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Received February 18, 2019
Accepted June 27, 2019
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Modeling of the solubility of H2S in [bmim][PF6] by molecular dynamics simulation, GA-ANFIS and empirical approaches
1Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran 2Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran 3Department of Chemical Engineering, University of Kashan, Kashan, Iran 4Institut de Recherche en Genie Chimique et Petrolier (IRGCP), Paris Cedex, France 5Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa 6Department of Molecular and Supramolecular Modelling, Chemistry and Chemical Engineering Research Center of Iran,, Tehran, Iran
amirdashti13681990@gmail.com
Korean Journal of Chemical Engineering, October 2019, 36(10), 1637-1647(11), 10.1007/s11814-019-0330-y
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Abstract
Predicting the solubility of acid gases in ionic liquids (ILs), has lately appeared as advantageous for natural gas purifying, which is equipped by powerful models considering technical and economic aspects. Important issue in the assessment of ILs for potential utilization in gas sweetening process is estimating the H2S solubility at various temperatures and pressures Experimental measurements are costly and take considerable time and effort. As a result, proposing methods for predicting the behavior of this system over a wide range of conditions is vital. In this regard, molecular dynamics simulation (MD) technique as well as artificial intelligence knowledge of hybrid genetic algorithmadaptive neuro fuzzy inference system (GA-ANFIS) and an empirical polynomial regression (PR) model were employed to estimate the solubility of H2S in [bmim][PF6] IL. Pressure and temperature are considered as the independent input variables and H2S solubility as the dependent output variable. The results of this study reveal that the simple fourthorder PR model and GA-ANFIS have the highest accuracy. As a result of the simplicity and accuracy of PR model, it can be used without any prior knowledge about MD and artificial intelligence (AI). According to the accuracy and precision of model proved by the obtained result, the solubility of H2S in ILs has been estimated. The results show that the PR method is more trustworthy than other models.
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Anthony JL, Maginn EJ, Brennecke JF, ACS Symposium Series (2002).
Bates ED, Mayton RD, Ntai I, Davis JH, J. Am. Chem. Soc., 124(6), 926 (2002)
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Brennecke JF, Maginn EJ, AIChE J., 47(11), 2384 (2001)
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Shariati A, Peters CJ, J. Supercrit. Fluids, 34(2), 171 (2005)
Shariati A, Gutkowski K, Peters CJ, AIChE J., 51(5), 1532 (2005)
Zhao H, Xia SQ, Ma PS, J. Chem. Technol. Biotechnol., 80(10), 1089 (2005)
Jalili AH, Rahmati-Rostami M, Ghotbi C, Hosseini-Jenab M, Ahmadi AN, Chem. Eng. Data, 54, 1844 (2009)
Shokouhi M, Adibi M, Jalili AH, osseini-Jenab M, Mehdizadeh A, J. Chem. Eng. Data, 55, 1663 (2009)
Jalili AH, Mehdizadeh A, Shokouhi M, Ahmadi AN, Hosseini-Jenab M, Fateminassab F, J. Chem. Thermodyn., 42(10), 1298 (2010)
Shariati A, Ashrafmansouri SS, Osbuei MH, Hooshdaran B, Korean J. Chem. Eng., 30(1), 187 (2013)
Ji XY, Adidharma H, Fluid Phase Equilib., 293(2), 141 (2010)
Eslamimanesh A, Gharagheizi F, Mohammadi AH, Richon D, Chem. Eng. Sci., 66(13), 3039 (2011)
Torrecilla JS, Palomar J, Garcia J, Rojo E, Rodriguez F, Chemometrics Intell. Lab. Syst., 93, 149 (2008)
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Kamps APS, Tuma D, Xia JZ, Maurer G, J. Chem. Eng. Data, 48(3), 746 (2003)
Shukla SK, Khokarale SG, Bui TQ, Mikkola JP, Front. Mater., 6 (2019)
Ghazani SHHN, Baghban A, Mohammadi AH, Habibzadeh S, J. Supercrit. Fluids, 133, 455 (2018)
Mesbah M, Shahsavari S, Soroush E, Rahaei N, Rezakazemi M, J CO2 Util., 25, 99 (2018)
Peng DY, Robinson DB, Ind. Eng. Chem. Res., 15, 59 (1976)
Soave G, Chem. Eng. Sci., 27, 1197 (1972)
Vega LF, Vilaseca O, Llovell F, Andreu JS, Fluid Phase Equilib., 294(1-2), 15 (2010)
Renon H, Prausnitz JM, AIChE J., 14, 135 (1968)
Wilson GM, J. Am. Chem. Soc., 86, 127 (1964)
Abrams DS, Prausnitz JM, AIChE J., 21, 116 (1975)
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Gross J, Sadowski G, Ind. Eng. Chem. Res., 40(4), 1244 (2001)
Karakatsani EK, Economou IG, J. Phys. Chem. B, 110(18), 9252 (2006)
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Shah JK, Brennecke JF, Maginn EJ, Green Chem., 4, 112 (2002)
Morrow TI, Maginn EJ, J. Phys. Chem. B, 106(49), 12807 (2002)
Shah JK, Maginn EJ, Fluid Phase Equilib., 222, 195 (2004)
Widom B, J. Chem. Phys., 39, 2808 (1963)
Pomelli CS, Chiappe C, Vidis A, Laurenczy G, Dyson PJ, J. Phys. Chem. B, 111(45), 13014 (2007)
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Jou FY, Mather AE, Int. J. Thermophys., 28, 490 (2007)
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Cadena C, Anthony JL, Shah JK, Morrow TI, Brennecke JF, Maginn EJ, J. Am. Chem. Soc., 126(16), 5300 (2004)
Jacquemin J, Husson P, Majer V, Gomes MFC, Fluid Phase Equilib., 240(1), 87 (2006)
Ahmadi MA, Haghbakhsh R, Soleimani R, Bajestani MB, J. Supercrit. Fluids, 92, 60 (2014)
Shafiei A, Ahmadi MA, Zaheri SH, Baghban A, Amirfakhrian A, Soleimani R, J. Supercrit. Fluids, 95, 525 (2014)
Ahmadi MA, Pouladi B, Javvi Y, Alfkhani S, Soleimani R, J. Supercrit. Fluids, 97, 81 (2015)
Baghban A, Ahmadi MA, Shahraki BH, J. Supercrit. Fluids, 98, 50 (2015)
Xia L, Wang J, Liu S, Li Z, Pan H, Processes, 7, 258 (2019)
Muldoon MJ, Aki SNVK, Anderson JL, Dixon JK, Brennecke JF, J. Phys. Chem. B, 111(30), 9001 (2007)
Liang J, Zhang R, Zhao Q, Dong J, Wang B, Li J, Comput. Theor. Chem., 980, 1 (2012)
Sarmiento-Perez RA, Rodriguez-Albelo LM, Gomez A, Autie-Perez M, Lewis DW, Ruiz-Salvador AR, Microporous Mesoporous Mater., 163, 186 (2012)
Velioglu S, Ahunbay MG, Tantekin-Ersolmaz SB, J. Membr. Sci., 417, 217 (2012)
Liu C, Dang Y, Zhou Y, Liu J, Sun Y, Su W, Zhou L, Adsorpt., 18, 321 (2012)
Li M, Huang X, Kang Z, J. Appl. Phys., 118, 084303 (2015)
Lei Z, Jiang J, Zhu GL, Cui P, Ling Q, Zhao ZG, Energy Fuels, 30(2), 1287 (2016)
Becke AD, J. Chem. Phys., 98, 5648 (1993)
Lee C, Yang W, Parr RG, Phys. Rev. B, 37, 785 (1988)
Derecskei B, Derecskei-Kovacs A, Mol. Simulat., 34, 1167 (2008)
Jang JSR, IEEE. T. Syst. Man. CY., 23, 665 (1993)
Mohammadi T, Esmaeelifar A, Desalination, 166(1-3), 329 (2004)
Chen X, Wang N, Chem. Eng. J., 150(2-3), 527 (2009)
Davis L, Handbook of genetic algorithms, Van Nostrand Reinhold, New York (1991).
Singh KP, Gupta S, Kumar A, Shukla SP, Sci. Total Environ., 426, 244 (2012)
Cecen A, Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data, Georgia Institute of Technology (2017).
Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary AN, Kalidindi SR, Integr. Mater. Manuf. Innov., 3, 8 (2014)
Shokrollahi A, Tatar A, Safari H, J. Taiwan Inst. Chem. E, 55, 17 (2015)
Darvish H, Nouri-Taleghani M, Shokrollahi A, Tatar A, J. Afr. Earth. Sci., 111, 409 (2015)
Jang JSR, Sun CT, Mizutani E, IEEE Trans Automat Contr., 42, 1482 (1997)
Rezakazemi M, Ghafarinazari A, Shirazian S, Khoshsima A, Polym. Eng. Sci., 53(6), 1272 (2013)
Yingjie L, Baoshu W, J. Sys. Eng. Electron., 16, 583 (2005)
Dashti A, Raji M, Azarafza A, Baghban A, Mohammadi AH, Asghari M, J. Env. Manage., 224, 58 (2015)
Dashti A, Raji M, Razmi A, Rezaei N, Zendehboudi S, Asghari M, Chem. Eng. Res. Des., 144, 405 (2019)
Raji M, Dashti A, Amani P, Mohammadi AH, J. Mol. Liq., 283, 804 (2019)
Dashti A, Harami HR, Rezakazemi M, Shirazian S, J. Mol. Liq., 271, 661 (2018)
Rezakazemi M, Azarafza A, Dashti A, Shirazian S, Int. J. Hydrog. Energy, 43(36), 17283 (2018)
