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Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

© Artificial Intelligence in Engineering, 2000, vol. 14, núm. 4, p. 307-317

Elsevier

Autor: Belanche Muñoz, Lluis
Valdés, Julio J.
Comas Matas, Joaquim
Rodríguez-Roda Layret, Ignasi
Poch, Manuel
Data: 2000
Resum: The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics
Format: application/pdf
ISSN: 0954-1810
Accés al document: http://hdl.handle.net/10256/2879
Llenguatge: eng
Editor: Elsevier
Col·lecció: Reproducció digital del document publicat a: http://dx.doi.org/10.1016/S0954-1810(00)00012-1
Articles publicats (D-EQATA)
És part de: © Artificial Intelligence in Engineering, 2000, vol. 14, núm. 4, p. 307-317
Drets: Tots els drets reservats
Matèria: Aigües residuals -- Depuració
Aigües residuals -- Plantes de tractament
Sewage disposal plants
Sewage -- Purification
Títol: Prediction of the bulking phenomenon in wastewater treatment plants
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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