Bioenergy and bioexergy analyses with artificial intelligence application on combustion of recycled hardwood and softwood wastes

Abstract

Novel biomass bioenergy-bioexergy analyses via thermogravimetry analysis and artificial intelligence are employed to evaluate the three biofuels from wood wastes (softwood-SW, hardwood-HW, and woods blend-WB). The chemical characterization of SW has the highest bioenergy (higher heating value – HHV: 18.84 MJ kg⁻¹) and bioexergy (specific chemical bioexergy – SCB: 19.65 MJ kg⁻¹) with the SCB/HHV ratio of wood waste as about 1.043–1.046. The high C-element has a significant influence on the HHV-SCB. The three distinct zones of wood waste combustion are identified: moisture evaporation (Zone I, up to 110 °C), combustion reaction – degradation of three major lignocellulosic components (hemicelluloses, cellulose, and lignin) at Zone II, 110–600 °C, and ash remains (Zone III, 600–800 °C). The ignition (D_ig = 0.01–0.04) and fuel reactivity (R_fuel = 3.82–6.97 %·min⁻¹·°C⁻¹) indexes are evaluated. The comprehensive combustion index (S_n:>5 × 10⁻⁷%² min⁻² °C⁻³) suggests that wood waste has a better combustion performance than bituminous coal. The statistical evaluation presents that the highest HHV-SCB values are obtained by performing combustion for SW-250 μm at 15 °C·min⁻¹. The S/N ratio and ANOVA results agree that the wood waste type and particle size denote the most influential parameters. The artificial neural network prediction shows an excellent result (R² = 1) with 1 hidden layer and 5 neuron configurations.