 Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIRHaobo Bi, Chengxin Wang, Qizhao Lin, Xuedan Jiang, Chunlong Jiang and Lin Bao Energy, 2020, vol. 213, issue C Abstract: 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 °C/min, 20 °C/min and 30 °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-Akahira-Sunose (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. Keywords: Co-combustion; Coal gangue; Peanut shell; TG-FTIR; Artificial neural network (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318971 DOI: 10.1016/j.energy.2020.118790 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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