Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Project: Reduction of energy and water consumption of mining operations by fusion of sorting technologies LIBS and ME-XRT (REWO-SORT)
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering. (Ore Geology)ORCID iD: 0000-0003-1867-2342
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering. (Ore Geology)
2018 (English)Other (Other (popular science, discussion, etc.))
Abstract [en]

In recent years, the mining industry has been faced with numerous challenges across Europe and worldwide. Among these is the need to process ore with successively lower grades due to the continuous depletion of high-grade deposits. This increases the consumption of energy and water and, thus, the operational costs at a mine site. Various approaches to solve this issue have been evaluated, but so far none of these could be validated as a satisfactory solution. The implementation of multimodal sorting techniques represents a promising approach by achieving a pre-concentration of valuable minerals already at an early stage in the metallurgical process.In this project we propose to develop a fusion technology including laser-induced breakdown spectroscopy (LIBS) and multi energy X-raytransmission (ME-XRT), which will be able to classify crushed mineral particles on a conveyor belt with the aid of deep learning technology.The combination of LIBS and ME-XRT is promising, as these sensors complement each other with regards to their analytical capabilities: LIBS can provide an elemental analysis of the sample surface, while ME-XRT produces volumetric data with lower accuracy. The technological fusion of both sensors will allow for the extrapolation of accurate surface data to the entire volume of the sample and therefore create representative data for the entire ore. In addition, the implementation of neural network technology will enable allow for automatic self-adjustments to varying ore types and geological parameters.The developed sensor fusion technology will enable constant and accurate monitoring of the mineralogy of the mined rock volume and will allow for on-line and in-situ measurement of geological, mineralogical, rock-mechanical and metallurgical properties of the ore. The development of an on-line feed of these data into 3D geological models of the ore bodies is envisaged, the accuracy and objectivity of which are crucial for successful mine planning.

Place, publisher, year, pages
2018.
Keywords [en]
Sensor fusion, LIBS, multi energy X-ray, mining, geological modelling
National Category
Geology
Research subject
Ore Geology
Identifiers
URN: urn:nbn:se:ltu:diva-68656OAI: oai:DiVA.org:ltu-68656DiVA, id: diva2:1204202
Funder
VINNOVA, 2018-00601Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-05-07

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Kampmann, Tobias ChristophBauer, Tobias
By organisation
Geosciences and Environmental Engineering
Geology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 24 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf