Prediction of CO2 loading capacity of chemical absorbents using a multi-layer perceptron neural network

A feed forward multi-layer perceptron neural network was developed to predict carbon dioxide loading capacity of chemical absorbents over wide ranges of temperature, pressure, and concentration based on the molecular weight of solution. To verify the suggested artificial neural network (ANN), regression analysis was conducted on the estimated and experimental values of CO2 solubility in various aqueous solutions. Furthermore, a comparison was performed between results of the proposed neural network and experimental data that were not previously used for network training, as well as a set of data for binary solutions. Comparison between the proposed multi-layer perceptron (MLP) network and other alternative models illustrated some notable points: (1) Better performance of the proposed model, (2) extrapolation capabilities of the network, (3) unlimited ranges of network performance regardless of parameters such as temperature, pressure, and concentration, and (4) ability of using MLP network as a correlation for prediction of carbon dioxide loading for different aqueous solutions