Rate-Based Process Modeling Study of CO2 Capture with Aqueous

Ind. Eng. Chem. Res. 2009, 48, 9233–9246

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Rate-Based Process Modeling Study of CO2 Capture with Aqueous Monoethanolamine Solution Ying Zhang,† Hern Chen,‡ Chau-Chyun Chen,*,‡ Jorge M. Plaza,§ Ross Dugas,§ and Gary T. Rochelle§ AspenTech Limited, Pudong, Shanghai 201203, China, Aspen Technology, Inc., Burlington, Massachusetts 01803, and Department of Chemical Engineering, The UniVersity of Texas at Austin, Austin, Texas 78712

Rate-based process modeling technology has matured and is increasingly gaining acceptance over traditional equilibrium-stage modeling approaches. [Taylor et al. Chem. Eng. Prog. 2003, 99, 28-39] Recently comprehensive pilot plant data for carbon dioxide (CO2) capture with aqueous monoethanolamine (MEA) solution have become available from the University of Texas at Austin. The pilot plant data cover key process variables including CO2 concentration in the gas stream, CO2 loading in lean MEA solution, liquid to gas ratio, and packing type. In this study, we model the pilot plant operation with Aspen RateSep, a second generation rate-based multistage separation unit operation model in Aspen Plus. After a brief review on ratebased modeling, thermodynamic and kinetic models for CO2 absorption with the MEA solution, and transport property models, we show excellent match of the rate-based model predictions against the comprehensive pilot plant data and we validate the superiority of the rate-based models over the traditional equilibrium-stage models. We further examine the impacts of key rate-based modeling options, i.e., film discretization options and flow model options. The rate-based model provides excellent predictive capability, and it should be very useful for design and scale-up of CO2 capture processes. Introduction The global surface temperature has risen significantly in the last hundred years, and the temperature rise has become even more pronounced in recent decades. These temperature changes are attributed to increased levels of greenhouse gases in the atmosphere resulting from human activities. Carbon dioxide is the primary greenhouse gas causing the global warming. CO2 capture from flue gas by absorption/stripping with monoethanolamine (MEA) or other physical and chemical solvents will be an important technology to reduce CO2 emissions from fossilfuel fired power plants and to address global climate change. While this capture technology is being considered for largescale application on existing power plants, it is necessary to further optimize the individual process units and improve the overall process economics. This requires rigorous modeling and simulation of the CO2 capture process and fundamental understanding of the underlying complex phenomena taking place in the process such as electrolyte thermodynamics, chemical reactions, and heat and mass transfer across the gas-liquid interface and in the bulk gas and liquid phases. CO2 emissions from coal-fired power plants can be captured via absorption and stripping processes with circulating chemical solvents that can be placed at the tail end of existing or new coal fired power plants with NOx and SOx controls. A typical CO2 absorption/stripping process flowsheet diagram is shown in Figure 1. A lean amine solvent (low CO2 concentration) is fed into the top of the absorber and is in countercurrent contact with the flue gas containing CO2. The CO2 chemically reacts with the amine solvent and the treated gas exits the top of the absorber. The rich (high CO2 concentration) amine leaves the * To whom correspondence should be addressed. E-mail: [email protected]. Phone: 781-221-6420. Fax: 781-2216410. † AspenTech Limited. ‡ Aspen Technology, Inc. § The University of Texas at Austin.

bottom of the absorber and is preheated by a cross heat exchanger before entering the top of the stripper. At stripper conditions, typically higher temperature, the reaction between the amine and CO2 is reversed, liberating the CO2. A concentrated CO2 stream is obtained from the top of the stripper. The lean solvent from the stripper is cooled and goes back to the absorber. Concentrated CO2 from the stripper can be compressed and sequestered into depleted oil or gas fields or deep saline reservoirs.2 Recent studies show that permanent storage in deep saline aquifers is feasible.3 In order to study the CO2 capture process with aqueous MEA, a pilot plant containing two columns, an absorber and a stripper, has been installed at the University of Texas at Austin4 (U.T.Austin). A pilot plant campaign, which consisted of 48 runs at 24 operating conditions, was carried out. Various packings, lean CO2 loadings, gas and liquid rates, and stripper pressures were tested, and a series of pilot plant data for the CO2 capture system was reported. In this study, a rate-based absorption model, RateSep, in Aspen Plus process simulator is used to simulate the pilot plant at U.T.-Austin. To successfully simulate the amine CO2 capture system with RateSep, we carefully examine thermodynamic, kinetic, transport, and hydraulic models and model parameters. Model predictions against the various groups of pilot plant data are presented. Rate-based modeling predictions and equilibrium-

Figure 1. Typical absorber/stripper flowsheet for CO2 capture.

10.1021/ie900068k CCC: $40.75  2009 American Chemical Society Published on Web 09/10/2009

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film is discretized into multiple film segments to accurately model the nonhomogeneous film layer. CO2 Capture Model

Figure 2. Discretized film concept combined with the countercurrent flow configuration in RateSep.

stage modeling predictions are compared, and the results confirm the superiority of the rate-based models for the CO2 capture process. In addition, key RateSep modeling options such as film discretization and flow models are discussed along with their impacts on the prediction results. The RateSep model provides excellent predictive capability and will be a very useful design tool to study various process variables, including liquid/gas ratio, CO2 concentration in the feed stream, CO2 loading and MEA concentration in the lean amine stream, operating pressure, packing height and type, etc. RateSep Aspen RateSep represents a second generation rate-based process modeling software for multistage separation operations. The rate-based modeling approach is rigorous and offers higher model fidelity over the traditional equilibrium-stage modeling approach.1 Rate-based multistage separation models assume that separation is caused by mass transfer between the contacting phases, equilibrium is achieved only at the vapor-liquid interface, and the Maxwell-Stefan theory is used to calculate mass transfer rates. Conversely, the traditional equilibrium-stage models assume that each theoretical stage is composed of a wellmixed vapor phase and liquid phase and that these two phases are in phase equilibrium with each other. This later assumption is inherently an approximation because the contacting phases are never in equilibrium in a real column. For an excellent summary on the difference between the rate-based modeling approach and the equilibrium-stage modeling approach, please see Taylor et al.1 Designed to provide rigorous and robust solutions to reactive multistage separation problems, Aspen RateSep solves the Maxwell-Stefan multicomponent mass transfer equation with the approximate solution proposed by Alopaeus et al.5 Separate balance equations are written for each distinct phase and mass and heat transfer resistances are considered according to the two-film theory6 with implicit calculation of interfacial fluxes and film discretization for the nonhomogeneous film layer. The film model equations are combined with relevant diffusion and reaction kinetics and include the specific features of electrolyte solution chemistry, electrolyte thermodynamics, and electroneutrality in the liquid phase. The column hydrodynamics are accounted for via specific correlations for vapor-liquid interfacial area, liquid hold-up, pressure drop, and mass transfer coefficients. Figure 2 illustrates the basic picture of CO2 transfer across the vapor and liquid films. Here Y is gas phase composition, X is liquid phase composition, T is temperature, I is interface, V is vapor, and L is liquid. Note that the liquid

To fully describe the complex phenomena taking place in the CO2 capture process with MEA, the model must properly account for thermodynamics of the CO2-water-MEA system, reaction kinetics of CO2 with the MEA solution, and the various transport properties affecting the mass and heat transfer. Thermodynamic and Kinetic Models. The Hilliard thermodynamic representation of the CO2-water-MEA system7 is used in this work. In the Hilliard representation, the liquid phase nonideality is accounted for with the electrolyte NRTL activity coefficient model.8 Hilliard considered the solution chemistry to include water dissociation, CO2 hydrolysis, bicarbonate dissociation, carbamate hydrolysis, and MEA protonation. Reaction kinetics is obtained from data by Aboudheir9 who generated rate data for CO2 absorption in MEA using a laminar jet at various amine concentrations, CO2 loadings, and temperatures. These data are used to evaluate the forward rate constants for the formation of carbamate. Specifically, we retain the water dissociation reaction and the bicarbonate dissociation reaction and we recombine the CO2 hydrolysis reaction, the carbamate hydrolysis reaction, and the MEA protonation reaction used in the Hilliard thermodynamic representation and present reaction kinetics with two reactions: carbamate formation and bicarbonate formation. Reactions 1 and 2 are the forward and reverse reactions for carbamate formation, and 3 and 4 are the forward and reverse reactions for bicarbonate formation. The resulting activity-based kinetic model remains consistent with the solution chemistry model in the Hillard thermodynamic representation. 2 MEA + CO2 f MEA+ + MEACOO-

(1)

MEA+ + MEACOO- f 2 MEA + CO2

(2)

MEA + CO2 + H2O f HCO3-+MEA+

(3)

MEA+ + HCO3- f MEA + CO2 + H2O

(4)

Equations 5-8 in Table 1 show the corresponding rate expressions for the forward and backward reactions. Here, kj is the reaction rate constant for reaction j, Ki is the chemical equilibrium constant for the formation of species i (i.e., MEACOO-, HCO3-), and ai is the activity of component i. Table 1. Initial Kinetic Rate Expressions Included in the Proposed CO2 Capture Model related species

reaction direction

kinetic expression

MEACOO-

forward

r1 ) k1aMEAaCO2

reverse

HCO3-

forward

reverse

r2 )

aMEACOO-aMEA+ k1 KMEACOOaMEA r3 ) k3aMEAaCO2

r4 )

k3 aHCO3-aMEA+ KHCO3aH2O

(5)

(6)

(7)

(8)

Ind. Eng. Chem. Res., Vol. 48, No. 20, 2009 9

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a

Table 2. Selected data from the Work of Aboudheir for Carbamate Rate Analysis loading (mol CO2/mol MEA)

temperature (K)

PCO2 (kPa)

liquid Flow rate (cm3/s)

contact height of the laminar jet (cm)

diameter of the laminar jet (cm)

experimental carbamate rate Rexp × 10-6 (gmol/s)

0.2767

313.15

87.06

0.351

0.2819

333.15

73.86

0.375

0.1104

333.15

75.64

0.408

1.884 1.410 1.082 2.142 1.758 1.323 2.197 1.872 1.415

0.0584 0.0584 0.0584 0.0563 0.0563 0.0563 0.0554 0.0554 0.0554

5.13 3.86 3.00 7.16 5.86 4.53 11.7 10.2 7.83

a

MEA concentration ) 7 kmol/m3.

In Aspen Plus, reaction rates are described by power law expressions:

( (

Ej 1 1 Rj ) k°T exp j R T 298.15 n

)) ∏ C

Hilliard’s study for the activity coefficients for CO2 and MEA as follows:

N

Rij i

(9)

ka3 )

i)1

Here Rj is the reaction rate for reaction j, k°j is the pre-exponential factor, T is the system temperature in Kelvin, n is the temperature factor, Ej is the activation energy, R is the gas constant, Ci is the concentration of species i, and Rij is the reaction order of component i in reaction j. The preexponential factor k°, j the temperature factor n, the activation energy Ej, and the concentration basis need to be specified. In the present study, the factor n in eq 9 is zero, the concentration basis is activity (i.e., product of mole fraction Ej for and activity coefficient or “mole gamma”), and k°and j the reactions were calculated using experimental data shown in Table 2. We first identify bicarbonate reaction constants, and then, built on the determined bicarbonate reaction constants, we identify carbamate reaction constants. On the basis of tertiary amine data, Rochelle et al.10 correlated the values of the forward bicarbonate reaction constant (reaction 3 in Table 1) at 298.15 K as a function of the base dissociation constant (pKb), as shown in Figure 3. Here, k in Figure 3 represents the forward bicarbonate reaction constant. The forward bicarbonate reaction rate constant at 298.15 K for MEA (pKb ) 4.45) was extracted from this correlation in this study. This result was converted from concentration basis to activity basis using values generated with the eNRTL model from

kc3Fs2 γMEAγ*CO2

Where, k3a is the forward reaction rate in activity basis, kmol/ m3 · s, k3c is the forward reaction rate in concentration basis, m3/ kmol · s, Fs is the molar density of the MEA solvent, kmol/m3, at 298.15 K, and γMEA and γ*CO2 are the activity coefficient of MEA and unsymmetric activity coefficient of CO2 at 298.15 K for one of the cases under study. This result was used as k°j in eq 9. The energy of activation, Ej, was approximated using the data for MDEA (49 kJ/gmol) reported by Pacheco et al.11 and presented in Rochelle et al.10 The forward reaction rate constant for the bicarbonate reaction was then calculated:

( (

ka3 ) k°3 exp -

E3 1 1 R T 298.15

))

(11)

Where, ka3 is the forward reaction rate, kmol/m3 · s, k°3 is the preexponential constant from eq 10 (9025.45 kmol/m3 · s), E3 is the energy of activation (49 kJ/gmol), T is the temperature of the point, and R is the gas constant. The results for k3a are used along with the equilibrium constants to determine the reverse rates for the bicarbonate reaction. Equilibrium was previously evaluated using a flash calculation block in Aspen to extract the activity coefficients and liquid equilibrium concentrations at each point condition. Nine data points from Aboudheir9 are used to determine the forward carbamate formation (reaction 1) rate. Three points are at 313.15 K with loadings of 0.2767, and the rest at 333.15 K with loadings of 0.1104 and 0.2819 (see Table 2). A laminar jet was modeled in Aspen using the found bicarbonate constants and Hilliard’s thermodynamics. Initially, the energy of activation was set to zero and the reported flux by Aboudheir was matched by changing the pre-exponential factor (k°) j in the power law. The resulting k°j was averaged among the same temperature and loading conditions and then regressed to obtain values for k°, 1 E1, and R for the following equation:

( (

k1 ) k°1 exp -

Figure 3. Second-order rate constants for the reaction of tertiary amines and CO2 at 298.15 K: ([) rate constants of tertiary amines, (9) rate constants of MEA, (s) correlation of amines’ rate constants.

(10)

))

E1 1 1 a R R T 298.15 MEA

(12)

This is a modified version of eq 11 that includes the activity of MEA raised to a power in an attempt to account for variations in ionic strength due to changes in loading. The regressed expression has R ) 0.9199. In the final forward rate expression, the activity of MEA need to be raised to the R + 1 power since there is already an activity term in this expression (See eq 5 in Table 1). Similarly, the activity term in the reverse rate is

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Table 3. Final Kinetic Rate Expressions Included in the Proposed CO2 Capture Model related species

reaction direction

kinetic expression

MEACOO-

forward

r1 ) k1aMEA2aCO2

r2 )

reverse

HCO3-

(13)

k1 a -a + KMEACOO- MEACOO MEA r3 ) k3aMEAaCO2

forward

reverse

r4 )

(14)

(15)

k3

aHCO3-aMEA+

KHCO3-

aH2O

(16) Figure 4. Simple schematic of the CO2 capture pilot plant at the University of Texas at Austin.

Table 4. Constants for Power Law Expressions for the Absorption of CO2 by MEA related species

reaction direction

k°j (kmol/m3 · s)

Ej (kJ/gmol)

forward reverse forward reverse

4.73 × 10 4.23 × 105 9025.5 3312.6

19.34 107.47 49.00 112.74

-

MEACOO HCO3-

9

raised to the R - 1 term in order to maintain compatibility with the equilibrium constant (see eq 6 in Table 1). This treatment results in an activity of MEA raised to the 1.9199 and to the -0.0801 in the forward and the reverse rates, respectively. In an effort to simplify the rate expressions, the MEA activity exponents were rounded. The final kinetic rate expressions included in the CO2 capture model are summarized in Table 3. The pre-exponential factor k°1 for each point was recalculated, averaged, and regressed by using these expressions to obtain E1 for the power law expression. The final new values for k°and 1 kinetic constants are presented in Table 4. Transport Property Models. Rate-based process models such as RateSep require quantitative models for various transport properties that are essential for applying various correlations of heat transfer, mass transfer, interfacial area, liquid holdup, pressure drop, etc. In parallel to this study, we examine the transport property models available in Aspen Plus process simulator12 and we match available literature data by adjusting the transport property model parameters for density, viscosity, surface tension, thermal conductivity, and diffusivity. Specifically, the Clarke density model13 for electrolytes solutions with mixed solvents is used to calculate density, the Jones-Dole viscosity model13 for liquid solutions with electrolytes is used to calculate viscosity, the Onsager-Samaras surface tension model13 is used to calculate the liquid mixture surface tension, the Riedel thermal conductivity model13 is used to calculate the liquid mixture thermal conductivity, and finally the Wilke-Chang diffusivity model13 is used to calculate CO2 diffusivity in H2O and MEA-H2O solutions. These models provide empirical frameworks for data correlation, interpolation, and extrapolation. Pilot Plant Data The pilot plant facility was set up as a closed-loop absorption/ stripping system for CO2 removal from a flue gas by means of a 32.5 wt % aqueous MEA solution. A test campaign consisting of 48 runs at 24 operating conditions was conducted over a period of 1 month.4

Pilot Plant Setup. Figure 4 shows a simple schematic of the closed-loop pilot plant. The pilot plant’s two carbon steel columns are nearly identically designed. Each has a total height of approximately 11.1 m and an inside diameter of 42.7 cm. Both have two 3.05 m beds for packing with a collector plate and redistributor between the beds. Both columns used chimney tray collector plates and orifice-riser liquid distributors. The pilot plant design included a 3.8 m3 lean solvent storage tank that holds the majority of the liquid inventory. The large storage tank minimized any unsteady state disruptions from the stripper, and it allows any composition disruptions in the solution to mix with the large liquid inventory and keep the absorber lean loading constant. Once the MEA solvent from the liquid storage tank had contacted the CO2 rich flue gas in the absorber, it was pumped to the heater. Industrially, a cross exchanger with fluid leaving the stripper would be used but the pilot plant used a heater and a cooler in place of a cross exchanger. The heater was found to be undersized for the liquid flow rates required for the absorber operation. Rather than sacrificing the absorber operating range, a subcooled feed was fed to the stripper. Once the solvent was stripped of its excess CO2 and exited the bottom of the stripper, it was pumped to the solvent cooler where the solvent temperature was lowered near 315 K. Carbon dioxide and water vapor exiting the top of the stripper went to a partial condenser where most of the water content was removed by cooling the gas to an exit temperature of 283-288 K. The dry CO2 from the top of the partial condenser was sent to a large, 2.8 m3 gas accumulation tank. This accumulation tank has the same purpose as the liquid storage tank. It buffers any unsteady state disturbances in the system and thus allows the absorber to obtain a more consistent feed. Pilot Plant Experiments. The MEA baseline campaign utilized a 32.5 wt % MEA solution defined with respect to water and with no CO2 loading. The campaign consisted of 48 runs at 24 different operating conditions. The absorber was operated at atmospheric pressure for all 48 runs and the stripper was operated at pressure slightly higher than atmospheric atm. On the last 4 runs, the stripper was operated in vacuum. Liquid samples were collected from the absorber feed, middle, and outlet and also from the stripper middle and outlet to check for CO2 loading. The stripper feed was not sampled since it was the same composition as the absorber outlet. Usually two runs were executed at each operating condition. The absorption/ stripping system was maintained at steady state for about an


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Table 5. Operation Conditions of MEA Campaign case

packings (absorber/stripper)

lean loading (molCO2/ molMEA)

mole-inlet CO2 (mol %)

gas rate (actual m3/min)

liquid rate (L/min)

CO2 removal (%)

1, 2 3, 4 5, 6 7, 8 9, 10 11, 12 13, 14 16, 17 18, 19 20, 21 22, 23 24 25, 26 27, 28 29, 30 31, 32 33, 34 35, 36 39, 40 41, 42 43, 44 45, 46 47, 48

Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 Flexipac 1Y/IMTP no. 40 IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y IMTP no. 40/Flexipac 1Y

0.18 0.16 0.18 0.17 0.15 0.15 0.33 0.37 0.27 0.27 0.28 0.28 0.28 0.29 0.28 0.28 0.28 0.28 0.23 0.23 0.23 0.29 0.28

16.9 16.4 17.4 16.5 16.7 16.8 16.8 17.8 17.0 17.0 17.3 15.2 16.6 16.7 16.6 17.5 17.9 17.0 16.8 17.1 17.0 16.9 18.0

6.88 6.87 13.74 13.75 13.75 13.75 12.37 9.75 5.50 5.50 5.49 8.74 11.00 11.00 11.00 5.50 5.50 5.62 11.00 10.97 11.00 8.25 8.23

18.8 13.2 29.4 37.7 29.4 25.9 56.8 80.4 28.4 23.1 20.4 39.5 104.1 82.1 54.9 40.7 42.6 42.8 83.1 56.8 39.4 60.8 30.1

99 99 61 96 87 75 62 94 95 87 72 92 93 86 70 95 80 95 94 87 72 96 69

hour before liquid samples were taken. After this initial sampling, the system was held steady for another hour until a second set of samples was taken at the condition. After this second sampling, a new operating condition was attempted.

at the beginning of the campaign. A total of 41 sets of pilot plant runs are used in this simulation study.

In addition to the liquid sampling, instantaneous online measurements were recorded for approximately 65 variables. These variables include gaseous CO2 concentrations, temperatures, pressures, densities, flow rates, and liquid levels. The values were logged into a spreadsheet with a recording interval of 1 min. For calculation purposes, these values were averaged around the sample time (10 min before and after). This dampens any temporary disturbances caused by the actual sample collection.

In order to limit this study to a manageable scope, we focus the RateSep simulation study on the absorber alone because the performance of the absorber is much more dependent on accurate modeling of the transfer phenomena and rates. The aforementioned subcooling of the stripper feed further makes it much more challenging to interpret the experimental data properly for modeling the stripper. For the random packing type of IMTP no. 40, mass transfer coefficients and interfacial area are predicted with the 1968 correlation by Onda.14 The correlation by Stichlmair et al.15 is used for the holdup calculations. For the structured packing type of Flexipac 1Y, the 1985 correlation of Bravo16 is used for the mass transfer coefficient and interfacial area predictions and the 1992 correlation of Bravo17 is used for holdup. The Chilton and Colburn correlation is used to predict heat transfer coefficients for both packings. In order to be consistent with those reported for the pilot plant, it is necessary to scale the interfacial area for the random packing as predicted by the 1968 Onda correlation. According to the pilot plant report,4 IMTP no. 40 has a surface area of 110 m2, as computed from the specific packing area of 145 m2/m3, the packing volume, and the average utilization factor of 87% under pilot plant operating conditions. For case 42, with a scale factor of 1.2, the 1968 Onda correlation computes the interfacial area to be 114 m2, close to 110 m2. IMTP, one of the third generation high performance packings developed after the mid 1970s,18 is expected to be underpredicted by the 1968 Onda correlation. As to be shown later, the interfacial area factor is further adjusted in this study. For the structure packing Flexipac 1Y, the interfacial area factor is set to 1. RateSep calculates film thickness as the ratio of the average mass transfer coefficient and average diffusivity. It allows several options for modeling film resistance. The “reaction condition” factor, the weighting factor for conditions (temperature and liquid composition) used to calculate reaction rates for the film or for the discretized film segment if the “film

The MEA baseline campaign used two different types of packing in the absorber and stripper. Initially Flexipac 1Y, a structured packing with a specific area of 420 m2/m3, was used in the absorber. IMTP no. 40, a random metal packing with a specific area of 145 m2/m3, was used in the stripper. Halfway through the campaign, the Flexipac 1Y was moved to the stripper and the IMTP no. 40 was placed in the absorber. For each lean loading, two gas rates were run. The higher gas rate was near capacity and the lower was approximately at half of the higher rate. For each of these gas rates, three liquid rates were attempted. Liquid rates were controlled such that the CO2 removal from the inlet flue gas would be approximately 70, 85, and 95%. At the end of the campaign, a few runs with vacuum stripping were performed. The actual operating conditions performed are presented in Table 5. Runs 37 and 38 are not included since they were conducted at different liquid flow rates. Run 15 was operated at the same conditions as runs 16 and 17. Flooding was observed at the collector plate between the two beds of packing during these runs, so the data of these runs is not accurate and was excluded from the study. The first four runs of the campaign were also excluded in the simulation study since they show inconsistent results compared to the other conditions. It is possible that some data was incorrectly sampled

Simulation with RateSep

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Table 6. Performance of the Absorber lean loading (molCO2/molMEA)

a

rich loading (molCO2/molMEA)

CO2 removal (%)

case

experimental

experimental

simulation

experimental

simulation

5 6 7 8 9 10 11 12 13 14 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

0.180 0.182 0.177 0.170 0.150 0.148 0.147 0.143 0.323 0.329 0.268 0.274 0.271 0.274 0.282 0.277 0.275 0.278 0.275 0.284 0.287 0.285 0.284 0.281 0.279 0.283 0.282 0.280 0.284 0.282 0.281 0.228 0.229 0.235 0.232 0.231 0.231 0.285 0.286 0.281 0.285

0.525 0.523 0.496 0.493 0.532 0.533 0.537 0.546 0.507 0.508 0.506 0.495 0.538 0.540 0.554 0.557 0.506 0.386 0.376 0.413 0.412 0.448 0.453 0.426 0.428 0.422 0.420 0.415 0.425 0.404 0.402 0.367 0.371 0.433 0.430 0.491 0.492 0.433 0.426 0.539 0.537

0.510 0.508 0.496 0.495 0.507 0.507 0.510 0.511 0.486 0.485 0.472 0.469 0.501 0.501 0.507 0.509 0.474 0.372 0.368 0.397 0.405 0.440 0.452 0.431 0.432 0.430 0.432 0.410 0.431 0.410 0.403 0.355 0.355 0.409 0.413 0.453 0.450 0.415 0.418 0.480 0.481

61 61 96 96 87 87 75 75 62 62 95 95 87 87 72 72 92 93 93 86 86 70 70 95 95 80 80 95 95 a a 94 94 87 87 72 72 96 96 69 69

66 71 86 87 76 76 68 67 71 72 95 95 88 86 75 74 90 81 79 77 74 70 64 90 90 90 90 93 89 91 89 86 86 82 80 76 76 85 83 68 65

No experimental data.

discretization” option is chosen, was set to 0.9. The condition used is the “factor × bulk condition + (1 - factor) × interface” condition. A factor of 0 indicates the interface, and a factor of 1 represents the edge of the film next to the bulk. A higher weighting factor means liquid conditions closer to the bulk liquid will carry higher weight. We choose the “film discretization” option and set the number of discretization points for the liquid film to 5, which gives six film segments. We set the film discretization ratio to 5, which is the ratio of the thickness of the adjacent discretization regions. A value of film discretization ratio greater than 1 means thinner film regions near the vapor-liquid interface. Furthermore, RateSep provides four different “flow models” to determine the bulk properties used to evaluate the mass and energy fluxes and reaction rates of a stage. The “mixed” flow model was used as the base calculation method. Twenty “mixed” stages were set up for the packed columns. The impact of the flow models and the number of stages will be presented later. It should also be noted that, in the simulations, heat loss of the column was ignored since it is expected to be negligible for the absorber. Pressure drop was also ignored. Performance of the Absorber. RateSep simulation results for the 41 cases are summarized in Table 6, along with the experimental performance data. The values in the “lean” column in Table 6 are the CO2 loading (i.e., mole ratios of CO2/MEA)

of the lean stream, which feeds into the top of the absorber. These values are feed stream data used in both pilot plant experiments and simulation. The values in the “rich” column are the mole ratios of CO2/MEA of the rich stream, which comes out of the bottom of the absorber and then goes into the top of the stripper. These values are indicative of the performance of the absorber. According to the pilot plant report,4 the mole ratio of CO2/ MEA is calculated from the density of the liquid at 313.15 K with the empirical equation of “y ) -898x2 + 2921x - 2028,” where x stands for the density of the liquid with a unit of kg/l, and y stands for CO2 concentration with a unit of gCO2/kgMEA. The density of the liquid at 313.15 K is in the range of 1.0-1.15 kg/L. In this range, there is an approximately linear relationship between the density and the CO2 concentration, and the empirical equation has roughly 10% uncertainty. Figures 5 and 6 show the parity plots of measured and calculated rich loadings and CO2 removal percent for the 17 cases with the structured packing Flexipac 1Y (cases 5-24). While Figure 5 shows that the calculated rich loadings are always lower than the experimental values, Figure 6 shows a roughly even distribution of over- and underpredictions for the corresponding CO2 removal percent. Given the 10% uncertainty level associated with the empirical correlation used to estimate


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Figure 5. Parity plot of CO2 rich loading for the cases with structured packing Flexipac 1Y: (0) CO2 loading.

Figure 7. Parity plot of CO2 rich loading for the cases with random packing IMTP no. 40: interfacial area factor ) 1.2, (0) CO2 loading.

Figure 6. Parity plot of CO2 removal percent for the cases with structured packing Flexipac 1Y: (0) CO2 removal percent.

Figure 8. Parity plot of CO2 removal percent for the cases with random packing IMTP no. 40: interfacial area factor ) 1.2, (0) CO2 removal percent.

the CO2 loading, the predictions for these 17 cases can be considered satisfactory. Figures 7 and 8 show the parity plots of measured and calculated rich loadings and CO2 removal percent for the remaining 24 cases with the random packing IMTP no. 40 (cases 25-48). Both figures show that the calculated rich loadings and CO2 removal percent are always lower than the experimental values, suggesting the 1968 Onda correlation underpredicts the mass transfer of IMTP no. 40. The “interfacial area” factor was then adjusted from 1.2 to 1.8 to overcome the deficiency of the Onda correlation. With the effective interfacial area factor set to 1.8, Figure 9 shows that the calculated rich loadings are in line with the measured values, and Figure 10 also shows improved matches for the CO2 removal percent. Again, given the 10% uncertainty level associated with the empirical correlation used to estimate the CO2 loading, the predictions for these 24 cases are considered acceptable. Note that in all the subsequent studies and those reported in Table 6, the interfacial area factor is set to 1.8 for IMTP no. 40. Absorber Temperature Profile. The absorber contains seven resistance temperature detectors. The locations of the sensors

are defined by the height from the bottom of the lower bed of packing. Between the two 3.05 m beds of packing, there is a liquid redistribution and packing change-out area which occupies 1.67 m of the column. Figure 11 shows the location of the temperature sensors. The heat of reaction of CO2 with MEA produces a temperature bulge in the column. This temperature bulge significantly affects the absorption rates in the column since the kinetics of the absorption reaction, the phase equilibrium of the system, and all fluid transport properties depend on the temperature. The temperature bulge can be explained based on the balance of the heat released from the reaction of CO2 with MEA and the heat consumed by processes including water evaporation, heating of the liquid and gas streams, heat loss to the environment, and stripping of CO2. If the heat released from the absorption reaction is more than the heat consumed, the temperature will rise. According to the shape of the temperature bulge, we observed three types of absorber temperature profiles. Figure 12 shows the three types of experimental temperature profile.4 The type A profile, resulting from a high gas to liquid

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Figure 12. Absorber temperature profiles: (s - s 0 s - s) experimental data of type A, case 7, (- - ∆ - -) experimental data of type B, case 19, (s - - s O s - - s) experimental data of type C, case 28. Table 7. Three Types of Absorber Temperature Profiles Figure 9. Parity plot of CO2 rich loading for the cases with random packing IMTP no. 40: interfacial area factor ) 1.8, (0) CO2 loading.

Figure 10. Parity plot of CO2 removal percent for the cases with random packing IMTP no. 40: interfacial area factor ) 1.8, (0) CO2 removal percent.

type

case

A B C

5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 43, 44, 47, 48 18, 19, 29, 30, 41, 42 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46

changes sharply at both ends of the column. The type C profile, resulting from a low gas to liquid ratio and represented by case 28, has a mild temperature bulge near the bottom. The type A profile is opposite to type C. The type B profile can be regarded as a transition from type A to type C. All 41 cases can be categorized into these three types. See Table 7. Figure 13 shows the excellent match between the simulation results and the experimental data. Simulation results for absorber temperature profiles match pilot plant data very well in all cases. Note that RateSep predicts temperature profiles for both the vapor phase and the liquid phase, and they are shown in Figure 13 for case 7. The temperature difference between the two phases is quite small. Only the liquid phase temperature profiles are shown for cases 19 and 28. The excellent match between the simulation results and the experimental data also supports our assumption that the heat loss is negligible for the absorber. To explain the factors controlling the types of temperature profiles, Figure 14 shows the CO2 reaction rate profiles in the absorber. Representing type A cases, case 7 shows initial sharp rise in CO2 reaction rate and then maintains high CO2 reaction rate throughout the column and that explains the continuing

Figure 11. Absorber temperature sensor locations.

ratio and represented by case 7, has a temperature bulge located near the top of the absorber. The type B profile, resulting from a medium gas to liquid ratio and represented by case 19, shows a very broad bulge in the temperature profile and the temperature

Figure 13. Absorber temperature profiles of type A, case 7: (0) Experimental data, (s - s) liquid temperature calculated by RateSep, (s s) vapor temperature calculated by RateSep; type B, case 19. (∆) Experimental data, (- - -) liquid temperature calculated by RateSep; type C, case 28. (O) Experimental data, (s - - s) liquid temperature calculated by RateSep.


Figure 14. CO2 reaction rate profiles calculated by RateSep: (s - s) CO2 reaction rate of type A, case 7, (- - -) CO2 reaction rate of type B, case 19, (s - - s) CO2 reaction rate of type C, case 28. Table 8. Four Calculation Models models

method for mass transfer

method for liquid phase chemical reaction

rate, kinetics rate, equilibrium equilibrium, kinetics equilibrium, equilibrium

rate-based rate-based equilibrium-stage equilibrium-stage

kinetics chemical equilibrium kinetics chemical equilibrium

temperature rise from the absorber bottom section to the top section. Representing type B cases, case 19 shows an initial high CO2 reaction rate at the absorber bottom and then a much lower constant rate across the rest of the absorber. That explains the initial rise in temperature and then the relatively flat temperature profile. Representing type C cases, case 28 shows a slowly declining CO2 reaction rate across the absorber. That explains the mild temperature bulge at the absorber bottom section. The study on the CO2 reaction rate profile in the absorber also shows us that essentially all of the CO2 is reacted in the film and none in the bulk liquid, i.e., the CO2 mass transfer profile is essentially the same as the profile of the CO2 reaction rate in the film. In other words, RateSep suggests that the film reaction dictates the mass transfer rate for CO2 capture with aqueous MEA. This insight could not have been recognized nor represented with the traditional equilibrium-stage modeling approach.

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equilibrium treatment for the liquid phase reactions. The “equilibrium, kinetics” model uses the equilibrium-stage modeling approach and reaction kinetic treatment for the CO2 absorption reactions. The “equilibrium, equilibrium” model uses the equilibrium-stage modeling approach and chemical equilibrium treatment for the liquid phase reactions. In this study, the rate, kinetics model is the default rate-based model unless the chemical equilibrium treatment for the liquid phase reactions is explicitly specified. The equilibrium, kinetics model is the default equilibrium-stage model unless the chemical equilibrium treatment for the liquid phase reactions is explicitly specified. Note that in specifying the equilibrium-stage model, 30 equilibrium stages are assumed for the structured packing and 16 equilibrium stages for the random packing. The number of equilibrium stages is equal to the packed height divided by the corresponding height of equivalent theoretical plates (HETPs). As an example, for cases 7 and 19, the value of the structured packing height is 6.1 m and the value of HETP estimated by RateSep from the converged vapor and liquid composition profiles is about 0.2 m. Therefore, the number of equilibrium stages for the structured packing is set to 30. For case 28, the value of HETP for the random packing is about 0.37 m and the number of equilibrium stages is set to 16. The following discussion presents RateSep simulation results for the absorber and all three absorber temperature profile types. Case 7 represents a type A temperature profile. Figure 15 shows the temperature profiles of the absorber as computed from the four models. Only the rate, kinetics model predicts the temperature profile that matches the experimental data. The temperature profiles predicted by the other three models are much too low in comparison to the experimental data. Note that the temperature profiles calculated from equilibrium, kinetics and equilibrium, equilibrium overlap each other. Case 19 is selected as an example for type B temperature profiles. Figure 16 shows the temperature profiles of the Case 19 absorber with the four models. The temperature profile calculated from the rate, kinetics model matches the experimental data very well. The rate, equilibrium and equilibrium, kinetics models give reasonable results while the equilibrium, equilibrium model predicts instant CO2 absorption at the absorber bottom section and yields a completely erroneous temperature profile. Case 28 is selected as an example for type C temperature profile. Figure 17 shows the predicted temperature profiles with these models. While the rate, kinetics model predicts a temper-

RateSep Simulation Options RateSep provides a number of simulation options. We investigate the impacts of several key simulation options, including modeling approach, film discretization, and flow models. Modeling Approach: Equilibrium-Stage vs Rate-Based. RateSep supports both an equilibrium-stage modeling approach and a rate-based modeling approach. Simulation results from both approaches are investigated and compared. In addition, two different options to account for chemical reactions in the liquid phase are examined. In the first option, the reaction kinetics for CO2 absorption by MEA are explicitly modeled while remaining liquid phase reactions are in chemical equilibrium. In the second option, chemical equilibrium conditions are assumed for all liquid phase reactions. Table 8 summarizes the four models investigated in this study. The “rate, kinetics” model uses a rate-based modeling approach and reaction kinetics treatment for the CO2 absorption reactions. The “rate, equilibrium” model uses rate-based modeling approach and chemical

Figure 15. Absorption temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated by rate, kinetics model, (s s) liquid temperature calculated by rate, equilibrium model, (- - -) liquid temperature calculated by equilibrium, kinetics model, (- s -) liquid temperature calculated by equilibrium, equilibrium model.

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Ind. Eng. Chem. Res., Vol. 48, No. 20, 2009 Table 10. Performance of the Absorber, Case 19, with a Lean Loading of 0.274 rich loading (molCO2/molMEA)

CO2 removal (%)

0.495 0.469 0.479 0.479 0.479

95 95 100 100 100

experimental rate, kinetics rate, equilibrium equilibrium, kinetics equilibrium, equilibrium

Table 11. Performance of the Absorber, Case 28, with a Lean Loading of 0.287

Figure 16. Absorber temperature profile of type B, case 19: (∆) experimental data, (s) liquid temperature calculated by rate, kinetics model, (s s) liquid temperature calculated by rate, equilibrium model, (- - -) liquid temperature calculated by equilibrium, kinetics model, (- s -) liquid temperature calculated by equilibrium, equilibrium model.

0.412 0.405 0.445 0.445 0.445

86 74 100 100 100

Table 12. Discretization Methods

nofilm filmrxn discretization discretization discretization discretization discretization discretization

ature profile consistent with the experimental data, the temperature profiles predicted with the other three models overlap and deviate completely from the observed pilot plant temperature profile. According to this investigation for all three types of temperature profile, the rate, kinetics model is the only one that consistently predicts correct temperature profiles for the absorber. CO2 loading and removal were also examined and the results for the three cases are summarized in Tables 9-11. It is interesting that the CO2 loadings calculated by the rate, kinetics model match the measured values well for cases 7 and 28. Also the CO2 removal percent calculated by the rate, kinetics model matches the measured value well for case 19. On the other hand, the other three models overpredict the rich CO2 loading for cases 7 and 28. They also overpredict the CO2 removal percent for cases 19 and 28.

CO2 removal (%)


number of film of reaction discretization points discretization ratio condition factor

type

Figure 17. Absorber temperature profile of type C, case 28: (O) experimental data, (s) liquid temperature calculated by rate, kinetics model, (s s) liquid temperature calculated by rate, equilibrium model, (- - -) liquid temperature calculated by equilibrium, kinetics model, (- s -) liquid temperature calculated by equilibrium, equilibrium model.


1 2 3 4 5 6

na na 5 5 1 10 5 5

na na 5 5 5 5 2 10

na 0.9 0.9 0.5 0.9 0.9 0.9 0.9

Film Discretization. Film discretization facilitates precise modeling of the chemical reactions taking place in the liquid film. Without film discretization, the liquid film reaction rates are computed based on an average liquid phase composition. With film discretization, the liquid film reaction rates are computed by multiple sets of liquid phase compositions with each set representing the average liquid phase composition for the particular film segment. The various schemes for film discretization are summarized in Table 12. The “nofilm” method assumes no liquid film and considers neither the film diffusion resistance nor film reactions. The “filmrxn” method considers the film resistance and reactions without film discretization. “Discretization 1” represents the default “discretization” scheme used in this study. The temperature profiles of cases 7, 19, and 28 calculated by the three methods are shown in Figures 18-20. The profiles of the discretization 1 method in Figures 18-20 are closer to

Table 9. Performance of the Absorber, Case 7, with Lean Loading of 0.177



CO2 removal (%)

0.496 0.496 0.521 0.516 0.526

96 86 92 91 93

Figure 18. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with filmrxn, (- - -) liquid temperature calculated with nofilm.


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Table 13. Performance of the Absorber, Case 7, with Lean Loading of 0.177 type experimental nofilm filmrxn discretization discretization discretization discretization discretization discretization

Figure 19. Absorber temperature profile of type B, case 19: (∆) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with filmrxn, (- - -) liquid temperature calculated with nofilm.

Figure 20. Absorber temperature profile of type C, case 28: (O) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with filmrxn, (- - -) liquid temperature calculated with nofilm.

1 2 3 4 5 6


CO2 removal (%)

0.496 0.515 0.511 0.496 0.500 0.474 0.496 0.490 0.495

96 90 90 86 87 80 86 84 86

(discretization 2), there are no significant changes in the temperature profiles, but the higher CO2 concentration used to calculated the film reaction rate reflects in more CO2 being absorbed and the CO2 loading changes slightly from 0.496 to 0.500. Three discretization schemes were studied with varying numbers of discretization points (discretization 1 5 discretization points; discretization 3 1 discretization point; discretization 4 10 discretization points). Figure 22 shows the absorber temperature profiles as computed with varying discretization points. As the number of discretization points becomes large, the impact of the discretization points diminishes and the predictions yield similar temperature profiles. For example, the profiles of discretization 1 (5 points) and discretization 4 (10 points) overlap each other and the values of CO2 loading calculated by the two schemes are almost the same. See Table 13. Finally, three discretization schemes with varying discretization ratios (discretization 1 ratio ) 5; discretization 5 ratio ) 2; discretization 6 ratio ) 10) were examined. The results in Figure 23 show that the discretization ratio is not as important as the numbers of discretization points. The temperature profiles of discretization 1 (ratio ) 5) and discretization 6 (ratio ) 10) overlap each other. The values of CO2 loading are also very close, as shown in Table 13. The results shown above highlight the critical importance of film discretization on modeling CO2 capture with MEA. The rate-based modeling with film discretization allows for rigorous account of the concentration gradients and the corresponding reaction rates in the various film segments. The success of modeling CO2 capture with MEA depends on whether and how film discretization is carried out. Flow Models: Mixed, CouterCurrent, Vplug, and VPlug-Pavg. RateSep provides four different flow models which determine the bulk properties required to evaluate the mass and

Figure 21. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with discretization 2.

the experimental values than the other two methods. The profiles of the other two methods deviate from the experimental data for all three cases. Interestingly, the nofilm and filmrxn methods yield results similar to those of equilibrium, kinetics. In short, the discretization 1 method is the best one which correctly predicts the temperature profiles. The results of varying the reaction condition factor for case 7 are summarized in Figure 21 and Table 13. As the reaction condition factor changes from 0.9 (discretization 1) to 0.5

Figure 22. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with discretization 3, (- - -) liquid temperature calculated with discretization 4.

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Figure 27. VPlug flow model.

Figure 23. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated with discretization 1, (s s) liquid temperature calculated with discretization 5, (- - -) liquid temperature calculated with discretization 6.

Figure 24. Flow models for a single stage in RateSep.

Figure 28. VPlug-Pavg flow model.

Figure 29. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated by the Mixed model, (s s) liquid temperature calculated by the CounterCurrent model, (- - -) liquid temperature calculated by the VPlug model, (- s -) liquid temperature calculated by the VPlug-Pavg model. Table 14. Performance of the Absorber, Case 7, with Lean Loading of 0.177

Figure 25. Mixed flow model.

Figure 26. CounterCurrent flow model.

energy fluxes and reaction rates. As shown in Figure 24, these models refer to a “single stage” in a column, with each stage having its own unique bulk properties. The four flow models are Mixed, CounterCurrent, Vplug, and VPlug-Pavg. Figures 25-28 show details of these four flow models. In the Mixed flow model, as is the case on equilibrium stages, the bulk properties for each phase are assumed to be the same as the outlet conditions for that phase leaving that stage. In the CounterCurrent flow model, the bulk properties for each phase are an average of the inlet and outlet properties. This Counter-

experimental Mixed CounterCurrent VPlug VPlug-Pavg


CO2 removal (%)

0.496 0.496 0.500 0.498 0.498

96 86 87 86 86

Current flow model gives more accurate results for packing but is more computationally intensive and sometimes unstable. See Figure 29. In the VPlug flow model, outlet conditions are used for the liquid and average conditions are used for the vapor. The outlet pressure is used. In the VPlug-Pavg flow model, outlet conditions are used for the liquid and average conditions are used for the vapor. The average pressure is used. Using cases 7, 19, and 28 as examples, we analyze the impact of flow models on the performance of the absorber. Tables 14-16 show that the flow models have only very minor influence on the overall CO2 absorption performance. Figures 29-31 show the predicted absorber temperature profiles with the four flow models. Again, flow models only have minor impacts on the predicted absorber temperature profiles. However, the Mixed flow model does yield the most reliable predictions. The VPlug model predictions and the Vplug-Pavg model predictions overlap each other. Finally, the impact of number of Mixed stages in RateSep was evaluated using cases 7, 19, and 28. Figures 32-34 show


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CO2 removal (%)

0.495 0.469 0.461 0.466 0.466

95 95 91 94 94




CO2 removal (%)

0.412 0.405 0.407 0.407 0.407

86 74 76 76 76

the temperature profiles predicted with 10, 20, 30, and 50 stages. As expected, the temperature profiles gradually “converge” as the number of Mixed stages increase. For example, the profiles of 30 and 50 stages are very close to each other. Likewise, Tables 17-19 show the rich loadings and CO2 removal percent also converge as the stage numbers increase. While the choice of flow models and number of stages are model parameters, it is important that one understands the significance of these model parameters and how they impact the simulation results.

Figure 32. Absorber temperature profile of type A, case 7: (0) experimental data, (s) liquid temperature calculated with 20 stages, (s s) liquid temperature calculated with 10 stages, (- - -) liquid temperature calculated with 30 stages, (- s -) liquid temperature calculated with 50 stages.

Figure 33. Absorber temperature profile of type B, case 19: (∆) experimental data, (s) liquid temperature calculated with 20 stages, (s s) liquid temperature calculated with 10 stages, (- - -) liquid temperature calculated with 30 stages, (- s -) liquid temperature calculated with 50 stages.

Figure 30. Absorber temperature profile of type B, case 19: (∆) experimental data, (s) liquid temperature calculated by the Mixed model, (s s) liquid temperature calculated by the CounterCurrent model, (- - -) liquid temperature calculated by the VPlug model, (- s -) liquid temperature calculated by the VPlug-Pavg model.

Figure 34. Absorber temperature profile of type C, case 28: (O) experimental data, (s) liquid temperature calculated with 20 stages, (s s) liquid temperature calculated with 10 stages, (- - -) liquid temperature calculated with 30 stages, (- s -) liquid temperature calculated with 50 stages.

Figure 31. Absorber temperature profile of type C, case 28: (O) experimental data, (s) liquid temperature calculated by the Mixed model, (s s) liquid temperature calculated by the CounterCurrent model, (- - -) liquid temperature calculated by the VPlug model, (- s -) liquid temperature calculated by the VPlug-Pavg model.

It was brought to our attention after completion of our study that Lawal and co-workers also have validated their rate-based model using the pilot plant data from U.T-Austin.19 Out of the 48 experimental cases, they selected cases 32 and 47 for steady state validation purposes. According to Table 7, case 32 has a type C temperature profile resulting from a low gas to liquid ratio while case 47 has a type A temperature

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experimental 10 stages 20 stages 30 stages 50 stages


CO2 removal (%)

0.496 0.491 0.496 0.498 0.499

96 84 86 86 86




CO2 removal (%)

0.495 0.472 0.469 0.468 0.466

95 96 95 94 93




CO2 removal (%)

0.412 0.402 0.405 0.406 0.406

86 73 74 75 75

profile resulting from a high gas to liquid ratio. While we are interested in comparing our simulation results against those reported by Lawal et al., we found the results are not comparable because there is no indication that film discretization was ever considered in the rate-based model of Lawal et al. In fact, they reported poor prediction of the absorber temperature profiles for both cases 32 and 47 from their models, equilibrium-based and rate-based. It should also be noted that additional pilot plant absorber data for CO2 capture with aqueous MEA have been reported by Tobiesen et al.20 While these pilot plant data were taken primarily at high gas to liquid ratio, they do represent extra useful data for future model validation studies. Conclusions This study shows the superiority of the rate-based models over the traditional equilibrium-stage models for the recently available pilot plant data from University of Texas at Austin for CO2 capture with aqueous monoethanolamine. Using proper models and model parameters, the rate-based modeling software RateSep successfully simulates the U.T.-Austin pilot plant and matches the absorber experimental data well. The model accurately predicts CO2 loading, CO2 removal %, and the three types of temperature profiles observed with the pilot plant absorber data. A key factor in the success of the CO2 absorption modeling is the use of the film discretization option to rigorously model the mass transfer resistance and the CO2 absorption reactions taking place in the liquid film. The RateSep model with proper model parameters provides excellent first principles-based predictive capability, and it should be a very useful tool for industrial research, development, and design of CO2 capture processes.

Acknowledgment Y.Z. and C.-C.C. are grateful for the extensive discussions and enthusiastic support from our colleagues and collaborators: Davy Zuo, Huiling Que, Jiangchu Liu, Hailing Li, Jianjun Peng, Brian Hanley, Paul Mathias, and Randy Field. Literature Cited (1) Taylor, R.; Krishna, R.; Kooijman, H. Real-World Modeling of Distillation. Chem. Eng. Prog. 2003, 99, 28–39. (2) Putting Carbon Back into the Ground; IEA Greenhouse Gas R&D Programme: Paris, 2006. (3) Kumar, A.; Ozah, R. C.; Noh, M.; Pope, G. A.; Bryant, S. L.; Sepehrnoori, K.; Lake, L. W. Reservoir Simulation of CO2 Storage in Deep Saline Aquifers. Soc. Pet. Eng. J. 2005, 10, 336–348. (4) Dugas, R. E. Pilot Plant Study of Carbon Dioxide Capture by Aqueous Monoethanolamine. Master thesis, Chemical Engineering, University of Texas at Austin, 2006. (5) Alopaeus, V.; Aittamaa, J.; Norden, H. V. Approximate High Flux Corrections for Multicomponent Mass Transfer Models and Some Explicit Methods. Chem. Eng. Sci. 1999, 54, 4267–4271. (6) Taylor, R.; Krishna, R. Multicomponent Mass Transfer; WileyInterscience: New York, 1993. (7) 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. thesis, Chemical Engineering, University of Texas at Austin, 2008. (8) Chen, C.-C.; Evans, L. B. A Local Composition Model for the Excess Gibbs Energy of Aqueous Electrolyte Systems. AIChE J. 1986, 32, 444– 454. (9) Aboudheir, A. Kinetics, Modeling and Simulation of CO2 Absorption into Highly Concentrated and Loaded MEA Solutions. Ph.D. thesis, Chemical Engineering, University of Regina, 2002. (10) Rochelle, G. T.; Bishnoi, S.; Chi, S.; Dang, H. -Y.; Santos, J. Research Needs for CO2 Capture from Flue Gas by Aqueous Absorption/ Stripping; Final Report on DOE P.O. No. DE-AF26-99FT01029, University of Texas at Austin, January 17, 2001. (11) Pacheco, M. A.; Kaganoi, S.; Rochelle, G. T. CO2 absorption into aqueous mixtures of diglycolamine and methyldiethanolamine. Chem. Eng. Sci. 2000, 55, 5125–5140. (12) Aspen Plus online documentation, 2008. (13) Aspen Properties Reference Manual; version number: 2006, Aspen Technology Inc.: Burlington, MA, October, 2006. (14) Onda, K.; Takeuchi, H.; Okumuto, Y. Mass Transfer Coefficients between Gas and Liquid Phases in Packed Columns. J. Chem. Eng. Jpn. 1968, 1, 56–62. (15) Stichlmair, J.; Bravo, J. L.; Fair, J. R. General Model for Prediction of Pressure Drop and Capacity of Countercurrent Gas/Liquid Packed Columns. Gas. Sep. Purif. 1989, 3, 19–28. (16) Fair, J. R.; Bravo, J. L. Prediction of Mass Transfer Efficiencies and Pressure Drop for Structured Tower Packings in Vapor/Liquid Service. Inst. Chem. Eng. Symp. Ser. 1985, , A183-200. (17) Bravo, J. L.; Rocha, J. A.; Fair, J. R. A Comprehensive Model for the Performance of Columns Containing Structured Packings. Inst. Chem. Eng. Symp. Ser. 1992, 129, A439-457. (18) Kister, H. Z. Distillation Design; McGraw-Hill: Boston, MA, 1992. (19) Lawal, A.; Wang, M.; Stephenson, P.; Yeung, H. Dynamic Modeling of CO2 Absorption for Post Combustion Capture in Coal-Fired Power Plants. Fuel, available online Dec 4, 2008, http://dx.doi.org/10.1016/ j.fuel.2008.11.009. (20) Tobiesen, F. A.; Svendsen, H. F.; Juliussen, O. Experimental Validation of a Rigorous Absorber Model for CO2 Postcombustion Capture. AIChE J. 2007, 53, 846–865.

ReceiVed for reView January 15, 2009 ReVised manuscript receiVed July 31, 2009 Accepted August 13, 2009 IE900068K

Rate-Based Process Modeling Study of CO2 Capture with Aqueous

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