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 Award||Apr 2021|
|Original language||English (US)|
- Physical Science and Engineering
|Supervisor||Ingo Pinnau (Supervisor)|
- Neural Network
- Fouling monitoring
- TMP Prediction
- Wastewater plant
- Non-linear control