Hydrogen solubility in ionic liquids: Application of a structure-based deep learning approach and equations of state

Abstract

Following the increasing threat to the environment, the application of hydrogen as an energy carrier is one of the solutions that has received much attention. Considering the importance of gas solubility in separation and gasification processes, the solubility of hydrogen in various solvents has particular importance. In this study, an extensive database containing 580 experimental data points (13 ionic liquids (ILs)) in a vast range of temperature (278.2–453.15 K) and pressure (0.433–552 bar) was employed to determine the solubility of hydrogen in ILs using intelligent models. In this regard, four intelligent models, including Random Forest (RF), Adaptive Boosting-Support Vector for Regression (AdaBoost-SVR), Deep Belief Network (DBN), and Multivariate Adaptive Regression Splines (MARS) models, were developed with two different approaches. In the first method, the chemical substructures were considered as inputs; in the second manner, the thermodynamic attributes of ILs were considered as inputs. Temperature and pressure were also two inputs of both methods. The results show that in both methods, the DBN paradigm has the best proficiency. The best root mean square error (RMSE) and coefficient of determination (R2) values in the former way were 0.00106 and 0.9991, respectively, and in the latter way, 0.00066 and 0.9996, respectively. The findings of sensitivity analysis show that among the chemical substructures and thermodynamic properties, –CH3 and pressure, with absolute relevancy factor values of 0.425 and 0.517, respectively, have the most impression on the hydrogen solubility in ILs. Also, the investigation of the influence of different parameters on hydrogen solubility shows that raising the pressure and alkyl chain length increases the hydrogen solubility in ILs. The acquired findings were also compared with equations of state (EOSs), and it was found that the intelligent models have better performance and high efficiency than EOSs. Finally, to validate the model data, the leverage method was used, which shows that over 96.7% of the data is in the authentic domain and only about 1% of the data is in the suspected data region.