Digital twins with distributed particle simulation for mine-to-mill material tracking

TL;DR

This paper introduces a particle-based digital twin approach for tracking and analyzing granular media in mining processes, enabling improved material property estimation and process prediction through distributed simulation and sensor data integration.

Contribution

The paper presents a novel particle simulation method for digital twins that models granular media as pseudo-particles, facilitating material tracking and property learning across mining processes.

Findings

Effective material tracking demonstrated in a mining simulator.

Enhanced property estimation from process observations.

Potential for process optimization through integrated data analysis.

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 complex interplay between the equipment, control, and the processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explore 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 can 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 makes it possible to better learn material properties from process observations, and to predict the effect on downstream processes. We test the technique on a mining simulator and demonstrate analysis that can be performed using data from cross-system material tracking.