Artificial neural network modeling for prediction of binary surface tension containing ionic liquid

Abstract

ABSTRACTIn this study, a feed-forward multilayer perceptron neural network is applied to predict the surface tension of 32 binary ionic liquids (ILs)/non-ILs systems using melting point (Tm), molecular weight (Mw) and mole fraction of ILs as well as Tm and Mw of non-IL components. The data are divided into two different subsets, namely training and testing subsets, to obtain the optimum parameters of the used network and to evaluate the correlative capability of the trained network. The results of the test stage show excellent capability of the proposed network to predict/correlate the binary surface tension of ILs/non-ILs systems (AARD%: 0.93, MSE: 6.67 × 10−7 and R2: 0.9950).