Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate

• Published in: The Korean journal of chemical engineering Vol. 30; no. 9; pp. 1681 - 1686
• Main Authors: Fazlali, Alireza, Koranian, Parvaneh, Beigzadeh, Reza, Rahimi, Masoud
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.09.2013; 한국화학공학회
• Subjects: Biotechnology; Catalysis; Chemistry; Chemistry and Materials Science; Industrial Chemistry/Chemical Engineering; Materials Science; Rapid Communication; 화학공학; Ionic Liquid; Ternary System; Artificial Neural Network; Vapor-liquid Equilibrium
• ISSN: 0256-1115; 1975-7220
• DOI: 10.1007/s11814-013-0109-5

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.