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

Perkins, Joseph; Williams, Orla; Wu, Tao; Lester, Edward

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 1300OC 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.

Citation

Perkins, J., Williams, O., Wu, T., & Lester, E. (2020). Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology. Fuel, 259, Article 116022. https://doi.org/10.1016/j.fuel.2019.116022

• Journal Article Type: Article
• Acceptance Date: Aug 13, 2019
• Online Publication Date: Sep 19, 2019
• Publication Date: Dec 1, 2020
• Deposit Date: Aug 19, 2019
• Publicly Available Date: Sep 30, 2019
• Journal: Fuel
• Print ISSN: 0016-2361
• Electronic ISSN: 1873-7153
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 259
• Article Number: 116022
• DOI: https://doi.org/10.1016/j.fuel.2019.116022
• Keywords: Coal characterisation, Macerals, Char Morphology, Automated Image Analysis, Combustion, Vitrinite
• Public URL: https://nottingham-repository.worktribe.com/output/2445933
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0016236119313766
• Additional Information: This article is maintained by: Elsevier; Article Title: Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology; Journal Title: Fuel; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.fuel.2019.116022; Content Type: article; Copyright: © 2019 The Author(s). Published by Elsevier Ltd.
• Contract Date: Aug 19, 2019