Wastewater Treatment Plant Optimization: Development of Membrane Bioreactor Fouling Monitoring Tool and Prediction of Transmembrane Pressure Using Artificial Neural Networks

  • Yasser Algoufily

Student thesis: Master's Thesis

Abstract

The construction and operation of central wastewater treatment plants started around the 20th century. With the advent of rigorous membrane research and development in the middle of the 20th century, more and more wastewater plants started incorporating a Membrane BioReactor, MBR, in their design. The MBR system however is far from perfect. Membrane systems continuously foul, and if fouling is incurred for a long period of time, maintenance and cleaning costs will rise in proportion. 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 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 MLSS around their desired setpoints. The controller is able to minimize disturbances in both states in a narrow band around their desired setpoints.
Date of AwardApr 2021
Original languageEnglish (US)
Awarding Institution
  • Physical Science and Engineering
SupervisorIngo Pinnau (Supervisor)

Keywords

  • Neural Network
  • Fouling monitoring
  • TMP Prediction
  • Wastewater plant
  • Non-linear control

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