Predictive Modeling of Membrane Fouling in Domestic Wastewater Membrane Bioreactor Using Machine Learning

Membrane fouling is widely recognized as a significant drawback of membrane technology, as it reduces filtration flux and impairs the overall efficiency of wastewater treatment systems. Accurate prediction of membrane fouling, therefore, offers a crucial pathway to optimizing system operation and developing proactive mitigation strategies. This study developed machine learning models-including linear regression, support vector regression, and decision tree regression-to predict transmembrane pressure, a key indicator of fouling severity. Input descriptors such as pH, ammonium, nitrate, and alkalinity, measured at multiple stages of the anoxic-aerobic membrane bioreactor system, were used to train and evaluate the models. Among the tested approaches, nonlinear models-particularly decision tree regression-demonstrated superior performance, achieving high prediction accuracy (R² = 0.99). Moreover, machine learning helped identify the most influential input descriptors and uncover hidden patterns within the collected data. This study presents a promising alternative approach for predicting membrane fouling in wastewater treatment systems.