Multi-objective genetic algorithm optimization with an artificial neural network for CO₂/CH₄ adsorption prediction in metal–organic framework

The industry requirement of separating gas mixtures via adsorption techniques is rapidly being imposed, as the adsorption method is regarded as superior in terms of thermodynamic efficiency and cost. Gas mixture adsorption investigations with metal–organic frameworks (MOFs) have been conducted both in experimental and molecular simulations. Molecular simulation studies are faster in predicting CO₂ adsorption performance but are difficult to conduct because they require detailed information on the characteristics of the MOF. Therefore, in this study, the separation factor of CO₂/CH₄, the gas adsorption performance, and the heat of adsorption were predicted using artificial neural networks (ANNs). MOF texture properties that contribute to the performance are the input in this simulation. Operating working pressure and temperature are also inputs in this simulation. Optimization is conducted using the multiobjective genetic algorithm method to maximize the separation factor and CO₂ uptake with mild heat of adsorption. Moreover, the optimal values will be determined via the technique for order of preference by similarity to the ideal solution (TOPSIS). Interestingly, the amount of CO₂ adsorption, selectivity, and heat of adsorption are in satisfactory agreement with the values that are predicted by ANN with high validity regressing (R = 0.99). The output optimum point to get maximum capacity of CO₂ and selectivity with mild heat of adsorption are 9.97 mmol/g, 362.92 kJ/kg, and 11.01 respectively. These results provide a basis for the use of machine learning algorithms in conjunction with multiobjective optimizations to investigate the output performance of gas adsorption under the requirements of industrial applications.