Prediction of thermo-physical properties of 1-Butyl-3-methylimidazolium hexafluorophosphate for CO2 capture using machine learning models

Abstract

Physical and thermodynamic properties of physical or chemical solvents are of utmost importance for mass and heat transfer calculations, process design and solvent regeneration. In recent times, machine learning has attracted interest for applications in several fields of engineering sciences. The ionic liquid 1-Butyl-3-methylimidazolium hexafluorophosphate [Bmim][PF6] is an emerging solvent for CO2 capture. In this study, three Gaussian process regression (GPR) models - the Matern 5/2 GPR model, rational quadratic GPR model, squared exponential GPR model - and one support vector machine (SVM) model (the nonlinear SVM)– are developed for predicting CO2 solubility, density, viscosity and molar heat capacity of [Bmim][PF6]. Detailed statistics of each model and comparative analyses between the models and their predicted results with experimental results is highlighted.