@Article{iasc.2020.013916,
AUTHOR = {Deisy Chaves, Maria Trujillo, Edward Garcia, Juan Barraza, Edward Lester, Maribel Barajas, Billy Rodriguez, Manuel Romero, Laura Fernández-Robles},
TITLE = {Automated Inspection of Char Morphologies in Colombian Coals Using Image Analysis},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {397--405},
URL = {http://www.techscience.com/iasc/v26n3/39999},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.013916}
}