Models for analysing the economic impact of ore sorting, using ROC curves
DOI:
https://doi.org/10.17159/Abstract
The past decade has seen a renewed possibility of using machine learning algorithms to solve a large collection of problems in several fields. Data acquisition for mining operations has increased with the boost of sensor-based technologies, therefore the amount of information available for mining applications has dramatically increased. Ore sorting equipment is available for separating ore from waste based on their different responses in terms of some physical properties detected by a real-time analyser. The efficiency of this separation depends on the contrast in the read properties, both in the ore mineral and gangue particles. This study investigates the application of machine learning models trained using data from the output of an ore sorting Dual-energy X-ray apparatus in a gold mine. The particles were first labelled according to their chemical and mineralogical composition into a binary class of ore and gangue particles, using hand sorting. Classification models were then used to help decide the balance between the number of true and false positives for ore in the concentrate, with a view to economic parameters, using their ROC curves.
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Copyright (c) 2024 Fernanda Gontijo Fernandes Niquini, David Alvarenga Drumond, Áttila Leães Rodrigues, joão felipe coimbra Coimbra Leite Costa, Mariana Gazire Lemos
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