PROJECT TITLE :

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

ABSTRACT:

Increasing demands on effluent quality and loads call for an improved control, monitoring, and fault detection of waste-water treatment plants (WWTPs). Improved control and optimization of WWTP cause increased pollutant removal, a reduced would like for chemicals also energy savings. An necessary step toward the optimal functioning of a WWTP is to minimize the influence of sensor faults on the management quality. To achieve this, a fault-detection system should be implemented. In this paper, the concept of using an evolving methodology as a base for the fault-detection/monitoring system is tested. The system is based on the evolving fuzzy model technique. This technique permits us to model the nonlinear relations between the variables with the Takagi-Sugeno fuzzy model. The tactic uses basic evolving mechanisms to feature and remove clusters and the adaptation mechanism to adapt the clusters' and native models' parameters. The proposed fault-detection system is tested on measured data from a real WWTP. The results indicate the potential improvement of the WWTP's management throughout a sensor malfunction.