On estimation of unknown state variables in wastewater systems

• Published in: 2009 IEEE Conference on Emerging Technologies & Factory Automation pp. 1 - 6
• Main Authors: Iratni, A., Katebi, R., Vilanova, R., Mostefai, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2009
• Subjects: Application specific processors; Biological system modeling; Filters; Purification; Riccati equations; Sludge treatment; Software systems; State estimation; Uncertainty; Wastewater
• DOI: 10.1109/ETFA.2009.5347055
• ISBN: 9781424427277; 1424427274
• ISSN: 1946-0740

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.