Utilization of optical image analysis and automatic texture classification for iron ore particle characterisation

Donskoi, E., Suthers, S. P., Fradd, S. B., Young, J. M., Campbell, J. J., Raynlyn, T. D. and Clout, J. M. F. (2007) Utilization of optical image analysis and automatic texture classification for iron ore particle characterisation. Minerals Engineering, 20 5: 461-471. doi:10.1016/j.mineng.2006.12.005


Author Donskoi, E.
Suthers, S. P.
Fradd, S. B.
Young, J. M.
Campbell, J. J.
Raynlyn, T. D.
Clout, J. M. F.
Title Utilization of optical image analysis and automatic texture classification for iron ore particle characterisation
Journal name Minerals Engineering   Check publisher's open access policy
ISSN 0892-6875
1872-9444
Publication date 2007-04-01
Sub-type Article (original research)
DOI 10.1016/j.mineng.2006.12.005
Volume 20
Issue 5
Start page 461
End page 471
Total pages 11
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Language eng
Abstract Opticalimageanalysis is a very convenient tool for obtaining comprehensive information about fine ironore size fractions. Data can be obtained on mineral abundances, porosity, particle shape and oretextures with a high level of accuracy. A range of techniques has been used to characterise ironore samples on a particle-by-particle basis. Automatic textural classification of ironoreparticles was used to establish classes containing particles with very similar mineral composition and texture. Imageanalysis coupled with probe analysis and mineral density measurements provided information about the chemical composition and density of each particle class. The combination of these results enabled a “virtual feed” to be created, which can be a key input into a beneficiation unit model for predicting its performance. Identification and classification of the textural type of each particle was performed according to the CSIRO-Hamersley IronOre Group Classification Scheme. If more detailed classification is needed, further classification can be performed based on dimensional, chemical or mineral criteria, such as the presence of certain minerals in particles or total iron content. Some deficiencies of the current imageanalysis procedures and their further improvement and automation are also discussed.
Keyword Iron ores
Mineral processing
Ore mineralogy
Particle morphology
Liberation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Special issue: Selected papers from Automated Mineralogy ’06

Document type: Journal Article
Sub-type: Article (original research)
Collection: Julius Kruttschnitt Mineral Research Centre Publications
 
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Created: Tue, 24 Jul 2012, 19:01:58 EST by Karen Holtham on behalf of Julius Kruttschnitt Mineral Research Centre