Journal
FUEL
Volume 219, Issue -, Pages 426-445Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2018.01.090
Keywords
Coking coal; Cokemaking; Coke quality; Prediction; Regression; Neural network
Categories
Funding
- Australian Coal Association Research Program [C25077]
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The prediction of coke quality from global coal basins is critical to coke producers and steel makers for both the selection and effective utilisation of coals. This review analysed the methods described within published models for the prediction of coke quality. Of particular focus were methods that sought to predict coke strength after reaction (CSR) and the related coke reactivity index (CRI). Using the cross industry standard process for data mining (CRISP-DM) as an analysis framework, the models were compared in terms of their data treatment and use of analytical techniques. On reviewing these papers, our results indicate that it is difficult to apply models beyond the conditions under which they were derived, and that many models do not report enough detail to allow complete replication.
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