Artificial Intelligence for Membrane Bioreactor Systems: A Review of Fouling Mechanisms, Prediction Approaches, and Intelligent Operation Pathways

With accelerated urbanization and increasing pressure on urban water systems, wastewater treatment is expected to deliver stable effluent quality within tighter spatial, energy, and environmental constraints. Membrane bioreactor (MBR) technology offers clear advantages in this context, but its wider application is still limited by membrane fouling, high aeration demand, and the resulting trade-off between process stability and operating cost. This review examines recent research on the use of artificial intelligence (AI) in MBR systems, with particular attention to fouling prediction, short-term operational warning, and control-oriented system integration. Rather than presenting a new predictive model, the paper synthesizes reported evidence on key fouling-related signals, the applicability of data-driven methods such as LSTM to short-horizon forecasting, and the role of AI within SCADA- and digital-twin-assisted operation. On this basis, a mechanism-informed framework is outlined to connect fouling processes, observable variables, predictive outputs, and operational responses. The review suggests that the practical value of AI in MBR systems depends not only on prediction accuracy, but also on interpretability, controllability, and ease of deployment. These observations provide a structured reference for the staged development of more intelligent, energy-efficient, and robust wastewater treatment systems.