Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil.
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/9054 https://doi.org/10.1590/0370-44672015690155 |
Resumo: | The construction of block models with an estimation of grades in situ is a common practice throughout resource evaluation. However, this information is not enough to understand the behavior of the ore in the beneficiation process. Geometallurgy proposes the addition of the ore´s metallurgical behavior in the block model, making it more dependable and adhering when it comes to production capacity, which generates financial earnings and brings risks down. Mass recovery is an important metallurgical variable for economic and mine planning. This is often underused, due to the lack of data, making it hard to use in the planning process. In order to achieve better use of the data available, the multiple regression analysis technique was used so as to develop a statistic model that would relate the mass recovery with the in situ grades, allowing that deposit regions with no available metallurgical information have an estimation of this variable’s values. |
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Fernandes, Fernanda GontijoCabral, Ivo Eyer2017-10-26T11:53:13Z2017-10-26T11:53:13Z2016FERNANDES, F. G.; CABRAL, I. E. Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. Revista Escola de Minas, v. 69, p. 75-77, 2016. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075>. Acesso em: 25 ago. 2017.1807-0360http://www.repositorio.ufop.br/handle/123456789/9054https://doi.org/10.1590/0370-44672015690155The construction of block models with an estimation of grades in situ is a common practice throughout resource evaluation. However, this information is not enough to understand the behavior of the ore in the beneficiation process. Geometallurgy proposes the addition of the ore´s metallurgical behavior in the block model, making it more dependable and adhering when it comes to production capacity, which generates financial earnings and brings risks down. Mass recovery is an important metallurgical variable for economic and mine planning. This is often underused, due to the lack of data, making it hard to use in the planning process. In order to achieve better use of the data available, the multiple regression analysis technique was used so as to develop a statistic model that would relate the mass recovery with the in situ grades, allowing that deposit regions with no available metallurgical information have an estimation of this variable’s values.A REM - International Engineering Journal - autoriza o depósito de cópia de artigos dos professores e alunos da UFOP no Repositório Institucional da UFOP. Licença concedida mediante preenchimento de formulário online em 12 set. 2013.info:eu-repo/semantics/openAccessGeometallurgyMultiple regression analysisPhosphateMass recoveryRegression model utilization to estimate the mass recovery of a phosphate mine in Brazil.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/123456789/9054/2/license.txt62604f8d955274beb56c80ce1ee5dcaeMD52ORIGINALARTIGO_RegressionModelUtilization.pdfARTIGO_RegressionModelUtilization.pdfapplication/pdf143111http://www.repositorio.ufop.br/bitstream/123456789/9054/1/ARTIGO_RegressionModelUtilization.pdf189b04b3c3cd48cd16ee1eeae0c17027MD51123456789/90542020-02-17 10:54:17.443oai:localhost: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ório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332020-02-17T15:54:17Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.pt_BR.fl_str_mv |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
title |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
spellingShingle |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. Fernandes, Fernanda Gontijo Geometallurgy Multiple regression analysis Phosphate Mass recovery |
title_short |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
title_full |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
title_fullStr |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
title_full_unstemmed |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
title_sort |
Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. |
author |
Fernandes, Fernanda Gontijo |
author_facet |
Fernandes, Fernanda Gontijo Cabral, Ivo Eyer |
author_role |
author |
author2 |
Cabral, Ivo Eyer |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Fernandes, Fernanda Gontijo Cabral, Ivo Eyer |
dc.subject.por.fl_str_mv |
Geometallurgy Multiple regression analysis Phosphate Mass recovery |
topic |
Geometallurgy Multiple regression analysis Phosphate Mass recovery |
description |
The construction of block models with an estimation of grades in situ is a common practice throughout resource evaluation. However, this information is not enough to understand the behavior of the ore in the beneficiation process. Geometallurgy proposes the addition of the ore´s metallurgical behavior in the block model, making it more dependable and adhering when it comes to production capacity, which generates financial earnings and brings risks down. Mass recovery is an important metallurgical variable for economic and mine planning. This is often underused, due to the lack of data, making it hard to use in the planning process. In order to achieve better use of the data available, the multiple regression analysis technique was used so as to develop a statistic model that would relate the mass recovery with the in situ grades, allowing that deposit regions with no available metallurgical information have an estimation of this variable’s values. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2017-10-26T11:53:13Z |
dc.date.available.fl_str_mv |
2017-10-26T11:53:13Z |
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 |
FERNANDES, F. G.; CABRAL, I. E. Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. Revista Escola de Minas, v. 69, p. 75-77, 2016. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075>. Acesso em: 25 ago. 2017. |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.ufop.br/handle/123456789/9054 |
dc.identifier.issn.none.fl_str_mv |
1807-0360 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1590/0370-44672015690155 |
identifier_str_mv |
FERNANDES, F. G.; CABRAL, I. E. Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil. Revista Escola de Minas, v. 69, p. 75-77, 2016. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075>. Acesso em: 25 ago. 2017. 1807-0360 |
url |
http://www.repositorio.ufop.br/handle/123456789/9054 https://doi.org/10.1590/0370-44672015690155 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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