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Combustion and pyrolysis kinetics of Australian lignite coal and validation by artificial neural networks

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  • Prabhakaran, SP Sathiya
  • Swaminathan, Ganapathiraman
  • Joshi, Viraj V.

Abstract

Lignite (AL), with a calorific value of 5.9 MJ/kg is the most abundant low-rank coal used widely in power generation. AL's combustion and pyrolysis characteristics were investigated to provide scientific findings using thermogravimetric analysis under non-isothermal conditions. Methods utilized in the kinetic investigation included Vyazovkin, Flynn-Ozawa-Wall (FOW), Kissinger-Akahira-Sunose (KAS), Freidman, Doyle, Arrhenius, Freeman-Caroll, and Sharp-Wentworth. Multiple heating rate methods delivered the activation energy (Ea) as 194–211 kJ/mol (combustion) and 450–470 kJ/mol (pyrolysis). Combustion process followed two dimensional diffusional reaction (2D), volume contracting (R3) solid-state reaction mechanism models and pyrolysis followed volume contracting (R3) as determined by CR (Coats-Redfern), KC (Kennedy-Clark) methods. Master Plot method validated the mechanisms and concluded that it is of deaccelerating type. Improper combustion at a higher heating rate (50 °C/min) was indicated with an increase in burnout Tb, ignition Ti, peak Tp temperatures. Combustion indices (CHCI, IG, IB) reported highest values of 4.99 E−10 mg2 min−2 OC−3, 4.19E-05 mg2 min−3, 2.65 mg2 min−4 at lowest heating rates. AL's analytical thermal degradation behavior results were validated using artificial neural networks with best-fit models NNA 7,8. The research study offers a useful guide for spontaneous AL combustion and pyrolysis prediction on site.

Suggested Citation

  • Prabhakaran, SP Sathiya & Swaminathan, Ganapathiraman & Joshi, Viraj V., 2022. "Combustion and pyrolysis kinetics of Australian lignite coal and validation by artificial neural networks," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221031984
    DOI: 10.1016/j.energy.2021.122949
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    4. Jiang, Xu & Xu, Jun & He, Qichen & Wang, Cong & Jiang, Long & Xu, Kai & Wang, Yi & Su, Sheng & Hu, Song & Du, Zhenyi & Xiang, Jun, 2023. "A study of the relationships between coal heterogeneous chemical structure and pyrolysis behaviours: Mechanism and predicting model," Energy, Elsevier, vol. 282(C).

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