National Natural Science Foundation of China (61503257 and 61374112)
; the Natural Science Foundation of Liaoning Province, China (2014020138)
; the Program for Liaoning Province Distinguished Young Scholars in University (LJQ2014045)
; and the University of Alberta (RES0014908)
A wastewater treatment plant is a large-scale nonlinear system including a series of biological reactors and a settler. In this work, we propose a distributed state estimation scheme for wastewater treatment processes in the context of extended Kalman filtering. Specifically, we consider a wastewater treatment process that includes a five-compartment reactor and an ideal splitter. First, we present a method to design the sensor network for the process and then discuss how the process may be decomposed into subsystems for distributed state estimation. We present a detailed design of the distributed filters and a detailed distributed state estimation algorithm to coordinate the actions of the different filters. Without loss of generality, we consider the entire system as being decomposed into two subsystems. The proposed approach can be extended in a straightforward fashion to include more subsystems. The distributed scheme is compared with the corresponding centralized extended Kalman filtering scheme under different weather conditions. Simulation results show that the distributed scheme can give comparable estimation performance to the centralized scheme or even better performance than the centralized scheme. Also, the distributed estimation scheme is shown to have more stable performance under different noise conditions.