Semi-automated iron ore characterisation based on optical microscope analysis: Quartz/resin classification

Delbem, Itamar Daniel; Galéry, Roberto; Brandão, Paulo Roberto Gomes; Peres, Antônio Eduardo Clark

doi:10.1016/j.mineng.2015.07.021

Cited by 26 publications

(10 citation statements)

References 13 publications

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“…OIA as a mineralogical quantification tool presents an important advance in ore characterization and predictive metallurgy [16,17]. As has been shown in previous research, automated quantification of mineral phases by digital techniques [6,9,22,25,32,55] is appropriate for systematical and automatic mineral quantification in polished sections. Our results of the mineral abundance measurement (A%, Figure 9) give an example of the contribution that the OIA technique can make to qualitative mineragraphic descriptions.…”

Section: Oia Systemmentioning

confidence: 98%

“…RGB images have been employed for the identification and quantification of opaque minerals in polished sections in ore mineral studies, material analysis in mining and mineral processing applications. These studies include determination and quantification of minerals in ore microscopy [19], gold particle quantification [20-22], analysis of particle size and shape [23], discrimination of major sulfide species [6,19,24,25], estimation of flotation froth grade [26] and automatic texture characterization of ore particles [16,27]. Segmentation of color images is often achieved by converting them into a grey-level image using a different false color representation.…”

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confidence: 99%

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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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Section: Oia Systemmentioning

confidence: 98%

mentioning

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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“…The MLA tool allows for quantitative single particle analysis of large number of grains. The total number of particles ranged from 27,244 (K23 < 2 mm) to 79,330 (K03), which can provide good statistics [34]. The variation in particle counts is due to the selection of a fixed scanning time (2 hours), which results in a larger number of particles per unit area of the polished section in the samples with finer particle size distribution (e.g., K03).…”

Section: Bioaccessible Arsenic In the Soil Samplesmentioning

confidence: 99%

Low arsenic bioaccessibility by fixation in nanostructured iron (Hydr)oxides: Quantitative identification of As-bearing phases

Ciminelli

Antônio

Caldeira

et al. 2018

Journal of Hazardous Materials

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A new analytical protocol was developed to provide quantitative, single-particle identification of arsenic in heterogeneous nanoscale mineral phases in soil samples, with a view to establishing its potential risk to human health. Microscopic techniques enabled quantitative, single-particle identification of As-bearing phases in twenty soil samples collected in a gold mining district with arsenic concentrations in range of 8 to 6354 mg kg. Arsenic is primarily observed in association with iron (hydr) oxides in fine intergrowth with phyllosilicates. Only small quantities of arsenopyrite and ferric arsenate (likely scorodite) particles, common in the local gold mineralization, were identified (e.g., 7 and 9 out, respectively, of app. 74,000 particles analyzed). Within the high-arsenic subgroup, the arsenic concentrations in the particle size fraction below 250μm ranges from 211 to 4304 mg kg. The bioaccessible arsenic in the same size fraction is within 0.86-22 mg kg (0.3-5.0%). Arsenic is trapped in oriented aggregates of crystalline iron (hydr)oxides nanoparticles, and this mechanism accounts for the low As bioaccessibility. The calculated As exposure from soil ingestion is less than 10% of the arsenic Benchmark Dose Lower Limit - BMDL. Therefore, the health risk associated with the ingestion of this geogenic material is considered to be low.

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“…2D images can be digitized on polished sections of particles by an ore microscope [18] or backscattered electron (BSE) imaging [19,20] on a scanning electron microscope (SEM). The optical micrographs can be evaluated with various image processing methods [11,[21][22][23][24][25][26] for the construction of a 2D mineral map on particles. However, the reflected light imaging in ore microscopy generates indistinguishable images of siliceous nonopaque minerals and epoxy resin as the reflected light spectra of the transparent minerals and epoxy resin are very similar [27].…”

Section: Introductionmentioning

confidence: 99%

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

Camalan¹,

Çavur²

2020

Turk J Elec Eng & Comp Sci

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The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.

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Semi-automated iron ore characterisation based on optical microscope analysis: Quartz/resin classification

Cited by 26 publications

References 13 publications

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

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

Low arsenic bioaccessibility by fixation in nanostructured iron (Hydr)oxides: Quantitative identification of As-bearing phases

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

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