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dc.contributor.authorSanz Bobi, Miguel Ángeles-ES
dc.contributor.authorRuiz Castelló, Pabloes-ES
dc.contributor.authorMontes Ponce de León, Julioes-ES
dc.date.accessioned2016-01-15T11:26:48Z-
dc.date.available2016-01-15T11:26:48Z-
dc.date.issued2012-11-08es_ES
dc.identifier.urihttp://hdl.handle.net/11531/5547-
dc.descriptionCapítulos en libroses_ES
dc.description.abstractes-ES
dc.description.abstractBioethanol is produced on an industrial scale by means of fermentation of a sugar substrate by Saccharomyces cerevisiae. Models for the detection of anomalies and their possible evolution are difficult to elaborate due to the biological nature of the fermentation process. This paper describes a method able to characterize patterns for explaining industrial bioethanol production using self-organised maps. Also, this method allows for an estimation of the probabilities of evolution to any pattern that the process may have from its last recognized state, therefore helping to take measures to correct a possible problem as soon as possible.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.publisherSin editorial (Coimbra, Portugal)es_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceLibro: 2nd International Symposium on Computational Intelligence for Engineering Systems - ISCIES 2011, Página inicial: 1-11, Página final:es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleThe process of industrial bioethanol production explained by self-organised mapses_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.keywordses-ES
dc.keywordsfermentation patterns; bioethanol production; chemical analysis monitoring; pattern recognition; selforganised maps; machine learning.en-GB
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