Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions

CO2 equilibrium solubility is an important parameter used to evaluate the performance of absorption solvents in CO2 capture processes. Back-propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) were proposed to predict the CO2 solubility in 12 known amine solutions. Both of the models were firstly conducted in monoethanolamine, diethanolmine and methyldiethanolamine solutions to evaluate their effectiveness, and were then applied in nine other amine solutions to further verify their adaptability. The results showed that both BPNN and RBFNN models provided excellent agreements with the experimental values for all the amine solutions with average absolute relative errors and root mean square errors less than 10%. A comparison between the predicted results and those of the eight published models showed that the proposed ANN models performed better than the literature models. Furthermore, scalability analysis was carried out to evaluate the adaptability of BPNN and RBFNN models in terms of the wide input parameter ranges.