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: | 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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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 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
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 |
instname_str |
Escola de Minas |
instacron_str |
ESCOLA DE MINAS |
institution |
ESCOLA DE MINAS |
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 |
_version_ |
1754122199062544384 |