Prediction of Thermophysical Properties for Binary Mixtures of Common Ionic Liquids with Water or Alcohol at Several Temperatures and Atmospheric Pressure by Means of Artificial Neural Network

Abstract

In this work, thermophysical properties such as density, dynamic viscosity, excess molar volume, refractive index and speed of sound of binary mixtures of common ionic liquids (ILs) with water or alcohol are predicted by the artificial neural network (ANN) technique. In each ANN proposed models, the density and dynamic viscosity of pure components IL, water or alcohol (including methanol, ethanol, 1-propanol and 2-propanol) and pure IL and the temperature as well as mole fractions of water or alcohol of studied binary mixtures were given as the inputs and the desired properties were predicted as the outputs. The obtained results revealed that the selected input parameters were appropriate and the high statistical quality represented by various criteria and the low prediction errors indicated that the presented models can accurately predict the properties of IL + water/alcohol binary mixtures