Improvements in blast fragmentation models using digital image processing

One of the fundamental requirements for being able to optimise 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 versus 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 [1]. Fragmentation models have a limited 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. This paper describes studies that we being conducted to improve blast fragmentation models. The Split image processing software is used for these studies [2, 3].