Modeling and estimation of water activity for the ionic-liquid-based aqueous ternary systems by smart paradigms

Abstract

In this study, different artificial intelligence (AI) techniques are employed to determine the water activity of ionic liquid-based ternary systems as a function of pressure, temperature, and the type and concentration of the components. The proposed model has been selected among five artificial neural networks (ANNs), bagged trees (BT), decision trees (DT), gaussian process regression (GPR), adaptive neuro-fuzzy inference systems (ANFIS), and least square support vector machines (LS-SVM) by trial-and-error process and ranking analysis of statistical and computational indicators. The most accurate model is the cascade feed-forward neural network with fifteen hidden neurons which are equipped with logarithm and hyperbolic tangent sigmoid transfer functions. This model estimates 1829 empirical water activity for ionic liquid-based ternary systems with AARD=0.25 %, MSE=1.73 × 10−5, RMSE=4.16 × 10−3, and R2=0.9919.