Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

Detalhes bibliográficos
Autor(a) principal: Chagas,Marcelo
Data de Publicação: 2017
Outros Autores: Machado,Marcelo Lucas Pereira, Souza,João Batista Conti, Frigini,Eduardo F. de Jesus
Tipo de documento: Artigo
Idioma: eng
Título da fonte: REM - International Engineering Journal
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000200187
Resumo: Abstract The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.
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spelling Use of an Artificial Neural Network in determination of iron ore pellet bed permeabilitypelletartificial neural networksiron oreAbstract The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.Fundação Gorceix2017-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000200187REM - International Engineering Journal v.70 n.2 2017reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672016700032info:eu-repo/semantics/openAccessChagas,MarceloMachado,Marcelo Lucas PereiraSouza,João Batista ContiFrigini,Eduardo F. de Jesuseng2017-04-19T00:00:00Zoai:scielo:S2448-167X2017000200187Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2017-04-19T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false
dc.title.none.fl_str_mv Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
title Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
spellingShingle Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
Chagas,Marcelo
pellet
artificial neural networks
iron ore
title_short Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
title_full Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
title_fullStr Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
title_full_unstemmed Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
title_sort Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
author Chagas,Marcelo
author_facet Chagas,Marcelo
Machado,Marcelo Lucas Pereira
Souza,João Batista Conti
Frigini,Eduardo F. de Jesus
author_role author
author2 Machado,Marcelo Lucas Pereira
Souza,João Batista Conti
Frigini,Eduardo F. de Jesus
author2_role author
author
author
dc.contributor.author.fl_str_mv Chagas,Marcelo
Machado,Marcelo Lucas Pereira
Souza,João Batista Conti
Frigini,Eduardo F. de Jesus
dc.subject.por.fl_str_mv pellet
artificial neural networks
iron ore
topic pellet
artificial neural networks
iron ore
description Abstract The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000200187
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000200187
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0370-44672016700032
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Fundação Gorceix
publisher.none.fl_str_mv Fundação Gorceix
dc.source.none.fl_str_mv REM - International Engineering Journal v.70 n.2 2017
reponame:REM - International Engineering Journal
instname:Fundação Gorceix (FG)
instacron:FG
instname_str Fundação Gorceix (FG)
instacron_str FG
institution FG
reponame_str REM - International Engineering Journal
collection REM - International Engineering Journal
repository.name.fl_str_mv REM - International Engineering Journal - Fundação Gorceix (FG)
repository.mail.fl_str_mv ||editor@rem.com.br
_version_ 1754734690562998272

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