Membrane Bioreactor control and Fouling Monitoring using Artificial Neural NetworkBased Approach

With the advent of rigorous membrane research and development in the middle of the 20th century, more wastewater plants started incorporating Membrane BioReactors (MBR) in their design. However, being a membrane system, the MBR is subject to fouling which may lead to maintenance and cleaning costs. In this paper, a fouling monitoring and prediction tool has been designed in MATLAB\Simulink. The model takes states related to membrane fouling, and calculates the membrane total resistance based on deterministic and stochastic models. The tool is capable of predicting future transmembrane pressure (TMP) cycles based on older TMP performance via an artificial neural network algorithm. TMP data have been synthetically generated from a validated mathematical model. Finally, an artificial neural network controller is implemented to control temperature and Mixed Liquor Suspended Solids (MLSS) around their desired setpoints. The controller is able to minimize disturbances in both states in a narrow band around their desired setpoints.

Automated summary

This paper presents a fouling monitoring and prediction tool designed for MBR, which calculates the membrane total resistance based on states related to membrane fouling and predicts future TMP cycles using an artificial neural network algorithm. Furthermore, an artificial neural network controller is implemented to control temperature and MLSS around their desired setpoints, minimizing disturbances in both states.