Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
214663 | International Journal of Mineral Processing | 2008 | 8 Pages |
Abstract
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 11 coal parameters, which include proximate analysis, group maceral analysis and rank. Non-linear 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.
Keywords
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Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Gülhan Özbayoğlu, A. Murat Özbayoğlu, M. Evren Özbayoğlu,