Artificial neural networks to prediction fuel rate in the blast furnace operation.

Detalhes bibliográficos
Autor(a) principal: Carvalho, Leonard de Araújo
Data de Publicação: 2018
Outros Autores: Assis, Paulo Santos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFOP
Texto Completo: http://www.repositorio.ufop.br/handle/123456789/10852
Resumo: This paper proposes the use of artificial neural networks for the prediction of fuel consumption in the blast furnace. For this purpose, a dataset of 270 records, with 19 input variables were considered, based on the historical data of operation from the years 2014 to 2017 of a blast furnace of a Brazilian steel mill, and it was verified that model presented good results with correlation coefficient of 0.837, consisting of an input layer with 19 neurons, intermediate layer with 19 neurons and output layer with 1 neuron.
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spelling Artificial neural networks to prediction fuel rate in the blast furnace operation.ModellingThis paper proposes the use of artificial neural networks for the prediction of fuel consumption in the blast furnace. For this purpose, a dataset of 270 records, with 19 input variables were considered, based on the historical data of operation from the years 2014 to 2017 of a blast furnace of a Brazilian steel mill, and it was verified that model presented good results with correlation coefficient of 0.837, consisting of an input layer with 19 neurons, intermediate layer with 19 neurons and output layer with 1 neuron.2019-03-28T15:52:42Z2019-03-28T15:52:42Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCARVALHO, L. de A.; ASSIS, P. S. Artificial neural networks to prediction fuel rate in the blast furnace operation. Indian Journal of Applied Research, v. 8, p. 431-432, 2018. Disponível em: <https://wwjournals.com/index.php/ijar/article/view/5680>. Acesso em: 15 fev. 2019.2249555Xhttp://www.repositorio.ufop.br/handle/123456789/10852This work is licensed under a Creative Commons Attribution 4.0 International License. Fonte: Indian Journal of Applied Research <https://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/> acesso em: 18 fev. 2019.info:eu-repo/semantics/openAccessCarvalho, Leonard de AraújoAssis, Paulo Santosengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOP2019-03-28T15:52:42Zoai:repositorio.ufop.br:123456789/10852Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332019-03-28T15:52:42Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false
dc.title.none.fl_str_mv Artificial neural networks to prediction fuel rate in the blast furnace operation.
title Artificial neural networks to prediction fuel rate in the blast furnace operation.
spellingShingle Artificial neural networks to prediction fuel rate in the blast furnace operation.
Carvalho, Leonard de Araújo
Modelling
title_short Artificial neural networks to prediction fuel rate in the blast furnace operation.
title_full Artificial neural networks to prediction fuel rate in the blast furnace operation.
title_fullStr Artificial neural networks to prediction fuel rate in the blast furnace operation.
title_full_unstemmed Artificial neural networks to prediction fuel rate in the blast furnace operation.
title_sort Artificial neural networks to prediction fuel rate in the blast furnace operation.
author Carvalho, Leonard de Araújo
author_facet Carvalho, Leonard de Araújo
Assis, Paulo Santos
author_role author
author2 Assis, Paulo Santos
author2_role author
dc.contributor.author.fl_str_mv Carvalho, Leonard de Araújo
Assis, Paulo Santos
dc.subject.por.fl_str_mv Modelling
topic Modelling
description This paper proposes the use of artificial neural networks for the prediction of fuel consumption in the blast furnace. For this purpose, a dataset of 270 records, with 19 input variables were considered, based on the historical data of operation from the years 2014 to 2017 of a blast furnace of a Brazilian steel mill, and it was verified that model presented good results with correlation coefficient of 0.837, consisting of an input layer with 19 neurons, intermediate layer with 19 neurons and output layer with 1 neuron.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019-03-28T15:52:42Z
2019-03-28T15:52:42Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv CARVALHO, L. de A.; ASSIS, P. S. Artificial neural networks to prediction fuel rate in the blast furnace operation. Indian Journal of Applied Research, v. 8, p. 431-432, 2018. Disponível em: <https://wwjournals.com/index.php/ijar/article/view/5680>. Acesso em: 15 fev. 2019.
2249555X
http://www.repositorio.ufop.br/handle/123456789/10852
identifier_str_mv CARVALHO, L. de A.; ASSIS, P. S. Artificial neural networks to prediction fuel rate in the blast furnace operation. Indian Journal of Applied Research, v. 8, p. 431-432, 2018. Disponível em: <https://wwjournals.com/index.php/ijar/article/view/5680>. Acesso em: 15 fev. 2019.
2249555X
url http://www.repositorio.ufop.br/handle/123456789/10852
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFOP
instname:Universidade Federal de Ouro Preto (UFOP)
instacron:UFOP
instname_str Universidade Federal de Ouro Preto (UFOP)
instacron_str UFOP
institution UFOP
reponame_str Repositório Institucional da UFOP
collection Repositório Institucional da UFOP
repository.name.fl_str_mv Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)
repository.mail.fl_str_mv repositorio@ufop.edu.br
_version_ 1813002864718512128

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