Prediction models of calorific value of coal based on wavelet neural networks

被引：44
|
作者：
Wen, Xiaoqiang [1 ]
Jian, Shuguang [2 ]
Wang, Jianguo [2 ]
机构：
[1] Northeast Elect Power Univ, Simulat Ctr, Jilin, Peoples R China
[2] Northeast Elect Power Univ, Dept Automat, Jilin, Peoples R China
来源：
FUEL | 2017年 / 199卷
基金：
中国国家自然科学基金;
关键词：
Gross calorific value; Wavelet neural networks; Proximate (ultimate) analysis; Oxide analyses; HIGHER HEATING VALUE; PROXIMATE ANALYSIS; BIOMASS FUELS; ULTIMATE ANALYSES; HHV; LIGNOCELLULOSICS; REGRESSION; ASH;
D O I：
10.1016/j.fuel.2017.03.012
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：
New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash. The coal samples, which have been employed to develop and verify the prediction models, are from United States Geological Survey (USGS) and China Huaneng Group. Some published methods have also been employed and redeveloped to make a comparison with the models proposed in this paper. The comparison reveals that the WNN models proposed here based on the proximate (ultimate) analysis components of coal, are consistently better than the published ones. The WNN models based on the oxide analyses of ash have higher accuracy in estimating the GCV of Chinese coals than US coals. Here we also analyze the possible reasons that could lead to the low estimated accuracy. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：512 / 522
页数：11
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