Abstract
Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S 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 H2S solubility in ILs and possesses R2, 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 H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.
Highlights
Absorption has always been an attractive process for removing hydrogen sulfide (H2S)
Untapped potentials and favorable characteristics of ionic liquids have been enticing for scientists to investigate their H 2S removal capacity
Since there are many affecting parameters and the interactions between ionic liquids (ILs) and H 2S molecules are complex, accurate results cannot be achieved by the equations of state
Summary
Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Numerous investigations have been performed to evaluate gas solubility in different ILs. Shariati and P eters[28] implemented the Peng–Robinson (PR) equation of state to obtain the solubility of C HF3 in [ C2mim][PF6] under various pressures and temperatures. A range of more general approaches must be applied to forecast gas solubility in ILs. Recently, many intelligent methods, such as artificial neural networks (ANNs)[33] have been applied for predicting various properties in chemical engineering, including c rstallinity[34,35], thermal conductivity36,37, viscosity[38], heat capacity[39], and solubility of different gases in solutions[40,41]. A feedforward MLPNN has three layers of input, interiors, and
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