The Fragmentation Energy-Fan Model in Quarry Blasts

Abstract

This paper investigates fragmentation in an aggregate quarry in the light of the fragmentation-energy fan concept. Six blasts were monitored, located one behind the other in the same quarry area. The rock structure was blocky, the water level and the usage of explosives were variable within and between the blasts; however, the distribution of explosive energy in the blocks was relatively uniform and the performance of both explosives per unit mass appeared to be similar. The powder factor above grade was between 0.28 and 0.44 kg/m 3 . Fragmentation of the plant feed was measured using an online digital image analysis system and three belt scales in the crushing plant. The oversize material that was not directly fed to the plant after the blast corresponds to 3-9.3% of the total. Data from image analysis was used to build the size distribution in the coarse range (fragments above 120 mm), and the passing fractions at 120 and 25 mm obtained from belt scales data were used in the range 120-25 mm. The non-dimensional percentile sizes between 10 and 90% for each blast are described through power functions of the powder factor above grade; the model coefficients (i.e. nine prefactors and nine exponents) are statistically significant. From them, using the principles of the fragmentation-energy fan and the Swebrec distribution properties, the fragmentation can be expressed in terms of the powder factor by means of five parameters: the fan focus coordinates and the three parameters of a function of the exponents versus the percentage passing. The model shows a good capability to describe fragmentation from 10 to 90 percentile sizes, with an expected error below 6% and a maximum likely error less than 15%.

Reduction of Fragment Size from Mining to Mineral Processing: A Review

“…This model was developed by Ouchterlony and coworkers (Ouchterlony et al 2017(Ouchterlony et al , 2021Ouchterlony and Sanchidrián 2018;Segarra et al 2018;Sanchidrián et al 2022). Sieving data obtained from tests with different values of specific charge q may be written where x P is the size at the percentage passing P. The exponent = (P) or P is, for a given blast geometry in a given material, a function of P only.…”

“…However, when fragment sizes are sought in an absolute, quantitative or predictive fashion, image analysis alone cannot provide a solution. Combinations of image analysis with on-site sieving, or derivation of rock cuts weights from crushing plant mass flows, have been used with advantage (Cho et al 2003;Ouchterlony et al 2006Ouchterlony et al , 2010Segarra et al 2018). 9 Concluding Remarks 1.…”

“…To achieve optimum fragmentation, it is necessary to be able to predict rock fragmentation results. With this purpose, several models or functions, such as the Kuz-Ram fragmentation model (Cunningham 1983;, the Swebrec function (Ouchterlony 2005a), the JK breakage appearance model (for DWT or drop weight testing, e.g., Napier-Munn et al 1996;Shi 2016), and the fragmentation-energy fan (Ouchterlony et al 2017;Sanchidrián and Ouchterlony 2017;Ouchterlony and Sanchidrián 2018;Segarra et al 2018;Ouchterlony and Sanchidrian 2019) were developed. These models, most of which describe the outcome of bench blasting, can describe the relation between particle/fragment size and accumulated mass passing quite well and the Swebrec function was found to be the bestfitting function in general, in nearly all groups of data and across the whole passing range (Sanchidrián et al 2012(Sanchidrián et al , 2014, compared with other functions.…”

Blasting efficiency in granite aggregate quarry based on the combined effects of fragmentation and weighted environmental hazards

“…However, fragmentation and environmental effects are influenced by the amount of powder factor [21], [22]. A higher amount of powder factor improves fragmentation and, thus, productivity [23]. Nevertheless, the blast-induced vibration and airblast also increase with an increase in the powder factor [24], [25].…”

“…Segarra et al [23] investigated the effects of powder factor on different percentage passing and concluded that fragmentation improves with powder factor. The most common tool for evaluating the impacts of blasting on structures and human beings is the Peak Particle Velocity (PPV) [26]- [29], as shown in Equation 1 [30], [31].…”