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Automated image analysis of iron-ore pellet structure using optical microscopy
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-6186-7116
2011 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 24, no 14, 1525-1531 p.Article in journal (Refereed) Published
Abstract [en]

Knowledge about pellet microstructure such as porosity and oxidation degree is essential in improving the pellet macro-behavior such as structural integrity and reduction properties. Manual optical microscopy is commonly used to find such information but is both highly time consuming and operator dependent. This paper presents research to automate image capture and analysis of entire cross-sections of baked iron ore pellets to characterize proportions of magnetite, hematite, and other components.The presented results cover: semi-automated image acquisition of entire pellets, separation of pellet and epoxy and calculation of total percentages of magnetite, hematite and pores. Using the Leica Qwin microscope software and a segmentation method based on Otsu thresholding these three objectives have been achieved with the phases labeled as magnetite, hematite and pores and additives. Furthermore, spatial distributions of magnetite, hematite and pores and additives are produced for each pellet, graphing proportions in relation to the distance to the pellet surface. The results are not directly comparable to a chemical analysis but comparisons with manual segmentation of images validates the method. Different types of pellets have been tested and the system has produced robust results for varying cases.

Place, publisher, year, edition, pages
2011. Vol. 24, no 14, 1525-1531 p.
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-6100DOI: 10.1016/j.mineng.2011.08.001Local ID: 44d39a6e-21e5-475d-b777-bb48717d77f5OAI: oai:DiVA.org:ltu-6100DiVA: diva2:978977
Projects
HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures
Note
Validerad; 2011; 20110630 (frinel)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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