Short-term mine scheduling targeting stationary grades

• Published in: REM - International Engineering Journal 2022-03, Vol.75 (1), p.73-82
• Main Authors: Toledo, Augusto Andres Torres, Marques, Diego Machado, Costa, João Felipe Coimbra Leite, Capponi, Luciano Nunes
• Format: Article
• Language: English
• Subjects: block sequencing; ENGINEERING, MECHANICAL; genetic algorithm; GEOLOGY; MECHANICS; METALLURGY & METALLURGICAL ENGINEERING; short-term planning; stochastic planning
• ISSN: 2448-167X; 2448-167X
• DOI: 10.1590/0370-44672020750134

Abstract 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 SiO2 values at a major iron ore mine.