Fault detection in a wastewater treatment plant based on neural networks and PCA

In this paper, a neural network PCA method that integrates neural networks (NN) and principal component analysis (PCA) is used to detect faults in a wastewater treatment plant. The neural networks are used to calculate a non-linear and dynamic model of the process in normal operating conditions. PCA is used to generate monitoring charts based on the residuals calculated as the difference between the process measurements and the output of the networks. It can evaluate the current performance of the process and detects the faults. This technique has been applied to the simulation of a benchmark of a biological wastewater treatment process, a highly non-linear process. The simulation results clearly show the advantages of using this NNPCA monitoring in comparison with classical PCA monitoring.