Hardgrove Grindability Index Estimation Using Neural Networks

Abstract

In a previous study, different techniques for the estimation of coal HGI values were investigated (Özbayoğlu et.al, 2008). As continuation of that research, in this study a revised neural network methodology is used for estimating the HGI values using the same data from 163 sub-bituminous coals from Turkey. The parameter set used for estimating HGI consisted of moisture, ash, volatile matter and Rmax ratios. These 4 coal parameters were fed into different neural network topologies. The network parameters were optimized by genetic algorithms. The test results indicate that estimation rate was improved %10-15 over the previous results (Özbayoğlu et.al, 2008) by using this new parameter set and optimized neural network configurations.