Abstract
Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types’ homogeneity. The proposed algorithm’s application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints.
Highlights
Deposits are routinely discretized into blocks, which are assigned economic values based on the cost of extraction and each block’s expected value [1]
High merging thresholds produce a clustering pattern with a high homogeneity of leach and mill destinations: when setting the merging “threshold-1” at 0.8, clustering of Marvin data leads to 75% leach, 89% mill destination homogeneities, and an average cluster size of 22
* Total blocks clustered on the bench = 100%; the algorithm run time = 10.97 s
Summary
Deposits are routinely discretized into blocks, which are assigned economic values based on the cost of extraction and each block’s expected value [1]. Interpretation of the economic block model defines the Ultimate Pit Limit (UPL) [2], which constrains the open pit dimensions in which maximum undiscounted value generation is predicted while honoring block precedence requirements, as well as physical and operational constraints. Given that a UPL may contain thousands to millions of blocks, defining a multi-period, long-term production schedule is challenging and computationally intensive. Grouping blocks leads to the definition of clusters that can assist both long- and short-term production planning. It should be noted that the long-term production schedule informs the short-term production planning process, albeit subject to its own set of specific constraints [3]. While long-term planning attempts to avoid any precedence constraints between target ore zones, short-term planning seeks to delineate mineable ore shapes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
