Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants
dc.contributor.author
dc.date.accessioned
2014-03-24T13:01:21Z
dc.date.available
2014-03-24T13:01:21Z
dc.date.issued
2008
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1991-8763
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dc.description.abstract
This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen
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application/pdf
dc.language.iso
eng
dc.publisher
World Scientific and Engineering Academy and Society (WSEAS)
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Reproducció digital del document publicat a: http://dl.acm.org/citation.cfm?id=1456035&CFID=426373287&CFTOKEN=19370478
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© WSEAS Transactions on Systems and Control, 2008, vol.7, núm. 6, p. 695-705
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Articles publicats (D-IMA)
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Tots els drets reservats
dc.title
Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
Cap
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.idgrec
015508
dc.identifier.eissn
2224-2856