Understanding the impact of coal blending decisions on the prediction of coke quality: a data mining approach

Understanding the impact of coal blending decisions on the prediction of coke quality: a data mining approach

Abstract The accurate prediction of coke quality is important for the selection and valuation of metallurgical coals. Whilst many prediction models exist, they tend to perform poorly for coals beyond which the model was developed. Further, these models general fail to directly account for physical i...

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Bibliographic Details
Main Authors: Lauren A. North, Karen L. Blackmore, Keith V. Nesbitt, Kim Hockings, Merrick R. Mahoney
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:International Journal of Coal Science & Technology
Subjects:
Coke
Quality
Prediction
Self-organizing maps
Vitrinite reflectance distribution
Online Access:http://link.springer.com/article/10.1007/s40789-018-0217-2

Internet

http://link.springer.com/article/10.1007/s40789-018-0217-2

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