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

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 lead to increased pollutant removal, a reduced need for chemicals as well as energy savings. An important step toward the optimal functioning of a WWTP is to minimize the influence of sensor faults on the control quality. To achieve this, a fault-detection system should be implemented. In this paper, the idea of using an evolving method as a base for the fault-detection/monitoring system is tested. The system is based on the evolving fuzzy model method. This method allows us to model the nonlinear relations between the variables with the Takagi-Sugeno fuzzy model. The method uses basic evolving mechanisms to add and remove clusters and the adaptation mechanism to adapt the clusters' and local 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 control during a sensor malfunction.