On estimation of unknown state variables in wastewater systems

A. Iratni, M.R. Katebi, R. Vilanova, M. Mostefai

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

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.

Conference

ConferenceIEEE Conference on Emerging Technologies and Factory Automation, 2009
CountrySpain
CityPalma de Mallorca
Period22/09/0925/09/09

Fingerprint

Wastewater
Activated sludge process
Extended Kalman filters
State estimation
Purification
Nonlinear systems
Sensors
Costs
Uncertainty

Keywords

  • chemical sensors
  • nonlinear control systems
  • purification
  • sludge treatment
  • state estimation
  • wastewater treatment

Cite this

Iratni, A., Katebi, M. R., Vilanova, R., & Mostefai, M. (2009). On estimation of unknown state variables in wastewater systems. 1-6. Paper presented at IEEE Conference on Emerging Technologies and Factory Automation, 2009, Palma de Mallorca, Spain. https://doi.org/10.1109/ETFA.2009.5347055
Iratni, A. ; Katebi, M.R. ; Vilanova, R. ; Mostefai, M. / On estimation of unknown state variables in wastewater systems. Paper presented at IEEE Conference on Emerging Technologies and Factory Automation, 2009, Palma de Mallorca, Spain.6 p.
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Iratni, A, Katebi, MR, Vilanova, R & Mostefai, M 2009, 'On estimation of unknown state variables in wastewater systems' Paper presented at IEEE Conference on Emerging Technologies and Factory Automation, 2009, Palma de Mallorca, Spain, 22/09/09 - 25/09/09, pp. 1-6. https://doi.org/10.1109/ETFA.2009.5347055

On estimation of unknown state variables in wastewater systems. / Iratni, A.; Katebi, M.R.; Vilanova, R.; Mostefai, M.

2009. 1-6 Paper presented at IEEE Conference on Emerging Technologies and Factory Automation, 2009, Palma de Mallorca, Spain.

Research output: Contribution to conferencePaper

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T1 - On estimation of unknown state variables in wastewater systems

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AU - Mostefai, M.

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N2 - 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.

AB - 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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KW - nonlinear control systems

KW - purification

KW - sludge treatment

KW - state estimation

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Iratni A, Katebi MR, Vilanova R, Mostefai M. On estimation of unknown state variables in wastewater systems. 2009. Paper presented at IEEE Conference on Emerging Technologies and Factory Automation, 2009, Palma de Mallorca, Spain. https://doi.org/10.1109/ETFA.2009.5347055