Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/136825
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Type: | Journal article |
Title: | A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion |
Author: | Peukert, D. Xu, C. Dowd, P. |
Citation: | Minerals, 2022; 12(11):1364-1-1364-22 |
Publisher: | MDPI AG |
Issue Date: | 2022 |
ISSN: | 2075-163X 2075-163X |
Statement of Responsibility: | Dylan Peukert, Chaoshui Xu and Peter Dowd |
Abstract: | Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques. |
Keywords: | sensor fusion; sensor-based sorting; ore sorting in mining and mineral processing; particle sorting; bulk sorting; simulation; X-ray fluorescence; X-ray transmission imaging; hyperspectral imaging; data synchronization |
Description: | Published: 27 October 2022 |
Rights: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
DOI: | 10.3390/min12111364 |
Grant ID: | http://purl.org/au-research/grants/arc/IC190100017 |
Published version: | http://dx.doi.org/10.3390/min12111364 |
Appears in Collections: | Civil and Environmental Engineering publications |
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hdl_136825.pdf | Published version | 975.28 kB | Adobe PDF | View/Open |
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