An investigation into control strategies for activated sludge wastewater treatment plants.
The activated sludge process is widely used throughout the world for the treatment of
wastewater from domestic and industrial users. This process is not normally efficiently
controlled and hence increasingly important financial incentives and environmental
considerations exist for improving the efficiency and quality of the treatment before
releasing the treated water into the environment.
This thesis presents the development of MATLAB computer simulation models for
activated sludge wastewater treatment plants. A comparison of control systems has
been made using these models for typical operating conditions of wastewater treatment
processess, uch as influent flow pattern and temperature.
The investigation identified the control of dissolved oxygen as an important area to
study because insufficient levels of dissolved oxygen in the wastewater prevent the
successful degradation of organic matter present, whereas too high a level causes
settling problems and inefficiencies. Three dissolved oxygen control methods, namely
PID, Fuzzy logic and self-tuning control have been investigated, applied and their
performances compared. As in most other processes, the number and location of
sensors and actuators within a water treatment plant can have large implications for
successful process control. Therefore, the model developed was used with real plant
data to test different designs and investigate the best location of sensors and actuators
for a specific North West Water plant to improve control of the process. Optimisation
of process operation has also been investigated with the objective of improving effluent
quality and reducing operation costs.
Simulations suggest that all three dissolved oxygen control methods investigated are
able to control the process satisfactorily with relatively little deviation from the setpoint.
The PID and fuzzy logic controllers needed retuning for changing process
conditions, but the adaptive nature of the self-tuner makes it more robust. Optimal
sensor and actuator placements have been identified and a cost/quality benefit analysis
performed. Significant cost reductions and effluent quality improvements may be
achieved by applying optimisation techniques to regulating the concentration of the
solids within the aeration stage. These objectives are conflicting and therefore
simultaneous improvement is not always achievable.
The project has demonstrated the potential benefits of employing models to simulate the
process, subject to availability of data to parameterise them. Process operation can be
significantly improved with the application of well-tuned controllers and optimisation