Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning

Ultrafiltration (UF) as one of the mainstream membrane-based technologies has been widely used in water and wastewater treatment. Increasing demand for clean and safe water requires the rational design of UF membranes with antifouling potential, while maintaining high water permeability and removal efficiency. This work employed a machine learning (ML) method to establish and understand the correlation of five membrane performance indices as well as three major performance-determining membrane properties with membrane fabrication conditions. The loading of additives, specifically nanomaterials (A_wt %), at loading amounts of > 1.0 wt % was found to be the most significant feature affecting all of the membrane performance indices. The polymer content (P_wt %), molecular weight of the pore maker (M_Da), and pore maker content (M_wt %) also made considerable contributions to predicting membrane performance. Notably, M_Da was more important than M_wt % for predicting membrane performance. The feature analysis of ML models in terms of membrane properties (i.e., mean pore size, overall porosity, and contact angle) provided an unequivocal explanation of the effects of fabrication conditions on membrane performance. Our approach can provide practical aid in guiding the design of fitfor-purpose separation membranes through data-driven virtual experiments.

Automated summary

Ultrafiltration (UF) is a widely used membrane-based technology for water and wastewater treatment. This study employed machine learning to establish the correlation between membrane performance indices, membrane properties, and fabrication conditions. The loading of additives and the polymer content were found to be the most significant features affecting membrane performance. Our approach provides practical guidance for the design of separation membranes through data-driven virtual experiments.