Prediction of CO2 loading capacities of aqueous solutions of absorbents using different computational schemes

In this communication, carbon dioxide solubility in aqueous solutions of various absorbents (2-amino-2-hydroxymethyl-1,3-propanediol; 2-amino-2-methyl-1-propanol; 2-amino-2-methyl-1,3-propanediol; potassium chloride; monoethanolamine; methyldiethanolamine; 2-piperidineethanol; potassium glycinate; piperazine; sodium glycinate; triethanolamine; potassium carbonate; diethanolamine) is predicted through four different methods namely Multi-layer Perceptron Artificial Neural Network (MLP-ANN), Radial Basis Function Artificial Neural Network (RBF-ANN), Least Square Support Vector Machine (LSSVM), and Adaptive Network-based Fuzzy Inference System (ANFIS). These tools predict CO2 loading capacity as a function of temperature, pressure, mass composition of solution, and average molecular weight of solution. In addition, an extrapolative ability of the proposed LSSVM model has been evaluated for different tertiary aqueous mixtures. Moreover, different statistical analyses have been conducted on the estimated and actual reported data. Among these approaches, results indicate that LSSVM model is more accurate than the other proposed ANFIS, MLP-ANN, and RBF-ANN models. Obtained values of mean squared error (MSE) and R-squared (R2) for the LSSVM, ANFIS, MLP-ANN, and RBF-ANN models are 0.001 and 0.991, 0.011 and 0.888, 0.004 and 0.964, 0.009 and 0.916 respectively. This LSSVM model can help us to have accurate prediction of carbon dioxide solubility in aqueous solutions and it can be utilized instead of thermodynamic approaches which have difficult concepts and calculations.