This paper presents an approach for developing accurate models to predict the fragmentation due to blasting. The approach makes use of drill monitoring data, which provides data throughout the rock mass to be blasted and is therefore superior to traditional data-gathering methods such as diamond core or field sampling. The approach also makes use of the Split image processing software for assessing post-blast fragmentation and the crushability and grindability of the ore. Finally the approach makes use of the explosive energy per unit volume of rock. These three types of data are collected and analyzed on a hole-by-hole basis, giving 50 or more data points for each blast. These data points form the basis for a statistical correlation between in-situ conditions, blasting parameters, and the resulting fragmentation size and strength. At a specific mine, the database is continually updated as mining progresses, resulting in an evolving and increasingly accurate model with time. Some sample results from the Phelps Dodge Sierrita mine are presented.
One of the fundamental requirements for being able to optimize blasting is the ability to predict fragmentation. An accurate blast fragmentation model allows a mine to adjust the fragmentation size for different downstream processes (mill processing rs. leach, for instance), and to make real time adjustments in blasting parameters to account for changes in rock mass characteristics (hardness, fracture density, fracture orientation, etc.). A number of blast fragmentation models have been developed in the past 40 years such as the Kuz-Ram model (Cunningham, 1983). Fragmentation models have a lim-
ited usefulness at the present time because:
1) The input parameters are not the most useful for the engineer to determine and data for these parameters are not available throughout the rock mass.
2) Even if the input parameters are known, the models still do not consistently predict the correct fragmentation. This is because the models capture some but not all of the important rock and blast phenomena.
3) The models do not allow for "tuning" at a specific mine site. At the University of Arizona, studies are being conducted to improve blast fragmentation models. The Split image processing software is used for these studies (Kemeny, 1994; Kemeny et al., 1999).
The Split software was originally developed at the University of Arizona, and in 1997 the technology was transferred to a newly formed company, Split Engineering. The Split software allows post-blast fragmentation to be determined on a regular basis throughout a mine, by capturing images of fragmented rock in muckpiles, on haul tracks, or from primary crusher feed or product. The resulting size distribution data can then be used to accurately assess the fragmentation associated with different parts of a shot. And in particular, this data can be used to assess and improve the accuracy of fragmentation models (Higgins, et al., 1999).
Fragmentation models are also being improved by utilizing drill-monitoring data. Drill-monitoring data includes raw drilling data such as rotary torque, penetration rate, and pull down pressure, as well as calculated quantitiesuch as drilling specific energy or the Aquila blastability index (Peck and Gray, 1995). Because drill-monitoring data is available from every blast hole, it provides data throughout the rock mass to be blasted.
As part of this project fragmentation studies are being conducted at the Phelps Dodge Sierrita Mine in Southern Arizona. The Sierdta mine has the Split-Online system installed at their in-pit primary crusher. On this system, cameras installed at the track dumps monitor primary c