Optimizing beneficial productivity in open pits using shovel-based sorting systems

The purpose of this study is to develop a model to assess the economic value delivered by deploying a new ore sorting solution in an open pit environment. ShovelSenseTM is a technology based on x-ray fluorescence sensors mounted on the bucket of the shovels in order to execute the ore sorting at the mining face in real time. This is a different application of the technology that was previously used in the mining industry on conveyors belts. The model was built using two steps. The first stage of this study was built using ExtendSim, a discrete event simulation software. This technology is widely used in the mining industry mainly modeling hauling and mineral processing systems. What was modeled in ExtendSim is a conventional open pit based on a shovel-truck system that has three possibilities or destinations (waste dump, heap leach and mill) to send the material extracted from the mine, where the destinations are constrained by a COG. Furthermore, the DES model is capable of reading the information coming from a resource block model via a text file. The second stage takes the outputs produced in the simulation (tonnage and grade distribution per destination) and applies a set of recoveries, metal prices and operating costs in order to model the economic case. The value delivered by the technology is estimated by running two sets of data in the form of block models, where one set is influenced by the technology and the other is not. Ultimately, the economic assessment compares the two options to determine which delivers the most benefit. Finally, the comparison shows a substantial economic value delivered by deploying ShovelsenseTM into the open pit operation analysed in this study. Furthermore, the unlocked value by the technology is the result of optimizing the material assignation to the different destinations in the system.Applied Science, Faculty ofMining Engineering, Keevil Institute ofGraduat