Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4133
Title: Hardgrove Grindability Index Estimation Using Neural Networks
Authors: Özbayoğlu, Gülhan
Özbayoğlu, Ahmet Murat
Issue Date: 2008
Source: Özbayoglu, A.M. and G. Özbayoglu. Hardgrove Grindability Index Estimation using Neural Networks”, International Mineral Processing Symposium (IMPS 2008), Antalya, Turkey.
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.
URI: https://hdl.handle.net/20.500.11851/4133
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering

Files in This Item:
File Description SizeFormat 
B47-Coal2008-619510.pdf147.07 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

Page view(s)

88
checked on Dec 26, 2022

Download(s)

10
checked on Dec 26, 2022

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons