Combining neural networks and phenomenological equations to model carbon dioxide adsorption on Zeolitic imidazolate framework (ZIF-8)

Abstract

This study proposes the combined use of a phenomenological model and neural networks trained by experimental data to predict Carbon dioxide adsorption on Zeolitic imidazolate framework – 8 (ZIF-8) considering different temperature and surface area conditions. The Sips model presented the best fit among the models studied and the perceptron networks presented the best performance to represent the phenomenon. The use of genetic algorithms assisted in the optimization of the neural network architecture, in a shorter computational time. The model indicates that at high pressures the area presents a strong positive correlation for carbon capture, however at lower pressures (of the order of 1 bar) the influence was negative, but with less significance. The study contributes to the development of more efficient carbon capture processes, helping to direct future research in the area of greenhouse gas mitigation, particularly in the field of developing products for carbon capture, among other applications involving carbon dioxide adsorption.