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 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 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 O C −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. • Various methods delivered Ea & K in 170–470 kJ/mol, 10 10 to 10 51 min −1 , R 2 (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 −2 O C −3 confirmed 20 ° C/min as optimum heating rate. • Reaction mechanisms confirmed combustion and pyrolysis as single and multi-step.