Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace.
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/12706 https://doi.org/10.21275/ART20203638 |
Resumo: | Being developed over the centuries, it currently occupies a prominent role in the world production scenario, being the stage of the process related to the obtaining of hot metal an element of great importance to establish the competitiveness of national steel. From this perspective, the control of the process of obtaining hot metal is relevant to ensure competitive prices and a sustainable process. Considering the presented situation, this research developed a committee machine, being three networks to predict each of the study variables, namely: i) fuel rate; ii) sulfur content in hot metal. The committee machine was developed to model the hot metal during the operation of a coke blast furnace, according to the input parameters provided. The results obtained by the committee machine were lower than those of the neural networks acting alone, and the following RMSE values were verified: i) fuel rate: 4.88 (network 1), 4.74 (network 2), 6.14 (network 3) and 4.67 (committee); ii) sulfur content: 0.00915 (network 1), 0.00917 (network 2), 0.00974 (network 3) and 0.00726 (committee). Considering the results obtained, the model can be used to provide important support in monitoring and decision making during the operation. |
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Assis, Paulo SantosCarvalho, Leonard de AraújoIrgaliyev, A.2020-09-08T21:17:32Z2020-09-08T21:17:32Z2019ASSIS, P. S.; CARVALHO, L. de A.; IRGALYEV, A. Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. International Journal of Science and Research, v. 8, n. 12, p. 1492-1495, dez. 2019. Disponível em: <https://www.ijsr.net/archive/v8i12/ART20203638.pdf>. Acesso em: 10 mar. 2020.2319-7064http://www.repositorio.ufop.br/handle/123456789/12706https://doi.org/10.21275/ART20203638Being developed over the centuries, it currently occupies a prominent role in the world production scenario, being the stage of the process related to the obtaining of hot metal an element of great importance to establish the competitiveness of national steel. From this perspective, the control of the process of obtaining hot metal is relevant to ensure competitive prices and a sustainable process. Considering the presented situation, this research developed a committee machine, being three networks to predict each of the study variables, namely: i) fuel rate; ii) sulfur content in hot metal. The committee machine was developed to model the hot metal during the operation of a coke blast furnace, according to the input parameters provided. The results obtained by the committee machine were lower than those of the neural networks acting alone, and the following RMSE values were verified: i) fuel rate: 4.88 (network 1), 4.74 (network 2), 6.14 (network 3) and 4.67 (committee); ii) sulfur content: 0.00915 (network 1), 0.00917 (network 2), 0.00974 (network 3) and 0.00726 (committee). Considering the results obtained, the model can be used to provide important support in monitoring and decision making during the operation.Licensed Under Creative Commons Attribution CC BY. Fonte: o próprio artigo.info:eu-repo/semantics/openAccessModelingArtificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPLICENSElicense.txtlicense.txttext/plain; charset=utf-8924http://www.repositorio.ufop.br/bitstream/123456789/12706/2/license.txt62604f8d955274beb56c80ce1ee5dcaeMD52ORIGINALARTIGO_ArtificialNeuralNetwork.pdfARTIGO_ArtificialNeuralNetwork.pdfapplication/pdf215359http://www.repositorio.ufop.br/bitstream/123456789/12706/1/ARTIGO_ArtificialNeuralNetwork.pdfb6b946332dfe68a97153d3039376c2ddMD51123456789/127062020-09-08 17:17:32.76oai:localhost: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ório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332020-09-08T21:17:32Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.pt_BR.fl_str_mv |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
title |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
spellingShingle |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. Assis, Paulo Santos Modeling |
title_short |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
title_full |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
title_fullStr |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
title_full_unstemmed |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
title_sort |
Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. |
author |
Assis, Paulo Santos |
author_facet |
Assis, Paulo Santos Carvalho, Leonard de Araújo Irgaliyev, A. |
author_role |
author |
author2 |
Carvalho, Leonard de Araújo Irgaliyev, A. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Assis, Paulo Santos Carvalho, Leonard de Araújo Irgaliyev, A. |
dc.subject.por.fl_str_mv |
Modeling |
topic |
Modeling |
description |
Being developed over the centuries, it currently occupies a prominent role in the world production scenario, being the stage of the process related to the obtaining of hot metal an element of great importance to establish the competitiveness of national steel. From this perspective, the control of the process of obtaining hot metal is relevant to ensure competitive prices and a sustainable process. Considering the presented situation, this research developed a committee machine, being three networks to predict each of the study variables, namely: i) fuel rate; ii) sulfur content in hot metal. The committee machine was developed to model the hot metal during the operation of a coke blast furnace, according to the input parameters provided. The results obtained by the committee machine were lower than those of the neural networks acting alone, and the following RMSE values were verified: i) fuel rate: 4.88 (network 1), 4.74 (network 2), 6.14 (network 3) and 4.67 (committee); ii) sulfur content: 0.00915 (network 1), 0.00917 (network 2), 0.00974 (network 3) and 0.00726 (committee). Considering the results obtained, the model can be used to provide important support in monitoring and decision making during the operation. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019 |
dc.date.accessioned.fl_str_mv |
2020-09-08T21:17:32Z |
dc.date.available.fl_str_mv |
2020-09-08T21:17:32Z |
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.citation.fl_str_mv |
ASSIS, P. S.; CARVALHO, L. de A.; IRGALYEV, A. Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. International Journal of Science and Research, v. 8, n. 12, p. 1492-1495, dez. 2019. Disponível em: <https://www.ijsr.net/archive/v8i12/ART20203638.pdf>. Acesso em: 10 mar. 2020. |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.ufop.br/handle/123456789/12706 |
dc.identifier.issn.none.fl_str_mv |
2319-7064 |
dc.identifier.doi.pt_BR.fl_str_mv |
https://doi.org/10.21275/ART20203638 |
identifier_str_mv |
ASSIS, P. S.; CARVALHO, L. de A.; IRGALYEV, A. Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. International Journal of Science and Research, v. 8, n. 12, p. 1492-1495, dez. 2019. Disponível em: <https://www.ijsr.net/archive/v8i12/ART20203638.pdf>. Acesso em: 10 mar. 2020. 2319-7064 |
url |
http://www.repositorio.ufop.br/handle/123456789/12706 https://doi.org/10.21275/ART20203638 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Licensed Under Creative Commons Attribution CC BY. Fonte: o próprio artigo. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Licensed Under Creative Commons Attribution CC BY. Fonte: o próprio artigo. |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFOP instname:Universidade Federal de Ouro Preto (UFOP) instacron:UFOP |
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UFOP |
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Repositório Institucional da UFOP |
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repositorio@ufop.edu.br |
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