Rigorous modeling of CO2 equilibrium absorption in ionic liquids

Abstract

Over the past few decades, solution of high amount of carbon dioxide in ionic liquids (ILs) has been the object of extensive studies. It is believed that ILs can be applied to capture CO2 and avoiding greenhouse gas emissions into the atmosphere. In this communication, the predictive capability of the Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), and Radial Basis Function Artificial Neural Network (RBF-ANN) has been evaluated for estimating carbon dioxide solubility in 67 different ILs as a function of the operational temperature (T), pressure (P) accompanied with the properties of ILs including the critical temperature (Tc), critical pressure (Pc) and, acentric factor (ω). In this regard, an extensive data bank containing 5368 data gathered from the literature was employed. Results obtained from the LSSVM approach indicate its satisfactory predictions than other strategies. Moreover, an outlier analysis was utilized to detect suspected data points. Obtained values of R-squared (R2), Mean Squared Error (MSE) were 0.9942 & 0.00035, 0.9135 & 0.004883, 0.9135 & 0.004883, and 0.9135 & 0.004883 for the LSSVM, ANFIS, MLP-ANN, and RBF-ANN respectively. Accordingly, the LSSVM strategy was introduced as a great tool for estimating CO2 solubility in such ionic liquids, which is easy to apply and can avoid time-consuming experimental measurement and expensive experimental apparatuses as well as complicated interpretation procedures. In addition, it can help chemists and chemical engineers to have a low parameter model with satisfactory results for the estimation of CO2 solubility in ILs.