Machine Learning-Assisted Prediction of Water Vapor Permeability in Polymer Membranes for Humidity Control and Gas Dehydration

Efficient water vapor removal is important for both building humidity control and industrial gas dehydration, where operating conditions may span broader temperature and pressure ranges. Driven by a pressure gradient, membrane-based dehumidification has emerged as an energy-efficient alternative, employing polymeric composite membrane materials to achieve effective moisture separation. However, traditional development of such membranes remains heavily reliant on inefficient trial-and-error approaches. To overcome this limitation, this study employs machine learning to directly predict the relationships between physicochemical structure, operational conditions, and water vapor permeation performance of composite membrane materials. A dataset comprising 138 experimental samples from 26 published studies was compiled, featuring five input features: selective layer thickness, operating temperature, feed pressure, relative humidity, and a newly proposed hydrophilicity score based on functional group composition. Among six machine learning models evaluated, the Gradient Boosting Decision Tree (GBDT) achieved superior predictive performance, yielding a test R2 of 0.912. SHAP analysis identified selective layer thickness as the dominant descriptor, followed by feed pressure, hydrophilicity score, operating temperature, and relative humidity, contributing 34.4%, 26.5%, 15.8%, 11.9%, and 11.5% to the model predictions, respectively. Within the investigated parameter space, a genetic algorithm integrated with the GBDT model identified a permeability-oriented parameter combination (18.25 μm thickness, 111.43 °C, 0.94 bar, 52.02%RH, and a hydrophilicity score of 5), achieving a predicted permeability of 136,418 Barrer. The framework offers a transferable strategy for accelerating the rational design of advanced membrane materials, significantly reducing the need for exhaustive experimental screening.