Modeling ofCO2absorption capabilities of amino acid solutions using a computational scheme

In this communication, modeling of carbon dioxide absorption by various amino acid solutions is presented as a function of operational parameters using the Least-Squares Support Vector Machine (LSSVM) algorithm integrated with three different evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid GA and PSO (HGAPSO). A databank containing 255 data of carbon dioxide absorption by amino acids of potassium taurate, potassium glycinate, potassium prolinate, and potassium lysinate at different temperatures, partial pressures, and concentrations was prepared from different sources to train and test the proposed algorithms. The models were applied to estimate carbon dioxide absorption by amino acid solutions in terms of temperature, molar concentration, equilibrium partial pressure, molecular weight, number of hydrogen bond donor, number of hydrogen bond acceptor, and number of rotatable bond. TheR(2)values of LSSVM optimized by HGAPSO, PSO, and GA are 0.9944, 0.9915, and 0.9891, respectively, and the various errors were determined close to zero. On the other hand, the visual comparison of models outputs and actual carbon dioxide adsorption data was used to clarify performances of the models. By comparison analysis, it was found that the LSSVM-HGAPSO is the most accurate model for estimation of carbon dioxide loading. Also, comparison of our proposed models results with previously reported artificial neural network results indicates the impressive estimation capability of LSSVM algorithm. According to sensitivity analysis, it becomes obvious that pressure is the most effective parameter on carbon dioxide absorption.