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

• Published in: Fuel (Guildford) Vol. 259; p. 116022
• Main Authors: Perkins, Joseph, Williams, Orla, Wu, Tao, Lester, Edward
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2020; Elsevier BV
• Subjects: Automated image analysis; Automation; Char morphology; Coal; Coal characterisation; Combustion; Decision trees; Histograms; Image analysis; Image processing; Intermediates; Macerals; Morphology; Performance prediction; Pulverized coal; Reflectance; Tube furnaces; Vitrinite; Char morphology; Combustion; Vitrinite; Coal characterisation; Macerals; Automated image analysis
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2019.116022

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.