Dynamic analysis for PFAS removal from contaminated water by foam separation based on machine learning models

Per- and polyfluoroalkyl substances (PFAS) have emerged as global environmental contaminants owing to their persistence, bioaccumulation potential, and toxicity. Among various remediation approaches, foam separation (FS) has attracted increasing attention for its simplicity, cost-effectiveness, and low energy demand. However, the PFAS removal efficiency of FS is highly sensitive to pollutant properties, operational parameters, and environmental conditions, which vary substantially across studies and pose challenges for process optimization. To address this issue, six machine learning (ML) models were evaluated, among which extreme gradient boosting (XGB) exhibited the highest predictive performance (R²). Based on this, an improved model, PFAS-XGB, was developed to streamline input variables and enhance prediction efficiency. Model interpretation identified aeration time and PFAS molecular weight as the most critical determinants of removal efficiency. Causal contribution analysis indicated that operational conditions accounted for 56.8 % of the influence, followed by PFAS properties (21.9 %), surfactant characteristics (16.8 %), and metal activators (4.5 %). These results underscore the predominant role of operational parameters while revealing the previously underestimated impact of surfactants and metal activators. Overall, this study establishes a data-driven framework for optimizing FS performance and offers actionable insights for enhancing PFAS remediation strategies.