Machine learning algorithms for predicting membrane bioreactors performance: A review

Membrane bioreactors (MBR) are recognized as a sustainable technology for treating polluted effluents. Machine learning (ML) algorithms have emerged as a modeling option to predict pollutant removal and operational variables such as membrane fouling, permeability, and energy consumption, which are significant challenges for MBR application. This review examines the use of ML algorithms in MBR-based wastewater treatment, focusing on the prediction of nitrogen and organic matter removal, and operational parameters related to membrane fouling. It presents the structures and fit quality of each model, noting that artificial neural networks (ANNs) are the most commonly used algorithm, appearing in 88 % of the 57 analyzed articles. Additionally, the review identified studies using random forests, support vector machines, k-nearest neighbors and boosting techniques, among other ML algorithms, although these were less frequently encountered. The review suggests potential in exploring less-utilized models for MBR data and identifies a gap in predicting membrane lifespan and replacement with ML models. This study aims to guide the development of new models for optimizing MBR performance by highlighting effective variables and algorithms, enhancing process control, real-time data analysis, parameter adjustment, and operational efficiency.