Prediction of Partial Miscibility of Binary Mixtures Consisting of Ionic Liquids

Abstract

Accurate knowledge and prediction of the liquid–liquid transition phase consisting of ionic liquids and organic solvents are key points in different industrial processes. In this way, the effects of 8 ionic liquids and 38 organic solvents through 58 binary mixtures on the miscibility gap and upper critical solution (or consolute) temperature are examined. Various approaches including 1) feedforward and cascade forward, which are trained with a backpropagation algorithm (that is, feedforward backpropagation network and cascade-forward backpropagation network) and 2) radial basis neural networks, an exact radial basis network, and generalized regression neural networks based on kernel regression, are used for the predictions and evaluations. Four independent variables (that is, molecular weight and density of ionic liquid and the organic solvent) are selected to differentiate among ionic liquids and organic solvents. In addition, the mole fraction of ionic liquid is the other input variable for the networks to predict the liquid–liquid phase transition. The obtained results reveal that the kernel regression-based networks are able to perform better as compared to two other considered networks trained with a backpropagation algorithm. The overall results indicated that the performance of the radial basis network leads to the lowest error, corresponding to the highest capability in predicting the partial miscibility condition of studied mixtures.