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Dynamic Modeling to Minimize Energy Use for CO2 Capture in Power Plants by Aqueous Monoethanolamine Sepideh Ziaii, Gary T. Rochelle,* and Thomas F. Edgar Department of Chemical Engineering, The UniVersity of Texas at Austin, Austin, Texas 78712
A dynamic rate-based model was developed for stripping in CO2 capture from coal-fired power plants with 30 wt % monoethanolamine (MEA). The model, created in a flow sheet of Aspen Custom Modeler, was based on the film theory for liquid and vapor phases. It takes into account the impact of equilibrium reactions on the mass transfer, thermodynamic nonidealities, and the hydraulics of the structured packing. With this model, steady state analyses were carried out for the stripper to understand the effect of the lean loading and the height of the packing on total equivalent work and find optimum operating conditions that minimize power plant lost work. Two dynamic strategies with control configurations are proposed to run the stripper in a flexible operation during the period of electricity peak load and prices. Open loop responses demonstrated some differences in dynamic behavior and steady state values for proposed dynamic strategies. One of the approaches increased the CO2 removal by 1% at the reduced steam rate and provided faster response of the stripper to a step change in the reboiler heat rate. 1. Introduction The removal of CO2 from flue gases in coal-fired power plants is an important technology to mitigate greenhouse gas emissions and meet environmental regulations. Recently, this technology has become of great interest due to the need to increase utilization of domestic fuels in the U.S. One of the well-known technological alternatives for CO2 capture is absorption/stripping with aqueous solvents such as alkanolamines and their blends. Aqueous monoethanaolamine (MEA; 30 wt %) is an effective solvent that has been used commercially in CO2 removal plants, but not yet with large coalfired boilers. This energy-intensive process is typically expected to reduce a coal-fired power plant output by 30-40%.1 As shown in Figure 1, the absorption/stripping process typically consists of two columns. In the absorber, which is operated at atmospheric pressure and 40-60 °C, the flue gas from coal-fired plants containing 10-12% CO2 is contacted with MEA and CO2 is absorbed into the solution by physical and chemical mechanisms. The rich solution coming out of the absorber, which typically has a loading of 0.4-0.5 mol of CO2/mol MEA, is directed to the reboiled stripper, operating at 1.5-2 atm and 100-120 °C. Water vapor accompanying CO2 from the top of the stripper is condensed and returned to the water wash section of the absorber. The hot lean solution coming out of the stripper is cooled by the cold rich solution in a cross heat exchanger (5-10 °C temperature approach) and is furthered cooled to 40 °C before entering the absorber. Several papers1-5 have been published on the modeling and simulation of absorption/stripping systems and mostly have focused on steady state analysis and optimization. The integrated models of the absorption/stripping process were created using commercial software. Oyenekan and Rochelle2 developed an equilibrium model for the stripper in Aspen Custom Modeler to minimize the energy requirement and investigate alternative solvents and stripper designs. Freguia and Rochelle1 developed a time-invariant model of the entire plant with RateFrac and performed sensitivity analyses to find the optimum operating conditions. Kvamsdal and Rochelle3 have prepared a dynamic * To whom correspondence should be addessed. E-mail: gtr@ che.utexas.edu.
model of MEA using Ratesep and gPROMS and compared steady state predictions with pilot plant data. Coal-fired power plants generate electricity at the base load and might be expected to run CO2 capture at its full capacity continuously. Operating CO2 capture flexibly, i.e., implementing an on/off operation for capture, would be invaluable for several reasons. By either turning off the capture or reducing the load at times with daily peak power demand or high electricity prices, all or part of the steam being used for solvent regeneration or for driving CO2 compression can be used for power generation. In addition, we can avoid building new power plants by turning off the capture during peak periods when electricity prices are high.6 There are several publications7-9 on dynamic modeling of the reactive distillation columns that have implemented the ratebased approach. Peng et al.8 compared the results of equilibrium and rate-base modeling. They found some differences in final steady state value, although dynamic responses were very similar.
Figure 1. Typical absorption/stripping process for CO2 removal with monoethanolamine.
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Table 1. Coefficients of Regressed Equilibrium Constants ln(Keq) ) A + B/T + C ln(T) + DT + Eldg + F[MEA]T
A B C D G F
Keq,carbamate
Keq,bicarbonate
-1294.4 46361 216.98 -0.334 7.282 -0.307
2727.5 -78283 -465.543 0.605 8.1898 -0.334
In this work, a rate-based dynamic model of the stripper was created in Aspen Custom Modeler (ACM). In the steady state mode, this model helps in understanding how the design variables of the stripper affect the energy requirement of the reboiler in order to find optimum operating conditions. The other objective of this paper is to introduce an effective on/off operation strategy and investigate the influences of flexible operation on the dynamic performance of the process. 2. Model Development A dynamic model of a stripper for 7 M MEA was created in ACM, which is an equation-oriented simulator for steady state and dynamic analyses. 2.1. Thermodynamic and Rate Models. In the stripper, mass transfer and chemical reactions occurring in the liquid phase result in desorption of CO2 from the rich solution. Due to the higher temperature in the stripper, the reactions can be considered as instantaneous and attain equilibrium. In the literature,10 different theoretical model approaches were presented to describe mass transport and chemical reactions in multicomponent systems. In the present study, the stripper was modeled by the rate-based approach based on film theory and kinetics was simplified by considering two dominant equilibrium reactions.
2.3. Model Equations. The packed column was discretized into a number of segments. The reboiler was modeled as an equilibrium stage. The following assumptions were made in modeling the stripper. (1) Each segment was assumed to be well-mixed liquid and vapor. (2) MEA is assumed to be nonvolatile, and the vapor phase contains only water and CO2. (3) The liquid-gas interface is at equilibrium. (4) The liquid level in the reboiler is controlled at a constant value. (5) The heat transfer coefficient of the liquid phase is much bigger than that of the vapor phase due to very high thermal conductivity of aqueous solution; therefore, the dominant heat transfer resistance is assumed to be in the vapor phase. (6) The vapor phase is assumed to be an ideal gas. The ordinary differential equations (ODEs) in the balances for each segment and the reboiler along with the vapor-liquid equilibrium, mass and heat transfer equations, and hydraulic relations comprise a set of algebraic differential equations (DAEs) which are solved by ACM solvers. Equations for the jth segment of the packed column Molar component balances for liquid and gas phases L dMCO 2,j
dt
L dMMEA,j π ) Lj-1xMEA,j-1 - LjxMEA,j + Dc2ljajNMEA,j dt 4
dMHL 2O,j dt V dMCO 2,j
MEACOO-- + MEA+ T 2MEA + CO2
(1)
dt
MEA+ + HCO3- T H2O + MEA + CO2
(2)
dMHV2O,j
The equilibrium constants, heat of desorption, and partial pressure of CO2 were represented by empirical expressions regressed from flash results of the Aspen plus electrolyte-NRTL model developed by Hilliard.11 The MEA-VLE model by Hilliard regresses and adequately represents data for CO2 solubility, heat of CO2 absorption, NMR speciation, MEA volatility, and heat capacity over a wide range of condition. In contrast, the thermodynamic model used by Freguia1 only represents CO2 solubility in 7 molal MEA with 0.2-0.5 loading. The regressed equilibrium constants are concentration based but include the rigorous effects of predicted activity coefficients. Table 1 lists the adjustable constants in equilibrium constant expressions. 2.2. Mass Transfer Model and Physical Properties. The mass transfer coefficients in the liquid and vapor phases were estimated by the equations of Onda et al.12 and the Chilton-Colburn analogy was used to calculate the heat transfer coefficient in the vapor. Mellapak 250Y was selected for the packed column. The correlation of Suess and Spiegel13 was used to calculate the liquid holdup. The pressure drop was estimated by the generalized pressure drop correlation of Kister et al.14 The model incorporates equations regressed from the data of Weiland et al.15 to calculate the density and viscosity. The data of the Hilliard11 model was used to calculate the heat capacity of the loaded MEA. The other physical properties of the solution and vapor phase were calculated by using empirical equations from the literature.16,17
π ) Lj-1xCO2,j-1 - LjxCO2,j + Dc2ljajNCO2,j 4
(3) (4)
π ) Lj-1xH2O,j-1 - LjxH2O,j + Dc2ljajNH2O,j 4
(5)
π ) Vj+1yCO2,j-1 - VjyCO2,j - Dc2ljajNCO2,j 4
(6)
π ) Vj+1yH2O,j-1 - VjyH2O,j - Dc2ljajNH2O,j dt 4 Energy balance for each phase
(7)
dELj π L L j CO j HL O,j) ) Lj-1Hj-1 - LjHLj + Dc2ljaj(NCO2,jH +NH2O,jH 2 2,j dt 4 (8) dEVj π V ) Vj+1Hj+1 - VjHVj - Dc2ljaj(hVj (TVj - TLj ) + dt 4 V j CO j HV O,j) (9) +NH2O,jH NCO2,jH 2,j 2 Definition of molar and energy holdups π L MCO ) Dc2ljhLtj CLj xCO2,j 2,j 4
(10)
π L MMEA,j ) Dc2ljhLtj CLj xMEA,j 4
(11)
π MHL 2O,j ) Dc2ljhLtj CLj xH2O,j 4
(12)
π V MCO ) Dc2lj(ε - hLtj )CVj yCO2,j 2,j 4
(13)
π MHV2O,j ) Dc2lj(ε - hLtj )CVj yH2O,j 4
(14)
π ELj ) Dc2ljhLtj CLj HLj 4
(15)
Ind. Eng. Chem. Res., Vol. 48, No. 13, 2009
π EVj ) Dc2lj(ε - hLtj )CVj HVj 4 Vapor-liquid equilibrium at the interface
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(16)
I *I ) PCO PCO 2,j 2,j
(17)
PHI 2O,j ) PH*I2O,j
(18)
Steady-state energy balance at the interface des + NH2O,j∆HHvap2O,j hVj (TVj - T Lj ) ) NCO2,j∆HCO 2,j
(19)
Molar fluxes of CO2 and H2O in gas phase (general case of binary diffusion and convective flow using film theory)
NCO2,j + NH2O,j ) kVj
(
ln
NCO2,j NCO2,j + NH2O,j NCO2,j NCO2,j + NH2O,j
- yCO2,j I - yCO 2,j
Molar balance in the bulk liquid
)
(20)
(29)
example, by taking into account the hydraulics of the column as a function of liquid and vapor flow rates, these variables not only appear in the mass and heat balances but also appear in algebraic hydraulic equations. The packing section of the column is axially divided into a number of segments, each segment is modeled as a mixed-flow element where liquid and vapor phases are well-mixed. For the investigation of the influence of the number of segments on the stripper solution and finding a reasonable number of segments, the stripper has been solved with different number of segments and, in each run, the heat requirement has been calculated with various packing height (Figure 2). In this analysis, the column is operated at 160 KPa with 5 °C temperature approach in the cross heat exchanger. The rich and lean loadings are kept constant at 0.53 and 0.4 mol of CO2/ mol MEA, which should permit 90% CO2 removal in an absorber of reasonable design. At 40 °C, these loadings give equilibrium PCO2 of 5 and 0.1 KPa, respectively. As demonstrated in Figure 2, calculations were performed with 1 to 10 segments with the same total height of packing. For the number of segments from 1 up to 5, adding a segment resulted in a 2% change in the calculated reboiler heat duty. By increasing the number of segments from 5 to 10, less than 0.01% difference of heat duty was achieved. Therefore, five segments would adequately represent the packing of the stripper working in this condition. Additionally, 5 segments may represent the number of equilibrium stages of the column since the impact of number of segments on the reboiler duty is independent of the height of packing. As a result, a five-segmented column filled by Mellapak 250Y is chosen for further stripper steady state and dynamic analyses.
(30)
4. Steady State Sensitivity Analysis and Energy Minimization
CLj xMEA,j ) [MEA]Bj + [MEA+]Bj + [MEACOO-]Bj
(21)
CLj xCO2,j ) [CO2]Bj + [MEACOO-]Bj + [HCO3-]Bj
(22)
Molar transfer flux in liquid phase NCO2,j ) kLj ([CO2]Bj + [MEACOO-]Bj + [HCO3-]Bj -[CO2]Ij [MEACOO-]Ij - [HCO3-]Ij ) (23) 0 ) ([MEA]Bj + [MEA+]Bj + [MEACOO-]Bj - [MEA]Ij [MEA+]Ij - [MEACOO-]Ij ) (24) Equilibrium reactions Keq,carb,j )
[MEACOO-]Bj [MEA+]Bj
Keq,carbamate,j )
*B PCO ([MEA]Bj )2 2,j
[MEACOO-]Ij [MEA+]Ij *I ([MEA]Ij )2 PCO 2,j
Keq,bicarbonate,j )
Keq,bicarb,j )
[HCO3-]Bj [MEA+]Bj *B PCO [MEA]Bj 2,j
[HCO3-]Ij [MEA+]Ij *I PCO [MEA]Ij 2,j
(25)
(26)
(27)
(28)
Charge balance in the liquid bulk 0 ) [MEA+]Bj - [MEACOO-]Bj - [HCO3-]Bj Charge balance at the interface 0 ) [MEA+]Ij - [MEACOO-]Ij - [HCO3-]Ij
Figure 2. Influence of the number of segments and height of packing on the heat requirement of the stripper, 90% removal, P ) 160 KPa, lean loading ) 0.4, rich loading ) 0.527, 5 °C hot-end temperature approach in the cross heat exchanger.
3. Numerical Solutions The complex rate-based approach and dynamic formulation of the stripper, including both molar and energy hold-up in liquid and vapor phases, lead to a system of differential and algebraic equations (DAEs). The resulting set of equations forms an index one system, which can be solved by numerical solvers of ACM. In order to prevent high index problems in this system, the model of the packed column is formulated such that no algebraic variables appear exclusively in the differential equations. For
The model was used to predict steady-state performance as a function of lean loading and packing height. In this analysis, the energy requirement for the system is given as a total equivalent work per mole of CO2 removed as defined by eq 31. The first term is the work needed for pumping the rich and lean solvents and the second term is the lost work associated with reboiler heat duty.
[
* Wtotal ) Wpumps + 0.75Qreb
(Treb + 10) - 313 Treb + 10
]
(31)
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5. Dynamic Strategies for CO2 Capture
Figure 3. Interaction of height of packing and lean loading on the energy requirement of the stripper.
Figure 4. Optimization of lean loading, 90% removal, P ) 160 KPa, * optimum height of packing, rich PCO at 40 °C ) 5 KPa, 5 °C hot-end 2 temperature approach in the cross heat exchanger.
In order to simplify the energy analysis of this system, the pressure was fixed at 160 KPa at the top of the column and CO2 compression work is not included in the total equivalent work. The following conditions were used in all runs: CO2 removal: 90% for a 500 MW power plant MEA concentration: mol MEA/kg H2O Rich loading: 0.527 mol CO2/mol MEA Column diameter: calculated based on 80% flooding Hot-end cross heat exchanger approach: 5 °C Pressure at the top of the column: 160 KPa 4.1. Packing Height. In order to investigate the interaction of lean loading and packing height on the energy requirement, the packing height was varied from 0 to 100 m and the reboiler heat duty was calculated to achieve the desired lean loading. As shown in Figure 3, at each lean loading, total equivalent work drops quickly with packing height but does not approach an asymptote. Instead, it increases slowly because of increasing pump work. The packing height at minimum work decreases from 4 to 1 m as the lean loading increases from 0.36 to 0.44. 4.2. Lean loading Optimization. The lean loading was varied to minimize the work requirement (Figure 4). The height of packing was optimized for each lean loading. At low lean loading, more heat is required to strip CO2 from the solvent, although less solvent is circulated. At high lean loading, a greater solvent rate is required, so the energy required to heat the solvent to the reboiler temperature dominates the reboiler heat duty. Fisher et al.4 found a lower optimum lean loading at similar conditions using a different thermodynamic package by Freguia and Rochelle.1
By implementing a dynamic operating strategy for CO2 capture, we should be able to avoid the high cost of energy during the electricity peak load. In one dynamic option, the absorber would operate continuously, but the reboiler steam rate would be reduced at the start of the peak period. Consequently, the absorber would provide variable CO2 removal. In this option, no additional inventory is needed for rich and lean solvents and the only input variable that significantly changes in the absorber would be the lean loading. In the stripper, we can keep the lean loading constant by reducing the rich solvent flow rate proportional to the heat rate, i.e. ratio control (strategy 1) or we can let the lean loading increase by regenerating all the rich solvent in the stripper (strategy 2). In the first strategy, the nonregenerated rich solvent stream is mixed with the lean solution coming from the stripper and then returned to the absorber (See Figures 5 and 6). This paper evaluates the steady-state performance and dynamic behavior of the stripper for these two cases. The overhead pressure of the column and reboiler liquid level are controlled at a constant value. The rich loading is also assumed constant since there will be a rich-end pinch at the bottom of absorber. The optimized design case was used to carry out the dynamic analyses Both strategies 1 and 2 were run with the following conditions, unless otherwise stated. • Packing height: 1.8 m • Column diameter: 5.4 m (80% flooding) * : 5 KPa at 40 °C • Rich loading: 0.527 or PCO 2 • Initial stripper lean loading: 0.42 (steady state value for 90% removal) • Stripper top pressure: 160 KPa • Cross heat exchanger temperature approach: 5 °C • Initial liquid holdup time in the reboiler: 5 min 6. Dynamic Results Three factors were considered in the analysis of the dynamic strategies: the performance of the system in the new steady state, the dynamic behavior of the key process variables, and the response time of the system to the input changes. To analyze the steady state and dynamics of the stripper, a negative 10% step change was made to the reboiler heat duty for both strategies and, additionally, a negative 10% step change was made to the rich solution flow rate to simulate control strategy 1. In both cases, the initial liquid residence time in the reboiler is set at 5 min in order to protect the pumps. The results show that, even with the same initial conditions in two strategies, there is some difference in the dynamic responses and new steady state values. The dynamic simulation details are given in Table 2. When the reboiler duty was reduced by 10% without changing the solvent flow, the lean loading increased 3%. However, when both reboiler duty and solvent rate were reduced by 10%, the change in the lean loading was negligible. Figure 7 shows that the lean loading approaches 98% of the new steady state in 30 min in strategy 2. However, it is almost constant in strategy 1. Figure 8 shows the reboiler pressure response with both strategies. In addition, the response of the reboiler pressure to a step reduction in the rich solvent rate is given to show how each manipulated variable affects the dynamic response of the system when changed individually. With a -10% change in the reboiler duty, the reboiler pressure decreases by 0.3%.
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Figure 5. Control strategy 1, keep lean loading constant in the stripper when the steam rate is reduced.
Figure 6. Control strategy 2, keep the rich solution flow rate constant in the stripper when the steam rate is reduced. Table 2. Detailed Simulation Results CO2 removal
strategy 1 strategy 2
lean loading
Treb°C
PrebKPa
L τpacking s
initial
final
initial
final
Initial
final
initial
final
average
90% 90%
81% 80.3%
0.42 0.42
0.4199 0.4315
162.76 162.76
162.36 162.27
103.23 103.23
103.19 101.93
4.98 5.10
Although the reduction of the steady state reboiler pressure is not changed significantly by implementing the ratio control strategy, this control strategy (strategy 1) causes the system to approach steady state much faster (in less than a minute) relative to strategy 2 because of the synergistic effects of rich solvent flow rate and reboiler heat rate. Both reboiler pressure and lean loading influence the temperature in the reboiler. Figure 9 indicates this combined effect on the reboiler temperature for the same cases shown for reboiler pressure responses. As shown in Figure 9, reboiler temperature increases as the rich solvent flow rate decreases. As the reboiler heat rate
decreases, reboiler pressure and lean loading both vary (Figures 7 and 8) and result in decreasing the reboiler temperature. With ratio control (strategy 1), although the pressure change is relatively large, reboiler temperature does not change significantly because the lean loading remains constant. So lean loading is the dominant factor influencing the temperature in the reboiler. As demonstrated in Figures 7-9, lean loading, reboiler pressure and temperature approach 98% of new steady state in about 30 min. The calculated time constants of these variables to the step change in the heat rate lie in the range of 5-9 min. As shown in Table 2, the liquid holdup in the packing section
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Figure 10. Comparison of CO2 removal between strategies 1 and 2. Figure 7. Lean loading steady state shifting in control strategies 1 and 2.
these factors could influence the dynamic response in the stripper. In addition, in this study it is assumed that the pressure at the top of the column is perfectly controlled at a constant set point. However, the CO2 multistage compressor and the steam turbine providing the required steam in the reboiler could interact with the stripper and thus influence its steady state and dynamic behavior significantly. 7. Conclusions
Figure 8. Reboiler pressure responses to the change of rich solvent flow rate and reboiler heat rate.
Figure 9. Reboiler temperature responses to a step change in the rich solvent flow rate and reboiler heat rate.
is much smaller than 5 min, the hold-up time in the reboiler; therefore, the reboiler residence time controls the response time of the system. When a step change is made to the heat rate of the reboiler, the liquid holdup time does not change significantly. However, if the liquid level is controlled at a constant set point, changing the solvent rate results in a different holdup time at the new steady state. Since liquid residence time reaches steady state quickly (less than a minute), the response time of the system is dominated by the residence time at the final operating condition. Therefore the lean loading takes a little more time to approach steady state with a -10% change in the rich solvent flow rate than with a -10% change in heat rate (see Figure 7). Figure 10 shows the variation of CO2 removal in response to the steam rate step change for both strategies. Strategy 1 shows a little better performance at the final condition and faster response to the load change. This behavior for removal is closely related to the lean loading behavior for both strategies. This study simplifies the model by ignoring the effects of the manipulated variable change on the absorber performance, loading, temperature and flow rate of the rich solvent, although
A rate-based dynamic model was developed for a CO2 stripper with 7 M MEA. The steady state analyses show that greater packing height in the stripper reduces the steam consumption while it increases pumping work. The packing height that minimizes the total equivalent work changes from 1 m at a lean loading of 0.44-4 m at a lean loading of 0.36. For operating the stripper at lower energy consumption during the period of high electricity demand and prices, two dynamic strategies were investigated: the reduction of steam rate with and without adjusting the rich solvent rate. The results showed that, in response to a 10% reduction in the steam rate without rich solvent rate adjustment, the lean loading increases by 3%. However, by implementing ratio control for the rich solution rate, the lean loading and temperature remained almost constant. The second strategy showed a 30 min response time for the lean loading and reboiler temperature and pressure. By implementing ratio control, the response time of the system decreased to less than one minute. With dynamic operation, the liquid residence time in the reboiler at the final steady state condition was found to be the dominant factor in the response time of the system. Acknowledgment The authors acknowledge Aspen Technology who provided the Aspen Custom Modeler software and provided help in creating rate-base dynamic models. This work was supported by the Luminant Carbon Management Program. Notation a ) effective specific area of the packing, 1/m C ) molar density, mol/m3 Dc ) column diameter, m E ) energy holdup, kJ h ) heat transfer coefficient, kW/(m2 K) ht ) total liquid holdup, m H ) enthalpy, kJ/mol j ) partial enthalpy, kJ/mol H k ) mass transfer coefficient, m/s Keq,carbamate ) equilibrium constant, 1/kPa Keq,bicarbonate ) equilibrium constant, kmol/(m3 kPa) l ) height of segment, m
Ind. Eng. Chem. Res., Vol. 48, No. 13, 2009 L ) liquid flow rate, mol/s ldg ) loading (mol of CO2/mol MEA) M ) molar holdup, mol MV ) manipulated variable [MEA]T ) total concentration of MEA, mol MEA/kg H2O N ) molar flux, mol/(m2 s) * PCO , PH* 2O ) equilibrium partial pressure, KPa 2 PCO2, PH2O ) partial pressure, KPa Qreb ) reboiler heat duty, kJ/mol T ) temperature, K Treb ) reboiler temperature, K V ) vapor flow rate, mol/s Wtotal ) equivalent work, kJ/mol Wpumps ) work of pumps, kJ/mol x ) liquid mole fraction y ) vapor mole fraction Greek Letters ε ) void fraction of the packing ∆Hdes,CO2 ) heat of adsorption of CO2, kJ/mol ∆Hvap,H2O ) heat of vaporization of H2O, kJ/mol τ ) response time, s Subscripts j ) segment index Superscripts B ) bulk I ) interface L ) liquid phase V ) vapor phase rich ) rich solution
Literature Cited (1) Freguia, S.; Rochelle, G. T. Modeling of CO2 capture by aqueous monoethanolamine. AIChE J. 2003, 49 (7), 7, 1676–1686. (2) Oyenekan, B. A.; Rochelle, G. T. Energy performance of stripper configurations for CO2 Capture by Aqueous Amines. Ind. Eng. Chem. Res. 2006, 45, 2457–64. (3) Kvamsdal, H. M.; Rochelle, G. T. Effects of the temperature bulge in CO2 absorption from flue gas by aqueous monoethanolamine. Ind. Eng. Chem. Res. 2008, 47 (3), 867–875. (4) Fisher, K. S.; Searcy, K.; Rochelle, G. T.; Ziaii, S.; Schubert, C. AdVanced Amine SolVent Formulations and Process Integration for Near-
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Term Capture Success; Submitted to U.S. Department of Energy,Grant No. DE-FG02-06ER84625, June 28, 2007. (5) Aboudheir, A.; Tontiwachwuthikul, P.; Idem, R. Rigorous Model for Predicting the Behavior of CO2 Absorption into AMP in Packed-Bed Absorption Columns. Ind. Eng. Chem. Res. 2006, 45 (8), 2553–2557. (6) Cohen, S. M.; Rochelle G. T.; Webber M. E. Turning CO2 capture on & off in response to electric grid baseline analysis of emissions and economics. Proceedings of ES 2008, Energy Sustainability, Jacksonville, FL, August 10-14, 2008. (7) Peng, J.; Lextrait, S.; Edgar, T. F.; Eldridge, R. B. A comparison of steady-State equilibrium and rate-based models for packed reactive distillation columns. Ind. Eng. Chem. Res, 2002, 41, 2735–2744. (8) Peng, J.; Edgar, T. F.; Eldridge, R. B. Dynamic rate-based and equilibrium models for a packed reactive distillation column. Chem. Eng. Sci. 2003, 58, 2671–2680. (9) Gunaseelan, P.; Wankat, P. C. Transient pressure and flow predictions for concentrated packed absorbers using a dynamic nonequilibrium model. Ind. Eng. Che. Res. 2002, 41, 5775–5788. (10) Schneider, R.; Sander, F.; Gorak, A. Dynamic simulation of industrial reactive absorption processes. Chem. Eng. Process. 2003, 42, 955– 964. (11) Hilliard, M. A predictive thermodynamic model for an aqueous blend of potassium carbonate, piperazine, and monoethanolamine for carbon dioxide capture from flue gas. Ph.D. Dissertation,The University of Texas at Austin, Austin, TX, 2008. (12) Onda, K.; Takeuchi, H.; Okumoto, Y. Mass transfer coefficients between gas and liquid phases in packed columns. J. Chem. Eng. Jpn. 1968, 1, 56–62. (13) Suess, P.; Spiegel, L. Hold-up of mellapak structured packings. Chem. Eng. Process. 1992, 31, 119–124. (14) Kister, H. Z.; Scherffius J.; Afshar, K.; Abkar. E. Realistically Predict Capacity and Pressure Drop for Packed Column. AIChE meeting, Houston, TX, Spring 2007. (15) Weiland, R. H.; Dingman, J. C.; Cronin, D. B.; Browning, G. J. Density and viscosity of some partially carbonated aqueous alkanolamine solutions and their blends. J. Chem. Eng. Data 1998, 43, 378–382. (16) Snijder, E. D.; te Riele, M. J. M.; Versteeg, G. F.; van Swaaij, W. P. M. Diffusion coefficients of several aqueous alkanolamine solutions. J. Chem. Eng. Data 1993, 38, 475–480. (17) Reid, R. C.; Prausnitz, J. M.; Poling B. E. The properties of gases and liquids, 4th ed.; McGraw-Hill: New York, 1978.
ReceiVed for reView September 14, 2008 ReVised manuscript receiVed December 26, 2008 Accepted January 7, 2009 IE801385Q
