Modelling Large Heaped Fill Stockpiles Using FMS Data

Abstract

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations.

Unmanned Aerial Vehicle Application in Mining User case in Rwanda

“…Mining operators may also easily determine the exact volume of stockpiles or regions to be mined by using a computerized system. [32] Certain modern mining systems may create data such as the heights of safety berms, their crests and toes, the limits of road networks, their widths and crests, and the length and altitude change of slopes. In addition to these constraints, artificial intelligence incorporated into the application may automatically identify objects that do not meet regulatory requirements [20].…”

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

“…Dumping commonly consists of the haul truck spotting itself into position and dropping ROM material from the back. This material forms a small heap if it is dumped on a flat surface, or cascades along the edge of a dump face if dumped over a developing dump, stockpile or dump/heap leach [7]. While there are minor differences in each of these earthworks, for the simplification of this article, they are referred to as rock piles [8].…”

“…Furthermore, dump faces are hazardous, and observation of the dumping process is challenging [13]. Additionally, dozers and other equipment handle the material subsequent to dumping, which compound with the issues mentioned previously [7]. Moreover, some complex engineering issues, such as reclamation and slope stability, are commonly considered to be linked to factors involving these material property variables in ways not fully understood, which increases the debate related to them [13][14][15].…”

“…Zhao [68] developed a real-time 3D modelling and mapping technique for the stockpiles formed by stacking/reclaiming machines for iron ore. Zhao's work is pioneering in the area of the digital transformation of small, intermediary stockpile construction, but does not consider the large rock fills made by haul truck enddumping. The authors of this present paper previously illustrated a method for modelling and mapping large heap-filled stockpiles using fleet management system (FMS) data [7]. However, while these data were amicable to modelling, no external data were available for the validation of the model.…”