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Recent Advances in the Prediction of Fouling in Membrane Bioreactors
Summary
This review examines recent advances in predicting membrane fouling in membrane bioreactors, covering artificial neural network models, mechanistic approaches, and hybrid prediction strategies. Researchers identify key challenges in fouling prediction including data quality, model generalizability, and the need for real-time monitoring integration.
Compared to the traditional activated sludge process, the membrane bioreactor (MBR) has several advantages such as the production of high-quality effluent, generation of low excess sludge, smaller footprint requirements, and ease of automatic control of processes. The MBR has a broader prospect of its applications in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle for its wider application. This paper reviews the techniques available to predict fouling in MBR, discusses the problems associated with predicting fouling status using artificial neural networks and mathematical models, summarizes the current state of fouling prediction techniques, and looks into the trends in their development.
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