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

Author

Listed:
  • Aniza, Ria
  • Chen, Wei-Hsin
  • Herrera, Christian J.A.
  • Quirino, Rafael
  • Petrissans, Mathieu
  • Petrissans, Anelie

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 (Dig = 0.01–0.04) and fuel reactivity (Rfuel = 3.82–6.97 %·min−1·°C−1) indexes are evaluated. The comprehensive combustion index (Sn:>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.

Suggested Citation

  • Aniza, Ria & Chen, Wei-Hsin & Herrera, Christian J.A. & Quirino, Rafael & Petrissans, Mathieu & Petrissans, Anelie, 2024. "Bioenergy and bioexergy analyses with artificial intelligence application on combustion of recycled hardwood and softwood wastes," Renewable Energy, Elsevier, vol. 237(PD).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pd:s0960148124019530
    DOI: 10.1016/j.renene.2024.121885
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