Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology
A new automated image analysis system that analyses individual coal particles to predict daughter char morphology is presented. 12 different coals were milled to 75–106 µm, segmented from large mosaic images and the proportions of the different petrographic features were obtained from reflectance hi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2020
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| Online Access: | https://eprints.nottingham.ac.uk/60135/ |
| _version_ | 1848799732451770368 |
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| author | Perkins, Joseph Williams, Orla Wu, Tao Lester, Edward |
| author_facet | Perkins, Joseph Williams, Orla Wu, Tao Lester, Edward |
| author_sort | Perkins, Joseph |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A new automated image analysis system that analyses individual coal particles to predict daughter char morphology is presented. 12 different coals were milled to 75–106 µm, segmented from large mosaic images and the proportions of the different petrographic features were obtained from reflectance histograms via an automated Matlab system. Each sample was then analysed on a particle by particle basis, and daughter char morphologies were automatically predicted using a decision tree-based system built into the program. Predicted morphologies were then compared to ‘real’ char intermediates generated at 1300 °C in a drop-tube furnace (DTF). For the majority of the samples, automated coal particle characterisation and char morphology prediction differed from manually obtained results by a maximum of 9%. This automated system is a step towards eliminating the inherent variability and repeatability issues of manually operated systems in both coal and char analysis. By analysing large numbers of coal particles, the char morphology prediction could potentially be used as a more accurate and reliable method of predicting fuel performance for power generators. |
| first_indexed | 2025-11-14T20:40:20Z |
| format | Article |
| id | nottingham-60135 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:40:20Z |
| publishDate | 2020 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
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| spelling | nottingham-601352020-03-23T01:53:21Z https://eprints.nottingham.ac.uk/60135/ Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology Perkins, Joseph Williams, Orla Wu, Tao Lester, Edward A new automated image analysis system that analyses individual coal particles to predict daughter char morphology is presented. 12 different coals were milled to 75–106 µm, segmented from large mosaic images and the proportions of the different petrographic features were obtained from reflectance histograms via an automated Matlab system. Each sample was then analysed on a particle by particle basis, and daughter char morphologies were automatically predicted using a decision tree-based system built into the program. Predicted morphologies were then compared to ‘real’ char intermediates generated at 1300 °C in a drop-tube furnace (DTF). For the majority of the samples, automated coal particle characterisation and char morphology prediction differed from manually obtained results by a maximum of 9%. This automated system is a step towards eliminating the inherent variability and repeatability issues of manually operated systems in both coal and char analysis. By analysing large numbers of coal particles, the char morphology prediction could potentially be used as a more accurate and reliable method of predicting fuel performance for power generators. Elsevier Ltd 2020-01-01 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/60135/1/Automated%20image%20analysis%20techniques%20to%20characterise%20pulverised%20coal%20particles%20and%20predict%20combustion%20char%20morphology.pdf Perkins, Joseph, Williams, Orla, Wu, Tao and Lester, Edward (2020) Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology. Fuel, 259 . p. 116022. ISSN 00162361 Coal characterisation; MaceralsChar morphology; Automated image analysis; Combustion; Vitrinite http://dx.doi.org/10.1016/j.fuel.2019.116022 doi:10.1016/j.fuel.2019.116022 doi:10.1016/j.fuel.2019.116022 |
| spellingShingle | Coal characterisation; MaceralsChar morphology; Automated image analysis; Combustion; Vitrinite Perkins, Joseph Williams, Orla Wu, Tao Lester, Edward Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title | Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title_full | Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title_fullStr | Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title_full_unstemmed | Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title_short | Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| title_sort | automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology |
| topic | Coal characterisation; MaceralsChar morphology; Automated image analysis; Combustion; Vitrinite |
| url | https://eprints.nottingham.ac.uk/60135/ https://eprints.nottingham.ac.uk/60135/ https://eprints.nottingham.ac.uk/60135/ |