Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology

Perkins, Joseph and Williams, Orla and 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

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Abstract

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.

Item Type: Article
Keywords: Coal characterisation; MaceralsChar morphology; Automated image analysis; Combustion; Vitrinite
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Chemical and Environmental Engineering
Identification Number: https://doi.org/10.1016/j.fuel.2019.116022
Depositing User: Wu, Cocoa
Date Deposited: 23 Mar 2020 01:53
Last Modified: 23 Mar 2020 01:53
URI: http://eprints.nottingham.ac.uk/id/eprint/60135

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