Process parameter optimization for ionic liquids as gas separation membranes based on a GWO–BPNN model

Abstract

Gas separation membranes offer a cost‐effective solution for capturing greenhouse gases, mitigating the global greenhouse effect. Ionic liquids (ILs) have emerged as one of the promising materials for greenhouse gas separation due to their strong affinity for CO2. In this study, we propose a laboratory‐scale method for preparing IL–PVDF blend membranes with high CO2/N2 selectivity. The separation performance of the membranes was evaluated using a custom gas permeation measurement system. The effects of casting solution composition, solidification method, and film‐forming processes on separation performance were experimental investigated, and the obtained experimental data were used to train a back propagation neural network (BPNN) optimized by the Gray Wolf Optimizer (GWO) algorithm. This hybrid GWO–BPNN model was utilized to predict separation membrane efficiency, optimize the film‐forming process, and identify the optimal range of process parameters. Notably, the GWO–BPNN model demonstrated a 2.76% higher prediction accuracy compared to a standalone BPNN. The results indicated that the GWO–BPNN algorithm has a great potential to accurately predict membrane separation efficiency and apply in optimal membrane process design (OMPD), and this method can significantly reduce the number of experimental trials required to achieve OMPD.