Emerging Trends in Machine Learning and Optimization for Membrane Gas Separation

This review provides a comprehensive analysis of the significance of machine learning (ML) and artificial intelligence (AI) in advancing membrane-based gas separation technologies. Particular emphasis is placed on recent applications involving polymeric and mixed matrix membranes for the separation of key gas pairs such as CO2/N2 and CO2/CH4. The review highlights how ML models including Random Forest, Gaussian Process Regression and Artificial Neural Networks (ANN) consistently achieve higher predictive accuracy in estimating permeability and selectivity. ANN achieved R2 up to 0.9996, while deep neural network ensembles reached ~0.92 on test data; similarly, optimized tree-based models delivered R2 values approaching 0.99. These models rely on influential input features such as temperature, pressure, filler loading, membrane thickness and polymer structure, which strongly govern separation efficiency. By capturing nonlinear relationships and integrating optimization techniques like genetic algorithms, particle swarm optimization and Bayesian methods, ML enables both accurate performance prediction and accelerated membrane design. Beyond prediction, ML has facilitated innovative material discovery and process optimization, offering clear pathways toward the development of energy-efficient, cost-effective and sustainable gas separation solutions.