The use of optical reflected light and SEM imaging systems to provide quantitative coal characterisation

O’Brien, Graham; Gu, Yi; Adair, B.J.I.; Firth, Bruce

doi:10.1016/j.mineng.2011.04.024

Cited by 24 publications

(9 citation statements)

References 11 publications

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“…It can be seen that pyrite was closely surrounded by aluminosilicate mineral species. In contrast, O'Brien et al 16 observed that pyrite in coal was mostly dotted in organic material or next to silicates. The different distribution of pyrite in coal gangue, as well as its mineral components, may lead to different SO 2 release behavior compared with coal.…”

Section: Characterization Of Coal Ganguementioning

confidence: 90%

Pyrite transformation and sulfur dioxide release during calcination of coal gangue

Nakano

Liu

et al. 2014

RSC Adv.

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Section: Characterization Of Coal Ganguementioning

confidence: 90%

Pyrite transformation and sulfur dioxide release during calcination of coal gangue

Nakano

Liu

et al. 2014

RSC Adv.

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“…Relative to the specific problem of distinguishing coal from mineral particles, OM techniques have been used in several applications. The coal grain analysis (CGA), which uses reflected light microscopy with an automated image processing system to characterize mineral particles, has been applied (albeit to polished samples) from urban environments to classify coal and non-coal airborne particulates [24,25]. Moreover, polarized light microscopy has been used to distinguish between mineral and coal dust particles in lung tissue samples from miners affected by occupational coal workers' pneumoconiosis (CWP or "Black Lung") [13,14,26].…”

Section: Of 15mentioning

confidence: 99%

Demonstration of Optical Microscopy and Image Processing to Classify Respirable Coal Mine Dust Particles

et al. 2021

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Respirable coal mine dust represents a serious health hazard for miners. Monitoring methods are needed that enable fractionation of dust into its primary components, and that do so in real time. Near the production face, a simple capability to monitor the coal versus mineral dust fractions would be highly valuable for tracking changes in dust sources—and supporting timely responses in terms of dust controls or other interventions to reduce exposures. In this work, the premise of dust monitoring with polarized light microscopy was explored. Using images of coal and representative mineral particles (kaolinite, crystalline silica, and limestone rock dust), a model was built to exploit birefringence of the mineral particles and effectively separate them from the coal. The model showed >95% accuracy on a test dataset with known particles. For composite samples containing both coal and minerals, the model also showed a very good agreement with results from the scanning electron microscopy classification, which was used as a reference method. Results could further the concept of a “cell phone microscope” type monitor for semi-continuous measurements in coal mines.

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“…Because the BSE grey value of this organic matter is the same as for epoxy resin, one should use an alternative embedding material [49]. C a r n a u b a wax can replace the epoxy resin [50], but it is a very soft substance and not easy to polish. For better stability, carnauba wax blocks of 25 mm can be double mounted in epoxy resin blocks of 30 mm diameter.…”

Section: Sample Preparation and Related Issuesmentioning

confidence: 99%

SEM-Based Automated Mineralogy and Its Application in Geo- and Material Sciences

Sandmann²,

2020

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Scanning electron microscopy based automated mineralogy (SEM-AM) is a combined analytical tool initially designed for the characterisation of ores and mineral processing products. Measurements begin with the collection of backscattered electron (BSE) images and their handling with image analysis software routines. Subsequently, energy dispersive X-ray spectra (EDS) are gained at selected points according to the BSE image adjustments. Classification of the sample EDS spectra against a list of approved reference EDS spectra completes the measurement. Different classification algorithms and four principal SEM-AM measurement routines for point counting modal analysis, particle analysis, sparse phase search and EDS spectral mapping are offered by the relevant software providers. Application of SEM-AM requires a high-quality preparation of samples. Suitable non-evaporating and electron-beam stable epoxy resin mixtures and polishing of relief-free surfaces in particles and materials with very different hardness are the main challenges. As demonstrated by case examples in this contribution, the EDS spectral mapping methods appear to have the most promising potential for novel applications in metamorphic, igneous and sedimentary petrology, ore fingerprinting, ash particle analysis, characterisation of slags, forensic sciences, archaeometry and investigations of stoneware and ceramics. SEM-AM allows the quantification of the sizes, geometries and liberation of particles with different chemical compositions within a bulk sample and without previous phase separations. In addition, a virtual filtering of bulk particle samples by application of numerous filter criteria is possible. For a complete mineral phase identification, X-ray diffraction data should accompany the EDS chemical analysis. Many of the materials which potentially could be characterised by SEM-AM consist of amorphous and glassy phases. In such cases, the generic labelling of reference EDS spectra and their subsequent target component grouping allow SEM-AM for interesting and novel studies on many kinds of solid and particulate matter which are not feasible by other analytical methods.

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The use of optical reflected light and SEM imaging systems to provide quantitative coal characterisation

Cited by 24 publications

References 11 publications

Pyrite transformation and sulfur dioxide release during calcination of coal gangue

Pyrite transformation and sulfur dioxide release during calcination of coal gangue

Demonstration of Optical Microscopy and Image Processing to Classify Respirable Coal Mine Dust Particles

SEM-Based Automated Mineralogy and Its Application in Geo- and Material Sciences

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