Automatic reactivity characterisation of char particles from pulverised coal combustion using computer vision

Q4 Computer Science
D. Chaves
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引用次数: 0

Abstract

Char morphologies produced during pulverised coal combustion may determine coal reactivity which affects the combustion efficiency and the emissions of CO2 in power plants. Commonly, char samples are characterised manually, but this process is subjective and time-consuming. This work proposes methods to automate the char reactivity characterisation using microscopy images and computer vision techniques. These methods are summarised in three contributions: the localisation of char particles based on candidate regions and deep learning methods; the classification of particles into char reactivity groups using morphological and texture features; and the integration in a system of the two previous proposals to characterise char sample reactivity. The proposed system successfully estimate char reactivity in a fast and accurate way.
用计算机视觉自动表征煤粉燃烧中炭颗粒的反应性
煤粉燃烧过程中产生的炭形态可以决定煤的反应性,从而影响发电厂的燃烧效率和二氧化碳的排放。通常,炭样品是手动表征的,但这个过程是主观的,耗时的。这项工作提出了使用显微镜图像和计算机视觉技术自动化炭反应性表征的方法。这些方法总结为三个贡献:基于候选区域和深度学习方法的char粒子定位;利用形态和结构特征将颗粒划分为炭反应性基团;并在一个系统的整合前两个建议表征炭样品的反应性。该系统成功地快速准确地估计了炭的反应性。
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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