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dc.contributor.authorCopete-López, Henry
dc.contributor.authorSánchez-Acevedo, Santiago
dc.date.accessioned2019-07-18T14:11:12Z
dc.date.accessioned2019-08-16T16:26:21Z
dc.date.available2019-07-18T14:11:12Z
dc.date.available2019-08-16T16:26:21Z
dc.date.issued2009-12-20
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/233
dc.identifier10.22430/22565337.233
dc.identifier.urihttp://hdl.handle.net/20.500.12622/841
dc.description.abstractEn este trabajo se presenta una técnica de control óptima aplicada al control del exceso de aire en el proceso de combustión de un horno reverbero mediante el monitoreo del porcentaje de O2 en la chimenea, el controlador es diseñado basado en un modelo no-lineal estimado con redes neuronales y se emplea para el previo entrenamiento una base de datos conformada por dos conjuntos: uno para entrenamiento y otro para la validación.spa
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)spa
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/233/239
dc.rightsCopyright (c) 2017 Tecno Lógicasspa
dc.source2256-5337
dc.source0123-7799
dc.sourceTecnoLógicas; Num. 23 (2009); 13-29eng
dc.sourceTecnoLógicas; Num. 23 (2009); 13-29spa
dc.subjectHorno reverberospa
dc.subjectcombustiónspa
dc.subjectcontrolador digitalspa
dc.subjectredes neuronalesspa
dc.subjectcontrol óptimospa
dc.titleAn Approach to Optimal Control of the Combustion System in a Reverberatory Furnacespa
dc.title.alternativeAn Approach to Optimal Control of the Combustion System in a Reverberatory Furnace
dc.subject.keywordsReverberatory furnaceeng
dc.subject.keywordscombustioneng
dc.subject.keywordsdigital controleng
dc.subject.keywordsneural networkseng
dc.subject.keywordsoptimal control.eng
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArticlesen-US
dc.typeArtículoses-ES
dc.description.abstractenglishIn this work an optimal control technique is applied to control the excess air in the combustion process of a reverberatory furnace by the monitoring of O2 percentage in the stack, the controller is designed based on a nonlinear model estimated by artificial neural networks and a data base is used for the previous training; the data base has two subsets one for training and other to validate the net.eng


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