Block Sequencing Using Geometallurgical Parameters in Stochastic Short-Term Models

Abstract

Abstract

One of the primary inputs to short-term mine planning is the block model, which includes estimated or simulated run-of-mine grades calculated using geostatistical techniques. The ore label is usually assigned to the blocks by analyzing cut-off grades, determined from the grades of the most important variable related to the mineral processed in the plant and sold as concentrate. Zones below the cut-off grade usually indicate low-recovery areas, but in some situations, the yield obtained has quantity and quality to be further used, paying its costs in the plant with a profit. Zones with values above the cut-off do not consistently guarantee mineral recovery, as even high-grade zones may prove non-recoverable due to contaminants that can affect the beneficiation process. These factors increase the complexity of mining planning, requiring consideration of geometallurgical properties in the selection and sequencing of blocks sent to the processing plant. This study presents a sequence of selected mineable blocks, with the yield estimated using neural networks applied to each simulated block containing the run-of-mine grades. This approach minimizes the metallurgical risk over short-term planning periods. New paradigms are proposed for short-term planning optimization, not relying solely on chemical variables but also incorporating geometallurgical variables.