Application of a model-based optimisation methodology for nutrient removing SBRs leads to falsification of the model

Recently, a model-based optimisation methodology for SBR operation has been developed and an optimal operation scenario proposed to improve N and P removal in a pilot-scale SBR. In this study, this optimal operation scenario was implemented and evaluated. The results of the implementation showed tha...

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Veröffentlicht in:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 53(2006), 4-5 vom: 05., Seite 95-103
1. Verfasser: Sin, G (VerfasserIn)
Weitere Verfasser: Villez, K, Vanrolleghem, P A
Format: Aufsatz
Sprache:English
Veröffentlicht: 2006
Zugriff auf das übergeordnete Werk:Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Nitrates Phosphorus 27YLU75U4W Nitrogen N762921K75
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520 |a Recently, a model-based optimisation methodology for SBR operation has been developed and an optimal operation scenario proposed to improve N and P removal in a pilot-scale SBR. In this study, this optimal operation scenario was implemented and evaluated. The results of the implementation showed that the SBR performance was improved by approximately 50 and 40% for total nitrogen and phosphorous removal, respectively, which was better than predicted by the model. However, the long-term SBR performance was found to be unstable, particularly owing to settling problems developed after the implementation. When confronted with reality, the model used for the optimisation of the operation was found to be invalid. The model was unable to predict the nitrite build-up provoked by the optimal operation scenario. These results imply that changing the operation of an SBR system using a model may significantly change the behaviour of the system beyond the (unknown) application domain of the model. This is simply because the mechanistic models currently do not cover all the aspects of activated sludge systems, e.g. settling and adaptation of the microbial community. To further improve model-application practices, expert knowledge (not contained in the models) can be valuable and should be incorporated into model-based process optimisations 
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