Sequenciamento direto de blocos em modelos estocásticos com multiminas e multidestinos

• Ano de defesa: 2018
• Autor(a) principal: Alex Flávio de Oliveira Miranda
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ENG - DEPARTAMENTO DE ENGENHARIA MINAS; Programa de Pós-Graduação em Engenharia Metalúrgica, Materiais e de Minas; UFMG
• Palavras-chave em Português: Planejamento de lavra; Sequenciamento direto de blocos; Planejamento mineiro; Engenharia de minas; Tecnologia mineral
• Link de acesso: http://hdl.handle.net/1843/31389

The methods that are widely known today for optimization of a discrete block model were based on graph theory and among those most used by the mining industry is a solution found by Lerchs and Grossmann in 1965, which was consolidated as the process of traditional mining planning. The exploitation of the mineral is basically conditioned by its economic viability. Once the mining and process route has been chosen that gives the best financial return to the investor, it is necessary to make a "path" (or trajectory) to exploit this in order to maximize the benefit from this activity and the steps for consummation of this objective consist of: determination of the final pit limit, definition of pushbacks and mining sequencing (usually annual). The most popular method for determining the optimal extraction sequence is the use of a final pit algorithm applied through successive metal price changes. However, this technique does not consider the value of money in time because it assumes that all blocks will be mining in the same time. Therefore, for multi-purpose and multidestine models, there is a limitation of the current methodology, since it consists of the optimization of each mine separately which may not be a global optimization solution. Currently, with the advancement of computational power to solve complex problems, it is possible to apply the discount rate for the mining sequence through direct block sequencing (DBS). DBS is the main advance for the application of the discount rate in traditional sequencing, without the need to define the final pit and pushbacks, since the method is able to analyze each block individually and apply the discount factor at the exact moment of its withdrawal in time and, thus, there is a sequence where the net present value (NPV) is more assertive compared to the traditional model. This work proposes the application of a global stochastic optimization model using the DBS for a copper mining complex with two mines, a pre-existing copper stockpile and two treatment streams, compares several scenarios analyzing the best alternative for the proposed problem.