Artificial neural networks applied to iron ore grinding process combined with empirical models

Detalhes bibliográficos
Autor(a) principal: Silva, Daniel Henrique Cordeiro
Data de Publicação: 2022
Outros Autores: Alves , Vladmir Kronemberger, Savio, Ernandes
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/32329
Resumo: Step by step, the technologies provided by Industry 4.0 are being inserted in the mining processes and one of the opportunities to be explored is the use of Big Data and Advanced Analytics tools. In iron ore beneficiation plants, in the milling processes, the potential gains from machine learning tools tend to be amplified when combined with mathematical models derived from process knowledge, whether empirical or phenomenological. This article presents the application of artificial neural networks for the prediction of the main product quality parameter of a milling plant, combined with empirical equations that describe the milling process, to establish whether such equations can contribute to a better performance of the predictive models.
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spelling Artificial neural networks applied to iron ore grinding process combined with empirical modelsRedes neuronales artificiales aplicadas a la molienda de hierro combinadas a modelos empíricosRedes neurais artificiais aplicadas à moagem de minério de ferro combinadas a modelos empíricosTratamiento de mineralesMena de hierroMoliendaRedes neuronales artificialesAprendizaje automáticoControlo de processos.Grinding MillArtificial Neural NetworksMachine LearningMineral processingIron oreProcess control.Tratamento de minériosMoagemRedes Neurais ArtificiaisAprendizado de MáquinaControle de processos.Step by step, the technologies provided by Industry 4.0 are being inserted in the mining processes and one of the opportunities to be explored is the use of Big Data and Advanced Analytics tools. In iron ore beneficiation plants, in the milling processes, the potential gains from machine learning tools tend to be amplified when combined with mathematical models derived from process knowledge, whether empirical or phenomenological. This article presents the application of artificial neural networks for the prediction of the main product quality parameter of a milling plant, combined with empirical equations that describe the milling process, to establish whether such equations can contribute to a better performance of the predictive models.Cada día, las tecnologías proporcionadas por la Industria 4.0 se están insertando en el Tratamiento de Minerales y una de las oportunidades a explorar es el uso de herramientas de Big Data y Analítica Avanzada. En el beneficio del mineral de hierro, en los procesos de molienda, las ganancias potenciales de las herramientas de aprendizaje automático tienden a amplificarse cuando se combinan con modelos matemáticos derivados del conocimiento del proceso, ya sea empírico o fenomenológico. Este artículo presenta la aplicación de redes neuronales artificiales para la predicción del principal parámetro de calidad del producto de una planta de molienda, combinado con ecuaciones empíricas que describen el proceso de molienda, con el fin de establecer como pueden contribuir a un mejor desempeño de los modelos predictivos.A cada dia, com o advento da Indústria 4.0, novas tecnologias são disponibilizadas e aplicadas ao Tratamento de Minérios, impulsionadas pela crescente disponibilidade de dados de chão de fábrica. Algumas das oportunidades a serem exploradas estão atreladas à utilização das ferramentas de Big Data, Advanced Analytics, Machine Learning e Inteligência Artificial. No beneficiamento de minério de ferro, nos processos de moagem, os ganhos potenciais oriundos de ferramentas de aprendizado de máquina tendem a ser ampliados quando combinados com modelos matemáticos consagrados, oriundos do conhecimento do processo, sejam eles empíricos ou fenomenológicos. Este artigo apresenta a aplicação de redes neurais artificiais para a predição da granulometria no produto de uma planta de moagem, principal parâmetro de qualidade, combinadas a modelos empíricos que descrevem o processo, visando estabelecer se tais equações podem contribuir para uma melhor performance dos modelos preditivos, de forma a suportar uma melhor e mais rápida tomada de decisão pelo operador da unidade.Research, Society and Development2022-10-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/3232910.33448/rsd-v11i13.32329Research, Society and Development; Vol. 11 No. 13; e84111332329Research, Society and Development; Vol. 11 Núm. 13; e84111332329Research, Society and Development; v. 11 n. 13; e841113323292525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/32329/29563Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Vladmir Kronemberger Alves ; Ernandes Saviohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilva, Daniel Henrique Cordeiro Alves , Vladmir KronembergerSavio, Ernandes2022-10-17T13:43:46Zoai:ojs.pkp.sfu.ca:article/32329Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:48:22.649503Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Artificial neural networks applied to iron ore grinding process combined with empirical models
Redes neuronales artificiales aplicadas a la molienda de hierro combinadas a modelos empíricos
Redes neurais artificiais aplicadas à moagem de minério de ferro combinadas a modelos empíricos
title Artificial neural networks applied to iron ore grinding process combined with empirical models
spellingShingle Artificial neural networks applied to iron ore grinding process combined with empirical models
Silva, Daniel Henrique Cordeiro
Tratamiento de minerales
Mena de hierro
Molienda
Redes neuronales artificiales
Aprendizaje automático
Controlo de processos.
Grinding Mill
Artificial Neural Networks
Machine Learning
Mineral processing
Iron ore
Process control.
Tratamento de minérios
Moagem
Redes Neurais Artificiais
Aprendizado de Máquina
Controle de processos.
title_short Artificial neural networks applied to iron ore grinding process combined with empirical models
title_full Artificial neural networks applied to iron ore grinding process combined with empirical models
title_fullStr Artificial neural networks applied to iron ore grinding process combined with empirical models
title_full_unstemmed Artificial neural networks applied to iron ore grinding process combined with empirical models
title_sort Artificial neural networks applied to iron ore grinding process combined with empirical models
author Silva, Daniel Henrique Cordeiro
author_facet Silva, Daniel Henrique Cordeiro
Alves , Vladmir Kronemberger
Savio, Ernandes
author_role author
author2 Alves , Vladmir Kronemberger
Savio, Ernandes
author2_role author
author
dc.contributor.author.fl_str_mv Silva, Daniel Henrique Cordeiro
Alves , Vladmir Kronemberger
Savio, Ernandes
dc.subject.por.fl_str_mv Tratamiento de minerales
Mena de hierro
Molienda
Redes neuronales artificiales
Aprendizaje automático
Controlo de processos.
Grinding Mill
Artificial Neural Networks
Machine Learning
Mineral processing
Iron ore
Process control.
Tratamento de minérios
Moagem
Redes Neurais Artificiais
Aprendizado de Máquina
Controle de processos.
topic Tratamiento de minerales
Mena de hierro
Molienda
Redes neuronales artificiales
Aprendizaje automático
Controlo de processos.
Grinding Mill
Artificial Neural Networks
Machine Learning
Mineral processing
Iron ore
Process control.
Tratamento de minérios
Moagem
Redes Neurais Artificiais
Aprendizado de Máquina
Controle de processos.
description Step by step, the technologies provided by Industry 4.0 are being inserted in the mining processes and one of the opportunities to be explored is the use of Big Data and Advanced Analytics tools. In iron ore beneficiation plants, in the milling processes, the potential gains from machine learning tools tend to be amplified when combined with mathematical models derived from process knowledge, whether empirical or phenomenological. This article presents the application of artificial neural networks for the prediction of the main product quality parameter of a milling plant, combined with empirical equations that describe the milling process, to establish whether such equations can contribute to a better performance of the predictive models.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-02
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/32329
10.33448/rsd-v11i13.32329
url https://rsdjournal.org/index.php/rsd/article/view/32329
identifier_str_mv 10.33448/rsd-v11i13.32329
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/32329/29563
dc.rights.driver.fl_str_mv Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Vladmir Kronemberger Alves ; Ernandes Savio
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Vladmir Kronemberger Alves ; Ernandes Savio
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 11 No. 13; e84111332329
Research, Society and Development; Vol. 11 Núm. 13; e84111332329
Research, Society and Development; v. 11 n. 13; e84111332329
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
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