Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks
Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution [(γ)_i^∞] for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.
[1] Ryo Kato, Jurgen Gmehling, "Systems with ionic liquids: Measurement
of VLE and (γ
) data and prediction of their thermodynamic behavior
using original UNIFAC, mod. UNIFAC(Do) and COSMO-RS(Ol)" , J.
Chem. Thermodynamics 37 (2005) 603–619.
[2] P. Wasserscheid, T. Welton (Eds.), Ionic Liquids in Synthesis, Wiley-
VCH, Weinheim, 2003.
[3] R.D. Rogers, K.R. Seddon (Eds.), Ionic Liquids – Industrial
Applications for Green Chemistry, ACS Symposium Series, vol. 818,
American Chemical Society, Washington, 2002.
[4] Mara G. Freire, Sonia P.M. Ventura, Luıs M.N.B.F. Santos, Isabel M.
Marrucho, Joao A.P. Coutinho, " Evaluation of COSMO-RS for the
prediction of LLE and VLE of water and ionic liquids binary systems" ,
Fluid Phase Equilibria 268 (2008) 74–84.
[5] J.G. Huddleston, H.D. Willauer, R.P. Swatloski, A.E. Visser, R.D.
Rogers, Chem. Commun. 44 (1998) 1765–1766.
[6] A.G. Fadeev, M.M. Meagher, Chem. Commun. 44 (2001) 295–296.
[7] J. McFarlane,W.B. Ridenour, H. Luo, R.D. Hunt, D.W. DePaoli,
D.W.R.X. Ren, Sep. Sci. Technol. 40 (2005) 1245–1265.
[8] R. Kato, J. Gmehling, Fluid Phase Equilib. 231 (2005) 38–43.
[9] U. Doman ska, E. Bogel-Łukasik, Ind. Eng. Chem. Res. 42 (2003)
6986–6992.
[10] U. Doman ska, E. Bogel-Łukasik, R. Bogel-Łukasik, Chem. Eur. J. 9
(2003) 3033–3041.
[11] M. Doker, J. Gmehling, Fluid Phase Equilib. 227 (2005) 255–266.
[12] R. Kato, J. Gmehling, J. Chem. Thermodyn. 37 (2005) 603–619.
[13] U. Domanska, Thermochim. Acta 448 (2006) 19–30.
[14] N. Calvar, B. Gonza lez, E. Gomez, A. Domı´nguez, J. Chem. Eng. Data
51 (2006) 2178–2181.
[15] T.M. Letcher, P. Reddy, Fluid Phase Equilib. 219 (2004) 107–112.
[16] T.M. Letcher, N. Deenadayalu, B. Soko, D. Ramjugernath, P.K.
Naicker, J. Chem. Eng. Data 48 (2003) 904–907.
[17] S.P. Verevkin, J. Safarov, E. Bich, E. Hassel, A. Heintz, Fluid Phase
Equilib. 236 (2005) 222–228.
[18] M. Bendov´ a, Z.Wagner, J. Chem. Eng. Data 51 (2006) 2126–2131.
[19] J.M. Crosthwaite, M.J. Muldoon, S.N.V.K. Aki, E.J. Maggin, J.F.
Brennecke, J. Phys. Chem. B 110 (2006) 9354–9361.
[20] A. Heintz, T.V. Vasiltsova, J. Safarov, E. Bich, S.V. Verevkin, J. Chem.
Eng. Data 51 (2006) 648–655.
[21] X. Hu, J. Yu, H. Liu, J. Chem. Eng. Data 51 (2006) 691–695.
[22] J. Safarov, S.P. Verevkin, E. Bich, A. Heintz, J. Chem. Eng. Data 51
(2006) 518–525.
[23] A. Shariati, C.J. Peters, J. Supercrit. Fluids 25 (2003) 109–111.
[24] L.P.N. Rebelo, J.N.C. Lopes, J.M.S.S. Esperanc¸ a, E. Filipe, J. Phys.
Chem. B 109 (2005) 6040–6043.
[25] J.O. Valderrama, P.A. Robles, Ind. Eng. Chem. Res. 46 (2007) 1338–
1344. [26] C. Knoop, D. Tiegs, J. Gmehling, J. Chem. Eng. Data 34 (1989) 240–
247.
[27] M. Krummen, D. Gruber, J. Gmehling, Ind. Eng. Chem. Res. 39 (2000)
2114–2123.
[28] El Ouahed AK, Tiab D, Mazouzi A (2005) Application of artificial
intelligence to characterize naturally fractured zones in Hassi Messaoud
Oil Field, Algeria. J Pet Sci Eng 49:122–141.
[1] Ryo Kato, Jurgen Gmehling, "Systems with ionic liquids: Measurement
of VLE and (γ
) data and prediction of their thermodynamic behavior
using original UNIFAC, mod. UNIFAC(Do) and COSMO-RS(Ol)" , J.
Chem. Thermodynamics 37 (2005) 603–619.
[2] P. Wasserscheid, T. Welton (Eds.), Ionic Liquids in Synthesis, Wiley-
VCH, Weinheim, 2003.
[3] R.D. Rogers, K.R. Seddon (Eds.), Ionic Liquids – Industrial
Applications for Green Chemistry, ACS Symposium Series, vol. 818,
American Chemical Society, Washington, 2002.
[4] Mara G. Freire, Sonia P.M. Ventura, Luıs M.N.B.F. Santos, Isabel M.
Marrucho, Joao A.P. Coutinho, " Evaluation of COSMO-RS for the
prediction of LLE and VLE of water and ionic liquids binary systems" ,
Fluid Phase Equilibria 268 (2008) 74–84.
[5] J.G. Huddleston, H.D. Willauer, R.P. Swatloski, A.E. Visser, R.D.
Rogers, Chem. Commun. 44 (1998) 1765–1766.
[6] A.G. Fadeev, M.M. Meagher, Chem. Commun. 44 (2001) 295–296.
[7] J. McFarlane,W.B. Ridenour, H. Luo, R.D. Hunt, D.W. DePaoli,
D.W.R.X. Ren, Sep. Sci. Technol. 40 (2005) 1245–1265.
[8] R. Kato, J. Gmehling, Fluid Phase Equilib. 231 (2005) 38–43.
[9] U. Doman ska, E. Bogel-Łukasik, Ind. Eng. Chem. Res. 42 (2003)
6986–6992.
[10] U. Doman ska, E. Bogel-Łukasik, R. Bogel-Łukasik, Chem. Eur. J. 9
(2003) 3033–3041.
[11] M. Doker, J. Gmehling, Fluid Phase Equilib. 227 (2005) 255–266.
[12] R. Kato, J. Gmehling, J. Chem. Thermodyn. 37 (2005) 603–619.
[13] U. Domanska, Thermochim. Acta 448 (2006) 19–30.
[14] N. Calvar, B. Gonza lez, E. Gomez, A. Domı´nguez, J. Chem. Eng. Data
51 (2006) 2178–2181.
[15] T.M. Letcher, P. Reddy, Fluid Phase Equilib. 219 (2004) 107–112.
[16] T.M. Letcher, N. Deenadayalu, B. Soko, D. Ramjugernath, P.K.
Naicker, J. Chem. Eng. Data 48 (2003) 904–907.
[17] S.P. Verevkin, J. Safarov, E. Bich, E. Hassel, A. Heintz, Fluid Phase
Equilib. 236 (2005) 222–228.
[18] M. Bendov´ a, Z.Wagner, J. Chem. Eng. Data 51 (2006) 2126–2131.
[19] J.M. Crosthwaite, M.J. Muldoon, S.N.V.K. Aki, E.J. Maggin, J.F.
Brennecke, J. Phys. Chem. B 110 (2006) 9354–9361.
[20] A. Heintz, T.V. Vasiltsova, J. Safarov, E. Bich, S.V. Verevkin, J. Chem.
Eng. Data 51 (2006) 648–655.
[21] X. Hu, J. Yu, H. Liu, J. Chem. Eng. Data 51 (2006) 691–695.
[22] J. Safarov, S.P. Verevkin, E. Bich, A. Heintz, J. Chem. Eng. Data 51
(2006) 518–525.
[23] A. Shariati, C.J. Peters, J. Supercrit. Fluids 25 (2003) 109–111.
[24] L.P.N. Rebelo, J.N.C. Lopes, J.M.S.S. Esperanc¸ a, E. Filipe, J. Phys.
Chem. B 109 (2005) 6040–6043.
[25] J.O. Valderrama, P.A. Robles, Ind. Eng. Chem. Res. 46 (2007) 1338–
1344. [26] C. Knoop, D. Tiegs, J. Gmehling, J. Chem. Eng. Data 34 (1989) 240–
247.
[27] M. Krummen, D. Gruber, J. Gmehling, Ind. Eng. Chem. Res. 39 (2000)
2114–2123.
[28] El Ouahed AK, Tiab D, Mazouzi A (2005) Application of artificial
intelligence to characterize naturally fractured zones in Hassi Messaoud
Oil Field, Algeria. J Pet Sci Eng 49:122–141.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:72612", author = "S. Mousavian and A. Abedianpour and A. Khanmohammadi and S. Hematian and Gh. Eidi Veisi", title = "Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks", abstract = "Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution [(γ)_i^∞] for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.
", keywords = "Ionic liquid, Neural networks, VLE, Dilute solution.", volume = "10", number = "4", pages = "405-5", }
