Determining Coke Moisture Content Through Images Analysis Methods and Machine Learning Models

                                
==Abstract==

Coke moisture content plays a crucial role as a quality indicator in ironmaking. Fast 
and accurate measurement of coke moisture content can effectively ensure operational stability, 
thereby guaranteeing the quality of pig iron. Coke with different moisture levels exhibits 
variations in light reflection, refraction, and powder adhesion characteristics. Drawing from this 
premise, we employed an image analysis approach to analyse the colour and texture features of 
coke images with varying moisture content. The results indicated that certain specific image 
features are highly sensitive to changes in coke moisture content. This study also conducted 
testing and comparison of performances of three common machine learning models in 
predicting coke moisture content based on image analysis. The Support Vector Machine (SVM) 
predictive model for coke moisture content based on image analysis showed optimal 
performance. This demonstrated a close connection between coke image features and moisture 
content.

== Full Paper ==
<pdf>Media:Draft_Sanchez Pinedo_794198937pap_47.pdf</pdf>