Integrating Geometallurgical Variables into Mine Planning Using Block Economic Value and Processing Time Constraints: A Case Study of a Copper Deposit

Abstract

Geometallurgy provides a framework for incorporating the spatial variability of ore processing responses into mine planning, although its practical implementation in operating mines remains limited due to the scarcity of dedicated metallurgical datasets. This study presents a case study using real operational data from the Salobo copper–gold mine and aims to evaluate how geometallurgical variables can be integrated into long-term mine planning using a limited geometallurgical dataset. Predictive models for Cu metallurgical recovery, Cu concentrate grade, and specific energy were developed from drillhole samples originally collected for resource evaluation. These models were incorporated into a Direct Block Scheduling framework through the formulation of block economic values, enabling the representation of processing-related variability within mine planning decisions. Two planning scenarios were evaluated: the Geomet scenario, which incorporates geometallurgical variables into block economic value without explicit operational time restrictions, and the Geomet_Constraints scenario, which additionally includes an explicit annual processing time constraint based on plant capacity. Although the predictive models exhibited low-to-moderate predictive performance (R2 = 0.45–0.60), their predictions were incorporated into the block model to represent the estimated spatial variability of processing responses. The results show that the Geomet_Constraints scenario generates operationally feasible schedules that comply with plant processing capacity and reduce overall material movement and stockpiling. In contrast, the Geomet scenario results in higher material movement and processing requirements exceeding plant capacity. From an economic perspective, the Geomet_Constraints scenario achieves a slightly higher cumulative NPV (approximately +2.8%), although this difference should be interpreted in the context of the predictive performance of the geometallurgical models. Overall, this case study illustrates how geometallurgical information can be incorporated into a long-term mine planning framework using a limited real-world dataset, and that the inclusion of processing time constraints plays a key role in improving the operational feasibility and economic performance of long-term production schedules.