Polymeric Hollow Fiber Membranes for Biogas Enrichment: Influence of Materials Selections Fabrication Parameters and the Potential of Artificial Intelligence

Abstract

Membrane based gas separation is a next-generation technology for CO₂ capture and biogas upgrading, offering lower energy consumption and reduced chemical use compared to conventional methods. Hollow fiber membranes have emerged as a leading solution due to their energy efficiency, compact design, and high separation performance. However, the trade-off between permeability and selectivity remains a challenge, driving innovations in advanced materials such as polymeric, blended, and mixed-matrix membranes, which enhance efficiency and stability for superior gas separation. To further optimize membrane performance and scale-up production, mathematical modeling plays a crucial role in predicting separation efficiency and guiding material selection. However, traditional models often struggle to accurately capture dynamic behaviors and variations in feed conditions. Recent advancements in artificial intelligence (AI) and machine learning have transformed membrane design by enabling rapid material screening, performance prediction, and process optimization, significantly reducing experimental efforts. The integration of advanced materials, AI-driven modeling, and digital optimization will drive the next generation of high-performance membranes, offering sustainable solutions to global environmental challenges.