Digital Twins with Distributed Particle Simulation for Mine-to-Mill Material Tracking

Abstract

Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries, these systems are chains of processes with a complex interplay among the equipment, control and processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explored a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics and simulation models at locations where no real sensors could see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This make it possible to better learn the material properties from process observations and to predict the effect on downstream processes. We tested the technique on a mining simulator and demonstrated the analysis that can be performed using data from cross-system material tracking.

A High-Fidelity Modelling Method for Mine Haul Truck Dumping Process

“…It is computationally intensive to simulate an entire dump area. Simulating individual dumps may allow for larger areas to be simulated with less computational power through the use of pseudo-particles [4].…”

“…Mine-to-mill optimization is a longstanding goal of the mining industry [1][2][3][4]. This approach focuses on optimizing the entire process around run-of-mine (ROM) material characteristics, rather than optimizing the unit operations to material characteristics individually.…”

“…The primary motivation for this approach is ensuring that the priority for optimization is given to the most demanding and costly process (grinding) [5]. Despite its success, one difficulty in holistic mine-to-mill optimization stems from a lack of understanding around material behavior during and between unit operations [4].…”

“…However, while they demonstrated the ability of UAV aerial photography to capture relevant data, they did not capture the volumes of individual dump profiles that could be used as a baseline to digitally transform the end-dumping rock pile construction process. Servin et al [4] present a digital transformation technique for holistic mine-to-mill distributed particle simulation where the gaps between unit operations are simulated using data from control systems and sensors. While other frameworks for similar integration have been proposed in the past, the focus Servin et al place on a unit operation-centered framework is consistent with the aims of this paper and the future direction of research in this area.…”

“…The resulting HFMs are akin to the model developed by Zhao [68], only they involve end-dumped rock piles as opposed to stacker-made intermediary stockpiles. These HFMs could be potentially suitable for the technique presented by Servin et al [4] for holistic mine-to-mill optimization, which includes large end-dumped rock piles. They may also be used as a modular test bed for the calibration of future simulations of mine haul truck dumping activity.…”