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.
翻译:微粒介质的运输和加工系统具有分析、操作和优化的难度。在采矿和矿物加工工业中,这些系统是设备、控制和加工材料之间复杂相互作用的流程链。材料特性具有自然变异,通常只在特定地点才知道。因此,我们探索对具有颗粒介质代表的粒子的数码双胞胎采取以材料为导向的方法。在数字形式中,材料被作为伪粒子处理,每个材料代表着大量不同大小、形状和矿物特性的实际粒子的集合。材料状态的移动和变化是由控制系统、传感器、车辆遥测和模拟模型中没有任何实际传感器可见的地方的综合数据决定的。基于粒子的表示使材料沿过程链进行跟踪成为材料。每个数字粒子都可以作为设备产生的观测数据的载体,与真实材料相互作用。这样就可以从过程观测中更好地学习材料特性,并预测对下游过程的影响。我们测试采矿模拟器的技术,并演示利用跨系统材料跟踪数据进行的分析。