AI-Optimized Membrane Bioreactor with Real-Time Fouling Prediction for Advanced Municipal Water Reclamation

Abstract

Advanced municipal water reclamation systems increasingly rely on membrane bioreactor (MBR) technology due to its superior treatment efficiency, compact design, and ability to produce high-quality reclaimed water. However, membrane fouling remains a major operational challenge that affects system performance, energy consumption, and maintenance requirements. This study presents an artificial intelligence (AI)-optimized membrane bioreactor integrated with real-time fouling prediction for advanced municipal water reclamation applications. The proposed framework combines machine learning algorithms, intelligent process control, and continuous sensor-based monitoring to enhance treatment efficiency and predict membrane fouling behavior under dynamic operational conditions. Key treatment parameters, including transmembrane pressure, permeate flux, dissolved oxygen concentration, mixed liquor suspended solids, pH, temperature, and organic loading rate, were continuously monitored and analyzed using AI-driven predictive models. The developed system employed advanced data analytics to identify nonlinear relationships between operational variables and fouling progression, enabling adaptive optimization of aeration, filtration cycles, and cleaning strategies. Experimental and simulation analyses demonstrated significant improvements in fouling prediction accuracy, membrane lifespan, pollutant removal efficiency, and energy optimization compared to conventional MBR operational methods. The AI-assisted control framework also enhanced process stability, reduced membrane cleaning frequency, and minimized operational downtime through intelligent real-time decision-making.