Estimation of Hardgrove grindability index of Turkish coals by neural networks

Ozbayoglu G., ÖZBAYOĞLU A. M. , Ozbayoglu M. E.

INTERNATIONAL JOURNAL OF MINERAL PROCESSING, vol.85, no.4, pp.93-100, 2008 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 85 Issue: 4
  • Publication Date: 2008
  • Doi Number: 10.1016/j.minpro.2007.08.003
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.93-100
  • Keywords: Hardgrove grindability index, Turkish coals, neural networks, non-linear regression, proximate analysis, petrographic analysis, MULTIVARIABLE REGRESSION, CHINESE COAL, PREDICTION


In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring I I coal parameters, which include proximate analysis, group maceral analysis and rank. Nonlinear regression and neural network techniques are used for predicting the HGI values for the specified coal parameters. Results indicate that a hybrid network which is a combination of 4 separate neural networks gave the most accurate HGI prediction and all of the neural network models, outperformed non-linear regression in the estimation process. (C) 2007 Elsevier B.V. All rights reserved.