Estimation of solubility of acid gases in ionic liquids using different machine learning methods

Abstract

Ionic liquids (ILs) can capture acid gases that damaged the environment. Due to the properties of low-cost and non-toxic, machine learning can be used to screen ILs for gas absorption. To find the most suitable machine learning method for estimating gas absorption in ILs, 12 different machine learning algorithms are used to train models to estimate CO2 and H2S solubility in different ILs. Temperature (T), pressure (P), molecular weight (Mw), critical temperature (Tc), and critical pressure (Pc) of solutions are used as the input variables; Solubility is used as the output variable in the model training. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R2) are used to evaluate the models. Stacking algorithm has the most accurate model in IL- CO2 system, with MSE, RMSE, MAE, and R2 of 0.001, 0.025, 0.018, and 0.969 respectively on average. Voting algorithm performs best in IL-H2S system; the four averaged metrics are 0.002, 0.032, 0.024, and 0.934 accordingly. By combining different algorithms, Voting and Stacking algorithms can balance out each model's weakness and produce a more accurate model. Stacking and Voting algorithms can be considered as a promising candidate for the estimation of acid gases solubility in ionic liquids.