Artificial neural networks applied to iron ore grinding process combined with empirical models
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
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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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 |
_version_ |
1797052717920681984 |