A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Absorption has always been an attractive process for removing hydrogen sulfide (H(2)S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H(2)S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H(2)S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H(2)S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H(2)S solubility in ILs and possesses R(2), RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H(2)S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM(+) and Tf(2)N(-), i.e., [OMIM][Tf(2)N] ionic liquid, is the best choice for H(2)S capture among the investigated absorbents. The H(2)S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.