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.

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