Estimation of CO2 equilibrium absorption in aqueous solutions of commonly used amines using different computational schemes

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• Computational models are presented to prognosticate CO 2 equilibrium absorption capacity of various amine solutions. • GA-ANFIS, PSO-ANFIS, CSA-LSSVM and RBF neural networks are employed for modeling purpose. • Among many different proposed models, LSSVM exhibits results that are more promising. • The proposed LSSVM model has better accuracy compared to the other models. In absorptive removal of CO 2 by aqueous alkanolamine solvent, as the most prevalent CO 2 capture technique, equilibrium absorption capacity of CO 2 is a significant parameter for assessing the efficiency of absorption systems. In this study, unique computational models are presented to estimate CO 2 solubility in commonly used amines. A series of models, including genetic algorithm-adaptive neuro fuzzy inference system (GA-ANFIS), particle swarm optimization ANFIS (PSO-ANFIS), coupled simulated annealing-least squares support vector machine (CSA-LSSVM) and radial basis function (RBF) neural networks were developed to estimate CO 2 equilibrium absorption capacity in twelve aqueous amine solutions. The model inputs comprise of CO 2 partial pressure, temperature, amine concentration in aqueous solution, molecular weight, hydrogen bond donor/acceptor count, rotatable bond count and complexity of the amines. The obtained results affirm that among proposed models, LSSVM exhibits more promising results with an excellent compatibility with experimental values. In detail, both mean square errors and average regression coefficient (R 2 ) of LSSVM model are 0.02 and 0.9338, respectively. Moreover, it is confirmed that the proposed LSSVM model has better accuracy compared to the other models.