Assessment of brazilian tailing dams by k means cluster analysis

Detalhes bibliográficos
Autor(a) principal: Paulo, Eliezer Antonio Amaral de
Data de Publicação: 2020
Outros Autores: Pereira, Carla Maria Silva Felisberto, Santos, Tatiana Barreto dos, Oliveira, Rudinei Martins de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/7811
Resumo: The exploitation of low content ores became possible due to the technological development. The tailing production from the mineral processing increased, leading the need of the number and capacity increase of the dams. As consequence, dam failure became more frequent, exemplified by Brumadinho/MG and Mariana/MG events in years 2019 and 2015. This article has the objective of applying the multivariate statistical cluster technique named k means to identifying the tailing dams registered in Brazilian Register of Dams of the National Mining Agency that presents similar characteristics to the failed dams from the last years. The technique was successfully applied and it was identified six cluster of dams. The failed dams were located in groups 1 and 2. Besides, the Brazilian tailing dams with high emergency level were located in the same cluster of failed dams and presents similar characteristics. This information does not attest that the dams from cluster 1 and 2 are unstable, but they must to be carefully evaluated.
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spelling Assessment of brazilian tailing dams by k means cluster analysisEvaluación de los diques de residuos mineros brasileros por análisis de cluster k meansAvaliação das barragens de rejeito brasileiras por meio da análise de agrupamentos k médiasDiques de residuos minerosAnálisis de conglomeradosk mediasRegistro brasileño de diques.Tailing damsCluster analysisk meansBrazilian register of dams.Barragem de rejeitosAnálise estatística de agrupamentosk médiasCadastro nacional de barragens.The exploitation of low content ores became possible due to the technological development. The tailing production from the mineral processing increased, leading the need of the number and capacity increase of the dams. As consequence, dam failure became more frequent, exemplified by Brumadinho/MG and Mariana/MG events in years 2019 and 2015. This article has the objective of applying the multivariate statistical cluster technique named k means to identifying the tailing dams registered in Brazilian Register of Dams of the National Mining Agency that presents similar characteristics to the failed dams from the last years. The technique was successfully applied and it was identified six cluster of dams. The failed dams were located in groups 1 and 2. Besides, the Brazilian tailing dams with high emergency level were located in the same cluster of failed dams and presents similar characteristics. This information does not attest that the dams from cluster 1 and 2 are unstable, but they must to be carefully evaluated.La explotación de minerales de bajo contenido se hizo posible debido al desarrollo tecnológico. La producción de residuos a partir del procesamiento de minerales aumentó, lo que llevó a la necesidad de aumentar el número y la capacidad de los diques. Como consecuencia, la falla de diques se hizo más frecuente, ejemplificada por los eventos de Brumadinho / MG y Mariana / MG en los años 2019 y 2015. Este artículo tiene el objetivo de aplicar la técnica estadística de conglomerados llamada k medias para identificar los diques de residuos mineros registrados en el Registro Brasileño de Diques de la Agencia Nacional de Minería que presentan características similares a los diques fallidos de los últimos años. La técnica fue aplicada con éxito y se identificaron seis grupos de diques. Los diques fallidos se ubicaron en los grupos 1 y 2. Además, los diques de residuos brasileños con alto nivel de emergencia se ubicaron en el mismo grupo de diques fallidos y presentan características similares. Esta información no significa que estos diques se encuentren en una situación inestable, pero deben ser evaluados cuidadosamente.Com a evolução tecnológica, tornou-se possível a lavra de minérios cada vez mais pobres em teor. Dessa forma, a produção de rejeitos oriundos do tratamento de minérios aumentou, levando à necessidade de ampliação das barragens em número e capacidade para armazenamento desses resíduos. Como consequência, rupturas barragens de grandes dimensões passaram a acontecer com uma frequência alarmante, como por exemplo os episódios de Brumadinho/MG em 2019 e Mariana/MG em 2015. Este artigo tem por objetivo a aplicação da técnica de estatística multivariada de agrupamento k médias para identificar as barragens de rejeito cadastradas no Cadastro Brasileiro de Barragens da Agência Nacional de Mineração que sejam semelhantes àquelas que romperam no país nos últimos anos. A técnica foi aplicada com sucesso e foram identificados seis grupos de barragens. Os grupos 1 e 2 acondicionaram as três últimas barragens de rejeito de mineração que se romperam. Foi possível notar que muitas das barragens que se encontram em estado de emergência tem características semelhantes às que se romperam. Essa informação não significa que essas barragens se encontram em situação instável, mas as mesmas devem ser avaliadas cuidadosamente.Research, Society and Development2020-09-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/781110.33448/rsd-v9i9.7811Research, Society and Development; Vol. 9 No. 9; e731997811Research, Society and Development; Vol. 9 Núm. 9; e731997811Research, Society and Development; v. 9 n. 9; e7319978112525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/7811/6942Copyright (c) 2020 Eliezer Antonio Amaral de Paulo; Carla Maria Silva Felisberto Pereira; Tatiana Barreto dos Santos; Rudinei Martins de Oliveirahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessPaulo, Eliezer Antonio Amaral dePereira, Carla Maria Silva Felisberto Santos, Tatiana Barreto dosOliveira, Rudinei Martins de 2020-09-18T01:42:11Zoai:ojs.pkp.sfu.ca:article/7811Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:30:25.443056Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Assessment of brazilian tailing dams by k means cluster analysis
Evaluación de los diques de residuos mineros brasileros por análisis de cluster k means
Avaliação das barragens de rejeito brasileiras por meio da análise de agrupamentos k médias
title Assessment of brazilian tailing dams by k means cluster analysis
spellingShingle Assessment of brazilian tailing dams by k means cluster analysis
Paulo, Eliezer Antonio Amaral de
Diques de residuos mineros
Análisis de conglomerados
k medias
Registro brasileño de diques.
Tailing dams
Cluster analysis
k means
Brazilian register of dams.
Barragem de rejeitos
Análise estatística de agrupamentos
k médias
Cadastro nacional de barragens.
title_short Assessment of brazilian tailing dams by k means cluster analysis
title_full Assessment of brazilian tailing dams by k means cluster analysis
title_fullStr Assessment of brazilian tailing dams by k means cluster analysis
title_full_unstemmed Assessment of brazilian tailing dams by k means cluster analysis
title_sort Assessment of brazilian tailing dams by k means cluster analysis
author Paulo, Eliezer Antonio Amaral de
author_facet Paulo, Eliezer Antonio Amaral de
Pereira, Carla Maria Silva Felisberto
Santos, Tatiana Barreto dos
Oliveira, Rudinei Martins de
author_role author
author2 Pereira, Carla Maria Silva Felisberto
Santos, Tatiana Barreto dos
Oliveira, Rudinei Martins de
author2_role author
author
author
dc.contributor.author.fl_str_mv Paulo, Eliezer Antonio Amaral de
Pereira, Carla Maria Silva Felisberto
Santos, Tatiana Barreto dos
Oliveira, Rudinei Martins de
dc.subject.por.fl_str_mv Diques de residuos mineros
Análisis de conglomerados
k medias
Registro brasileño de diques.
Tailing dams
Cluster analysis
k means
Brazilian register of dams.
Barragem de rejeitos
Análise estatística de agrupamentos
k médias
Cadastro nacional de barragens.
topic Diques de residuos mineros
Análisis de conglomerados
k medias
Registro brasileño de diques.
Tailing dams
Cluster analysis
k means
Brazilian register of dams.
Barragem de rejeitos
Análise estatística de agrupamentos
k médias
Cadastro nacional de barragens.
description The exploitation of low content ores became possible due to the technological development. The tailing production from the mineral processing increased, leading the need of the number and capacity increase of the dams. As consequence, dam failure became more frequent, exemplified by Brumadinho/MG and Mariana/MG events in years 2019 and 2015. This article has the objective of applying the multivariate statistical cluster technique named k means to identifying the tailing dams registered in Brazilian Register of Dams of the National Mining Agency that presents similar characteristics to the failed dams from the last years. The technique was successfully applied and it was identified six cluster of dams. The failed dams were located in groups 1 and 2. Besides, the Brazilian tailing dams with high emergency level were located in the same cluster of failed dams and presents similar characteristics. This information does not attest that the dams from cluster 1 and 2 are unstable, but they must to be carefully evaluated.
publishDate 2020
dc.date.none.fl_str_mv 2020-09-05
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/7811
10.33448/rsd-v9i9.7811
url https://rsdjournal.org/index.php/rsd/article/view/7811
identifier_str_mv 10.33448/rsd-v9i9.7811
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/7811/6942
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv 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. 9 No. 9; e731997811
Research, Society and Development; Vol. 9 Núm. 9; e731997811
Research, Society and Development; v. 9 n. 9; e731997811
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
_version_ 1797052739842211840

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