Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil

Detalhes bibliográficos
Autor(a) principal: Fernandes,Fernanda Gontijo
Data de Publicação: 2016
Outros Autores: Cabral,Ivo Eyer
Tipo de documento: Artigo
Idioma: eng
Título da fonte: REM. Revista Escola de Minas (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075
Resumo: Abstract 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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spelling Regression model utilization to estimate the mass recovery of a phosphate mine in Brazilgeometallurgymultiple regression analysismass recoveryphosphateAbstract 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.Escola de Minas2016-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075Rem: Revista Escola de Minas v.69 n.1 2016reponame:REM. Revista Escola de Minas (Online)instname:Escola de Minasinstacron:ESCOLA DE MINAS10.1590/0370-44672015690155info:eu-repo/semantics/openAccessFernandes,Fernanda GontijoCabral,Ivo Eyereng2017-10-18T00:00:00Zoai:scielo:S0370-44672016000100075Revistahttp://www.scielo.br/remhttps://old.scielo.br/oai/scielo-oai.phpeditor@rem.com.br1807-03530370-4467opendoar:2017-10-18T00:00REM. Revista Escola de Minas (Online) - Escola de Minasfalse
dc.title.none.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
mass recovery
phosphate
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
mass recovery
phosphate
topic geometallurgy
multiple regression analysis
mass recovery
phosphate
description Abstract 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.none.fl_str_mv 2016-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672016000100075
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0370-44672015690155
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 Escola de Minas
publisher.none.fl_str_mv Escola de Minas
dc.source.none.fl_str_mv Rem: Revista Escola de Minas v.69 n.1 2016
reponame:REM. Revista Escola de Minas (Online)
instname:Escola de Minas
instacron:ESCOLA DE MINAS
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reponame_str REM. Revista Escola de Minas (Online)
collection REM. Revista Escola de Minas (Online)
repository.name.fl_str_mv REM. Revista Escola de Minas (Online) - Escola de Minas
repository.mail.fl_str_mv editor@rem.com.br
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