Powder factor is understood as the amount of explosive used per unit volume or tonnage to fragment a certain volume or tonnage of rock in blasting operations (Taiwo et al., 2024). On a surface mine, bench blasting serves as the main method of breaking down rock masses to fragments. One of the important variables in bench blasting is the ore hardness, comprising the rock compressive strength, density, and brittleness. Matching the explosive energy input to these inherent rock characteristics is crucial to ensure efficient blasting. The mismatch leads to ineffective fragmentation causing either oversize boulders impeding the crushing process or the overabundance of fine particles limiting mineral recovery.
One of the operational difficulties is that ore hardness is rarely the same throughout one blasting bench. Rock masses are heterogenic, having different weathering degree, structural discontinuity, and mineralogical composition. With bench drilling, the natural difference in rock mass intact strength and fracture density creates different blasting zones. High-hardness rock zones require more explosive energy input to provide proper fracturing, while low-hardness or very jointed ones – less explosive energy to prevent over-crushing. The failure to make powder factor correction for these natural differences results in the uneven muck pile creating extreme loading variation and excessive loading equipment wear.
Nowadays, modern mines operate on more complex data-based models rather than simple empirical formulas to adjust powder factor across different natural zones. Old ways of estimating powder factor are based on generalized assumptions that cannot reflect dynamic site conditions in heterogenic bench. Modern practices include measuring while drilling (MWD) data including drill penetration rate and torque to determine the heterogenic hardness profile of the bench. With input of the geological data in real time to artificial neural networks and other machine learning algorithms, it is possible to predict the local powder factor needed at different blocks in the blasting pattern (Taiwo et al., 2024).
Upon defining hardness profile of the bench, blasting engineers use practical and localized blasting design modifications. At zones that have high ore hardness, the powder factor should be increased by decreasing the bore burden and hole spacing or using explosive with higher energy input like high detonation velocity. In turn, the powder factor should be decreased at soft geological zones of the same bench by increasing the drill pattern or using lower-density explosive blends. Such an energy distribution allows concentrating explosive energy at the places of rock strength necessity, providing uniform rock mass fragmentation.
Adjustment of powder factors according to hardness profile affects the efficiency of subsequent comminution circuits. Variability of fragmentation created by non-adjustable powder factor in heterogeneous ore body limits the mill throughput and increases the specific energy consumption (Saldana et al., 2024). When the powder factor is locally adjusted in the hard zone, microfractures caused by blasting allow easier crushing and grinding. Customization of the blasting design according to rock hardness variations provides significant throughput increase and energy consumption decrease during the milling process (Mboyo et al., 2024).
In summary, the adjustment of powder factors to varying ore hardness is a dynamic and important engineering practice. Through delineation of the geological zones with real-time drilling data and machine learning, it is possible to distribute the explosive energy accurately. This engineering practice helps to mitigate expenses related to poor fragmentation and protect downstream equipment. Ultimately, the customization of powder factor to local rock characteristics allows closing the gap between extraction and comminution.
References
Mboyo, H. L., Huo, B., Mulenga, F. K., Fogang, P. M., & Kasongo, J. K. K. (2024). Assessing the Impact of Surface Blast Design Parameters on the Performance of a Comminution Circuit Processing a Copper-Bearing Ore. Minerals, 14, 1226. https://doi.org/10.3390/min14121226
Saldana, M., Gallegos, S., Arias, D., et al. (2024). Applications of Kuz–Ram Models in Mine-to-Mill Integration and Optimization—A Review. Minerals, 14, 1162. https://doi.org/10.3390/min14111162
Taiwo, B. O., Gebretsadik, A., Abbas, H. H., et al. (2024). Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics. Heliyon, 10, e33099. https://doi.org/10.1016/j.heliyon.2024.e33099
