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 at...

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Bibliographic Details
Published in2009 IEEE Conference on Emerging Technologies & Factory Automation pp. 1 - 6
Main Authors Iratni, A., Katebi, R., Vilanova, R., Mostefai, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2009
Subjects
Application specific processors
Biological system modeling
Filters
Purification
Riccati equations
Sludge treatment
Software systems
State estimation
Uncertainty
Wastewater
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Summary: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.
ISBN:9781424427277
1424427274
ISSN:1946-0740
1946-0759
DOI:10.1109/ETFA.2009.5347055