Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review

TL;DR: A review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment, is presented in this article , focusing on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict faults early, thereby enabling the development of novel cleaning strategies.

Abstract: This study offers a review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment. The review focuses on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict faults early, thereby enabling the development of novel cleaning strategies. Key ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forest, and reinforcement learning (RL) are briefly introduced, with an emphasis on their potential and limitations in advanced wastewater applications. The main challenges obstructing the implementation, namely data quality, interpretability, and transferability of ML, are identified. Finally, future research trends are proposed, including ML integration with big data, the Internet of Things (IoT), and hybrid model development. The review also underscores the need for interdisciplinary collaboration and investment in data management, along with the implementation of new policies addressing data privacy and security. By addressing these challenges, the integration of ML into MBRs has the potential to significantly enhance performance and reduce the energy footprint, providing a sustainable solution for advanced wastewater treatment.