Soft modelling of the Hardgrove grindability index of bituminous coals: An overview

James C. Hower, Amir H. Bagherieh, Saeid R. Dindarloo, Alan S. Trimble, Saeed Chehreh Chelgani

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Predictions of the Hardgrove grindability index, a predictor of the crushing and pulverization propensity of coal, have been made using both regression and neural network techniques. All techniques suffer from shortcomings. In general, input parameters must be selected based on a sound knowledge of coal chemistry and petrology, with avoidance of redundant parameters, avoidance of closure in the data sets that add to 100% (individually the proximate and ultimate analyses, petrology, and (approximately) major oxides), and a constrained coal rank and provenance setting. Predictions based on a specific set of coals are not necessarily translatable to different ranks or maceral suites. In general, for high volatile bituminous coals, combinations of coal rank (vitrinite reflectance or volatile matter), liptinite content, and ash percentage produce the best predictions.

Original languageEnglish
Article number103846
JournalInternational Journal of Coal Geology
Volume247
DOIs
StatePublished - Nov 1 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Artificial intelligence
  • Coal rank
  • HGI
  • Maceral
  • Statistics

ASJC Scopus subject areas

  • Fuel Technology
  • Geology
  • Economic Geology
  • Stratigraphy

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