçament des de 2003

Direcció Tècnica d'Urbanisme

Universitat de Barcelona. Centre de Recerca Polis

Repositori Obert de Coneixement de l'Ajuntament de Barcelona

Gato

Sepideh Ziaii

Gary T. Rochelle

Thomas F. Edgar

Crossref

Energy Procedia

Optimum design and control of amine scrubbing in response to electricity and CO2 prices

Botero, Fernando (escultor)Pla general frontal de l'obra. De singular 
iconografia, d'hImatge i Producció Editorialrtròfia sense mida i 
generosos volums. De bronze , mesura 
2,30 x 7 x 2,30 metres. Del 1990, en 
l'actual emplaçament des de 2003

Elsevier - Publisher Connector 

Available online at www.sciencedirect.com
   
 
Energy  Procedia  00 (2010) 000–000 
 
Energy 
Procedia 
 
www.elsevier.com/locate/XXX
 
GHGT-10 
Optimum Design and Control of Amine Scrubbing in Response to Electricity and 
CO2 prices 
Sepideh Ziaiia , Gary T. Rochellea,* , Thomas F. Edgara 
aChemical Engineering Department, University of Texas, Austin, Texas 78712, USA 
 
Elsevier use only: Received date here; revised date here; accepted date here 
Abstract 
This paper presents steady state and dynamic modelling of post combustion CO2 capture using 
30 wt% MEA integrated with models of CO2 compression and the steam power cycle. It uses 
multivariable optimization tools to maximize hourly profit of a 100 MWe coal-fired power plant. 
Steady state optimization for design provided optimum lean loading and CO2 removal as a 
function of price ratio (CO2 price / electricity price). The results indicated that for price ratio 
between 2.1 and 7, the plant should be designed at removal between 70% and 98% and lean 
loading in the range of 0.22–0.25. Dynamic optimization determined the operation of the capture 
system in response to two partial load scenarios (reboiler steam load reduction and power plant 
boiler load reduction) and provided optimum set points for steam rate, solvent circulation rate 
and stripper pressure control loops. Maximum profit is maintained by allowing the stripper 
pressure to drop and implementing a ratio control between solvent and steam rate (and flue gas 
rate for partial boiler load operation). 
© 2010 Elsevier Ltd. All rights reserved 
Keywords: Dynamic modeling; Absorption; Stripping; Optimization; Control 
 
1. Introduction 
     Dynamic modelling of a process is a helpful tool that is commonly used not only for 
analyzing the dynamic behaviour and designing simple control strategies but also for optimizing 
the operation of the plant in response to possible disturbances. However, this strategy has not 
been previously employed for optimizing the operation and control of CO2 absorption plants 
with aqueous amines.  In the few works found on dynamic modelling (Kvamsdal et al. [1]; Ziaii 
et al. [2]), the dynamic simulation of CO2 capture was used to investigate the dynamic behaviour 
c⃝ 1 Published by Elsevier Ltd.
Energy Procedia 4 (2011) 1683–1690
www.elsevier.com/locate/procedia
doi:10.1016/j.egypro.2011.02.041
Open access under CC BY-NC-ND license.
 s.ziaii/ Energy Procedia 00 (2010) 000–000 
 2
of the absorber and stripper separately in response to the partial load operational scenarios.  
Lawal et al. [3] analyzed the effects of possible upstream disturbances on the dynamic responses 
of a combined model of the absorber and regenerator. Several works have focused on reducing 
overall energy consumption of the capture and/or improvement of absorption performance [4-6]. 
Since those studies are based on steady state analysis of the plant running continuously at full 
capacity, they cannot provide insight into dynamic and control performance of the system during 
transitional operation. 
      This study presents a fully integrated model of an absorber/stripper using 
monoethanolamine(MEA) for CO2 capture.  The model was, developed in Aspen Custom 
Modeller (ACM) and also included an approximation of steady state models of CO2 compression 
and power cycle steam turbines to  account for the interaction of these components with capture 
during dynamic operation. This paper, presents the implementation of steady state multivariable 
optimization tool of ACM® to optimize the most important design parameters, lean loading and 
CO2 removal. In an economical study presented by Abu Zahra et al. [7], the lean loading and 
amine concentration were optimized based on minimization of cost of electricity for a plant 
designed at 90% removal, which included the terms of total capital cost, operating cost and 
energy cost. The analysis neglected the effects of capital cost and operating cost of the power 
plant, capture, and only considered energy cost by using power plant hourly profit as the cost 
function.  
     The second part of this study presents the implementation of a multivariable dynamic 
optimization tool, which is tied with dynamic simulation of the MEA plant, to find final 
optimum set points for the control loops when two main possible dynamic scenarios are applied.  
This optimization maximized the power plant profit at the time when the new steady state is 
reached. The scenarios considered in this study were introduced as possible operational cases in 
previous work [1, 3, 8]. The first operational scenario is a partial load reboiler steam operation in 
response to the variation of electricity or CO2 market conditions. This scenario indicates when 
the operation of flexible capture is economical, and is discussed in detail in the paper authored 
by Ziaii et al. [8]. The second one represents the variation of the load of power plant boilers, 
which can directly affect the operation of the capture. 
 
2. Model development 
A model of CO2 absorption/stripping with 30 wt% monoethanolamine (MEA) was created in a 
flow sheet of ACM®. The model includes dynamic rate-based models of packed columns 
(absorber and stripper), simplified steady state models for the heat exchangers, general 
performance curves for multi-stage CO2 compressor and pumps and an approximate steady state 
model for the steam turbine. The stages for steam turbines (HP, IP, and LP) with the extracted 
steam was extracted for solvent regeneration from the IP/LP crossover of the three-stage steam 
turbine (HP, IP, and LP). Each stage the turbines was represented with the ellipse law with 
design conditions reported by Lucquiaud [9]. The flue gas from 100 MWe coal- fired power 
1684 S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690
 Author name / Energy Procedia 00 (2010) 000–000  
 3
plant enters the absorber with 13% CO2. Figure 1 gives the flow sheet of the absorption/stripping 
process with specified design conditions. 
3. Steady state and dynamic optimization 
    This work uses power plant profit ($/hour) as the objective function for steady state and 
dynamic optimization: 
)hr/ton(removedCO)ton/($priceCO
)kW.(gen.Elect)kWh/($price.Elec)hr/($profit
22 

                                       (1)                
    Where the electricity generation is the total work produced by steam in the power cycle, minus 
lost work due to CO2 capture and compression. It is also assumed that the total work produced 
by steam is only a function of total steam rate in the steam cycle and varies proportionally with 
that variable.  The influence of factors such as turbine inlet/outlet conditions is neglected. 
Equation 2 calculates the lost work of CO2 capture and compression: 
pumpscomp
)extP(
sat
ksin)extP(
sat
reblost WW
T
TT
Q.W 






	

 
 750
                                          (2)
 
      The FEASOPT method in ACM® was connected to the steady state model of the MEA plant 
and operated at steady state to maximize the profit by adjusting the lean loading and CO2 
removal.  Other independent process variables were set at their design specification as shown in 
Figure 1.  Optimization was performed with the ratio of CO2 price to the electricity price varying 
from 2 to 7($/ton CO2)/(cents/kWh)).  This steady state optimization provides optimum design 
curves for lean loading and removal as a function of price ratio.  
    Dynamic optimization was used to find optimum operating curves for manipulated variables to 
maximize the final power plant profit as the disturbance occurs.  Four valves (figure 1) were 
used to control.  The valve on the lean solution is manipulated to control the reboiler level.  The 
other three valves regulate the steam rate, solvent rate, and the stripper vapor rate.  The operating 
curves provided by dynamic optimization give the optimum final values of the controlled 
variables so that they can be used to establish a set point.  
    The FEASOPT method performed multivariable dynamic optimization and ultimately 
optimized the set point paths of steam rate, solvent rate, and stripper pressure simultaneously 
over a range of variations in disturbances for two dynamic scenarios:  
1. Partial steam load operation in flexible capture with a price ratio of 2 to 5. 
2. Partial boiler load operation in a variable load power plant with the same simultaneous 
relative decrease in both flue gas rate and power cycle steam rate  up to 70% of the full 
load. 
S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690 1685
 s.ziaii/ Energy Procedia 00 (2010) 000–000 
 4
       
 
Figure 1: MEA absorption/stripping integrated with power cycle and CO2 compression system 
along with control valves    
4. Results and Discussion 
4.1. Optimization in Response to the Price Ratio  
    Steady state optimization was performed to optimize design variables (lean loading and CO2 
removal) for different values of price ratios.  Then dynamic optimization was carried out for a 
case study designed at a specific point on the optimum design curve (price ratio = 2.6, removal = 
90.1%, lean loading = 0.225) to find the optimum operating curve as the price ratio varies.  This 
means that if the controller set points move on the optimum operating curves as the price ratio 
varies, we can make the highest profit at the new steady state condition. Figure 2 illustrates the 
design and operating curves of CO2 penetration versus price ratio.  CO2 penetration is defined as 
the fraction of the CO2 in the flue gas that is released to the atmosphere: 
removalCOnpenetratioCO 22 1                (3) 
    As shown in Figure 2, for price ratio of 2 to 3 the rate of the reduction is much higher relative 
to the higher price ratio.  The design curve shows a large change around price ratio = 2.1, which 
means that there are two local optima around this point, at 53% and 70% removal.  The optimum 
operating curve deviates from the design curve specifically when the new price ratio is higher 
than the initial price ratio because of using different specifications in steady state and dynamic 
1686 S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690
 Author name / Energy Procedia 00 (2010) 000–000  
 5
simulation for the liquid level in the absorber sump.  Even at the design point (price ratio = 2.6) 
the optimum operating point does not lie on the design curve. No significant deviation is seen 
between the design and operating curves for lean loading for the selected case study (Figure 3). 
    Considering the design curve, at medium and high price ratio where the system is designed at 
removal higher than 70%, the optimum lean loading is lower than 0.25, while at low level of 
removal ( 53%), the optimum lean loading greater than 0.39.  Figure 4 illustrates how the 
optimum lean loading moves from low to high as removal increases.  Comparing the equivalent 
work curve of 90% removal to 70% and 53%, we can see a flat minimum area at lower removal 
so that the global minimum can easily change from low to high as the removal varies slightly.  
That is why the optimum lean loading changes significantly around price ratio = 2.1. 
     One of the variables that was optimized during dynamic optimization is pressure at the top of 
the stripper.  The results suggest that in order to stay on maximum profit and minimum energy 
lost, the pressure valve should be kept wide open and the stripper pressure should be allowed to 
drop as the steam rate reduces. 
   
 
Figure 2: CO2 penetration at the optimum 
design and operating conditions, Habs=15m, 
Hstrip = 10m, cross heat exchanger Tdesign = 
5°C, reboiler Tdesign=10°C, reboiler Tdesign 
= 120°C, sumps=2 min 
                                                              
Figure 3: Lean loading at the optimum 
design and operating conditions, Habs=15m, 
Hstrip = 10m, cross heat exchanger Tdesign=5 
°C, reboiler Tdesign = 10°C, reboiler Tdesign 
=120°C, sumps =2 min  
   At the optimum operating conditions the optimum solvent rate is a linear function of the steam 
rate.   Although the slope of the line deviates slightly from one, we can use a ratio control on the 
steam rate and solvent rate and still stay close to the optimum path. (Figure 5)  
 
 
 
 
S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690 1687
 s.ziaii/ Energy Procedia 00 (2010) 000–000 
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Figure 4: Capture total lost work versus at 
optimum design conditions, Habs=15m, Hstrip 
= 10m, cross heat exchanger Tdesign = 5 °C, 
reboiler Tdesign = 10 °C, reboiler Tdesign = 
120 °C 
 
 
                                                                    
Figure 5: Steam flow at optimum operating 
conditions, plant designed at price ratio=2.6 
(removal=90% and lean loading=0.225), 
Habs=15m, Hstrip = 10m, cross heat exchanger 
Tdesign = 5 °C, reboiler Tdesign = 10 °C, 
reboiler Tdesign =120 °C, sumps = 2 min 
4.2.      Optimization in Response to the Partial Boiler Load Operation 
    When the boiler load decreases, the flow of flue gas directed to the absorber and total steam 
rate entering the first stage of steam turbine decreases.  Variation of steam rate in the power 
cycle leads in changing the rate and the pressure of extracted steam entering the reboiler, which 
can influence the stripper operation.  In the simulation, we assumed that both flue gas rate and 
total steam rate vary proportionally with the boiler load. 
      The simulation and optimization of this dynamic scenario is performed for the plant designed 
initially at 90.1% removal and 0.225 lean loading.  As in partial steam load operation, optimizing 
the pressure shows that keeping the pressure valve always wide open. Therefore allowing the 
stripper pressure to drop with decreasing steam rate is the most energy efficient and profitable 
strategy for partial boiler load operation. 
     Figure 6 indicates that optimum steam rate and solvent rate vary linearly with the boiler load.  
The deviation of the slope of the solvent rate from 1 is slightly more than steam rate.  Since the 
deviation of slopes of solvent and steam rates are not significant, placing a ratio control among 
flue gas rate, steam rate, and solvent rate will keep the plant close to the optimum path. 
    Both optimum removal and lean loading increase as boiler load decreases (Figure 7).  From a 
process control perspective, the current results indicate that keeping L/G constant in both 
1688 S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690
 Author name / Energy Procedia 00 (2010) 000–000  
 7
absorber and stripper is a control strategy that enables the plant to run close to the optimum path 
during variable load operation of power plant. 
 
    
 
Figure 6: Optimum steam rate and solvent 
rate (normalized by initial rates) with 
variable boiler load, Habs=15m, Hstrip = 10m, 
cross heat exchanger Tdesign = 5 °C, 
reboiler Tdesign = 10 °C, reboiler Tdesign = 
120 °C, sumps = 2 min  
Figure 7: Optimum CO2 removal and lean 
loading with variable boiler load, Habs=15m, 
Hstrip = 10m, cross heat exchanger Tdesign = 
5 °C, reboiler Tdesign = 10 °C, reboiler 
Tdesign = 120 °C, sumps = 2 min 
5. Conclusions 
    The dynamic model of the absorption/stripping process was integrated with the first order 
approximation model of the power plant steam turbines.  By doing so, the variation of steam 
pressure at the IP/LP crossover point is taken into account in dynamic simulation. After 
implementing the multivariable steady state optimization tools of ACM® , power plant profit was 
maximized to optimize lean loading and CO2 removal at different values of price ratio (CO2 
price/electricity price) for design purposes.  As a result, for price ratio between 2.1 and 7, the 
plant should be designed at removal between 70% and 98% and lean loading in the range of 
0.22–0.25. For a price ratio lower than 2 , the plant should be designed at high lean loading ( 
0.39). 
   Two important operational scenarios were dynamically simulated: partial reboiler steam load 
and partial boiler load operations.  After implementing the multivariable dynamic optimization 
tools of ACM® to maximize profit, solvent rate, steam rate, and stripper pressure were 
optimized.  The results show that for both scenarios, keeping the pressure valve wide open and 
allowing the stripper pressure to swing is found to be the most profitable strategy. For reboiler 
steam partial load operation, a linear relationship exists between the optimum solvent rate and 
reboiler steam rate with the slope very close to 1. For boiler partial load operation, a linear 
S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690 1689
 s.ziaii/ Energy Procedia 00 (2010) 000–000 
 8
relationship exists between the optimum solvent rate and reboiler steam rate with the boiler load 
(or flue gas rate).  The slope of the line is relatively close to 1. This significant observation leads 
to a practical application in which the ratio control between the solvent rate and steam rate in 
scenario 1 and ratio control among the solvent rate, steam rate, and flue gas rate in scenario 2 can 
be proposed as optimum strategies in response to the discussed disturbances.   
6. Acknowledgments  
This work was supported by the Luminant Carbon Management Program at the University of 
Texas at Austin.  
7. References 
- [1] Kvamsdal HM, Jakobsen JP, Hoff KA. Dynamic modelling and simulation of a CO2 
absorber column for post-Combustion CO2 capture. Chem Eng & Proc. 2009; 48:135–144. 
- [2] Ziaii S, Rochelle GT, Edgar TF. Dynamic modeling to minimize energy use for CO2 
capture in power plants by aqueous Monoethanolamine.  Ind Eng Chem Res. 2009; 48(13):6105–
6111. 
- [3] Lawal A, Wang M, Stephenson P, Koumpouras G, Yeung H. Dynamic modeling and 
analysis of post-combustion CO2 chemical absorption process for coal-fired power plants. Fuel 
2010, doi:10.1016/j.fuel.2010.05.030. 
- [4] Freguia, S, Rochelle GT. Modeling of CO2 capture by aqueous monoethanolamine. AICHE 
J 2003; 49(7):1676-1686. 
- [5] Oyenekan BA , Rochelle GT, Energy performance of stripper configurations for CO2 
capture by aqueous amines, Ind Eng Chem Res. 2006; 45(8): 2457–2464. 
- [6] Plaza JM, Van Wagener D, Rochelle GT. Modeling CO2 capture with aqueous 
Monoethanolamine. International Journal of Greenhouse Gas Control 2009; 1(1):1171–1178. 
- [7] Abu-Zahra MRM, Niederer JPM, Feron PHM, Versteeg GF. CO2 capture from power 
plants: Part II. A parametric study of the technical performance based on monoethanolamine. 
International Journal of Greenhouse Gas Control 2007; 1(2):135–142. 
- [8] Ziaii S, Cohen S, Rochelle GT, Edgar TF, Webber ME. Dynamic operation of amine 
scrubbing in response to   electricity demand and pricing. International Journal of Greenhouse 
Gas Control 2009; 1(1):4047–4053. 
- [9] Lucquiaud M, Steam cycle options for capture-ready power plants, retrofits and flexible 
operation with post combustion CO2 capture. Ph.D. Dissertation, Imperial College London, 
2010. 
 
 
1690 S. Ziaii et al. / Energy Procedia 4 (2011) 1683–1690


Optimum design and control of amine scrubbing in response to electricity and CO2 prices 

Botero, Fernando (escultor)Primer pla frontal de l'obra. De singular 
iconografia, d'hImatge i Producció Editorialrtròfia sense mida i 
generosos volums. De bronze , mesura 
2,30 x 7 x 2,30 metres. Del 1990, en 
l'actual emplaçament des de 2003

Botero, Fernando (escultor)Pla generanal de la part posterior 
de l'obra. De singular 
iconografia, d'hImatge i Producció Editorialrtròfia sense mida i 
generosos volums. De bronze , mesura 
2,30 x 7 x 2,30 metres. Del 1990, en 
l'actual emplaçament des de 2003

https://doi.org/10.1016/j.egypro.2011.02.041

Optimum design and control of amine scrubbing in response to electricity and CO2 prices

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