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 fit-for-purpose
separation membranes through data-driven virtual experiments.

超滤（Ultrafiltration，UF）作为主流膜基技术之一，已广泛应用于水处理与废水处理领域。当前对清洁安全用水的需求日益增长，亟需合理设计兼具抗污染性能、同时维持高水渗透性能与去除效率的超滤膜。本研究采用机器学习（Machine Learning，ML）方法，构建并解析了五项膜性能指标、三项决定膜性能的关键膜属性与膜制备条件之间的关联。研究发现，添加剂（尤其是纳米材料）的投加量（A_wt %）在投加量高于1.0 wt%时，是影响所有膜性能指标的最显著特征因素。聚合物含量（P_wt %）、致孔剂分子量（M_Da）以及致孔剂含量（M_wt %）同样对膜性能预测具有显著贡献。值得注意的是，在膜性能预测中，M_Da的重要性高于M_wt %。针对膜属性（即平均孔径、总孔隙率与接触角）开展的机器学习模型特征分析，清晰阐释了制备条件对膜性能的影响机制。本研究方法可通过数据驱动的虚拟实验，为定制化分离膜的设计提供切实可行的指导。