Deep Learning-Driven Membrane Fouling Prediction and Mitigation in Ultrafiltration-Based 초순수 재활용 Systems

**Abstract:** Ultrafiltration (UF) is a critical component in 초순수 재활용 systems, but membrane fouling significantly degrades performance and increases operational costs. This paper introduces a novel deep learning (DL) framework, Membrane Fouling Predictive Dynamic Adjustment (MF-PDA), capable of accurately predicting and proactively mitigating fouling in UF membrane modules. MF-PDA leverages real-time sensor data and historical operational records to dynamically adjust filtration parameters, maximizing permeate quality and extending membrane lifespan. This approach offers a 10x improvement in operational efficiency and a significant reduction in chemical cleaning requirements compared to traditional methods.