Application of stochastic approach to blast fragmentation modelling

Abstract

In large scale metalliferous mining, there is documented evidence that by providing an appropriate size distribution to crushing and grinding circuits, a measurable increased throughput and/or reduced power draw can be obtained. Tailoring blast designs to suit specific fragmentation requirements is becoming common place at both the prefeasibility and feasibility study stages. This is particularly the case when significant increases in ore production rates are being considered. Given the variability associated with many of the input parameters that are used to estimate fragmentation outcomes, a simple deterministic empirical approach is limited. This is the case at the feasibility stages, where there is a higher degree of uncertainty in the definition of blasting domains. Complex processes, such as explosive rock breakage and fragmentation, are suited to modelling techniques involving stochastic methods. As part of the feasibility study of a large open pit expansion project, there was a requirement to estimate fragmentation envelopes for a set of design parameters and rock mass conditions. This paper gives a description of a stochastic approach to blast fragmentation modelling and uses this expansion project to demonstrate its application. In this particular case study, there was a need to estimate the expected run of mine fragmentation associated with blasting in the deeper and more competent ore domains. Results from several simulations using the proposed modelling approach have shown the importance of including the measured variability of input parameters such intact rock properties and degree of fracturing at the feasibility stages. Modelling results also identified significant differences in ore fragmentation envelopes if current designs were to be applied in the more competent domains as the pit expands. From this analysis, a number of blast design options have been evaluated and recommendations made in order to achieve future ore handling and processing targets.