Data-driven soft-sensors for monitoring and fault diagnosis in wastewater treatment plants

Identificador:  TDX:3387

Handle: https://hdl.handle.net/20.500.11797/TDX3387

Autores:  Kazemi, Pezhman

Resumen:
Failing to reach the specific effluent properties in wastewater treatment plants can adversely affect human health and environmental. Due to this, there are significant pressures on authorities for efficient design and operation of wastewater treatment plants (WWTPs). Therefore, to achieve regulatory standards for wastewater effluent in a cost-efficient way, the development of an advanced information framework for the control and supervision of the WWTPs is mandatory. For the implementation of this framework, the real-time measurements of crucial parameters (e.g., concentrations of nitrate and total nitrogen, phosphate and total phosphorus, suspended solids, biochemical oxygen demand (BOD) and chemical oxygen demand (COD), total volatile fatty acids (VFA)) are necessary. Measurement of such parameters is often associated with capital and maintenance costs, as well as the time delay. The focus of this thesis was to design soft-sensors that can be used besides conventional instrumentation to improve the process operation and safety. Due to the availability of the massive amount of process data in most modern WWTPs, data-driven methods have attracted significant attention. Therefore, in this thesis, we developed different data-driven soft-sensors for online prediction of a crucial parameter (VFA) and fault detection (FD) and diagnosis in WWTPs.