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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| creator | Iratni, A. Katebi, R. Vilanova, R. Mostefai, M. |
| description | 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. |
| doi_str_mv | 10.1109/ETFA.2009.5347055 |
| format | Conference Proceeding |
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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.</abstract><pub>IEEE</pub><doi>10.1109/ETFA.2009.5347055</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1946-0740 |
| ispartof | 2009 IEEE Conference on Emerging Technologies & Factory Automation, 2009, p.1-6 |
| issn | 1946-0740 1946-0759 |
| language | eng |
| recordid | cdi_ieee_primary_5347055 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application specific processors Biological system modeling Filters Purification Riccati equations Sludge treatment Software systems State estimation Uncertainty Wastewater |
| title | On estimation of unknown state variables in wastewater systems |
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