Artificial Neural Network and Response Surface Methodology Modelling of Surface Tension of 1-Butyl-3-methylimidazolium Bromide Solution

Abstract

The thermo-physical properties of the ionic liquids are required for engineering and product design applications in many pharmaceutical and food industries. Among the many unique properties of ionic liquids, their surface tension plays an important role for various reasons. In the present study, the aim is to investigate the effect of temperature and concentration on the surface tension of the binary solution of 1-butyl-3-methylimidazolium bromide + water. The concentration of the ionic liquid is varied from 0.1–0.6%w/w and temperature ranges from 302.85–337.45 K. A quadratic mathematical model has been formulated for predicting the surface tension using response surface methodology with central composite rotatable design having a coefficient of determination R 2 as 0.9807. In addition, a two-layered feed forward back propagation neural network 2-4-1 is also modelled which provides a better performance when compared to response surface model. The developed ANN model can predict the surface tension with mean square error, root mean square error and percentage absolute average error equal to 0.156, 0.395 and 0.623, respectively.