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Automated Inspection of Char Morphologies in Colombian Coals Using Image Analysis

Deisy Chaves1,5,*, Maria Trujillo1, Edward Garcia2, Juan Barraza2, Edward Lester3, Maribel Barajas4, Billy Rodriguez4, Manuel Romero4, Laura Fernández-Robles5

1 Multimedia and Computer Vision Group, Universidad del Valle, Cali, Colombia
2 Chemical Engineering School, Universidad del Valle, Cali, Colombia
3 Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham, United Kingdom
4 Colombian Geological Service, Bogota, Colombia
5 Group for Vision and Intelligent Systems (former VARP), Universidad de León, León, Spain

* Corresponding Author: Deisy Chaves,

Intelligent Automation & Soft Computing 2020, 26(3), 397-405.


Precise automated determination of char morphologies formed by coal during combustion can lead to more efficient industrial control systems for coal combustion. Commonly, char particles are manually classified following the ICCP decision tree which considers four morphological features. One of these features is unfused material, and this class of material not characteristic of Colombian coals. In this paper, we propose new machine learning algorithms to classify the char particles in an image based system. Our hypothesis is that supervised classification methods can outperform the 4 ‘class’ ICCP criteria. In this paper we evaluate several morphological features and specifically assess the contribution of the unfused material feature on the overall classification performance. The results from this work confirm that the proposed method is able to accurately identify and automatically classify chars.


Cite This Article

D. Chaves, M. Trujillo, E. Garcia, J. Barraza, E. Lester et al., "Automated inspection of char morphologies in colombian coals using image analysis," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 397–405, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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