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

Abstract

Predicting hydrogen sulfide (H₂S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization. In this work, the qualitative analysis of the influence of cations and anions on the H₂S solubility in ILs has been conducted. The results indicate that anions play an important role in determining the H₂S solubility in ILs. Subsequently, two novel quantitative structure–property relationship (QSPR) models are developed based on charge distribution area (S_σ-profile) descriptors and an extreme learning machine (ELM) algorithm. To develop the QSPR models, a total of 1282 pieces of data belonging to 27 ILs are employed to validate the models. The average absolute relative deviation (AARD%) and coefficient of determination (R²) of the two QSPR models of the entire data set are 3.73% and 0.998, as well as 3.80% and 0.997, respectively. These results suggest that the proposed QSPR models can be useful for the prediction of H₂S solubility in ILs.