Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning

Data-driven machine learning (ML) provides a promising
approach
to understanding and predicting the rejection of trace organic contaminants
(TrOCs) by polyamide (PA). However, various confounding variables,
coupled with data scarcity, restrict the direct application of data-driven
ML. In this study, we developed a data-knowledge codriven ML model
via domain-knowledge embedding and explored its application in comprehending
TrOC rejection by PA membranes. Domain-knowledge embedding enhanced
both the predictive performance and the interpretability of the ML
model. The contribution of key mechanisms, including size exclusion,
charge effect, hydrophobic interaction, etc., that dominate the rejections
of the three TrOC categories (neutral hydrophilic, neutral hydrophobic,
and charged TrOCs) was quantified. Log D and
molecular charge emerge as key factors contributing to the discernible
variations in the rejection among the three TrOC categories. Furthermore,
we quantitatively compared the TrOC rejection mechanisms between nanofiltration
(NF) and reverse osmosis (RO) PA membranes. The charge effect and
hydrophobic interactions possessed higher weights for NF to reject
TrOCs, while the size exclusion in RO played a more important role.
This study demonstrated the effectiveness of the data-knowledge codriven
ML method in understanding TrOC rejection by PA membranes, providing
a methodology to formulate a strategy for targeted TrOC removal.