Prediction of thermo-physical properties of 1-Butyl-3-methylimidazolium hexafluorophosphate for CO2 capture using machine learning models
Document Type
Article
Publication Date
4-1-2021
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 andmolar 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. (C) 2020 Elsevier B.V. All rights reserved.
Keywords
CO2 capture, Bmim]PF6], Physical properties, Machine learning, Gaussian process regression, Support vector machine
Funders
Office of Research, Innovation and Commercialization (ORIC), Dawood University of Engineering and Technology[03/DRFP/ORIC/DUET/2018]
Publication Title
Journal of Molecular Liquids
Volume
327
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS