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Machine learning algorithms for predicting membrane bioreactors performance: A review
Marina Muniz de Queiroz1, Victor Rezende Moreira1, Míriam Cristina Santos Amaral1
1Department of Sanitation and Environmental Engineering, School of Engineering, Federal University of Minas Gerais, 6627 Antônio Carlos Avenue, Campus Pampulha, Belo Horizonte, Minas Gerais, Brazil.
Journal of Environmental Management
|March 18, 2025
Summary
Machine learning (ML) models effectively predict pollutant removal and operational issues in membrane bioreactors (MBRs). Artificial neural networks are most common, but exploring other ML algorithms could further optimize MBR performance.
Area of Science:
- Environmental Engineering
- Water Treatment Technologies
- Computational Science
Background:
- Membrane bioreactors (MBRs) offer sustainable wastewater treatment but face challenges like membrane fouling and energy consumption.
- Machine learning (ML) presents a promising approach for modeling and predicting MBR performance and operational variables.
- Accurate prediction is crucial for optimizing MBR efficiency and overcoming application hurdles.
Purpose of the Study:
- To review the application of ML algorithms in MBR-based wastewater treatment.
- To focus on ML's role in predicting pollutant removal (nitrogen, organic matter) and membrane fouling-related operational parameters.
- To identify effective ML algorithms and data gaps for future MBR optimization.
Main Methods:
- Systematic review of 57 articles analyzing ML algorithms used in MBR wastewater treatment.
- Evaluation of model structures and fit quality for various ML algorithms.
- Identification of commonly used and less-utilized ML techniques for MBR applications.
Main Results:
- Artificial neural networks (ANNs) are the most prevalent ML algorithm, used in 88% of reviewed studies.
- Other ML algorithms like random forests, support vector machines, and k-nearest neighbors were less frequently encountered.
- Key areas for ML application include pollutant removal and membrane fouling prediction.
Conclusions:
- ML, particularly ANNs, shows significant utility in predicting MBR performance and operational parameters.
- Further exploration of underutilized ML models could enhance MBR optimization.
- A notable gap exists in using ML for predicting membrane lifespan and replacement needs.
