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Automated Inspection of Char Morphologies in Colombian Coals using Image Analysis


Authors



Abstract

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.


Keywords


Pages

Total Pages: 9

DOI
10.31209/2019.100000071


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Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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