Abstract
The definition of ore and waste blocks is typically made using run-of-mine cut-off grades, calculated with Lane’s formula. This approach implicitly considers metallurgical recovery and yield; however, it overlooks one important ore property: the quality of the product concentrate. This problem can be overcome by leveraging extensive geometallurgical knowledge and this paper presents an example of the gains that can be achieved by using this information. The study was conducted in a phosphate mine, where the current definition of ore/waste blocks is based on P 2 O 5 and CaO grades. The general solution proposed here is to incorporate geometallurgical information in the cut-off’s decision-making process by applying hierarchical indicator kriging to assign a concentrate category to each block and to apply a neural network to forecast each block yield. This information (yield and block category) is then used to define a block as ore, marginal ore, or waste. Using the proposed approach, the percentage of blocks classified as ore in the bebedourite domain was 61.6% of all bebedourite blocks, whereas using the current criterion, this number was only 23%, highlighting a significant amount of ore not taken into account when using the traditional method. For the carbonatite domain, the impact was in the opposite direction, as 27.6% of the carbonatite blocks were classified as ore using the current criterion, and when the geometallurgical cut-off was applied, the number of ore blocks became only 8.2% of all carbonatite blocks, showing that several waste blocks would have been misclassified as ore.