Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning.

Membrane fouling remains a significant challenge in the operation of membrane bioreactors (MBRs). Plant operators rely heavily on observations of filtration performance from noisy sensor data to assess membrane fouling conditions and lab-based protocols for plant maintenance, often leading to inaccurate estimations of future performance and delayed membrane cleaning. This challenge is further compounded by the difficulty in integrating existing complex mechanistic models with the Internet of Things (IoT) systems of wastewater treatment plants (WWTPs).