State estimation for large-scale wastewater treatment plants
Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliabili...
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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
5 April 2013
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| In: |
Water research
Year: 2013, Jahrgang: 47, Heft: 13, Pages: 4774-4787 |
| ISSN: | 1879-2448 |
| DOI: | 10.1016/j.watres.2013.04.007 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.watres.2013.04.007 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0043135413003126 
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| Verfasserangaben: | Jan Busch, David Elixmann, Peter Kühl, Carine Gerkens, Johannes P. Schlöder, Hans G. Bock, Wolfgang Marquardt |
| Zusammenfassung: | Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity. |
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| Beschreibung: | Gesehen am 10.12.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1879-2448 |
| DOI: | 10.1016/j.watres.2013.04.007 |