Journal
ENERGY
Volume 213, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.118790
Keywords
Co-combustion; Coal gangue; Peanut shell; TG-FTIR; Artificial neural network
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Funding
- National Natural Science Foundation of China [51376171]
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The combustion behavior and gas product characteristics of coal gangue (CG) and peanut shell (PS) in air atmosphere were studied by thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). Artificial neural network (ANN) method was used to establish the optimal prediction model of CG and PS co-combustion. The heating rate of TG-FTIR experiment was set to 10 degrees C/min, 20 degrees C/min and 30 degrees C/min. The mass fractions of PS in the experimental samples were 0%, 25%, 50%, 75% and 100%. Some functional groups in the gas products were detected by Fourier transform infrared spectrometer. Moreover, the apparent activation energy (E) was calculated by Flynn-Wall-Ozawa (FWO) and Kissinger-AkahiraSunose (KAS). The activation energies of CG and PS mixture combustion are significantly lower than that of pure substance. ANN models have been established to predict the relationship between mass loss and experimental conditions. By comparing errors and correlation coefficients, it is found that the ANN20 model is optimal. (C) 2020 Elsevier Ltd. All rights reserved.
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