Impact variables in mining scheduling
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/34146 |
Resumo: | Mine planning is developed considering economic variables, grades, lithology, spatial position. These variables are used to determine the final pit limit and sequencing of operations. Normally, only the variables related to the grade are exhaustively sampled. The other variables are configured with average values. Multivariate statistical techniques make it possible to determine the variables with the greatest impact. Using a geological model of copper and gold, the final pit and mining sequencing will be determined using the Lerchs-Grossmann algorithm. The resulting block model will be evaluated for non-standard variables in the population. The population elements were standardized and properly transformed into continuous variables. The principal component analysis technique will be used to determine the most important variables of the mine and final pit sequencing. The objective of this work is to determine the most influential variables in determining the final pit and mining sequencing. Mine planning tools only present the end result of planning. They do not point out the most sensitive variables. It is important to determine the variables in which a small change in value is capable of turning a mined block into barren. The work confirmed the importance of economic variables related to the benefit function, however, it quantified that the spatial positioning of the block has similar importance to some economic variables. |
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Impact variables in mining scheduling Variables de impacto en la programación mineraVariáveis de impacto no sequenciamento de lavraOptimizationPrincipal component analysisAdvancesScheduling.MejoramientoAnálisis de componentes principalesAvancesSecuenciación.OtimizaçãoAvançosSequenciamentoAnálise de componentes principais.Mine planning is developed considering economic variables, grades, lithology, spatial position. These variables are used to determine the final pit limit and sequencing of operations. Normally, only the variables related to the grade are exhaustively sampled. The other variables are configured with average values. Multivariate statistical techniques make it possible to determine the variables with the greatest impact. Using a geological model of copper and gold, the final pit and mining sequencing will be determined using the Lerchs-Grossmann algorithm. The resulting block model will be evaluated for non-standard variables in the population. The population elements were standardized and properly transformed into continuous variables. The principal component analysis technique will be used to determine the most important variables of the mine and final pit sequencing. The objective of this work is to determine the most influential variables in determining the final pit and mining sequencing. Mine planning tools only present the end result of planning. They do not point out the most sensitive variables. It is important to determine the variables in which a small change in value is capable of turning a mined block into barren. The work confirmed the importance of economic variables related to the benefit function, however, it quantified that the spatial positioning of the block has similar importance to some economic variables.La planificación de la mina se desarrolla considerando variables económicas, leyes, litología, posición espacial. Estas variables se utilizan para determinar el límite final del tajo y la secuencia de operaciones. Normalmente, sólo se muestrean exhaustivamente las variables relacionadas con el grado. Las demás variables se configuran con valores medios. Las técnicas estadísticas multivariantes permiten determinar las variables de mayor impacto. Utilizando un modelo geológico de cobre y oro, la secuencia final del tajo y la extracción se determinará utilizando el algoritmo de Lerchs-Grossmann. El modelo de bloques resultante se evaluará para variables no estándar en la población. Los elementos de la población fueron estandarizados y debidamente transformados en variables continuas. La técnica de análisis de componentes principales se utilizará para determinar las variables más importantes de la secuenciación de la mina y el tajo final. El objetivo de este trabajo es determinar las variables más influyentes en la determinación del rajo final y la secuenciación del minado. Las herramientas de planificación minera solo presentan el resultado final de la planificación. No señalan las variables más sensibles. Es importante determinar las variables en las que un pequeño cambio de valor es capaz de convertir un bloque minado en estéril. El trabajo confirmó la importancia de las variables económicas relacionadas con la función de beneficio, sin embargo, cuantificó que el posicionamiento espacial de la manzana tiene una importancia similar a algunas variables económicas.O planejamento de mina é desenvolvido considerando variáveis econômicas, teores, litologia, posição espacial. Estas variáveis são utilizadas para determinar o limite final de cava e sequenciamento das operações. Normalmente são amostradas exaustivamente somente as variáveis relacionadas ao teor. As demais variáveis são configuradas com valores médios. As técnicas de estatística multivariada permitem determinar as variáveis de maior impacto. Utilizando um modelo geológico de cobre e ouro será determinado a cava final e sequenciamento de lavra utilizando o algoritmo de Lerchs-Grossmann. O modelo de blocos resultante será avaliado quanto as variáveis fora do padrão da população. Foram padronizados e transformados em variáveis contínuas adequadamente os elementos da população. Será utilizado a técnica de análise de componentes principais para determinar as variáveis mais importantes do sequenciamento de lavra e cava final. O objetivo deste trabalho é determinar as variáveis mais influentes na determinação de cava final e sequenciamento de lavra. As ferramentas de planejamento de mina apenas apresentam o resultado final do planejamento. Não apontam as variáveis mais sensíveis. É importante determinar as variáveis em que uma pequena variação no valor é capaz de transformar um bloco minerado em estéril. O trabalho confirmou a importância das variáveis econômica relacionadas a função benefício, entretanto quantificou que o posicionamento espacial do bloco possui importância semelhante a algumas variáveis econômicas.Research, Society and Development2022-09-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/3414610.33448/rsd-v11i12.34146Research, Society and Development; Vol. 11 No. 12; e107111234146Research, Society and Development; Vol. 11 Núm. 12; e107111234146Research, Society and Development; v. 11 n. 12; e1071112341462525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/34146/28935Copyright (c) 2022 Barbara Isabela Silva Campos; Felipe Ribeiro Souza; Hernani Mota de Limahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCampos, Barbara Isabela Silva Souza, Felipe Ribeiro Lima, Hernani Mota de 2022-09-26T11:56:08Zoai:ojs.pkp.sfu.ca:article/34146Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:49:33.980688Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Impact variables in mining scheduling Variables de impacto en la programación minera Variáveis de impacto no sequenciamento de lavra |
title |
Impact variables in mining scheduling |
spellingShingle |
Impact variables in mining scheduling Campos, Barbara Isabela Silva Optimization Principal component analysis Advances Scheduling. Mejoramiento Análisis de componentes principales Avances Secuenciación. Otimização Avanços Sequenciamento Análise de componentes principais. |
title_short |
Impact variables in mining scheduling |
title_full |
Impact variables in mining scheduling |
title_fullStr |
Impact variables in mining scheduling |
title_full_unstemmed |
Impact variables in mining scheduling |
title_sort |
Impact variables in mining scheduling |
author |
Campos, Barbara Isabela Silva |
author_facet |
Campos, Barbara Isabela Silva Souza, Felipe Ribeiro Lima, Hernani Mota de |
author_role |
author |
author2 |
Souza, Felipe Ribeiro Lima, Hernani Mota de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Campos, Barbara Isabela Silva Souza, Felipe Ribeiro Lima, Hernani Mota de |
dc.subject.por.fl_str_mv |
Optimization Principal component analysis Advances Scheduling. Mejoramiento Análisis de componentes principales Avances Secuenciación. Otimização Avanços Sequenciamento Análise de componentes principais. |
topic |
Optimization Principal component analysis Advances Scheduling. Mejoramiento Análisis de componentes principales Avances Secuenciación. Otimização Avanços Sequenciamento Análise de componentes principais. |
description |
Mine planning is developed considering economic variables, grades, lithology, spatial position. These variables are used to determine the final pit limit and sequencing of operations. Normally, only the variables related to the grade are exhaustively sampled. The other variables are configured with average values. Multivariate statistical techniques make it possible to determine the variables with the greatest impact. Using a geological model of copper and gold, the final pit and mining sequencing will be determined using the Lerchs-Grossmann algorithm. The resulting block model will be evaluated for non-standard variables in the population. The population elements were standardized and properly transformed into continuous variables. The principal component analysis technique will be used to determine the most important variables of the mine and final pit sequencing. The objective of this work is to determine the most influential variables in determining the final pit and mining sequencing. Mine planning tools only present the end result of planning. They do not point out the most sensitive variables. It is important to determine the variables in which a small change in value is capable of turning a mined block into barren. The work confirmed the importance of economic variables related to the benefit function, however, it quantified that the spatial positioning of the block has similar importance to some economic variables. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-10 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/34146 10.33448/rsd-v11i12.34146 |
url |
https://rsdjournal.org/index.php/rsd/article/view/34146 |
identifier_str_mv |
10.33448/rsd-v11i12.34146 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/34146/28935 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Barbara Isabela Silva Campos; Felipe Ribeiro Souza; Hernani Mota de Lima https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Barbara Isabela Silva Campos; Felipe Ribeiro Souza; Hernani Mota de Lima https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 12; e107111234146 Research, Society and Development; Vol. 11 Núm. 12; e107111234146 Research, Society and Development; v. 11 n. 12; e107111234146 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052771321511936 |