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Inspecting the bioenergy potential of noxious Vachellia nilotica weed via pyrolysis: Thermo-kinetic study, neural network modeling and response surface optimization

Author

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  • Sahoo, Abhisek
  • Saini, Komal
  • Negi, Shweta
  • Kumar, Jitendra
  • Pant, Kamal K.
  • Bhaskar, Thallada

Abstract

In the present study, Vachellia nilotica (VN) was valorized via thermal pyrolysis. A thermogravimetric analyzer studied the thermal degradation of VN under non-isothermal conditions. Kinetic parameters were determined using seven isoconversional methods. The Eavg was ranged from 142.15 to 166.21 kJ/mol, with the advanced isoconversional approach showing significantly better performance than general methods. Thermodynamic analysis of VN pyrolysis suggested the possibility of conversion. Neural network modeling was significantly employed to predict the value of dα/dt. The effect of temperature (350–550 °C), heating rate (10–50 °C/min), and particle size (0.1–1 mm) on process performance in terms of products yield were systematically studied and analyzed. The process variables of temperature, heating rate, and particle size were also optimized using response surface methodology (RSM). The ideal operating parameters of temperature at 500 °C, and a heating rate of 25 °C/min with 0.4 mm particle size were determined and experimentally confirmed. The characteristics of the resulting product were determined using ultimate analysis, FTIR, and GC-MS, and it was discovered that the principal constituents in the VN pyrolysis process include phenolics, furfural, and dehydroacetic acid. VN has a high thermochemical conversion potential for bioenergy, as evidenced by their physicochemical properties and thermo-kinetic findings.

Suggested Citation

  • Sahoo, Abhisek & Saini, Komal & Negi, Shweta & Kumar, Jitendra & Pant, Kamal K. & Bhaskar, Thallada, 2022. "Inspecting the bioenergy potential of noxious Vachellia nilotica weed via pyrolysis: Thermo-kinetic study, neural network modeling and response surface optimization," Renewable Energy, Elsevier, vol. 185(C), pages 386-402.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:386-402
    DOI: 10.1016/j.renene.2021.12.007
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    1. Rawat, Shweta & Wagadre, Lokesh & Kumar, Sanjay, 2024. "Multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending," Renewable Energy, Elsevier, vol. 225(C).
    2. Nawaz, Ahmad & Kumar, Pradeep, 2022. "Pyrolysis behavior of low value biomass (Sesbania bispinosa) to elucidate its bioenergy potential: Kinetic, thermodynamic and prediction modelling using artificial neural network," Renewable Energy, Elsevier, vol. 200(C), pages 257-270.

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