Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches.

ABSTRACT: Ionic liquids (ILs) have emerged as suitable options for gas storage applications over the past decade. Consequently, accurate prediction of gas solubility in ILs is crucial for their application in the industry. In this study, four intelligent techniques including Extreme Learning Machine (ELM), Deep Belief Network (DBN), Multivariate Adaptive Regression Splines (MARS), and Boosting-Support Vector Regression (Boost-SVR) have been proposed to estimate the solubility of some gaseous hydrocarbons in ILs based on two distinct methods. In the first method, the thermodynamic properties of hydrocarbons and ILs were used as input parameters, while in the second method, the chemical structure of ILs and hydrocarbons along with temperature and pressure were used. The results show that in the first

SUBMITTER: Nakhaei-Kohani R 

PROVIDER: S-EPMC9395420 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature