A neural network approach for on-line fault detection of nitrogen sensors in alternated active sludge treatment plants
In this paper, an effective strategy for fault detection of nitrogen sensors in alternated active sludge treatment plants is proposed and tested on a simulated set-up. It is based on two predictive neural networks, which are trained using a historical set of data collected during fault-free operatio...
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 62(2010), 12 vom: 01., Seite 2760-8 |
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| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2010
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Sewage Nitrogen N762921K75 |
| Zusammenfassung: | In this paper, an effective strategy for fault detection of nitrogen sensors in alternated active sludge treatment plants is proposed and tested on a simulated set-up. It is based on two predictive neural networks, which are trained using a historical set of data collected during fault-free operation of a wastewater treatment plant and their ability to predict reduced (ammonium) and oxidized (nitrates and nitrites) nitrogen is tested. The neural networks are also characterized by good generalization ability and robustness with respect to the influent variability with time and weather conditions. Then, simulations have been carried out imposing different kinds of fault on both sensors, as isolated spikes, abrupt bias and increased noise. Processing of residuals, based on the difference between measured concentration values and neural networks predictions, allows a quick revealing of the fault as well as the isolation of the corrupted sensor |
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| Beschreibung: | Date Completed 24.02.2011 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2010.025 |