On estimation of unknown state variables in wastewater systems

This paper focuses on the estimation of the non-measurable physical states of wastewater systems when nonlinear models with uncertainties describe the processes. The activated sludge process (ASP), as the most commonly applied biological wastewater purification technique, attracts a great deal of attention from the research community. We developed for this class of processes a state dependent differential Riccati filter (SDDRF) for state estimation of nonlinear model describing the system. The resulting software sensor is simple to implement and has a relatively low computational cost. The results are compared with the extended Kalman filter (EKF) in order to demonstrate the better performance of the SDDRF filter. The filter allows the on-line tracking of process variables, which are not directly measurable. The simulation results point out to the advantage of using this approach.