Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium L-asparaginate, potassium L-glutaminate, sodium L-phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization-adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models' results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO2 loading capacities of AAs aqueous solutions.