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Added: April 15, 20252025-04-15T07:55:14-04:00 2025-04-15T07:55:14-04:00In: General Information

Powder Factor (PF) and its impact on fragmentation

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Definition

The Powder factor is a relationship between how much rock is broken and how much explosive is used to break it. It can serve a variety of purposes, such as an indicator of how hard the rock is, or the cost of the explosives needed, or even as a guide to planning a shot. Powder factor can be expressed as a quantity of rock broken by a unit weight of explosives. Or, alternatively, it can be the amount of explosives required to break a unit measure of rock. Since rock is usually measured in pounds, there are several possible combinations that can express the powder factor (Chapter8.Pdf, n.d.).

How to calculate?

The powder factor for a single borehole is calculated as suggested in (“(PDF) Comparative Analysis of Rock Fragmentation Models – A Case Study,” 2024):

PF = (27 × (PC × 0.34ρ × d2)) / B × S × H

Where:

PF = powder factor, pounds of explosives per bank cubic yard of rock;

PC = Powder column, feet of explosive charge*;

ρ = density, in g/cm³, of the explosive;

d = charge diameter‡ in inches;

B = burden dimension in feet;

S = spacing dimension in feet;

H = bench height (or hole depth) in feet*.

Typically, blasters will round the powder factor to the nearest tenth or hundredth.

What are the impacts on fragmentation?

Higher powder factors generally yield smaller fragment sizes, improving fragmentation efficiency. For example, a study in a granite quarry showed that increasing powder factor reduced the 50% passing size (D50), enhancing crushing productivity (Shehu et al., 2023).

Low powder factors result in larger, irregular fragments, increasing excavator digging time. At PT. Ansaf, average fragment sizes of 38.33 cm (Komatsu PC 400) and 33.97 cm (Hyundai R 850) correlated with digging times of 7.95 seconds and 8.44 seconds, respectively (Lawangan et al., 2022).

Powder factor optimization requires balancing fragmentation goals with environmental and operational constraints. Empirical models, computational tools, and machine learning approaches are increasingly used to predict ideal powder factors for site-specific conditions.

Reference

Chapter8.pdf. (n.d.). Retrieved April 15, 2025, from https://www.nps.gov/parkhistory/online_books/npsg/explosives/Chapter8.pdf

Lawangan, F., Oktaviani, R., & Winarno, A. (2022). Studi Pengaruh Powder Factor Terhadap Fragmentasi Peledakan Dan Produktivitas Alat Gali Muat Di PT. Ansaf Inti Resources Kabupaten Kutai Kartanegara Provinsi Kalimantan Timur. MINERAL, 7(2), Article 2. https://doi.org/10.33019/mineral.v7i2.3346

(PDF) Comparative Analysis of Rock Fragmentation Models – A Case Study. (2024, December 15). ResearchGate. https://www.researchgate.net/publication/307594360_Comparative_Analysis_of_Rock_Fragmentation_Models_-_A_Case_Study

Shehu, S. A., Yusuf, K. O., Zabidi, H., Jimoh, O. A., & Hashim, M. H. M. (2023). Blasting efficiency in granite aggregate quarry based on the combined effects of fragmentation and weighted environmental hazards. Mining of Mineral Deposits, 17(1), 120–128. https://doi.org/10.33271/mining17.01.120

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