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

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 (E-a) as 194-211 ki/mol (combustion) and 450-470 ki/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-Red-fern), 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 degrees C/min) was indicated with an increase in burnout T-b, ignition T-i , peak T-p temperatures. Combustion indices (CHCI, IG, IB) reported highest values of 4.99 E-10 mg(2) min(-2) C-o-3, 4.19E-05 mg(2) min(-3), 2.65 mg(2) 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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Lignite (AL), a low-rank coal with a calorific value of 5.9 MJ/kg, was extensively studied for its combustion and pyrolysis characteristics using thermogravimetric analysis under non-isothermal conditions. The kinetic investigation involved various methods to determine activation energy, reaction mechanisms, and combustion indices, providing scientific findings and guidance for spontaneous combustion and pyrolysis prediction of AL on site. The research results were further validated using artificial neural networks, confirming the analytical thermal degradation behavior of AL.