Integrating Underground Blast Fragmentation Modeling for Sustainable Mine-to-Mill Optimization: A Focus on Blast Fragmentation and Energy Efficiency in Comminution Circuits

This paper presents a novel approach aimed at optimizing underground mine bench blasting practices to minimize energy consumption in downstream comminution processes. The proposed methodology integrates multivariate regression analysis modeling of blast-induced particle size distributions with the Work Index calculation, offering a comprehensive framework for bench blast design optimization. The efficacy of this approach is demonstrated through a comparative analysis of three bench blast designs conducted at an underground aggregate mine in central Kentucky. Comminution processes, particularly in underground mining operations, account for a significant portion of energy consumption. By focusing on bench blast design fragmentation optimization, it is possible to achieve the optimum particle sizes for the comminution process, thereby reducing the energy requirements for subsequent crushing and grinding operations. To this end, the proposed algorithmic approach leverages advanced statistical modeling techniques to predict particle size distributions resulting from different bench blast designs. These predictions are then integrated with the Work Index calculation, providing insights into the energy efficiency of each blast design. The case study conducted at the underground aggregate mine in central Kentucky serves as a practical demonstration of the proposed methodology. It compares the energy consumption associated with three distinct bench blast designs to evaluate the efficacy of the algorithmic approach. The results highlight the potential for significant energy savings achievable through optimized bench blast design strategies. Overall, this paper contributes to the growing body of research aimed at promoting sustainability in mining operations. By optimizing underground mine bench blasting practices, mining companies can reduce their environmental footprint, improve operational efficiency, and enhance long-term economic viability. The proposed methodology offers a practical framework for achieving these objectives, with demonstrated effectiveness in real-world applications.