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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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Bibliographic Details
Main Authors: Busch, Jan (Author) , Elixmann, David (Author) , Kuehl, Peter (Author) , Gerkens, Carine (Author) , Schlöder, Johannes P. (Author) , Bock, Hans Georg (Author) , Marquardt, Wolfgang (Author)
Format: Article (Journal)
Language:English
Published: 5 April 2013
In: Water research
Year: 2013, Volume: 47, Issue: 13, Pages: 4774-4787
ISSN:1879-2448
DOI:10.1016/j.watres.2013.04.007
Online Access: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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Author Notes:Jan Busch, David Elixmann, Peter Kühl, Carine Gerkens, Johannes P. Schlöder, Hans G. Bock, Wolfgang Marquardt
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Summary: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.
Item Description:Gesehen am 10.12.2020
Physical Description:Online Resource
ISSN:1879-2448
DOI:10.1016/j.watres.2013.04.007

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