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

2007 ◽  
Vol 20 (5) ◽  
pp. 461-471 ◽  
Author(s):  
E. Donskoi ◽  
S.P. Suthers ◽  
S.B. Fradd ◽  
J.M. Young ◽  
J.J. Campbell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Iron Ore ◽  
Texture Classification ◽  
Optical Image ◽  
Particle Characterisation

Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysis

2013 ◽  
Vol 122 (4) ◽  
pp. 217-229 ◽  
Author(s):  
E. Donskoi ◽  
J. R. Manuel ◽  
P. Austin ◽  
A. Poliakov ◽  
M. J. Peterson ◽  
...  
Keyword(s):  
Image Analysis ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Comparative Study ◽  
Iron Ore ◽  
Optical Image ◽  
Scanning Electron

Novel developments in optical image analysis for iron ore, sinter and coke characterisation

2015 ◽  
Vol 124 (4) ◽  
pp. 227-244 ◽  
Author(s):  
E. Donskoi ◽  
A. Poliakov ◽  
J. R. Manuel ◽  
M. Peterson ◽  
S. Hapugoda
Keyword(s):  
Image Analysis ◽  
Iron Ore ◽  
Optical Image ◽  
Iron Ore Sinter

Automated optical image analysis of natural and sintered iron ore

Iron Ore ◽  
2015 ◽  
pp. 101-159 ◽  
Author(s):  
E. Donskoi ◽  
A. Poliakov ◽  
J.R. Manuel
Keyword(s):  
Image Analysis ◽  
Iron Ore ◽  
Optical Image ◽  
Sintered Iron

scholarly journals Automated Optical Image Analysis of Iron Ore Sinter

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 562
Author(s):  
Eugene Donskoi ◽  
Sarath Hapugoda ◽  
James Robert Manuel ◽  
Andrei Poliakov ◽  
Michael John Peterson ◽  
...  
Keyword(s):  
Image Analysis ◽  
Iron Ore ◽  
Deep Understanding ◽  
Furnace Operation ◽  
Optical Image ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Iron Ore Sinter ◽  
Very High

Sinter quality is a key element for stable blast furnace operation. Sinter strength and reducibility depend considerably on the mineral composition and associated textural features. During sinter optical image analysis (OIA), it is important to distinguish different morphologies of the same mineral such as primary/secondary hematite, and types of silico-ferrite of calcium and aluminum (SFCA). Standard red, green and blue (RGB) thresholding cannot effectively segment such morphologies one from another. The Commonwealth Scientific Industrial Research Organization’s (CSIRO) OIA software Mineral4/Recognition4 incorporates a unique textural identification module allowing various textures/morphologies of the same mineral to be discriminated. Together with other capabilities of the software, this feature was used for the examination of iron ore sinters where the ability to segment different types of hematite (primary versus secondary), different morphological sub-types of SFCA (platy and prismatic), and other common sinter phases such as magnetite, larnite, glass and remnant aluminosilicates is crucial for quantifying sinter petrology. Three different sinter samples were examined. Visual comparison showed very high correlation between manual and automated phase identification. The OIA results also gave high correlations with manual point counting, X-ray Diffraction (XRD) and X-ray Fluorescence (XRF) analysis results. Sinter textural classification performed by Recognition4 showed a high potential for deep understanding of sinter properties and the changes of such properties under different sintering conditions.

scholarly journals Advances in Optical Image Analysis Textural Segmentation in Ironmaking

2020 ◽  
Vol 10 (18) ◽  
pp. 6242 ◽  
Author(s):  
Eugene Donskoi ◽  
Andrei Poliakov
Keyword(s):  
Image Analysis ◽  
Chemical Composition ◽  
Software Package ◽  
Iron Ore ◽  
Optical Image ◽  
Point Counting ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Iron Ore Sinter ◽  
Feedstock Material

Optical image analysis is commonly used to characterize different feedstock material for ironmaking, such as iron ore, iron ore sinter, coal and coke. Information is often needed for phases which have the same reflectivity and chemical composition, but different morphology. Such information is usually obtained by manual point counting, which is quite expensive and may not provide consistent results between different petrologists. To perform accurate segmentation of such phases using automated optical image analysis, the software must be able to identify specific textures. CSIRO’s Carbon Steel Futures group has developed an optical image analysis software package called Mineral4/Recognition4, which incorporates a dedicated textural identification module allowing segmentation of such phases. The article discusses the problems associated with segmentation of similar phases in different ironmaking feedstock material using automated optical image analysis and demonstrates successful algorithms for textural identification. The examples cover segmentation of three different coke phases: two types of Inert Maceral Derived Components (IMDC), non-reacted and partially reacted, and Reacted Maceral Derived Components (RMDC); primary and secondary hematite in iron ore sinter; and minerals difficult to distinguish with traditional thresholding in iron ore.

Automated optical image analysis of natural and ­sintered iron ore

2022 ◽  
pp. 127-178
Author(s):  
E. Donskoi ◽  
A. Poliakov ◽  
J.R. Manuel
Keyword(s):  
Image Analysis ◽  
Iron Ore ◽  
Optical Image ◽  
Sintered Iron
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