Deisy Chaves1,5,*, Maria Trujillo1, Edward Garcia2, Juan Barraza2, Edward Lester3, Maribel Barajas4, Billy Rodriguez4, Manuel Romero4, Laura Fernández-Robles5
Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 397-405, 2020, DOI:10.32604/iasc.2020.013916
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 More >
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