Improved Rate-Based Modeling of H2S and CO2 Removal by

Article pubs.acs.org/IECR

Improved Rate-Based Modeling of H2S and CO2 Removal by Methyldiethanolamine Scrubbing Stefania Moioli,*,† Laura A. Pellegrini,† Barbara Picutti,§ and Paolo Vergani§ †

Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy § Tecnimont S.p.A., Via Gaetano de Castillia 6/A, I-20124 Milano, Italy S Supporting Information *

ABSTRACT: Gas treatment for H2S and CO2 removal has been representing for more than 50 years an important industrial application; nowadays these processes are becoming increasingly important as environmental tools also for controlling the composition of the flue gas streams. Natural gas, exhaust gases from power plants, and refinery gases are all sources of carbon dioxide and/or hydrogen sulfide, two components whose presence is strictly limited by the commercial specifications of the final product (i.e., natural gas or syngas) and/or by environmental regulations. In addition, especially H2S is a poison for many catalysts, thus the necessity for removal in order to obtain the best performance in some processes. In this paper, a new model is proposed, where Eddy diffusivity theory instead of film theory is taken into account. The new model is used to develop an external subroutine for ASPEN Plus, a commercial simulation software, that is used as a framework. The use of this tool on different case studies taken from operating plants shows an improvement in the representation of the absorption phenomenon, in comparison with the experimental data.

1. INTRODUCTION Gas treatment for H2S and CO2 removal has been representing for more than 50 years an important industrial application; nowadays these processes are becoming increasingly important as environmental tools also for controlling the composition of the flue gas streams. Natural gas, exhaust gases from power plants, and refinery gases are all sources of carbon dioxide and/or hydrogen sulfide, two components whose presence is strictly limited by the commercial specifications of the final product (i.e., natural gas or syngas) and/or by environmental regulations. In addition, H2S, a toxic and malodorous compound, causes poisoning of many catalysts. Because of these characteristics, its concentration in the exhaust gases must be reduced to parts per million and, in the gaseous streams to be fed to some processes, even to a few parts per million.1 Other sulfur compounds, such as carbonyl sulfide, are often present together with hydrogen sulfide in low concentration, but must still be reduced to ppm because of their poisoning action on catalysts. CO2 removal is required in the ammonia production process and in the production of hydrogen; moreover, the scientific community generally agrees that it is a significant greenhouse gas2 and according to the Intergovernmental Panel on Climate Change,3 it constitutes the major man-made contribution to global warming. Many countries have agreed to reduce their emissions into the atmosphere to help prevent dangerous alterations to the climate system. This has led to a number of measures to address this problem, so several mature technologies are available, including adsorption, cryogenics, membrane, and absorption. Absorption is the most commonly used process when it comes to gas treating. In particular, absorption by chemical solvents, such as alkanolamines, is one of the most effective methods for acid gas removal and it has been used in industry for over half a century.4 © 2013 American Chemical Society

The process described in this paper is a conventional amine absorption system, with a solution of methyldiethanolamine (MDEA) as amine solvent. It can selectively absorb H2S, if required, because of its relatively slow kinetics with CO2. Moreover, it is characterized by a lower heat of regeneration than primary and secondary amines, allowing energy saving in the amine scrubbing system. Commercial software packages5,6 are based on different methods for the column calculations, such as equilibrium or rate-based approaches. It is commonly accepted that a ratebased approach has potential to be the most accurate description of the phenomenon,7 but thermodynamics and mass transfer with kinetics should be properly taken into account.8,9 The commercial simulation software ASPEN Plus is used as a framework for the new model, based on the Eddy diffusivity theory,10 developed in this work and validated by comparison with experimental data. Results show the reliability of the proposed model in the representation of the absorption phenomenon.

2. MODELING Amine scrubbing presents several advantages, in particular in the mass transfer phenomenon, promoted by chemical reactions in the liquid phase. The presence of the amine dramatically influences the solubility of the acid gas in water.11 From the bulk of the gas phase to the bulk of the liquid phase, the following steps occur: Received: Revised: Accepted: Published: 2056

July 24, 2012 December 21, 2012 January 10, 2013 January 10, 2013 dx.doi.org/10.1021/ie301967t | Ind. Eng. Chem. Res. 2013, 52, 2056−2065

Industrial & Engineering Chemistry Research

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Figure 2. Experimental data, literature profiles, and proposed correlation of kinetic constant of reaction eq 11 vs temperature (a) and detail in logarithmic scale (b).

(2) diffusion of reagents from the gas−liquid interface to the bulk of the liquid phase, (3) simultaneous reaction between dissolved gas and liquid reactant, and (4) diffusion of reaction products in the bulk of the liquid phase promoted by the concentration gradient due to chemical reaction. An accurate description of the phenomenon, in terms of thermodynamics, kinetics, and mass transfer, is then needed for a reliable representation of the absorption process. 2.1. Vapor−Liquid and Chemical Equilibria. Since chemical reactions occur in the liquid phase, the description of the vapor−liquid equilibrium is strictly connected to the

Figure 1. Vapor−liquid equilibrium, expressed as partial pressure over loading, of (a) CO2−MDEA−H2O, (b) H2S−MDEA−H2O, and (c) toluene−MDEA−H2O systems.

(1) diffusion of the component from the bulk of the gas phase to the gas−liquid interface, 2057

dx.doi.org/10.1021/ie301967t | Ind. Eng. Chem. Res. 2013, 52, 2056−2065


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Figure 5. Density of the aqueous MDEA solution vs CO2 loading.

Figure 3. Experimental data,42,44,45,50−52 literature profiles, and proposed correlation of equilibrium constant of reaction 14 vs temperature.

K a,CO2

CO2 + 2H 2O ←⎯⎯→ HCO3− + H3O+ K a,HCO3−

H 2O + HCO3− ←⎯⎯⎯⎯⎯→ CO32 − + H3O+

consumption or production of volatile, but also nonvolatile, species in the liquid phase. For this reason, chemical equilibrium reactions (eqs 1−6) must be considered, with the equilibrium constant computed as in eq 7: K H2O

2H 2O ←→ ⎯ H3O+ + OH−

K a,MDEAH+

MDEAH+ + H 2O ←⎯⎯⎯⎯⎯⎯→ MDEA + H3O+ K a,H2S

H 2S + H 2O ←⎯⎯→ H3O+ + HS−

(1)

(2) (3) (4) (5)

Figure 4. Experimental data and literature profiles of density of the amine solution with (a) 10% w/w MDEA, (b) 30% w/w MDEA, (c) 40% w/w MDEA, and (d) 50% w/w MDEA. 2058


Industrial & Engineering Chemistry Research K a,HS−

HS− + H 2O ←⎯⎯→ H3O+ + S2 − ln Kj = Aj +

Bj T

+ Cj ln T + DjT

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(6)

(7)

where j is the considered reaction. Henry’s constant is calculated with a correlation analogous to eq 7. Values of parameters are taken from the literature12 and from ASPEN Plus5 (see the Supporting Information). Because of chemical reactions, acid gases and amine partially dissociate in the aqueous phase to form a complex mixture consisting of molecular species and nonvolatile ionic species, in a strongly nonideal system. The description of these system can be accurately obtained by using a γ/ϕ approach, with the electrolyte-NRTL model developed by Chen et al.,13,14 Chen and Evans,15 and Mock et al.16 for the liquid phase and the SRK17 EoS for the vapor phase, where a negligible deviation from ideality, because of the low operating pressure, is observed. A large number of parameters are involved in the VLE calculation, because the model takes into account interactions between molecule and molecule, molecule and ion pair, and ion pair and ion pair. Optimized values of the most important parameters have been previously obtained, also considering interactions of aromatics and sulfur compounds such as mercaptans, and are used for simulation. Figure 1 shows a comparison of obtained results with experimental data18−20 for some analyzed systems. Further details can be found in Pellegrini et al.21,22 2.2. Reaction Kinetics. Absorption of carbon dioxide in MDEA solution involves finite rate reactions. These reactions are quite fast, but chemical equilibrium is not attained, so a proper kinetic characterization is important for a reliable description of the phenomenon.23−25 Carbon dioxide and the hydroxyl ion react according to a mechanism already known in the literature (eqs 8 and 9) with kinetic constants calculated according to the Arrhenius form (eq 10) whose parameters are taken from Pinsent et al.26 (see the Supporting Information). k1

CO2 + OH− → HCO3− k −1

HCO3− ⎯→ ⎯ CO2 + OH− ⎛ Eatt, j ⎞ kj = Aj exp⎜ − ⎟ ⎝ RT ⎠

Figure 6. Experimental data and literature profiles of viscosity of the amine solution.

Figure 7. Viscosity of the aqueous MDEA solution vs CO2 loading.

(8) (9)

(10)

MDEA cannot directly react with carbon dioxide to form a carbamate, like other primary and secondary amines do, because of its being a tertiary amine. In aqueous solution, however, hydrolysis of carbon dioxide takes place, which promotes mass transfer from the vapor phase to the liquid one. The reaction mechanism is well-defined in the literature, but there are still some differences in the representation of kinetic data. In particular, many researchers27−40 have studied the kinetics of this reaction in order to obtain a dependence on temperature (see the Supporting Information). k2

CO2 + MDEA + H 2O → HCO3− + MDEAH+

Figure 8. Experimental data and literature profiles of diffusivity of the amine solution.

reported. Curves are plotted only in the temperature ranges of validation reported by each author. As can be seen, trends are very different from each other and are not able to properly describe experimental data. ASPEN Plus uses the correlation developed by Rinker et al.,37 which shows a significant deviation from experimental data at high temperature. On the other hand, Jamal et al.39 have proposed an expression that fits experimental data at elevated temperature but that cannot properly reproduce data at low

(11)

In Figure 2 experimental data28,29,31−33,38,39,41 and literature profiles5,28−32,34,35,38,40 of kinetic constant vs temperature are 2059


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0.93 0.0693 4.06 × 10−04 0 1.74 × 10−05 0.93 0.0693 4.05 × 10−04 0 6.17 × 10−06 0.93 0.0694 4.88 × 10−04 0 1.16 × 10−05 0.93 0.0694 4.52 × 10−04 0 1.16 × 10−05 8.74 × 10−04 0 0.0348 0.964 5.50 × 10−05

4 319.26 54.28 439.11 2 310.93 54.28 772.32 1 309.26 54.28 644.28 5 306.48 54.44 1506.09

3 317.04 54.28 922.03

0.93 0.0697 5.23 × 10−04 0 5.84 × 10−06

Table 2. Characteristics of the Absorption Column of the Dome’s North Caroline Plant60 characteristic

value

pressure (bar) diameter (m) type of column type of tray material valve lift

55 1.28 tray column ballast carbon steel 0.313 in. (7.95 mm)

temperature. A new generalized correlation (eq 12), based on all the available experimental data, was then needed and developed by the authors. ⎛ 49100.82 J ⎞ mol ⎟ k 2 = 1.8314 × 10 exp⎜⎜ − ⎟ RT ⎝ ⎠ 14

k −2

HCO3− + MDEAH+ ⎯→ ⎯ CO2 + MDEA + H 2O

K eq MDEAH+

MDEAH+ + H 2O XooooooooooY MDEA + H3O+

8.74 × 10−04 0 0.0347 0.964 5.61 × 10−05

8.74 × 10−04 0 0.0347 0.964 5.80 × 10−05

CO2 + 2H 2O XooooooY HCO3− + H3O+ Keq2 =

(14) (15)

KeqCO2 K eqMDEAH+

(16)

The reaction of eq 15 is already known in the literature, but the reaction of eq 14 is still being studied, and a unique correlation for the expression of the equilibrium constant is not available in the literature,42−49 as shown in Figure 3 and in the Supporting Information. By regression of experimental data, a new equilibrium constant is obtained (eq 17) and used, together with the equilibrium constant for reaction eq 15 taken from Read,53 in order to obtain the overall equilibrium constant Keq 2 and so the kinetic constant k‑2 (eq 18).

8.74 × 10−04 0 0.0352 0.964 5.00 × 10−05

8.74 × 10−04 0 0.0347 0.964 5.80 × 10−05

4 306.48 54.44 1470.23 3 305.37 54.44 1642.64 2 302.59 54.44 1553.9

(13)

The equilibrium reaction can be considered as the sum of two equilibrium reactions, eq 14 and the inverse reaction of eq 15, so that the equilibrium constant of eq 11 can be computed according to eq 16.

test temp (K) pressure (atm) molar flow (kmol/h) composition (molar fraction) H2O MDEA CO2 CH4 H2S

1 302.04 54.44 1541.94

(12)

The kinetic constant of the inverse reaction eq 13 is calculated considering the kinetic constant k2 and the equilibrium constant.

K eq CO2

gas in

Table 1. Characteristics of the Streams Entering the Absorption Column for Different Cases of the Dome’s North Caroline Plant60

lean in

5 322.04 54.28 1129.84


⎛ 4504 ⎞ ⎟ K eqMDEAH+ = 8.9999 × 10−3 exp⎜ − ⎝ T ⎠

(17)

⎛ 9861.7 J ⎞ mol ⎟ k −2 = 6.26427 × 1012 exp⎜⎜ − ⎟ RT ⎝ ⎠

(18)

2.3. Mass Transfer. For amine scrubbing systems, a square root dependence on diffusivity for mass transfer coefficient is observed, and it can be correctly predicted25 using the Eddy diffusivity theory.10 This theory considers the presence of little eddies that influence the mass transfer rate, and it takes into account both molecular diffusion and turbulent transport. Considering the contribution of Eddy diffusivity to mass transfer and the presence of chemical reactions, the mass balance equation for carbon dioxide can be expressed as in eq 19: 2060



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Figure 9. Temperature profiles along the absorption column of the Dome’s North Caroline Plant for (a) test 1, (b) test 2, (c) test 3, (d) test 4, and (e) test 5.

⎤ ∂ ⎡ 2 ∂[CO2 ] ⎢(DCO2 + εx ) ⎥ − R CO2 = 0 ∂x ⎣ ∂x ⎦

ASPEN Plus is used as a framework for simulation, where the model developed in this work is introduced and successfully used for simulating an absorption column. The specific effective area is calculated according to the correlation of Bravo et al.55 for structured packed columns and the correlation of Scheffe and Weiland56 for tray columns. The obtained effective liquid mass transfer coefficient, which influences the mass transfer rates, is obtained by integrating eq 19:

(19)

where RCO2 is the rate of consumption of carbon dioxide due to chemical reactions occurring in the liquid phase and ε = (k1[OH−]i + k2[MDEA]i)/2, obtained by analytically solving the mass balance equation. ASPEN Plus is provided with a rate-based model that takes directly into account the mass transfer limitations occurring in the absorption phenomenon. However, they are described with the film theory,54 which provides a linear dependence on diffusivity.

kL,CO2 = 2061

π 2

1 (k1[OH −]i + k 2[MDEA]i )DCO2 2

(20)



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2.4. Properties of the Amine Solution. ASPEN Plus uses generalized correlations for physical properties, in order to describe different mixtures by simply changing values of parameters. This generalization, however, affects the accuracy in describing the behavior of the specific case. These properties are involved in the calculation of mass transfer coefficient and interfacial area for mass transfer modeling, so a proper definition is fundamental. For this reason, density and viscosity of the amine solution and diffusivity of carbon dioxide in the solvent are analyzed in detail. The density of the amine solution and its dependence on temperature and amine concentration have been widely studied in the literature.57−59 Moreover, the density of the liquid phase is influenced also by the presence of other compounds in solution, such as carbon dioxide, studied by Weiland.58 As shown in Figure 3, the correlation developed by Al-Ghawas et al.57 significantly deviates from experimental data, especially at low MDEA concentrations. Weiland58 and Hsu and Li59 proposed correlations showing a similar behavior, even if they differ in the description of the volume of the mixtures, considered constant by the former and dependent on temperature by the latter. Moreover, as already stated, the model developed by Weiland58 takes into account the amount of carbon dioxide in the liquid phase. In this paper a new model is used, where the molar volume of the aqueous solution is calculated as by Hsu and Li,59 but the influence of carbon dioxide is considered as in the work of Weiland58 (Figure 4). In the operative ranges of the absorption process, values of viscosity change significantly, so a proper description of this property is required. Like density, also the viscosity of the liquid phase shows a dependence on temperature, on the amine concentration, and on the amount of other compounds, like carbon dioxide (Figure 5). Different models were developed in the literature, but only the one proposed by Weiland58 takes into account the influence of CO2, as reported in Figures 6 and 7. As for the dependence of density on the amount of hydrogen sulfide, in the literature no data were found, neither for density nor viscosity. The diffusivity of carbon dioxide is fundamental in the description of the absorption phenomenon, since it influences the mass transfer coefficient. In the literature several correlations with dependence on temperature and on the amine concentration are reported and give different results, as shown in Figure 8. The correlation proposed by ASPEN Plus is far from experimental data, while the one by Versteeg29 seems to be the closest, in particular at the operating temperature range of the absorption process, and is then used for simulation.

phase (carbon dioxide and hydrogen sulfide) for test 1 is shown in Figures 10 and 11. Figure 12 shows the CO2 concentration in the sweetened gas at different amine circulation rates.

Figure 10. Profiles of CO2 molar fraction of vapor stream along the column of the Dome’s North Caroline Plant for test 1.

Figure 11. Profiles of H2S molar fraction of vapor stream along the column of the Dome’s North Caroline Plant for test 1.

3. RESULTS OF SIMULATIONS Different cases of acid gas removal with MDEA solutions are simulated by applying the proposed thermodynamic21,22 and mass transfer models. In particular, the Dome’s North Caroline Plant60 and another plant from the literature7 are considered. Simulations are performed with the ASPEN Plus default model and with our model, which is integrated in the simulator by using an external Fortran subroutine. The Dome’s North Caroline Plant for the sweetening of natural gas consists of one absorber and one regenerator. Five tests were reported by Daviet et al.60 and are considered for simulation. Data of the five cases are listed in Table 1, and characteristics of the absorption tray column are detailed in Table 2. In Figure 9, the temperature profile of the column for the five cases is reported, while the composition of the vapor

Figure 12. CO2 molar fraction of the purified gas at different amine circulation rates of the Dome’s North Caroline Plant.

In the literature, other experimental data of a CO2 absorber containing structured packing7 are available and used for simulation. The column is run at atmospheric pressure. It has a 2062



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diameter of 0.275 m and is packed with stainless steel structured packing Sulzer DX (a = 900 m2/m3) for 2.15 m. Three cases are considered, as by Naami et al.7 Characteristics of the streams entering the pilot column for each case are reported in Table 3. The carbon dioxide composition profiles along the absorber are shown in Figure 13. The temperature profile along the

Figure 14. Temperature profiles along the column of the pilot plant from Naami et al.7 for test 1.

increase is not observed, as results with our model. Though experimental data of temperature profiles are not available, it can be inferred that the profile obtained with the proposed model is more reliable than the one obtained with ASPEN Plus default model.

4. DISCUSSION The model is tested by using experimental data of two different plants, 7,60 and in both cases, it well-reproduces the experimental values. Figures 9 and 14 show that our model is reliable in representing the temperature profile. For all the cases of Dome’s North Caroline Plant, results are closer to experimental data than those obtained with the ASPEN Plus default model. Moreover, in particular for tests 3, 4, and 5 (Figure 8c−e), it is able to reproduce the trend of temperature, with a good representation of the position and of the extension of the bulge. This increase in temperature is due to the heat released because of the exothermic reactions that occur in liquid phase, so a proper description of this phenomenon is an index of the simulator reliability. Removal of hydrogen sulfide is almost complete (Figure 11) because reactions attain chemical equilibrium and the amount in the gas fed to the Dome’s North Caroline Plant (as well as to the pilot plant from Naami et al.7) is low. Removal of carbon dioxide, on the contrary, is kinetically controlled, and the absorption process is mainly influenced by mass transfer and, consequently, by a proper description of the phenomenon. For this reason, there are some differences in results obtained for CO2 with ASPEN Plus standard model and our model: the former predicts a molar fraction of carbon dioxide lower than the experimental value, to which the latter is closer. This behavior can be seen both in Figure 10 (Dome’s North Caroline Plant) and in Figure 13 (pilot plant from Naami et al.7), where, according to ASPEN Plus, the experimental value of CO2 in the outlet stream can be obtained with a shorter absorption column. The difference in the performances of the two models is emphasized in Figure 13, especially in part a, where results of test 1 of the pilot plant from Naami et al.7 are shown. Unlike the other cases, in test 1 the liquid flow is low, much lower than that of test 3. On the other hand, generally ASPEN Plus standard model overestimates the amount of absorbed CO2 and, consequently, the amount of heat released because of the

Figure 13. Profiles of CO2 molar fraction of vapor stream along the column of the pilot plant from Naami et al.7 for (a) test 1, (b) test 2, and (c) test 3.

absorber for test 1 is reported in Figure 14. Because of the liquid to gas ratio used in this test, a significant temperature 2063



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Table 3. Characteristics of the Streams Entering the Absorption Column of the Simulated Pilot Plant7 gas in test temp (K) pressure (atm) molar flow (kmol/h) composition (molar fraction) H2O MDEA CO2 N2

lean in

1 296.35 1 1.2458

2 296.35 1 1.2458

3 296.35 1 1.2458

0 0 0.149 0.851

0 0 0.149 0.851

0 0 0.149 0.851

exothermicity of reactions. In this case, the heat cannot be efficiently dissipated because of the low liquid flow, so a significant increase in temperature occurs, with a strong impact on the kinetics of the absorption process. For this reason, ASPEN Plus standard model shows in Figure 13a an unconventional profile, with a trend very far from experimental data.

ASSOCIATED CONTENT

S Supporting Information *

Table S.1, values of parameters A, B, C and D for the calculation of the constant of equilibrium reactions and for the Henry’s constant; Table S.2, values of parameters for the calculation of the kinetic constant of reactions involving carbon dioxide and the hydroxyl ion; Table S.3, expressions for the kinetic constant [L/(mol s)] of reaction eq 11 according to different sources in the literature [activation energy in (J/mol)]; and Table S.4, expressions for the equilibrium constant of reaction 14 according to different sources in the literature. This material is available free of charge via the Internet at http://pubs.acs.org.

■

■

2 296.75 1 5

3 296.75 1 7

0.949872 0.043972 0.006156 0

0.949872 0.043972 0.006156 0

0.949872 0.043972 0.006156 0

REFERENCES

(1) Shangguan, J.; Liang, L.; Fan, H.; Shen, F. Influence of Gas Components on the Formation of Carbonyl Sulfide over Water−Gas Shift Catalyst B303Q. J. Nat. Gas Chem. 2007, 16, 53−59. (2) Kamal, W. A. Improving Energy EfficiencyThe Cost-Effective Way To Mitigate Global Warming. Energy Convers. Manage. 1997, 38 (1), 39−59. (3) Rogner, H. H.; Zhou, D.; Bradley, R.; Crabbé, P.; Edenhofer, O.; Hare, B.; Kuijpers, L.; Yamaguchi, M. Introduction. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Metz, B., Davidson, O. R., Bosch, P. R., Dave, R., Meyer, L. A., Eds.; Cambridge University Press: New York, 2007. (4) Astarita, G.; Savage, D. W.; Bisio, A. Gas Treating with Chemical Solvents; Wiley: New York, 1983. (5) ASPEN Plus; AspenTech: Burlington, MA, 2010. (6) ASPEN HYSYS; AspenTech: Burlington, MA, 2010. (7) Naami, A.; Edali, M.; Sema, T.; Idem, R.; Tontiwachwuthikul, P. Mass Transfer Performance of CO2 Absorption into Aqueous Solutions of 4-Diethylamino-2-butanol, Monoethanolamine, and NMethyldiethanolamine. Ind. Eng. Chem. Res. 2012, 51 (18), 6470− 6479. (8) Pellegrini, L. A.; Moioli, S.; Picutti, B.; Vergani, P.; Gamba, S. Design of an Acidic Natural Gas Purification Plant by Means of a Process Simulator. Chem. Eng. Trans. 2011, 24, 271−276. (9) Pellegrini, L. A.; Moioli, S.; Picutti, B.; Vergani, P.; Gamba, S. Design of an Acidic Natural Gas Purification Plant by Means of a Process Simulator. In ACOS−AIDIC Conference Series, Vol. 10 Selected Papers of ICheaP-10 Proceedings; Pierucci, S., Ed.; AIDIC The Italian Association of Chemical Engineering: Milano, 2011; pp 285− 294. (10) King, C. J. Turbolent Liquid Phase Mass Transfer at a Free Gas−Liquid Interface. Ind. Eng. Chem. Fundam. 1966, 5 (1), 1−8. (11) Kohl, A. L.; Riesenfeld, F. C. Gas Purification; 5th ed.; Gulf Publishing Co., Book Division: Houston, TX, 1997. (12) Edwards, T. J.; Maurer, G.; Newman, J.; Prausnitz, J. M. Vapor− Liquid Equilibria in Multicomponent Aqueous Solutions of Volatile Weak Electrolytes. AIChE J. 1978, 24 (6), 966−976. (13) Chen, C. C.; Britt, H. I.; Boston, J. F.; Evans, L. B. Extension and Application of the Pitzer Equation for Vapor−Liquid Equilibrium of Aqueous Electrolyte Systems with Molecular Solutes. AIChE J. 1979, 25, 820−831. (14) Chen, C. C.; Britt, H. I.; Boston, J. F.; Evans, L. B. Local Composition Model for Excess Gibbs Energy of Electrolyte Systems. Part I: Single Solvent, Single Completely Dissociated Electrolyte Systems. AIChE J. 1982, 28, 588−596. (15) 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. (16) Mock, B.; Evans, L. B.; Chen, C. C. Thermodynamic Representation of Phase Equilibria of Mixed-Solvent Electrolyte Systems. AIChE J. 1986, 32 (10), 1655−1664. (17) Soave, G. Equilibrium Constants from a Modified Redlich− Kwong Equation of State. Chem. Eng. Sci. 1972, 27, 1197−1203.

5. CONCLUSIONS Mass transfer significantly influences the absorption process of acid gases in aqueous amines solutions, in particular when removal of carbon dioxide is performed. In this paper, a new model is proposed, where Eddy diffusivity theory instead of film theory is taken into account. The new model is introduced in ASPEN Plus, a commercial simulation software, that is used as a framework and integrated with an external subroutine. The model considers also different correlations for density and viscosity of the aqueous amine solution as well as modified parameters for thermodynamic vapor−liquid equilibrium calculation. The use of this tool on different case studies taken from operating plants shows an improvement in the representation of the absorption phenomenon, in comparison with experimental data.7,60

■

[m3/(m2 h)]

1 296.75 1 4

AUTHOR INFORMATION

Corresponding Author

*Tel: +39 02 2399 3237. Fax: +39 02 7063 8173. E-mail: [email protected]. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest. 2064



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Improved Rate-Based Modeling of H2S and CO2 Removal by

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