Rigorous correlations for predicting the solubility of H₂S in methylimidazolium‐based ionic liquids

Abstract

Two general and simple models, a group contribution correlation (model I) and an empirical relation (model II), were proposed to predict the solubility of H2S in methylimidazolium based ionic liquids (ILs) over wide range of temperatures (303.15‐363.15 K) and pressures (60.8‐2016.8 kPa). The constants of the suggested functionality relations were found via the Nelder‐Mead simplex algorithm. Both correlations were trained with 407 data points of H2S solubility in 9 methylimidazolium based ILs and tested through 121 H2S solubility data points of 3 different methylimidazolium based ILs to ensure generality. A comprehensive statistical evaluation showed that both suggested correlations are vigorous and have satisfactory error trends. The dataset was subjected to a statistical outlier diagnostic test and the validity of the database was confirmed. In addition, the sensitivity analysis revealed that the experimental data and both models have the same responses toward pressure and temperature, which indicates the reliability of the proposed correlations.

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

“…Thus, the trained ANN model can then be used to correctly estimate the desired outputs from the given inputs. Due to a large number of data points associated with solubility of H 2 S in absorbents, ANNs have been applied for predicting the H 2 S solubility at various operating conditions. − However, most of the literature focused on single absorbent types either organic physical solvents, amines, or ionic liquids. The present work therefore aims to construct an ANN model from the experimental data available in the literature for predicting the solubility of H 2 S for both single and blended absorbents among the abovementioned types.…”

Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors

“…Mahmoudabadi and Pazuki 33 developed a predictive PC-SAFT EOS incorporating COSMO computations, comparing it favorably with other models. Mesbah et al 31 proposed group contribution correlation and empirical models for H 2 S solubility in methylimidazolium-based ILs, demonstrating their robustness and accuracy. Song et al 34 successfully predicted CO 2 solubility in various ILs using machine learning algorithms, with the artificial neural network-group contribution (ANN-GC) model outperforming others.…”

“…Over the past decade, numerous methods have been reported for correlating solid compound solubility in different solvents, primarily categorized into thermodynamics-based models, soft computing methods, and quantitative structure−property relationship (QSPR) techniques, 30 each with its own merits and limitations. 31 al., 32 who applied four soft computing algorithms and five equations of state (EOS) models to estimate SO 2 solubility in ILs, finding the deep belief network (DBN) model most accurate. Mahmoudabadi and Pazuki 33 developed a predictive PC-SAFT EOS incorporating COSMO computations, comparing it favorably with other models.…”

Estimation of CO2 Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

“…The literature has already addressed the utilization of empirical correlations [11], equations-of-state (EOSs) [12], and molecular descriptors [13] for the investigation and modeling of gas solubility. These methods typically have limitations, being applicable only to specific systems and compositions within defined temperature and pressure ranges.…”