Studies of the relationship between petrography and grindability for Kentucky coals using artificial neural network

被引:37
作者
Baghereih, A. H. [2 ]
Hower, James C. [1 ]
Bagherieh, A. R. [3 ]
Jorjani, E. [2 ]
机构
[1] Univ Kentucky, Ctr Appl Energy Res, Lexington, KY 40511 USA
[2] Islamic Azad Univ, Dept Mining Engn, Tehran, Iran
[3] Shiraz Univ, Dept Civil Engn, Shiraz, Iran
关键词
Hardgrove grindability index (HGI); coal petrography; artificial neural network (ANN); coal;
D O I
10.1016/j.coal.2007.04.002
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Although there are several formulas available for predicting Hardgrove grindability of coal, most of them are linear and do not simultaneously take into consideration most of the relevant factors. The artificial neural network is an information processing tool that is capable of establishing an input-output relationship by extracting controlling features from a database presented to the network. In this paper, a neural network approach was proposed to deal with the grindability behavior of coal. 195 sets of experimental data were evaluated with artificial neural network to predict the HGI of Kentucky coals. Two different kinds of the trained artificial neural network were undertaken using the database created in this study. It is shown from the examples that the artificial neural network adequately recognized the characteristics of the coal experimental data sets, retaining a generality for further prediction. It is believed that an artificial neural network based prediction procedure shown in this paper can be further employed for Hardgrove grindability index prediction. The influence of liptinite, vitrinite, ash, and sulfur content on HGI was studied by a parametric study. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:130 / 138
页数:9
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