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

Joseph Perkins, Orla Williams, Tao Wu, Edward Lester

    Research output: Journal PublicationArticlepeer-review

    10 Citations (Scopus)
    1 Downloads (Pure)

    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.

    Original languageEnglish
    Article number116022
    JournalFuel
    Volume259
    DOIs
    Publication statusPublished - 1 Jan 2020

    Keywords

    • Automated image analysis
    • Char morphology
    • Coal characterisation
    • Combustion
    • Macerals
    • Vitrinite

    ASJC Scopus subject areas

    • Chemical Engineering (all)
    • Fuel Technology
    • Energy Engineering and Power Technology
    • Organic Chemistry

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