Classifying coke using CT scans and landmark multidimensional scaling

International Journal of Coal Science & Technology

Research

Stephan Chalup¹  Merrick Mahoney¹  Bishnu P. Lamichhane¹  Keith Nesbitt¹  Fayeem Aziz¹ 

[1] School of Information and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia;

关键词: Coke;    Microstructure;    Clustering;    Classification;    Computer tomography;   

DOI  :  10.1007/s40789-023-00570-z

 received in 2021-12-02, accepted in 2023-01-16,  发布年份 2023

来源: Springer

【 摘 要 】

One factor that limits development of fundamental research on the influence of coke microstructure on its strength is the difficulty in quantifying the way that microstructure is both classified and distributed in three dimensions. To support such fundamental studies, this study evaluated a novel volumetric approach for classifying small (approx. 450 μm3) blocks of coke microstructure from 3D computed tomography scans. An automated process for classifying microstructure blocks was described. It is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means clustering. The approach was evaluated using 27 coke samples across a range of coke with different properties and reliably identified 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural density. The lower class (1–2) subblocks tend to be dominated by pores and thin walls. Typically, there is an increase in wall thickness and reduced pore sizes in the higher classes. Inert features are also likely to be seen in higher classes (5–6). In general, this approach provides an efficient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 5

Fig. 1

Fig. 3

12302_2023_718_Article_IEq20.gif

Fig. 2

Fig. 4

Fig. 1

Fig. 1

40708_2023_184_Article_IEq4.gif

Fig. 6

40854_2023_456_Article_IEq31.gif

40517_2023_248_Article_IEq34.gif

40517_2023_248_Article_IEq24.gif