3D stockpile modelling and quality calculation for continuous stockpile management

Zhao, Shunyi; Lu, Tien‐Fu; Koch, Ben; Hurdsman, Alan

doi:10.1016/j.minpro.2015.04.012

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…Indeed, the agreement between the outline and shape of the real and reconstructed stockpiles is very good. Moreover, the errors are much smaller than the ones presented in other works [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], always being much less than 20 cm, which is an acceptable level [ 12 ].…”

Section: Resultsmentioning

confidence: 60%

“…Geo-referencing the radar measurements during the scanning process is essential for post-processing the retrieved data and reconstructing the stockpile shape. In this context, encoders as well as Global Navigation Satellite System (GNSS) solutions have been adopted [ 15 , 16 , 17 , 18 , 19 ]. However, the former one, which is the most widely used in the literature, usually provides a relatively low resolution for imaging purposes, as it is designed to provide a coarse estimation of the position and its information is not always prepared to be read from external systems.…”

Section: Introductionmentioning

confidence: 99%

“…Other works deal with simulated [ 17 ] or extremely small scaled laboratory experiments [ 11 , 12 , 17 , 18 , 22 , 23 ]. Hence, they did not consider real measured data, which is affected by more complex features such as noise, weather conditions and reflectivity from surrounding elements.…”

Section: Introductionmentioning

confidence: 99%

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A Portable Electromagnetic System Based on mm-Wave Radars and GNSS-RTK Solutions for 3D Scanning of Large Material Piles

Álvarez

Álvarez-Narciandi

García-Fernández

et al. 2021

Sensors

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In this paper, a portable three-dimensional (3D) scanning system for the accurate characterization of large raw material (e.g., cereal grain, coal, etc.) stockpiles is presented. The system comprises an array of high resolution millimeter-wave radars and a cm-level accuracy positioning system to accurately characterize large stockpiles by means of a high-resolution 3D map, making it suitable for automation purposes. A control unit manages the data received by the sensors, which are sent to a computer system for processing. As a proof of concept, the entire sensor system is evaluated in a real environment for electromagnetically scan a scaled stockpile of coal, used in the industry for handling raw materials. In addition, a highly efficient processing adaptive algorithm that may reconstruct the scanned structure in real-time has been introduced, enabling continuous dynamic updating of the information. Results are compared with those from a photogrammetry-like technique, revealing an excellent agreement.

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Section: Resultsmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

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A Portable Electromagnetic System Based on mm-Wave Radars and GNSS-RTK Solutions for 3D Scanning of Large Material Piles

Álvarez

Álvarez-Narciandi

García-Fernández

et al. 2021

Sensors

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“…For example, iron ore is often shipped directly from the mine to the customer. Since the tonnages involved are immense, extensive work has already been carried out to model the way that iron ore should be stockpiled for re-handle [16,17,24,25]. Likewise, due to hazards around coal (loss of energy through oxidation, spontaneous combustion, etc.)…”

Section: Classification Of Stockpilesmentioning

confidence: 99%

Modelling Large Heaped Fill Stockpiles Using FMS Data

Young¹,

Rogers²

2021

Minerals

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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.

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“…According to the literature survey conducted by the authors in the previous paper [15], a scanning system developed by a company named QMASTOR is built through mounting a 3D Lidar at the back of a vehicle [16].…”

Section: Stockpile Modellingmentioning

confidence: 99%