Le, B.T. and Xiao, D. and Mao, Y. and He, D. (2018) Coal analysis based on visible-infrared spectroscopy and a deep neural network. Infrared Physics and Technology, 93. pp. 34-40. ISSN 13504495
Full text not available from this repository. (Upload)Abstract
The proximate analysis of coal is the umbrella term for the six indexes that include the moisture, ash, volatile matter, fixed carbon, and sulphur contents and the heating value. Burning of coal creates carbon dioxide, sulphur dioxide and nitrogen dioxide which are main reasons causing air pollution. Therefore, before utilizing coal, it is indispensable to analyse coal. The traditional proximate analysis of coal mainly relies on chemical analysis, which is time-consuming and costly. Hence, a method to construct a coal analysis is introduced in this paper. By using the method to analyse moisture (%), ash (%), volatile matter (%), fixed carbon (%), and sulphur (%) contents and the low heating value (J/g). We first obtained different coal sample from different coal areas in China. Then, measured the spectral data through the spectral analysis instrument and extracted spectral features through a convolutional neural network. Finally, we applied the extreme learning machine algorithm to construct the prediction and analysis model of the spectral feature data. The experimental result shows that the model in the study can predict the components of coal. Compared with the chemical analysis method, this method has unparalleled advantages in terms of financial efficiency, speed and accuracy. © 2018
Item Type: | Article |
---|---|
Divisions: | Faculties > Faculty of Control Engineering |
Identification Number: | 10.1016/j.infrared.2018.07.013 |
Uncontrolled Keywords: | Carbon dioxide; Coal; Convolution; Infrared spectroscopy; Knowledge acquisition; Moisture; Neural networks; Nitrogen oxides; Optimization; Spectrum analysis; Sulfur determination; Sulfur dioxide; Artificial bee colonies (ABC); Convolutional neural network; Convolutional Neural Networks (CNN); Extreme learning machine; Financial efficiency; Low heating values; Prediction and analysis; Proximate analysis; Deep neural networks |
Additional Information: | Language of original document: English. |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/9531 |