Drill and blast design is a cornerstone of mining and quarrying operations. Its optimization directly determines the efficiency of downstream processes such as loading, hauling, crushing, and milling. Understanding the key parameters that govern blast performance is therefore essential for any mining engineer. Let’s explore the various parameters that influence drill and blast design.
Geometrical parameters
The most fundamental controllable parameters in blast design are geometrical. These include burden, the distance from the explosive charge to the nearest free face, spacing between blast holes, bench height, drill hole diameter, stemming height, sub-drilling depth, and the powder factor, expressed as kilograms of explosive per cubic metre of rock (Shirazi et al., 2015). Integrating these geometrical parameters with the geological model and mine topography is critical for effective blast-hole pattern positioning on the actual drill bench surface (Wilkinson & Kecojevic, 2005).
Rock mass properties
Beyond geometry, the mechanical properties of the rock mass play a decisive role. Factors such as the type of explosives, rock mass classification, and the influence of applied blast energy must be accounted for when predicting fragmentation outcomes (Mutinda et al., 2021). Rock density, uniaxial compressive strength (UCS), and the nature of natural discontinuities all shape how energy is transmitted and distributed through the rock.
Explosive characteristics and powder factor
The choice of explosive and the resulting powder factor are critical design variables. Optimization of blasting parameters is essential since the fragmentation obtained affects the cost of all interrelated mining activities, including drilling, blasting, loading, hauling, and crushing (Sankovsky, 2017). Technical and economic uncertainties such as rock density, uniaxial compressive strength, bit life, and operating costs must all be considered in an integrated optimization approach (Abbaspour et al., 2018).
Fragmentation modelling
Empirical models help engineers predict outcomes. Cunningham’s Kuz-Ram model, introduced in the early 1980s and built upon Kuznetsov’s mean fragment size equation and the Rosin-Rammler fragment size distribution, remains the most widely used approach in the industry for predicting rock fragmentation after blasting (Gheibi et al., 2009).
To conclude, effective drill and blast design requires balancing geometrical, geological, and explosive parameters within a consistent, model-driven framework.
References
Abbaspour, H., Drebenstedt, C., Badroddin, M., & Maghaminik, A. (2018). Optimized design of drilling and blasting operations in open pit mines under technical and economic uncertainties by system dynamic modelling. International Journal of Mining Science and Technology, 28(6), 839–848. https://doi.org/10.1016/j.ijmst.2018.06.009
Gheibi, S., Aghababaei, H., Hoseinie, S. H., & Pourrahimian, Y. (2009). Modified Kuz—Ram fragmentation model and its use at the Sungun Copper Mine. International Journal of Rock Mechanics and Mining Sciences, 46, 967–973. https://doi.org/10.1016/j.ijrmms.2009.05.003
Mutinda, E. K., Alunda, B. O., Maina, D. K., & Kasomo, R. M. (2021). Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model. Journal of the Southern African Institute of Mining and Metallurgy, 121(3), 107–112. https://doi.org/10.17159/2411-9717/1401/2021
Sankovsky, M. M. Y. and A. A. (2017). DRILLING AND BLASTING DESIGN BASED ON INVARIABLE MINING PARAMETERS. Journal of Industrial Pollution Control, 931–936.
Shirazi, A., Mohebbi, J., & Tabatabaee, H. (2015). ADAPTIVE-NEURO FUZZY INFERENCE SYSTEM (ANFIS) MODEL FOR PREDICTION OF BLAST-INDUCED GROUND VIBRATION a b c. SCIENCE INTERNATIONAL, 27, 2079–2091.
Wilkinson, W., & Kecojevic, V. (2005). Elements of Drill and Blast Design and 3D Visualization in Surface Coal Mines. Mining Engineering, 57, 77–82.
