Drill-and-blast optimization is a structured activity designed to maximize rock breaking efficiency and minimize the costs of operations. One of the key indicators in drill and blast optimization is powder factor, which is the amount of explosives utilized per unit volume of rock. Fragmentation is concerned with the distribution in size of the broken rock that affects the efficiency of further processing of the rock, including crushing, grinding, and milling processes.
In this respect, the main objective of drill and blast optimization is to decrease the powder factor without negatively impacting rock fragmentation. An increased quantity of explosives increases operating costs of mining operations, whereas a reduced powder factor produces large boulder sizes. Badly fragmented rocks have significant negative impacts on the efficiency of further processes as it blocks the primary crushers, reduces excavators’ bucket fill factors, and requires expensive secondary breaking of rock (Cardu et al., 2015).
A proper analysis for an optimization process should begin by identifying a reliable baseline and measuring rock mass characteristics. This includes considering uncontrollable geological variables such as the compressive strength of rock and structural discontinuities. By considering these variables, one will understand the in-situ behavior of rocks under blasting which will form the base needed in controlling the explosives.
Once the baseline is set, further refinement of the blasting parameters becomes possible by manipulation. Besides making changes based purely on high powder factors, there are other ways that can be considered such as manipulation of drilling variables like hole diameter, burden, spacing, and stemming. What is more important is the fact that manipulation of the detonating time process has proven effective in rock fragmentation while reducing explosive consumption (Cardu et al., 2015).
Predictive modeling assumes a key significance in ensuring safe reductions in powder factor. Predictive modeling using mathematical tools like the Kuz-Ram fragmentation model enables engineers to model the rock fragment size distributions prior to their execution (Phamotse & Nhleko, 2019). Comparing such predicted size distributions against the optimum size distribution expected from the crusher enables the calculation of the lowest powder factor acceptable for the process (Phamotse & Nhleko, 2019).
Lastly, the predictive optimization process has to be tested under field conditions to validate the outcomes of theoretical calculations. After performing trial blasting processes, image analysis tools are employed to determine the size of the rock fragments generated after blasting (Phamotse & Nhleko, 2019). In case the fragments generated conform to predictions and fit easily into downstream circuits, the new powder factor will be accepted.
References
Cardu, M., Seccatore, J., Vaudagna, A., et al. (2015). Evidences of the influence of the detonation sequence in rock fragmentation by blasting – Part II. Rem: Revista Escola de Minas, 68, 455-462. https://doi.org/10.1590/0370-44672014680219
Phamotse, K. M., & Nhleko, A. S. (2019). Determination of optimal fragmentation curves for a surface diamond mine. Journal of the Southern African Institute of Mining and Metallurgy, 119. https://doi.org/10.17159/2411-9717/494/2019
