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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Published in | 2009 IEEE Conference on Emerging Technologies & Factory Automation pp. 1 - 6 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2009
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 9781424427277 1424427274 |
ISSN: | 1946-0740 1946-0759 |
DOI: | 10.1109/ETFA.2009.5347055 |