Improving geometry dynamic prediction of in-pit stockpiles using geospatial data and polygon models APCOM 2021

The Southern African Institute of Mining and Metallurgy
M. Balamurali K. M. Seiler
Organization:
The Southern African Institute of Mining and Metallurgy
Pages:
12
File Size:
3471 KB
Publication Date:
Sep 1, 2021

Abstract

Modelling stockpiles is a key factor of a project’s economics and operation in mining, because not all the mined ores are not able to mill for many reasons. Furthermore, the financial value of the ore in the stockpile needs to be reflected on the balance sheet. Therefore, automatically tracking the boundaries of the stockpile facilitates the mine scheduling engineers to calculate the tonnage of the ore remaining in the stockpile. This paper suggests how the dynamic of stockpile shape changes caused by dumping and reclaiming operations can be inferred using polygon models. The paper also demonstrates how the geometry of stockpiles can be inferred in the absence of reclaimed bucket information, in which case the reclaim polygons are established using the digger’s global positioning system (GPS) positional data at the time of truck loading. This work further compares two polygon models for creating 2D shapes.
Citation

APA: M. Balamurali K. M. Seiler  (2021)  Improving geometry dynamic prediction of in-pit stockpiles using geospatial data and polygon models APCOM 2021

MLA: M. Balamurali K. M. Seiler Improving geometry dynamic prediction of in-pit stockpiles using geospatial data and polygon models APCOM 2021. The Southern African Institute of Mining and Metallurgy, 2021.

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