Machine learning for membrane bioreactor research: principles, methods, applications, and a tutorial

Highlights

	● Principles and methods of machine learning for MBR application are summarized.
	● Available models for MBR pollutant removal and membrane fouling are reviewed.
	● A tutorial is given to illustrate machine learning models for fouling prediction.
	● Limitations and future improvements for MBR intelligent operation are discussed.

Abstract

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor (MBR) technologies for wastewater treatment. The accurate prediction of the membrane filtration process is of great importance for identifying and controlling fouling. Machine learning methods address the limitations of traditional statistical approaches, such as low accuracy, poor generalization ability, and slow convergence, particularly in predicting complex filtration and fouling processes within the realm of big data. This article provides an in-depth exposition of machine learning theory. The study then reviews advances in MBRs that utilize machine learning methods, including artificial neural networks (ANN), support vector machines (SVM), decision trees, and ensemble learning. Based on current literature, this study summarizes and compares the model input and output characteristics (including foulant characteristics, solution environments, filtration conditions, operating conditions, and time factors), as well as the selection of models and optimization algorithms. The modeling procedures of SVM, random forest (RF), back propagation neural network (BPNN), long short-term memory (LSTM), and genetic algorithm-back propagation (GA-BP) methods are elucidated through a tutorial example. The simulation results demonstrated that all five methods yielded accurate predictions with R² > 0.8. Finally, the existing challenges in the implementation of machine learning models in MBRs were analyzed. It is notable that integration of deep learning, automated machine learning (AutoML) and explainable artificial intelligence (XAI) may facilitate the deployment of models in practical engineering applications. The insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

Graphical abstract

Keywords

Membrane bioreactor / Machine learning / Pollutant removal / Membrane fouling / Model prediction

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52370059), the Beijing Natural Science Foundation (No. JQ22027), and the Fundamental Research Funds for the Central Universities (No. E2EG0502X2).

Conflict of Interests

Kang Xiao and Xia Huang are editorial board members of Frontiers of Environmental Science & Engineering. The authors declare that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-025-1954-2 and is accessible for authorized users.

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