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Optimization of Torrefaction Parameters Using Metaheuristic Approach

Author

Listed:
  • Alok Dhaundiyal

    (Center for Energy Research, Konkoly-Mikos Utca. 29-33, 1121 Budapest, Hungary
    Department of Mechanical Engineering, THDC-Institute of Hydropower Engineering and Technology, Uttarakhand Technical University, Tehri Garhwal 249124, Uttarakhand, India)

  • Laszlo Toth

    (Institute of Technology, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary)

Abstract

The probabilistic technique was used to optimize the torrefaction parameters that indirectly influence the yield of end-products obtained through the pyrolysis of biomass. In the same pursuit, pine cones underwent thermal pre-treatment at 210 °C, 220 °C, 230 °C, 240 °C, and 250 °C in the presence of N 2 gas with a flowing rate of 0.7 L∙s −1 , whereas the duration of the pre-treatment process was 5 min, 10 min, and 15 min at each. To facilitate the processing of pine waste, a muffle furnace was improvised for pilot-scale testing. The thermal process used to carry out torrefaction was quasi-static. The average dynamic head of volatile gases inside the chamber was 1.04 m. The criteria for determining the optimal solution were based on calorific value, solid yield, energy consumption during the pre-treatment process, and ash handling. In absolute terms, time and temperature did not influence the statistical deviation in cellulose and hemicellulose decomposition after thermal pre-treatment. While considering ash content as a primal factor, thermal processing should be conducted for 5 min at 210 °C for the bounded operating conditions, which are similar to the operating conditions obtained experimentally. The optimal solid yield would be obtained if the thermal pre-treatment is performed at 250 °C for 5 min. The solution derived through a simulated annealing technique provided a better convergence with the experimental dataset.

Suggested Citation

  • Alok Dhaundiyal & Laszlo Toth, 2024. "Optimization of Torrefaction Parameters Using Metaheuristic Approach," Energies, MDPI, vol. 17(13), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3314-:d:1429728
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    References listed on IDEAS

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    1. A.F. Atiya & A.G. Parlos & L. Ingber, 2003. "A reinforcement learning method based on adaptive simulated annealing," Lester Ingber Papers 03rl, Lester Ingber.
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