Automated optical image analysis of natural and sintered iron ore

Donskoi, E.; Poliakov, A.; Manuel, James

doi:10.1016/b978-1-78242-156-6.00004-6

Cited by 14 publications

(24 citation statements)

References 33 publications

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“…Two major imaging techniques are currently used to characterise iron making materials: SEM (scanning electron microscopy) [5][6][7] and OIA (optical image analysis) [8][9][10][11]. Both techniques have their advantages and drawbacks.…”

Section: mentioning

confidence: 99%

“…2 for mineral segmentation by Mineral 4, reproduced from Ref. [9]), has better resolution for massive screening and better characterises porosity (for detailed comparison of the two techniques see Refs. [3,12]).…”

Section: mentioning

confidence: 99%

“…Mineral 4 is responsible for image collection, image processing, measurement of large sets of image/particle features, and output of the measurement results to Microsoft Excel and a particle/frame database. One of the key features of Mineral 4 is that it performs mineral identification and segmentation of different morphologies of the same mineral or identification of phases with similar reflectivity but different structures [2,9]. As an example, Fig.…”

Section: Mineral 4/recognition 4 Softwarementioning

confidence: 99%

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Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization

Donskoi¹,

Poliakov²,

Vining³

et al. 2017

JEPE

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OIA (optical image analysis) has traditionally been used for reliable identification of different iron oxides and oxyhydroxides in iron ore. The automated CSIRO OIA system Mineral 4/Recognition 4 was created for rapid mineral and textural characterisation of iron ore providing identification of different minerals and different morphologies. The technique has further been applied to processed iron ore products such as iron ore sinter to determine key parameters such as porosity, different morphologies of hematite (primary and secondary), and different morphologies of SFCA (silicon ferrite of calcium and aluminium). Application of textural identification has recently been extended to coke characterisation where the software gives comprehensive characterisation of porosity, IMDC (inert material derived components), RMDC (reactive material derived components) and the boundaries between IMDC and RMDC. The software also has many unique features needed for iron ore research including characterisation of large objects like pellets and ore lumps; automated gangue (including quartz) identification; automated particle separation; multiple image set processing and on-line measurements. All these features make the Mineral 4/Recognition 4 OIA system a unique, reliable, industry/research focused tool for ore, sinter, pellet and coke characterisation.

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Section: mentioning

confidence: 99%

Section: mentioning

confidence: 99%

Section: Mineral 4/recognition 4 Softwarementioning

confidence: 99%

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Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization

Donskoi¹,

Poliakov²,

Vining³

et al. 2017

JEPE

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show abstract

“…This applies as well for SEM-based systems. According to [57,58], a combined approach using OIA techniques and new mineral quantification systems (e.g., QEMSCAN) will provide the most detailed understanding of ore mineral samples characterized.…”

Section: Oia Systemmentioning

confidence: 99%

Ore Petrography Using Optical Image Analysis: Application to Zaruma-Portovelo Deposit (Ecuador)

Berrezueta

Ordóñez-Casado

Bonilla³

et al. 2016

Geosciences

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Optical image analysis (OIA) supporting microscopic observation can be applied to improve ore mineral characterization of ore deposits, providing accurate and representative numerical support to petrographic studies, on the polished section scale. In this paper, we present an experimental application of an automated mineral quantification process on polished sections from Zaruma-Portovelo intermediate sulfidation epithermal deposit (Ecuador) using multispectral and color images. Minerals under study were gold, sphalerite, chalcopyrite, galena, pyrite, pyrrhotite, bornite, hematite, chalcocite, pentlandite, covellite, tetrahedrite and native bismuth. The aim of the study was to quantify the ore minerals visible in polished section through OIA and, mainly, to show a detailed description of the methodology implemented. Automated ore identification and determination of geometric parameters predictive of geometallurgical behavior, such as grade, grain size or liberation, have been successfully performed. The results show that automated identification and quantification of ore mineral images are possible through multispectral and color image analysis. Therefore, the optical image analysis method could be a consistent automated mineralogical alternative to carry on detailed ore petrography.Geosciences 2016, 6, 30 2 of 23 with important contributions in metallurgical processes [13][14][15] and provide rapid, statistically reliable and repeatable mineralogical, petrographic and metallurgical data.OIA is a convenient, accessible, and inexpensive tool for obtaining comprehensive information about fine fractions of the ore [13][14][15][16]. However, a limiting factor in direct OIA is the discrimination between minerals with similar reflective properties [17]. A digital image is a numerical representation of a two-dimensional image. Digital cameras used in optical microscopy usually incorporate a charge-coupled device (CCD) to capture images. The CCD transfers the optical photon data received through a filter into electronic pulse or photo. The generated voltage is then converted into pixels (converting analogical data to digital data) and stored as a digital image that contains a fixed number of rows and columns of pixels [17,18]. A color image is a digital image that includes color information for each pixel. Color images can be acquired using a CCD digital camera (i.e., 3CCD camera, Bayer mosaic filter or three shot color sampling). For visually acceptable digital color images, it is necessary to provide grey levels (GL) in three bands/channels for each pixel, which are interpreted as coordinates in one of the existing color spaces.The Red, Green and Blue (RGB) color model is an additive color model in which red, green, and blue light is added together in various ways to reproduce a broad array of colors. It is commonly applied in computer displays but other models such as hue-saturation-value (HSV) are also in use. RGB images have been employed for the identification and quantification of opaque minerals in polished secti...

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“…Several methods applied for the mineral and textural characterization of iron ores have been proposed [24], and optical reflected light image analysis is among the most powerful ones [25][26][27]. However, it is still rare that any of those considers maghemite.…”

Section: Introductionmentioning

confidence: 99%

Maghemite in Brazilian Iron Ores: Quantification of the Magnetite-Maghemite Isomorphic Series by Χ-ray Diffraction and the Rietveld Method, and Confirmation by Independent Methods

2021

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Maghemite (γ-Fe2O3) is a mineral formed from magnetite oxidation at low temperatures, an intermediate metastable term of the magnetite to hematite oxidation and could be mixed with both. It has magnetic susceptibility similar to magnetite, crystal structure close to magnetite with which it forms a solid solution, while compositionally it equals hematite. Maghemite is thus easily misidentified as magnetite by Χ-ray diffraction and/or as hematite by spot chemical analysis in iron ore characterization routines. Nonstoichiometric magnetite could be quantified in samples of Brazilian soils and iron ores by the Rietveld method using a constrained refinement of the Χ-ray patterns. The results were confirmed by reflected light microscopy and Raman spectroscopy, thus qualitatively validating the method. Χ-ray diffraction with the refinement of the isomorphic substitution of Fe2+ by Fe3+ along the magnetite-maghemite solid solution could help to suitably characterize maghemite in iron ores, allowing for the evaluation of its ultimate influence on mineral processing, as its effect on surface and breakage properties.

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Automated optical image analysis of natural and sintered iron ore

Cited by 14 publications

References 33 publications

Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization

Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization

Ore Petrography Using Optical Image Analysis: Application to Zaruma-Portovelo Deposit (Ecuador)

Maghemite in Brazilian Iron Ores: Quantification of the Magnetite-Maghemite Isomorphic Series by Χ-ray Diffraction and the Rietveld Method, and Confirmation by Independent Methods

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