A Multilayer Dig-Limit Approach for Reducing Ore and Profit Losses in an Open-Pit Mine Having Complex Orebody

Rock fragment movement during blasting operations is a major cause of ore and profit losses in hard rock open-pit mines having a complex-orebody. To address this issue, a novel multilayer dig-limit approach, which well considers blast movement in dig-limit optimization, was proposed in this study by combining machine learning techniques and practical heuristic algorithms. First, horizontal and vertical blast-induced rock movement distances were predicted using a supervised learning model. Then, the movement direction of rock fragments was computed based on the initiation sequence. After meshing the blast block into rock units, the blasted muckpile and post-blast ore boundary were determined, providing a good basis for dig-limit determination. Finally, the optimized dig-limit with maximum profit can be calculated using a practical heuristic algorithm. By applying this method in a case study, the ore recovery and economic profit were improved, compared with manually drawn dig-limit method. Additionally, the impact of equipment size, number of layers and powder factor on the application of this method was discussed. The obtained results indicated that ore and profit losses can be reduced with a decreased equipment size, increased number of layers and decreased powder factor.