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Description:
The progressive deterioration of water resources and the large amount of polluted water generated in industrialized societies gives to wastewater treatment processes a fundamental importance in the water pollution prevention. Inside a biological wastewater treatment plant, the Activated Sludge Process (ASP) is the most commonly used technology to remove organic pollutant from wastewater. This is because it is the most cost-effective, it is very flexible (it can be adapted to any kind of wastewater), and it is reliable and has the capacity of producing high quality effluent. For all these reasons, our interest during this work will focus on demonstrating that a better understanding and management of the process can lead to benefits for the overall wastewater treatment plant.
For the improvement of reliability, safety and efficiency of the plant advanced methods of supervision, fault detection and fault diagnosis become increasingly important. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection were developed by using input and output signals and applying dynamic process models. The knowledge of on ASP modeling and simulation acquired by Dr. Mulas, during her PhD and the internationally proved reputation of the Laboratory of Process Control and Automation at the HUT, will give a real benefit in improving the wastewater treatment plant management.
Fault and failures are very unfavorable to performance of a control system. In this project we will propose a framework for incorporating and detect faults and failures of sensors and actuators in the simulation of wastewater treatment plants. Focusing on ASP, the process model will be developed in Matlab/Simulink environment including the sensors and actuators models. The final goal will be to represent the real process in order to define innovative and control strategies for the wastewater treatment plant and, on the other hand, to detect and isolate faults in the system. The goal will be achieved generating several symptoms indicating the difference between nominal and faulty status. Based on different symptoms fault diagnosis procedures follow, determining the fault by applying classification or inference methods.
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