Mine-to-mill optimization represents a paradigm shift that integrates mine extraction and mineral processing into a unified value chain (Dance, 2013; Ahmad, 2026). Mechanical crushing and grinding dominate plant operations, consuming between 5 to 25 kWh per tonne and accounting for 50% to 70% of total operational power requirements (Ahmad, 2026). Explosive energy provides the first and most cost-effective stage of size reduction because explosive chemical forces fracture solid rock more efficiently than mechanical equipment (Dance, 2013). Poorly designed blasts generate oversized boulders and inconsistent feed material, which cause crusher jams and severe operational bottlenecks (Uplatz, 2023). Strategic investment in upstream blast design achieves finer fragmentation, preventing downstream crushing delays and maximizing throughput across the comminution circuit (Dance, 2013; Khajouei Sirjani et al., 2026).
Chemical explosive energy during blasting breaks intact rock at a fraction of the cost required by mechanical crushing (Dance, 2013). Increases in mine drilling and blasting expenditures are typically outweighed 7 to 10 times over by direct reductions in downstream milling operating costs (Dance, 2013). Higher-energy blasting yields finer primary fragmentation, specifically generating fine particles under 12 mm that pass directly through primary mills into secondary grinding circuits (Dance, 2013). Finer feed size distributions reduce total comminution circuit energy requirements by 8% to 15% (Ahmad, 2026). Furthermore, tailored blast fragmentation increases concentrator mill throughput by up to 20% to 30% (Dance, 2013). Elimination of oversized boulders also prevents costly secondary blasting, loader downtime, and primary crusher blockages (Pal Roy et al., 2023).
Blast pattern geometry and explosive spatial distribution directly dictate fragment size distributions (Hosseinzadeh et al., 2025). Specific charge (powder factor) and burden distance serve as the two most critical parameters controlling median particle size (Hosseinzadeh et al., 2025). Advanced technique implementation, such as air-deck blasting, introduces discrete air gaps within the explosive column to alter shock wave reflection and gas pressure propagation (Khajouei Sirjani et al., 2026). Field measurements and LS-DYNA numerical simulations at the Gol-e-Gohar Iron Ore Mine show that air-decking reduces median fragment size (D50) by 10% to 15% and oversize material (D80) by 15% to 18% (Khajouei Sirjani et al., 2026). Precision adjustments to burden and spacing ratios further enhance explosive energy distribution without increasing overall explosive consumption (Cotrina-Teatino et al., 2025).
Traditional empirical fragmentation models like the Kuz–Ram equation fail to capture complex, non-linear rock mass interactions (Hosseinzadeh et al., 2025). Machine learning algorithms provide superior predictive accuracy for median particle size (P50 or X50), with hybrid XGBoost and Support Vector Regression (SVR) models achieving coefficient of determination (R²) values above 0.90 on field datasets (Cotrina-Teatino et al., 2025; Hosseinzadeh et al., 2025). Evolutionary multi-objective optimization algorithms, such as NSGA-II, Genetic Algorithms (GA), and Particle Swarm Optimization (PSO), combine with predictive machine learning models to solve conflicting operational priorities (Cotrina-Teatino et al., 2025; Hosseinzadeh et al., 2025). These hybrid AI frameworks generate Pareto-optimal solution sets, enabling engineers to select blast parameters that balance drilling and blasting expenditures against targeted fragment sizes (Hosseinzadeh et al., 2025).
Blast design optimization serves as an essential first stage in mineral comminution rather than an isolated excavation cost (Dance, 2013; Ahmad, 2026). Precision pattern engineering, air-decking techniques, and AI-driven multi-objective optimization provide reliable mechanisms for controlling rock fragmentation distributions (Khajouei Sirjani et al., 2026; Hosseinzadeh et al., 2025). Mineral extraction operations achieve smooth equipment operation, reduced energy consumption, and increased profitability when they shift from cost-minimised blasting to value-maximised fragmentation (Dance, 2013).
References
Ahmad, A. (2026). Innovative comminution strategies for optimizing mineral concentrate quality. Frontiers in Engineering, Science & Management, 1(1), 40–44.
Cotrina-Teatino, M. A., Marquina-Araujo, J. J., Mamani-Quispe, J. N., Arango-Retamozo, S. M., Ccatamayo-Barrios, J. H., Gonzalez-Vasquez, J. A., Donaires-Flores, T., & Calla-Huayapa, M. A. (2025). Optimization of Fragmentation and Operational Costs of Drilling and Blasting using Hybrid Machine Learning Models in an Open-Pit Mine in Peru. Journal of Mining and Environment, 16(4), 1195–1219.
Dance, A. (2013). Mine-To-Mill Optimisation: Effect Of Feed Size On Mill Throughput. SRK Consulting / SRK News, Issue 48.
Hosseinzadeh, S., et al. (2025). A Novel AI-Driven Framework for Predicting Fragmentation and Cost-Oriented Design Optimization Using a Hybrid XGBoost–NSGA-II Approach. Journal of Artificial Intelligence, Applications and Innovations, 2(2), 85–99.
Khajouei Sirjani, M., et al. (2026). Field-validated numerical investigation of rock fragmentation in conventional and air-deck blasting using LS-DYNA. Journal of Mining and Environment.
Pal Roy, P., et al. (2023). Optimization of powder factor, fragmentation and oversized boulders through subsystem studies in an opencast coal mine. International Journal of Mining and Geo-Engineering, 57(2), 195–203.
Uplatz. (2023). Rock Fragmentation Fundamentals | Mining Engineering [Video]. YouTube.
