Precise prediction of CO2 separation performance of metal-organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal-organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.