Preliminary Blast Fragmentation Modeling in an Underground Aggregate Operation

ABSTRACT: The availability of fragmentation models for underground mine-to-mill optimization is fundamental, given that fragmentation models establish which blasting parameters can be manipulated to produce a desired particle size distribution. Given the ever-changing conditions in which mining takes place (geology, stress conditions, mining procedures, etc.), any applicable model must be flexible, adaptable, and dynamic to provide a range of predictions with acceptable accuracy. This paper discusses the results and model validation of using multivariate regression analysis to analyze blast fragmentation data from an underground aggregate operation. These are the initial results with respect to the development of a probabilistic model for underground bench blasts that predicts the resultant particle size distribution. 1. INTRODUCTION In underground mining, optimizing blasting practices to achieve the desired particle size distribution (PSD) is crucial for enhancing downstream processing efficiency, energy consumption reduction, and overall ore recovery (Fuerstenau Kuznetsov, 1973, Lilly, 1986; Ouchterlony, 2003). However, the unique challenges posed by underground environments, including confinement conditions, excavation stability, and limited visibility, demand innovative solutions (Siddiqui et al., 2009; Demenagas, 2008). The efficiency of aggregate quarry operations heavily relies on optimizing blasting processes, which directly impact subsequent stages of the mine-to-mill workflow. One critical aspect of this optimization lies in predicting the particle size distribution resulting from blasting activities (McKee, 2013). A thorough understanding of particle size distribution facilitates better control over downstream processes such as crushing, grinding, and sorting, ultimately enhancing overall operational efficiency and profitability. Traditionally, the prediction of particle size distribution following blasting has been approached using empirical models or simplistic statistical techniques. However, the inherent complexity and variability of geological formations, coupled with the diverse characteristics of explosive materials, demand a more sophisticated and data-driven approach (Nadolski et al., 2015). In this context, Multivariate Regression Analysis (MRA) or Multivariate Multiple Regression emerges as a powerful tool for accurately predicting particle size distribution by incorporating multiple variables that influence blasting outcomes.