Estimation of Hardgrove grindability index of Turkish coals by neural networks

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.