Computer-Aided Design of New Physical Solvents for Hydrogen Sulfide Absorption

Abstract

Treatment of hydrogen sulfide (H 2 S) is important in many industrial processes including oil refineries, natural and biogas processing, coal gasification. The most mature technology for the selective H 2 S capturing is based on its absorption by chemical or physical solvents. However, only several compounds are currently used as physical solvents in industry, and the search for the new ones is an important task. The experimental screening of physical solvents requires a lot of time and resources, while solubility modeling might enable one to reduce the number of solvents for the experimental evaluation. In this study, a workflow for the in silico discovery of new physical solvents for H 2 S absorption was suggested and experimentally validated. A dataset composed of 99 H 2 S physical solvents was collected and predictive quantitative structure-property relationships for H 2 S solubility were built using random forest algorithm and two types of molecular descriptors: ISIDA fragments and quantum-chemical descriptors. Virtual screening of industrially produced chemicals and their structural analogs enabled to identify the ones with high predicted solubility values. They can be suggested as starting points for further exploration of H 2 S physical solvents chemical space. The predicted solubility value for one of the compounds found in virtual screening, 1,3-Dimethyl-2-imidazolidinone, was confirmed experimentally.

Selective H2S Absorption in Aqueous Tertiary Alkanolamine Solvents: Experimental Measurements and Quantitative Kinetic Model

“…Physical solvents can be used for bulk selective H 2 S removal. Many molecules can be used, ,, and common examples are the Rectisol, Purisol, and Selexol processes. The selectivity is thermodynamic …”

Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents

“…Driven by both the expanded chemical database and the advanced algorithms, machine learning (ML) has been finding powerful functions and wide applications in designing molecules and infrastructure for broad engineering areas, including chemistry, material, biology, medicine, , environment, and electronics. , ML algorithms have been used to aid solvent discovery by predicting the solubilities of various species, diffusion coefficients, and reaction paths . Quantitative structure–activity relationship (QSAR) models were explored using extensive training data sets and descriptors. , Using sufficient solubility data, Orlov et al and Shi et al have successfully used ML methods to achieve solubility prediction and solvent identification for the absorption of H 2 S and CO 2 , respectively. However, the valid solubility data are still lacking for most of the environmentally unfriendly sulfides including volatile thioether compounds.…”

“…24 Quantitative structure−activity relationship (QSAR) models were explored using extensive training data sets and descriptors. 25,26 Using sufficient solubility data, Orlov et al 27 and Shi et al 28 have successfully used ML methods to achieve solubility prediction and solvent identification for the absorption of H 2 S and CO 2 , respectively. However, the valid solubility data are still lacking for most of the environmentally unfriendly sulfides including volatile thioether compounds.…”

Chemoinformatics-Driven Design of New Physical Solvents for Selective CO2 Absorption

“…Although good predictive performance was achieved, the applicability domain (AD) of this model is limited to the specific classes of compounds used for model building. Except for recent publications on H 2 S solubility modeling, , to our knowledge, there were no papers describing the application of the chemoinformatics-driven methods for physical solubility modeling of the major components encountered in natural gas treatment or in the pre-combustion CO 2 capture process: carbon dioxide (CO 2 ), methane (CH 4 ), carbon monoxide (CO), hydrogen (H 2 ), and nitrogen (N 2 ). Hence, the investigation of the perceptiveness of using chemoinformatics for the rational design of new solvents for the absorption of CO 2 and other industrial gases is an important task.…”