Estimation of Hardgrove grindability index of Turkish coals by neural networks

Date

2008-01-31

Author

Ozbayoglu, Gulhan
ÖZBAYOĞLU, AHMET MURAT
Ozbayoglu, M. Evren

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Geotechnical Engineering and Engineering Geology, Geochemistry and Petrology

URI

https://hdl.handle.net/11511/66627

Journal

INTERNATIONAL JOURNAL OF MINERAL PROCESSING

DOI

https://doi.org/10.1016/j.minpro.2007.08.003

Collections

Department of Mining Engineering, Article

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Citation Formats

G. Ozbayoglu, A. M. ÖZBAYOĞLU, and M. E. Ozbayoglu, “Estimation of Hardgrove grindability index of Turkish coals by neural networks,” INTERNATIONAL JOURNAL OF MINERAL PROCESSING, pp. 93–100, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66627.