Inclusion of the geometallurgical variable specific energy in the mine planning using direct block scheduling
Inclusão da variável geometalúrgica energia específica no planejamento de lavra utilizando sequenciamento direto de blocos
Jônatas Franco Campos da Mata, Alizeibek Saleimen Nader, Douglas Batista Mazzinghy
Abstract
In mining projects, it is necessary to have a wide knowledge of the main variables of the mineral deposit before strategic mine planning takes effect. In the meantime, the application of geometallurgy has allowed the modeling of parameters related to the lithologies present in the deposit, such as the specific energy in comminution. This work intends to carry out a mine planning case study with the Direct Block Scheduling (DBS) methodology implemented in the MiningMath software and using the Marvin block model. The results indicate that the processing time of each block required more complex decision-making from the DBS algorithm to fulfill the objectives of mine planning. It is also noticed that the algorithms prioritize the extraction of blocks more released in the first years of the mine, anticipating profits and leaving, for the second half of the life of the project, the intensification of development, aiming to release more blocks for mining.
Keywords
Resumo
Em projetos de mineração, faz-se necessário o amplo conhecimento das principais variáveis do depósito mineral antes da efetivação do planejamento estratégico de lavra. Neste interim, a aplicação da geometalurgia tem permitido o modelamento dos parâmetros relacionados às litologias presentes na jazida, como a energia específica de cominuição. Este trabalho pretende realizar um estudo de caso de planejamento de lavra com a metodologia de Sequenciamento Direto de Blocos (SDB) implementada no software MiningMath e utilizando o modelo de blocos Marvin. Os resultados indicam que o tempo de processamento de cada bloco exigiu do algoritmo de SDB tomadas de decisão mais complexas para cumprir os objetivos do planejamento de lavra. Percebe-se, também, que os algoritmos priorizam a extração de blocos mais liberados nos primeiros anos da mina, antecipando lucros e deixando, para a segunda metade da vida do projeto, a intensificação do desenvolvimento, visando liberar maior quantidade de blocos para a lavra.
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Referências
1 Whittle D, Whittle J, Wharton C, Hall G. Strategic mine planning. 8nd ed. Vancouver: Gemcom Software International Inc; 2005.
2 Elkington T, Durham R. Integrated open pit pushback selection and production capacity optimization. Journal of Mining Science. 2011;47(2):177-190.
3 Lerchs H, Grossmann IF. Optimum design of open pit mines. In: Joint CORS and ORSA Conference. Montreal: Transactions CIM; 1965. p. 17-24.
4 Johnson TB. Optimum open pit mine production scheduling. Berkeley: Operations Research Department, University of California; 1968.
5 Newman AM, Rubio E, Caro R, Weintraub A, Eurek K. A review of operations research in mine planning. Interfaces. 2010;40(3):222-245.
6 Almeida AM. Surface constrained stochastic life-of-mine production schedulling [thesis]. Quebec, Canada: Department of Mining and Materials Engineering, McGill University; 2013.
7 Miranda A, Nader AS. Direct sequencing of blocks in stochastic models with multi-mines and multi-destinations. REM: International Engineering Journal. 2019;72(4):661-666.
8 Deutsch JL. Multivariate spatial modeling of metallurgical rock properties [thesis]. Alberta: Department of Civil and Environmental Engineering, University of Alberta; 2015.
9 Morales N, Seguel S, Cáceres A, Jélvez E, Alarcón M. Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning. Minerals. 2019;9(2):108.
10 Dunham S, Vann J. Geometallurgy, geostatistics and project value: does your block model tell you what you need to know? In: Project Evaluation Conference; 2007 June 19-20; Melbourne, Australia. Melbourne: Australasian Institute of Mining and Metallurgy; 2007. p. 189-196.
11 Espinoza D, Goycoolea M, Moreno E, Newman AN. MineLib: a library of open pit mining problems. Operations Research. 2012 [cited 2021 Nov 1];206(1):91-114. Available at: http://mansci-web.uai.cl/minelib/
12 MiningMath. MiningMath’s Knowledge Base. 2021 [cited 2021 Oct 5]. Available at: https://knowledge.miningmath. com/
13 London Metal Exchange – LME. 2021 [cited 2021 Nov 10]. Available at: https://www.lme.com/
14 Bergerman M, Delboni H, Nankran M. Variability study and optimization of the SAG milling circuit at Sossego. REM - International Engineering Journal. 2009;62(1):93-97
Submetido em:
13/01/2022
Aceito em:
20/06/2022
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