期刊
FUEL
卷 173, 期 -, 页码 230-238出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2016.01.043
关键词
Back-propagation neural network; Power coal blends; Ignition temperature; Activation energy
资金
- National Natural Science Foundation - China [51176163, 51476141]
- National Basic Research Program - China [2012CB214906]
Back-propagation (BP) neural network models were developed to accurately predict the ignition temperature and activation energy of 16 typical Chinese coals and 48 of their blends. Pearson correlation analysis showed that ignition temperature and activation energy were most relevant to the moisture, volatile matter, fixed carbon, calorific value and oxygen of coals. Accordingly, three-layer BP neural network models with five input factors were developed to predict the ignition characteristics of power coal blends. The BP neural network for ignition temperature gave a relative mean error of 1.22%, which was considerably lower than 3.7% obtained by the quadratic polynomial regression. The BP neural network for activation energy gave a relative mean error of 3.89%, which was considerably lower than 10.3% obtained by the quadratic polynomial regression. The accuracy of the BP neural network was significantly higher than that of traditional polynomial regression. (C) 2016 Elsevier Ltd. All rights reserved.
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