焦炭
椭圆
纹理(宇宙学)
煤
各向异性
材料科学
反应后焦炭强度
人工智能
计算机科学
矿物学
光学
数学
石油焦
图像(数学)
冶金
工程类
化学
几何学
物理
废物管理
作者
Sun Weizhou,Mingdong Zheng,Ping Cui,Desheng Hu,Xiu Kan
摘要
Abstract The optical texture of coke is undoubtedly one of the important factors that determined many properties of coke, but it still mostly depends on the skills and experience of manual analysis. In this paper, a novel automatic measurement method is proposed for coke optical texture. this method through two analyzer angle images(10° and 170°) under polarizing microscope to recognize isochromatic regions which occurs color changed between blue and red by image processing, then characterize the dimensional sizes of isochromatic regions and classify them into different anisotropic texture type. Two fitted ellipse methods are used to characterize the regions' dimensional sizes by the range of shape factor S , followed by classifying different types of anisotropic texture according to the long and short axes of fitted ellipse. An automatic image acquisition and analysis system is established based on the above method. Finally, eight coke samples derived from different ranks of coking coal were selected to test the proposed method. The result shows that it can effectively recognize different types of anisotropic texture and provide composition of different anisotropic texture type for each coke. Moreover, the anisotropic degree of coke shows a significant positive correlation with parent coal rank and a negative correlation with coke reactivity index (CRI).
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