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Received June 13, 2017
Accepted November 22, 2017
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Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids
Reza Soleimani
Amir Hossein Saeedi Dehaghani1†
Navid Alavi Shoushtari2
Pedram Yaghoubi3
Alireza Bahadori4
Young Researchers and Elite Club, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran 1Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran 2Calgary, 210 15 Ave SE, Calgary, Alberta, Canada T2G 0B5 3Department of Physics, University of Kashan, Kashan, Iran 4School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales 2480, Australia
asaeedi@modares.ac.ir
Korean Journal of Chemical Engineering, July 2018, 35(7), 1556-1569(14), 10.1007/s11814-017-0326-4
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Abstract
Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.
References
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Dong Q, Muzny CD, Kazakov A, Diky V, Magee JW, Widegren JA, Chirico RD, Marsh KN, Frenkel M, J. Chem. Eng. Data, 52(4), 1151 (2007)
Eslamimanesh A, Gharagheizi F, Mohammadi AH, Richon D, Chem. Eng. Sci., 66(13), 3039 (2011)
Eslamloueyan R, Khademi M, Int. J. Therm. Sci., 48, 1094 (2009)
Eslamloueyan R, Khademi MH, J. Chem. Eng. Data, 54(3), 922 (2009)
Fatehi MR, Raeissi S, Mowla D, J. Supercrit. Fluids, 95, 60 (2014)
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Gacino FM, Regueira T, Lugo L, Comunas MJP, Fernandez J, J. Chem. Eng. Data, 56(12), 4984 (2011)
Garcia-Miaja G, Troncoso J, Romani L, J. Chem. Thermodyn., 41(2), 161 (2009)
Geppert-Rybczynska M, Lehmann JK, Safarov J, Heintz A, J. Chem. Thermodyn., 62, 104 (2013)
Gharagheizi F, Eslamimanesh A, Mohammadi AH, Richon D, Chem. Eng. Sci., 66(13), 2959 (2011)
Gharagheizi F, Eslamimanesh A, Sattari M, Mohammadi AH, Richon D, AIChE J., 59(2), 613 (2013)
Gharagheizi F, Eslamimanesh A, Tirandazi B, Mohammadi AH, Richon D, Chem. Eng. Sci., 66(21), 4991 (2011)
Gharagheizi F, Ilani-Kashkouli P, Mohammadi AH, Chem. Eng. Sci., 78, 204 (2012)
Golbraikh A, Tropsha A, J. Mol. Graphics Modelling, 20, 269 (2002)
Golzar K, Amjad-Iranagh S, Modarress H, Ind. Eng. Chem. Res., 53(17), 7247 (2014)
Harris KR, Kanakubo M, Woolf LA, J. Chem. Eng. Data, 51(3), 1161 (2006)
Hashemkhani M, Soleimani R, Fazeli H, Lee M, Bahadori A, Tavalaeian M, J. Mol. Liq., 211, 534 (2015)
Haykin S, Neural networks: A comprehensive foundation: Macmillan college publishing company, New York (1994).
He XZ, Zhang XP, Zhang SJ, Liu JD, Li CS, Fluid Phase Equilib., 238(1), 52 (2005)
Hekayati J, Raeissi S, J. Mol. Liq., 231, 451 (2017)
Hezave AZ, Lashkarbolooki M, Raeissi S, Fluid Phase Equilib., 352, 34 (2013)
Hezave AZ, Lashkarbolooki M, Raeissi S, Fluid Phase Equilib., 314, 128 (2012)
Hezave AZ, Raeissi S, Lashkarbolooki M, Ind. Eng. Chem. Res., 51(29), 9886 (2012)
Jiang HC, Zhao Y, Wang JY, Zhao FY, Liu RJ, Hu YQ, J. Chem. Thermodyn., 64, 1 (2013)
Kauffman GW, Jurs PC, J. Chem. Information Computer Sci., 41, 408 (2001)
Kazakov A, Magee J, Chirico R, Diky V, Muzny C, Kroenlein K, Frenkel M, version 2.0, national institute of standards and technology, gaithersburg md, 20899.
Kermanpour F, Niakan HZ, J. Chem. Thermodyn., 48, 129 (2012)
Lashkarblooki M, Hezave AZ, Al-Ajmi AM, Ayatollahi S, Fluid Phase Equilib., 326, 15 (2012)
Lashkarbolooki M, Sep. Sci. Technol., 52(8), 1454 (2017)
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Lashkarbolooki M, Hezave AZ, babapoor A, Korean J. Chem. Eng., 30(1), 213 (2013)
Lashkarbolooki M, Shafipour ZS, Hezave AZ, J. Supercrit. Fluids, 73, 108 (2013)
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Lashkarbolooki M, Vaferi B, Shariati A, Hezave AZ, Fluid Phase Equilib., 343, 24 (2013)
Laugier S, Richon D, Fluid Phase Equilib., 210(2), 247 (2003)
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Machida H, Taguchi R, Sato Y, Smith J, Richard L, J. Chem. Eng. Data, 56, 923 (2010)
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Mehra P, Wah BW, Artificial neural networks: Concepts and theory, IEEE Computer Society Press Los Alamitos (1992).
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Mirarab M, Sharifi M, Ghayyem MA, Mirarab F, Fluid Phase Equilib., 371, 6 (2014)
Mohanty S, Int. J. Refrigeration, 29, 243 (2006)
Mohebbi A, Taheri M, Soltani A, Int. J. Refrigeration, 31, 1317 (2008)
Nami F, Deyhimi F, J. Chem. Thermodyn., 43(1), 22 (2011)
Okuyucu H, Kurt A, Arcaklioglu E, Mater. Des., 28, 78 (2007)
Oliveira MB, Dominguez-Perez M, Freire MG, Llovell F, Cabeza O, Lopes-da-Silva JA, Vega LF, Coutinho JAP, J. Phys. Chem. B, 116(40), 12133 (2012)
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Roy PP, Paul S, Mitra I, Roy K, Molecules, 14, 1660 (2009)
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Rilo E, Pico J, Garcia-Garabal S, Varela LM, Cabeza O, Fluid Phase Equilib., 285(1-2), 83 (2009)
Rohani AA, Pazuki G, Najafabadi HA, Seyfi S, Vossoughi M, Expert Systems with Applications, 38, 1738 (2011)
Ross T, J. Appl. Bacteriol., 81, 501 (1996)
Sadrzadeh M, Mohammadi T, Ivakpour J, Kasiri N, Chem. Eng. Process., 48(8), 1371 (2009)
Sadrzadeh M, Mohammadi T, Ivakpour J, Kasiri N, Chem. Eng. J., 144(3), 431 (2008)
Safamirzaei M, Modarress H, Fluid Phase Equilib., 332, 165 (2012)
Safamirzaei M, Modarress H, Thermochim. Acta, 545, 125 (2012)
Sedghamiz MA, Rasoolzadeh A, Rahimpour MR, J. CO2 Utilization, 9, 39 (2015)
Seki S, Tsuzuki S, Hayamizu K, Umebayashi Y, Serizawa N, Takei K, Miyashiro H, J. Chem. Eng. Data, 57(8), 2211 (2012)
Shafiei A, Ahmadi MA, Zaheri SH, Baghban A, Amirfakhrian A, Soleimani R, J. Supercrit. Fluids, 95, 525 (2014)
Soleimani R, Dehaghani AHS, Bahadori A, J. Mol. Liq., 242, 701 (2017)
Sterjovski Z, Nolan D, Carpenter K, Dunne D, Norrish J, J. Mater. Process. Technol., 170, 536 (2005)
Tariq M, Freire MG, Saramago B, Coutinho JA, Lopes JNC, Rebelo LPN, Chem. Soc. Rev., 41, 829 (2012)
Taskinen J, Yliruusi J, Adv. Drug Deliv. Rev., 55, 1163 (2003)
Torrecilla JS, Palomar J, Garcia J, Rojo E, Rodriguez F, Chemometrics Intelligent Laboratory Systems, 93, 149 (2008)
Torrecilla JS, Rodriguez F, Bravo JL, Rothenberg G, Seddon KR, Lopez-Martin I, Phys. Chem. Chem. Phys., 10, 5826 (2008)
Troncoso J, Cerdeirina CA, Sanmamed YA, Romani L, Rebelo LPN, J. Chem. Eng. Data, 51(5), 1856 (2006)
Urata S, Takada A, Murata J, Hiaki T, Sekiya A, Fluid Phase Equilib., 199(1-2), 63 (2002)
Vakili-Nezhaad G, Vatani M, Asghari M, Ashour I, J. Chem. Thermodyn., 54, 148 (2012)
Vega LF, Vilaseca O, Llovell F, Andreu JS, Fluid Phase Equilib., 294(1-2), 15 (2010)
Wandschneider A, Lehmann JK, Heintz A, J. Chem. Eng. Data, 53(2), 596 (2008)
Wang JY, Jiang HC, Liu YM, Hu YQ, J. Chem. Thermodyn., 43(5), 800 (2011)
Wang JY, Zhao FY, Liu YM, Wang XL, Hu YQ, Fluid Phase Equilib., 305(2), 114 (2011)
Wei Y, Zhang QG, Liu Y, Li XR, Lian SY, Kang ZH, J. Chem. Eng. Data, 55(7), 2616 (2010)
Witten IH, Frank E, Hall MA, Pal CJ, Data mining: Practical machine learning tools and techniques, Morgan Kaufmann (2016).
Zupan J, Gasteiger J, Neural networks for chemists: An introduction, John Wiley & Sons, Inc. (1993).
