Quantitative Relationship between CO2 Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

CO 2 emissions reduction has become a hot issue to relieve global warming in recent years. CO 2 absorption with amines aqueous solutions is a promising method for its capture, and increasing CO 2 capacity and reducing energy consumption are essential for its application. In this work, a data set of 29 different amines was regressed with genetic function approximation (GFA) and artificial neural network (ANN) algorithm to find a predictive quantitative relationship between amine structure and CO 2 absorption capacity. Density functional theory (DFT) methods at levels B3LYP and 6-311+g (d,p) were used to optimize the structures. Both reactants and products were introduced in regression, and an optimal model with best relevance and predictability for CO 2 absorption at 313 K was obtained and experimentally confirmed. Descriptors analysis showed that reducing the number of hydroxyl groups, increasing molecular mass, and changing steric effect of amine structure would increase its CO 2 absorption capacity. Two new amine structures predicted with high absorption capacity were proposed.