Membrane-informed multi-mechanistic predictive maintenance for MBR plants: Early determination of membrane cleaning with biologically driven, physically deposited, and chemically induced fouling model

Abstract

Membrane bioreactors (MBRs) are widely employed in wastewater treatment for their superior performance, though maintaining membrane efficiency remains costly and energy-intensive because of fouling accumulation. This study introduces a novel membrane-informed predictive maintenance (membrane-PM) system that accurately predicts cleaning intervals for membrane fouling in a full-scale MBR plant. By integrating biologically informed, physically deposited, and chemically induced fouling data via an activated sludge model, resistance-in-series model, and multiple linear regression model, we captured the complex dynamics of fouling. A day-to-day calibration approach, utilizing global sensitivity analysis and a genetic algorithm (GA), improves model precision by reflecting temporal fouling changes. Additionally, membrane-informed multivariate statistical monitoring (membrane-MSM), based on Hotelling's T2 statistic, was developed to predict optimal chemical cleaning intervals, helping to prevent MBR operational failures. Results indicate that the membrane-PM system effectively estimated membrane fouling progress via transmembrane pressure (TMP) with an R2 of 88.4 %, achieving high accuracy and extending membrane operational lifespan by an average of 17.5 %.