Qian, Xiangchen, Li, Fengjie, Yan, Yong, Lu, Gang, Liu, Hao (2020) Characterisation of the Combustion Behaviours of Individual Pulverised Coal Particles Entrained by Air Using Image Processing Techniques. Measurement Science and Technology, 32 (3). Article Number 034005. ISSN 0957-0233. (doi:10.1088/1361-6501/abc2ea) (KAR id:83573)
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Official URL: http://dx.doi.org/10.1088/1361-6501/abc2ea |
Abstract
The combustion characteristics of individual coal particles are the basis for a deep understanding of the macroscopic pulverised coal combustion in power plant boilers. This work proposes a quantitative method to characterise the combustion behaviours of individual pulverised coal particles by measuring a set of physical parameters from digital images of the particles. The combustion process of pulverised particles of bituminous coal in a visual drop tube furnace was recorded by a high-speed camera with a frame rate of 6200 frames per second. An improved-Canny algorithm was developed to extract the combustion zones of a coal particle in both the volatile and char combustion phases. Using the improved-Canny and Otsu algorithms, the unburned part of the particle was identified in the volatile combustion phase. Characteristic parameters of coal particles, including the area, brightness, length, width and aspect ratio of volatile flame, and falling velocity, were derived from the processed images. The results obtained show that the volatile and char combustion took place successively and the volatile matter was combusted almost as soon as it was released. The particle travelled upward for around 14 ms during the early stage of combustion due to the influence of devolatilisation and volatile combustion. The particle also exhibited a slight difference in the rotation frequency at different combustion phases.
Item Type: | Article |
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DOI/Identification number: | 10.1088/1361-6501/abc2ea |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Yong Yan |
Date Deposited: | 20 Oct 2020 09:28 UTC |
Last Modified: | 05 Nov 2024 12:49 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/83573 (The current URI for this page, for reference purposes) |
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