Machine learning-assisted solvent molecule design for efficient absorption of ethanethiol
To solve the problems of low organic sulfur removal efficiency,long solvent development cycle and high cost in the traditional amine elution desulfurization process,the quantitative structure-activity relationship(QSPR)model for ethanethiol solubility was established by using seven machine learning algorithms.Besides,the absorption mechanism of ethanethiol was elucidated by using the SHapley Additive exPlanations(SHAP)method and the virtual screening for candidate molecules was conducted to identify efficient solvents for the absorption removal of ethanethiol.Molar solubilities of ethanethiol in 14732 solvents,which cover a wide range of chemical space,were calculated by using the conductor-like screening model for real solvents(COSMO-RS).XGBoost was identified as the optimal algorithm for predicting the molar solubility of ethanethiol,having R2test of 0.66,RMSE of 1.22,and MAE of 0.84.The complexity of molecular structure,covalent bonding,and electron distribution in molecules were identified as the key factors for the molar solubility of ethanethiol.Four solvents,including 3-ethoxypropylamine,3-diethylaminopropylamine,1,4-dimethylpiperazine,and 3-butoxypropylamine were identified as potential solvents.The results of the equilibrium solubility determination experiments show that 3-butoxypropylamine has the best ethanethiol dissolution with Henry's law constant of 37.34 kPa.
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