Using ANFIS and MLP Neural Networks in Predicting the Extraction of Aromatic Compounds from Aliphatic Compounds by Ionic Liquids

Document Type : Research Article

Authors

Department of Chemical Engineering, Faculty of Engineering, University of Guilan, Rasht, I.R. IRAN

Abstract

One of the main processes in the refining industries of the oil industry is the extraction of aromatic hydrocarbons from aliphatic hydrocarbons. Accordingly, accurate prediction of the phase behavior of these systems can improve liquid-liquid extraction.  In this study, the phase thermodynamic behavior of the ternary system of aliphatic and aromatic hydrocarbons with ionic liquids is predicted by the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Multilayer Perceptron (MLP)neural network. The model inputs were considered in modeling the liquid-liquid extraction system, the molar ratio of aliphatic, aromatic, and ionic compounds in the feed, as well as the molecular mass of the ions and the temperature of the extraction system, and the model output was the molar ratio. Aliphatic and aromatic compounds in the alkane-rich phase and molar ratio of aromatic compounds and ionic liquids in the iron-rich phase were considered. The design parameters of these neural networks, including the number of neurons and the clustering radius of the MLP and ANFIS networks, were optimized by the genetic algorithm evolution method (GA) in order to improve their prediction accuracy. Comparison of prediction accuracy of ANFIS and MLP networks with experimental data based on statistical parameters R2, RMSD, and MAD for ANFIS model was calculated 0.9999, 0.0190, and 0.0129 respectively and for MLP neural network model was 0.996, 0.0204, and 0.0127 respectively. Also, a comparison was made between the prediction accuracy of ANFIS, MLP networks and the NRTL thermodynamic model for two different liquid-liquid extraction systems, their RMSD for the two extraction systems were 0.0093, 0.0110, and 0.0113, respectively. The results of statistical parameters show that these networks have relatively good accuracy in predicting the thermodynamic behavior of liquid-liquid equilibrium and are an effective method.

Keywords

Main Subjects


[1] Ghanadzadeh H., Ghanadzadeh A., Liquid-Liquid Equilibria in Water + Ethanol + 2-Ethyl-1-hexanol) at T = (298.2, 303.2, 308.2, and 313.2) K, J. Chem. Thermodynamics, 35: 1393–1401 (2003).
[2] Ghanadzadeh H., Haghi A. K., Liquid–Liquid Equilibrium Data for Water+Ethanol+trans-Decalin: Measurement and Predication, J. Fluid Phase Equilib., 243: 45–50 (2006).
[3] Villaluenga J.G., Tabe-Mohammadi A., A Review on the Separation of Benzene/Cyclohexane Mixtures by Pervaporation Processes, J. Memb. Sci., 169: 159–174 (2000).
[4] Al-Jimaz A.S., Fandary M.S., Alkhaldi K.H.E., Al-Kandary J.A., Fahim M.A., Extraction of Aromatics from Middle Distillate Using N-Methyl-2-Pyrrolidone: Experiment, Modeling, and Optimization, J. Ind. & Eng. Chem. Res. 46: 5686–5696 (2007).
[6] Wlazło M., Marciniak A., Ternary Liquid-Liquid Equilibria of Trifluorotris (Perfluoroethyl) Phosphate Based Ionic Liquids +Methanol + Heptane, J. Fluid Phase Equilib., 338: 253–256 (2013).
[7] Marsh K.N., Boxall J.A., Lichtenthaler R., Room Temperature Ionic Liquids and Their Mixtures– A Review, J. Fluid Phase Equilib., 219: 93–98 (2004).
[8] Kamankesh A., Vossoughi M., Shamloo A., Mirkhani S., Akbari J., Liquid–Liquid Equilibrium (LLE) Data for Ternary Mixtures of {Aliphatic+ P-Xylene+ [EMpy][ESO 4]} at T= 313.15 K, J. Fluid Phase Equilib., 332: 48–54 (2012).
[9] Larriba M., Navarro P., García J., Rodríguez F., Liquid–Liquid Extraction of Toluene from Heptane Using [Emim][DCA], [Bmim][DCA], and [Emim][TCM] Ionic Liquids, J.  Ind. & Eng. Chem. Res., 52: 2714–2720 (2013).
[10] Al-Jimaz A.S., Alkhaldi K.H., Al-Rashed M.H., Fandary M.S., AlTuwaim M.S., Study on the Separation of Propylbenzene from Alkanes Using Two Methylsulfate-Based Ionic Liquids at (313 and 333) K, J.  Fluid Phase Equilib. 354: 29–37 (2013).
[11] Larriba M., Navarro P., García J., Rodríguez F., Selective Extraction of Toluene from N-Heptane Using [Emim][SCN] and [Bmim][SCN] Ionic Liquids as Solvents, J. Chem. Thermodynamics, 79: 266–271 (2014).
[12] García S., Larriba M., García J., Torrecilla J.S., Rodríguez F., Alkylsulfate-Based Ionic Liquids in the Liquid-Liquid Extraction of Aromatic Hydrocarbons,  J. Chem. Thermodynamics, 45: 68–74 (2012).
[13] Heidari M.R., Mokhtarani B., Seghatoleslami N., Sharifi A., Mirzaei M., Liquid-Liquid Extraction of Aromatics from Their Mixtures with Alkanes Using 1-Methyl 3-Octylimidazolium Thiocyanate Ionic Liquid, J. Chem. Thermodynamics, 54: 310–315 (2012).
[16] González E.J., Calvar N., Gómez E., Domínguez Á., Application of [EMim][ESO 4] Ionic Liquid as Solvent in the Extraction of Toluene from Cycloalkanes: Study of Liquid–Liquid Equilibria at T= 298.15 K, J. Fluid Phase Equilib., 303: 174–179 (2011).
[17] Calvar N., Domínguez I., Gómez E., Domínguez Á., Separation of Binary Mixtures Aromatic+ Aliphatic Using Ionic Liquids: Influence of the Structure of the Ionic Liquid, Aromatic and Aliphatic, J. Chem. Eng., 175: 213–221 (2011)
[18] Fandary M.S., Alkhaldi K.H., Al-Jimaz A.S., Al-Rashed M.H., Al-Tuwaim M.S., Evaluation of [Bmim][PF 6] as an Ionic Solvent for the Extraction of Propylbenzene from Aliphatic Compounds, J. Chem. Thermodynamics, 54: 322–329 (2012).
[21] Ganguly S., Prediction of VLE Data Using Radial Basis Function Network, J. Comput. Chem. Eng., 27: 1445–1454 (2003).
[22] Mjalli F.S., Neural Network Model-Based Predictive Control of Liquid-Liquid Extraction Contactors, J. Chem. Eng. Sci., 60: 239–253 (2005).
[23] Torrecilla J.S., Deetlefs M., Seddon K.R., Rodríguez F., Estimation of Ternary Liquid-Liquid Equilibria for Arene/Alkane/Ionic Liquid Mixtures Using Neural Networks, J. Chem. Phys., 10: 5114–5120 (2008).
[26] Powell M.J.D., Mason J.C., Cox M.G., “Radial Basis Functions for Multivariable Interpolation: A Review in Algorithms for Approximation”, Clarendon Press, Oxford, UK, (1987).
[27] Park J., Sandberg I., Universal Approximation Using Radial Basis Function Networks, J. Neural Comput., 3 (2): 246–257 (1991).
[28] Ivakhnenko A.G., Polynomial Theory of Complex Systems, IEEE Trans. Syst. Man. Cybern., ggg1: 364–378 (1971).
[29] Farlow S.J., “Self-Organizing Method in Modelling: GMDH-Type Algorithm”, Marcel Dekker, New York, (1984).
[30] Reyhani S.Z., Ghanadzadeh H., Puigjaner L., Recances F., Estimation of Liquid–Liquid Equilibrium for a Quaternary System Using the GMDH Algorithm, J. Ind. Eng. Chem. Res., 48: 2129–2134 (2009).
[31] Ketabchi S., Ghanadzadeh H., Ghanadzadeh A., Fallahi S., Ganji M., Estimation of VLE of Binary Systems (Tert-Butanol + 2-Ethyl-1 Hexanol) and (Nbutanol + 2-Ethyl-1-Hexanol) Using GMDH-Type Neural Network, J. Chem. Thermodynamics, 42 (11): 1352–1355 (2010).
[32] Ghanadzadeh H., Ganji M., Fallahi S., Mathematical Model of Liquid–Liquid Equilibrium for a Ternary System Using the GMDH-Type Neural Network and Genetic Algorithm, J. Appl. Math. Model, 36: 4096–4105 (2012).
[34] Hakim M., Behmardikalantari G., Abedini Najafabadi H., Pazuki G., Vosoughi A., Vossoughi M., Prediction of Liquid–Liquid Equilibrium Behavior for Aliphatic + Aromatic + Ionic Liquid Using Two Different Neural Network-Based Models, J. Fluid Phase Equilib., 394: 140–147 (2015).
[36] García S., Larriba M., García J., Torrecilla J.S., Rodríguez F., 1-Alkyl-2,3-Dimethylimidazolium Bis(Trifluoromethylsulfonyl)Imide Ionic Liquids for the Liquid-Liquid Extraction of Toluene from Heptane, J. Chem. Eng. Data, 56: 3468–3474 (2011).
[37] Melin P., Castillo O., Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, J. Fuzz. Inf. and Eng. 4: 345–355(2005).
[38] Mamdani E.H., Assilian S., An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, J. Man-Machine Studies, 7 (1): 1–13 (1975).
[39] Sugeno M., “Industrial Applications of Fuzzy Control”, Elsevier, Amsterdam, (1985).
[40] Jang J., ANFIS: Adaptive Network-Based Fuzzy Inference Systems, IEEE Trans. Syst. Man. Cybern., 23: 665–668 (1993).
[41] Brown M., Harris C., “Neuro-Fuzzy Adaptive Modeling and Control”, Prentice-Hall, New York, (1994).
[42] Haykin S., “Neural Networks: A Comprehensive Foundation”, Prentice-Hall, New York, (2007).
[43] Rojas R., “Neural Networks: A Systematic Introduction”, Springer, Berlin, (1996).
[44] Preechakul C., Kheawhom S., Modified Genetic Algorithm With Sampling Techniques for Chemical Engineering Optimization, J. Ind. Eng. Chem. 15: 110-119 (2009).