naa a22 4500 510819559 CHVBK 20180411083524.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11814-013-0109-5 doi (NATIONALLICENCE)springer-10.1007/s11814-013-0109-5 Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate [Elektronische Daten] [Alireza Fazlali, Parvaneh Koranian, Reza Beigzadeh, Masoud Rahimi] A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1, 0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-random-two-liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid. Korean Institute of Chemical Engineers, Seoul, Korea, 2013 Vapor-liquid Equilibrium nationallicence Ionic Liquid nationallicence Ternary System nationallicence Artificial Neural Network nationallicence Fazlali Alireza Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran aut Koranian Parvaneh Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran aut Beigzadeh Reza Young Researchers Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran aut Rahimi Masoud CFD Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran aut Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/9(2013-09-01), 1681-1686 0256-1115 30:9<1681 2013 30 11814 https://doi.org/10.1007/s11814-013-0109-5 text/html Onlinezugriff via DOI 1 brief-communication jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11814-013-0109-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fazlali Alireza Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran aut NATIONALLICENCE 700 1- Koranian Parvaneh Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran aut NATIONALLICENCE 700 1- Beigzadeh Reza Young Researchers Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran aut NATIONALLICENCE 700 1- Rahimi Masoud CFD Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran aut NATIONALLICENCE 773 0- Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/9(2013-09-01), 1681-1686 0256-1115 30:9<1681 2013 30 11814 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer