Model-based nonlinear filtering for Activated Sludge Process

The paper address the design of new nonlinear filtering algorithm for the estimation of the non-measurable states in wastewater treatment plants (WWTPs). Two approaches are considered based on the state-dependent formulation and the H-infinity control design technique. It differs from other well-known methods for state estimation, because not only nonlinear, but also the robustness and the load estimation are considered. The filter is designed and implemented on a benchmark model of an Activated Sludge Process (ASP). The different nonlinear filters presented have been tested using the benchmark´s data of different weather conditions, i.e. dry, rainy and storm, showing good agreement in all the estimated states. The simulation study presents a comparison of the Extended Kalman Filter (EKF), the state-dependent Riccati filter (SDRF), the extended H-infinity filter (EHF) and finally the new proposed state-dependent H-infinity filter (SDHF).