Explaining relationships between coke quality index and coal properties by Random Forest method

S. Chehreh Chelgani, S. S. Matin, James C. Hower

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


In this study was shown that random forest (RF) can be used as a sensible new data mining tool for variable importance measurements (VIMs) through various coal properties for prediction of coke quality (Free Swelling Index (FSI)). The VIMs of RF within coal analyses (proximate, ultimate, and petrographic analyses) were applied for the selection of the best predictors of FSI over a wide range of Kentucky coal samples. VIMs assisted by Pearson correlation through proximate, ultimate, and petrographic analyses indicated that volatile matter, carbon, vitrinite, and Rmax (coal rank parameters) are the most effective variables for the prediction of FSI. These important predictors have been used as inputs of RF model for the FSI prediction. Outputs in the testing stage of the model indicated that RF can predict FSI quite satisfactorily; the R2 was 0.93 and mean square error from actual FSIs was 0.15 (had less than interval unit of FSI; 0.5). According to the result, by providing nonlinear inter-dependence approximation among parameters for variable selection and also non-parametric predictive model RF can potentially be further employed as a reliable and accurate technique for the determination of complex relationship through fuel and energy investigations.

Original languageEnglish
Pages (from-to)754-760
Number of pages7
StatePublished - Oct 15 2016

Bibliographical note

Publisher Copyright:
© 2016 Published by Elsevier Ltd.


  • Coal rank
  • Coke quality
  • Random forest
  • Swelling index
  • Variable importance

ASJC Scopus subject areas

  • Chemical Engineering (all)
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry


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