Prediction of solubility of sulfur dioxide in ionic liquids using artificial neural network

In this work, artificial neural network (ANN) has been used to estimate the solubility of sulfur dioxide in different ionic liquids. The studied ionic liquids are consisting of 1-butyl-3-methylimidazolium acetate [BMIM][Ac], 1-n-butyl-3-methylimidazolium methylsulfate [BMIM][MeSO4], 1-Butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]), 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]), 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [BMIM][Tf2N], 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]), 1-Hexyl-3-methylimidazolium tetrafluoroborate ([HMIM][BF4]) and 1-Hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [HMIM][Tf2N]. The model has five inputs including temperature, pressure, critical temperature, critical pressure and acentric factor. The network has been trained with about 70% of data sets and tested with other 30% of data points. The most accurate network has one hidden layer with 27 neurons. The results of this study show that the ANN model is successful in estimation of the solubility of sulfur dioxide in ionic liquids. (C) 2015 Elsevier B.V. All rights reserved.