Machine Learning-Assisted Prediction of Water Vapor Permeability in Polymer Membranes for Humidity Control and Gas Dehydration
Year: 2026
Type: article
Abstract: 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.
Source: Buildings
Authors: Ziyao Li, Yilin Liu, Ruiting Wu, Yanhui Zou, Liwen Jin
Institution: Xi'an Jiaotong University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7170682867