Abstract
Material cut-off grade (COG) is one of the most critical factors in determining the profitability of a mining operation. Traditional approaches to COG analysis determine the split between ore and waste by using a variety of costs with a fixed set of grades. However, the reality is that mining is a complex process and this simplified approach may not be appropriate for many operations.
A detailed analysis of COG should include many additional factors such as: variable costs and commodity prices, multiple material destinations and ore sources, physical limitations on processing plants, material stockpiling and rehandle, ore constituents, and revenues based on a final blended product. This paper illustrates how these factors can be included in the COG analysis by using linear programming (LP) and mixed-integer linear programming (MILP). This provides an optimized COG that accounts for the complexities of real-world mining.