Mechanism-driven reinforcement learning for ultrafiltration scheduling: From event triggering to proactive cycle orchestration

Abstract

Ultrafiltration systems are significantly affected by dynamic evolution of membrane fouling during long-term operation, exhibiting highly nonlinear operational behaviors and intricate control coupling. To overcome the constraints encountered by conventional control strategies in industrial applications, including response latency, empirical reliance, and the difficulty of day-scale predictive optimization, this study proposes a mechanism-driven reinforcement learning predictive control framework for industrial supervisory control and data acquisition systems, enabling proactive operational optimization at the chemically enhanced backwashing cycle scale. To achieve this objective, a dual-time-scale dynamical model is established based on membrane resistance decomposition, simultaneously characterizing short-term reversible resistance growth and long-term irreversible resistance accumulation to provide a unified physical representation. This mechanistic model is subsequently integrated into the reinforcement learning environment to construct a physically consistent state-transition structure. Furthermore, the proximal policy optimization algorithm is employed to jointly optimize energy consumption, membrane fouling levels, and operational safety constraints under a unified reward framework. This framework directly generates filtration and backwash control strategies at the cycle level, enabling end-to-end predictive control from state perception to action generation. Validation using real-world industrial data demonstrates that the proposed method effectively adjusts CEB cycle parameters during the transition toward stable operation and achieves coordinated multi-objective optimization during stable operation. It effectively reduces transmembrane pressure and specific energy consumption during stable operation, while supporting progressive stabilization under unstable conditions. The results indicate that the proposed framework provides a novel predictive intelligent control paradigm for industrial ultrafiltration systems that can be directly integrated into existing industrial platforms.