Predicting fouling resistance in spiral-wound ultrafiltration membranes: a machine learning approach for high-recovery wastewater treatment

Ultrafiltration (UF) plays a crucial role in industrial and municipal wastewater treatment, yet membrane fouling continues to limit system efficiency, increase operational costs, and shorten membrane lifespan. This study explores fouling dynamics in spiral-wound UF membranes using a 100-h pilot-scale dataset collected under high-recovery conditions from a secondary municipal wastewater facility. Key operational and feedwater quality parameters were used to model fouling resistance computed via Darcy’s law as a mechanistic performance indicator. To capture the complexity of fouling behavior, multiple machine learning (ML) models were applied, including multiple linear regression (MLR), ensemble methods (ENS), Gaussian process regression (GPR), and neural networks (NN). Among these, GPR and ENS demonstrated strong predictive capabilities and robustness. Importantly, the study integrates these ML models into a Supervisory Control and Data Acquisition (SCADA)-compatible framework, enabling real-time fouling prediction and the implementation of proactive maintenance strategies. By supporting dynamic cleaning schedules and early intervention, this approach offers a practical tool for optimizing UF performance and advancing sustainable membrane operations in real-world applications.