Predicting H₂S solubility in ionic liquids by the quantitative structure–property relationship method using S_σ-profile molecular descriptors

Abstract

Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization.

A review on machine learning algorithms for the ionic liquid chemical space

“…76 Using COSMO-RS s-proles as data for ML methods, seems promising and has been implemented in various classical property regression models with very promising results, [77][78][79] but so far with only few implementations to ML algorithms. [80][81][82][83][84][85] classication and prediction. The predictive ability of these algorithms is being investigated in depth in ILs research, with the main aim being the accurate prediction of physical and chemical properties.…”

The solubility of gases in ionic liquids

“…Katritzky et al developed two‐ to four‐parameter models for the partition coefficients of organic solutes in eight different ILs . Recently, QSPR models have been used to examine CO 2 and H 2 S solubility in ILs.…”

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

“…Due to above discussions, development of an accurate and reliable approach for estimation of solubility of hydrocarbons and non-hydrocarbons in aqueous electrolyte solutions has been highlighted. Nowadays, machine learning approaches have shown extensive applications in different topics [27][28][29][30][31][32][33][34][35]. This work organizes a novel artificial intelligence method called extreme learning machine (ELM) to estimate solubility of hydrocarbons in aqueous electrolyte mixtures in terms of types of gas, mole fractions of gases, pressure, temperature, and ionic strength.…”