Fault Detection in Waste Water Treatment Plants using Improved Particle Filter-based Optimized EWMA

Environmental, health, and safety concerns are of major importance world-wide. These concerns are closely tied to the availability and quality of water that can be used in various domestic and industrial applications. Therefore, the objective of this paper is to develop a general framework for modeling and monitoring technique that aims at enhancing the operation of wastewater treatment plants. In this work, an improved PF (IPF) method will be developed to better handle the nonlinear and high dimensional state estimation problem involved in modeling wastewater treatment plants. Then, an improved detection control chart to enhance the monitoring of WWTP will be developed. The contributions of this work are the foorfold: 1) to estimate a nonlinear state variables of WWTPs using improved particle filter in three types of weathers (dry, storm and rain). 2) to develop an new optimized EWMA (OEWMA) based on the best selection of smoothing parameter ($\lambda$) and control width L. 3) to combine the advantages of state estimation technique, with OEWMA chart to improve the fault detection of WWTP. 4) to investigate the effect of fault types (change in variance and mean in shift) and sizes on the monitoring performances.