Disturbance detection and isolation in the activated sludge process.

This paper proposes a new fault detection and isolation (FDI) method. This method monitors the distribution of process data and detects changes in this distribution, which reflect changes in the corresponding operating condition. A modified dissimilarity index and a FDI technique are defined to quantitatively evaluate the difference between data sets. This technique considers the importance of each transformed variable in the multivariate system. The FDI technique is applied to a benchmark simulation and to data from a real wastewater treatment plant. Simulation results show that it immediately detects disturbances and automatically distinguishes between serious and minor anomalies for various types of fault. The method not only detects the disturbances, but also isolates the scale of the disturbance, facilitating the interpretation of the disturbance source. The proposed monitoring technique is found to be appropriate for analyzing the biological wastewater treatment process, which is characterized by a variety of fault and disturbance sources and non-stationary characteristics.