The development of a model predicting long-term fouling for membrane bioreactors

Abstract

A membrane bioreactor (MBR) is a wastewater treatment process which uses activated sludge to remove organic substances from wastewater. The membrane is used to separate activated sludge from treated water in MBRs. The concentration of mixed liquor suspended solids (MLSS) is important for long-term fouling prediction and process control in MBRs, but it requires much time and cost to measure the concentration of MLSS. In this study, we developed a regression models between the concentration of MLSS and other variables such as operating conditions and water quality variables. To develop a widely-used and accurate model, we analyzed three data sets measured in different MBR plants. The model constructed with three data sets could achieve high predictive performance. From the results of the variable selection using a genetic algorithm-based partial least squares (GAPLS) method and the comparison of GAPLS models with different explanatory variables, it was suggested that the viscosity in the membrane tank and treated water quality variables are important for prediction of the concentration of MLSS.