Incorporation of geological uncertainty in pit optimization with geostatistics simulation
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | REM - International Engineering Journal |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000300325 |
Resumo: | Abstract Risk mapping processes in mine planning and ore recovery are constantly used in the mining industry to increase decision making certainty based on the available information. However, it is not possible to predict the risk behavior in all of the project's boundary conditions and small variations in some of these conditions can cause a great impact on its financial return. Among the countless uncertainties existing in a mining project (operational, costs, market change), many authors define the geological uncertainty as the most critical one, capable of influencing the success of the project. Measurement and evaluation of the geological uncertainty of a mine planning project is crucial because the calculated risk can be translated into a financial risk of the project. This article presents a possible way to consider the geological uncertainty in the pit optimization step by using sequential Gaussian simulation. The results obtained from the case study on a copper deposit results in a simple procedure with significant increase in reliability for the project. |
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REM - International Engineering Journal |
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Incorporation of geological uncertainty in pit optimization with geostatistics simulationrisksgeological uncertaintysequential Gaussian simulationmine planningpit optimizationAbstract Risk mapping processes in mine planning and ore recovery are constantly used in the mining industry to increase decision making certainty based on the available information. However, it is not possible to predict the risk behavior in all of the project's boundary conditions and small variations in some of these conditions can cause a great impact on its financial return. Among the countless uncertainties existing in a mining project (operational, costs, market change), many authors define the geological uncertainty as the most critical one, capable of influencing the success of the project. Measurement and evaluation of the geological uncertainty of a mine planning project is crucial because the calculated risk can be translated into a financial risk of the project. This article presents a possible way to consider the geological uncertainty in the pit optimization step by using sequential Gaussian simulation. The results obtained from the case study on a copper deposit results in a simple procedure with significant increase in reliability for the project.Fundação Gorceix2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000300325REM - International Engineering Journal v.70 n.3 2017reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672016700134info:eu-repo/semantics/openAccessSouza,Rafael AlvarengaCabral,Ivo Eyereng2017-07-21T00:00:00Zoai:scielo:S2448-167X2017000300325Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2017-07-21T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false |
dc.title.none.fl_str_mv |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
title |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
spellingShingle |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation Souza,Rafael Alvarenga risks geological uncertainty sequential Gaussian simulation mine planning pit optimization |
title_short |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
title_full |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
title_fullStr |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
title_full_unstemmed |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
title_sort |
Incorporation of geological uncertainty in pit optimization with geostatistics simulation |
author |
Souza,Rafael Alvarenga |
author_facet |
Souza,Rafael Alvarenga Cabral,Ivo Eyer |
author_role |
author |
author2 |
Cabral,Ivo Eyer |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Souza,Rafael Alvarenga Cabral,Ivo Eyer |
dc.subject.por.fl_str_mv |
risks geological uncertainty sequential Gaussian simulation mine planning pit optimization |
topic |
risks geological uncertainty sequential Gaussian simulation mine planning pit optimization |
description |
Abstract Risk mapping processes in mine planning and ore recovery are constantly used in the mining industry to increase decision making certainty based on the available information. However, it is not possible to predict the risk behavior in all of the project's boundary conditions and small variations in some of these conditions can cause a great impact on its financial return. Among the countless uncertainties existing in a mining project (operational, costs, market change), many authors define the geological uncertainty as the most critical one, capable of influencing the success of the project. Measurement and evaluation of the geological uncertainty of a mine planning project is crucial because the calculated risk can be translated into a financial risk of the project. This article presents a possible way to consider the geological uncertainty in the pit optimization step by using sequential Gaussian simulation. The results obtained from the case study on a copper deposit results in a simple procedure with significant increase in reliability for the project. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-01 |
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=S2448-167X2017000300325 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2017000300325 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0370-44672016700134 |
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 |
Fundação Gorceix |
publisher.none.fl_str_mv |
Fundação Gorceix |
dc.source.none.fl_str_mv |
REM - International Engineering Journal v.70 n.3 2017 reponame:REM - International Engineering Journal instname:Fundação Gorceix (FG) instacron:FG |
instname_str |
Fundação Gorceix (FG) |
instacron_str |
FG |
institution |
FG |
reponame_str |
REM - International Engineering Journal |
collection |
REM - International Engineering Journal |
repository.name.fl_str_mv |
REM - International Engineering Journal - Fundação Gorceix (FG) |
repository.mail.fl_str_mv |
||editor@rem.com.br |
_version_ |
1754734690588164096 |