Resumo Na mineração a céu aberto, na etapa de planejamento a curto prazo são desenhados constantemente polígonos usados no planejamento de produção, onde geralmente o processo de desenho é manual, demandando o tempo e esforço dos planejadores. Os polígonos gerados no planejamento de curto prazo devem apresentar a menor variabilidade possível entre eles, para que as restrições da planta de processamento sejam atendidas. Desenhar polígonos de curto prazo de forma automática, sequenciar todos os blocos de minério em cada polígono, e conectar um polígono a outro é o foco deste trabalho. Para isso, foi feito uso de algoritmos genéticos e dinâmicos, desenvolvidos em linguagem de programação Python e operacionalizados no software geoestatístico SGeMS. Gerou-se múltiplas iterações para cada um dos avanços individuais de blocos de minério, gerando regiões ou polígonos, e selecionando as regiões de menor variabilidade de teores. As funções de distribuição de probabilade dos teores de cada avanço foram comparadas a função da distribuição de teores global do corpo de minério, os resultados mostram que os polígonos possuem distribuições semelhantes à distribuição de referência, então foi possível sequenciar os blocos de forma operacional garantindo quasi-estacionaridade dos parâmetros. Palavras-chave: Planejamento de Curto prazo; Sequenciamento de blocos; Estocástico; Função de distribuição provável; Algoritmo genético.
In short-term mine planning, mining scheduling is generally defined by designing dig-lines, allocated on benches. The mined ore will be sent to stockpiles, homogenization piles, or a concentration plant. The process to design dig-lines is usually done manually, whereby multiple simultaneous mining fronts are time-consuming and labour-intensive. The manual design of dig-lines tends to produce high variability of the grades throughout certain periods. Due to the limited time to manually multiple test dig-line design alternatives in short term planning, it is impossible to ensure production under stationary mean grades and variance. This article proposes an alternative to design short-term dig-lines, through an optimization process that joins and sequences the blocks in the block model over weeks or months, ensuring low variability of grades among periods. The methodology proposed generates multiple random paths starting at seed-points representing the locations and numbers of shovels previously selected by the mine planner. It tests multiple polygons representing a set of first dig-lines, comparing them with others, and keeping the dig-lines of low variability closer to a specific ore grade probability distribution, discarding the rest of the iterations. The process is repeated for the next dig-line. The block grades' probability distribution of all iterations is compared to a reference-grade histogram, and the iterations with the grade histogram more adherent are selected. Union-find and genetic algorithms were used to optimize the dig-lines aiming at the possible stationary grade distribution. The mean and variance of the reference model are 2.13% and 0.64% 2 , respectively. The mean for the automated draw dig-lines is closer to these values than the ones manually drawn. The method ensures more constant quality and quantity of ore production along a period planned, matching a target grade probability distribution. The methodology is illustrated using SiO 2 values at a major iron ore mine.
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