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

• Published in: Energy (Oxford) Vol. 242; p. 122949
• Main Authors: Prabhakaran, SP Sathiya, Swaminathan, Ganapathiraman, Joshi, Viraj V.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2022; Elsevier BV
• Subjects: Artificial neural network; Artificial neural networks; Burnout; Calorific value; Combustion; Combustion indices; Contraction; Heating rate; Immunoglobulins; Kinetics; Lignite; Neural networks; Pyrolysis; Reaction mechanisms; Solid-state reaction model; Thermal degradation; Thermogravimetric analysis; Two dimensional models; Pyrolysis; Combustion; Combustion indices; Kinetics; Artificial neural network; Solid-state reaction model
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.122949

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. [Display omitted] •Various methods delivered Ea & K in 170–470 kJ/mol, 1010 to 1051 min−1, R2 (0.98).•R2, R3 and D2 were best fit reaction mechanism models by CR, KC, MP methods.•NNA 7,8 were best fit models with MSE, MSEREG functions for neural networks.•CHCI value of 3.05E-9 mg min−2OC−3 confirmed 20 °C/min as optimum heating rate.•Reaction mechanisms confirmed combustion and pyrolysis as single and multi-step.