Coal free-swelling index modeling by an ordinal-based soft computing approach

Abstract

ABSTRACT Coking coal is the most important reductant agent in the steel-making industry and approximately does not have any substitution. Thus, it is globally still on the list of critical raw materials. The free swelling index (FSI) as a qualitative index is used to assess and rank the coking-ability of coal samples. Although the accurate classification of FSI values is one of the most crucial issues in the coke-making industry, it has become one of the most challenging data-driven problems due to the specific difficulties that lie in coal data characteristics. In this study, by analyzing a robust dataset with over 3600 records from U.S. Geological Survey Coal Quality, an insight was obtained into the FSI classification problem factors that have led to the poor performance of machine learning models. By settling the class imbalance at the data level and applying an efficient model training strategy based on the ordinal classification at the algorithmic level, a novel structure for FSI modeling has been proposed. This study also demonstrates direct relation between the model accuracy and the impact of nonlinear feature selection using Mutual Information instead of traditional linear methods.