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Development and validation of torrefaction optimization model applied element content prediction of biomass

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  • Oh, Kwang Cheol
  • Kim, Junghwan
  • Park, Sun Yong
  • Kim, Seok Jun
  • Cho, La Hoon
  • Lee, Chung Geon
  • Roh, Jiwon
  • Kim, Dae Hyun

Abstract

In terms of heat energy, woody biomass is not as effective as fossil fuels owing to its hydrophilic characteristics and low calorific value. These disadvantages can be overcome through torrefaction, a low-temperature pyrolysis method that can increase the energy density of woody biomass by increasing its carbon content ratio. However, it is difficult to increase the calorific value within a short processing time, while long processing times decrease the useful heating value. Therefore, optimal conditions need to be determined. Accordingly, this study attempted to optimize the torrefaction of woody biomass using a one-dimensional simulation analysis. Changes in the elemental contents of biomass were predicted by analyzing the mass reduction and characteristics of volatile matter emission due to torrefaction, and changes in the calorific value were derived. Comparing experiments and simulations. We estimated the calorific value and optimal conditions according to the process temperature and time (200 °C at 40 min, 230 °C at 30 min, 250 °C at 20 min). This study provides preliminary findings for the effective utilization of biomass, a material that is usually discarded.

Suggested Citation

  • Oh, Kwang Cheol & Kim, Junghwan & Park, Sun Yong & Kim, Seok Jun & Cho, La Hoon & Lee, Chung Geon & Roh, Jiwon & Kim, Dae Hyun, 2021. "Development and validation of torrefaction optimization model applied element content prediction of biomass," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321344
    DOI: 10.1016/j.energy.2020.119027
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    References listed on IDEAS

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    1. Oh, Kwang Cheol & Park, Sun Young & Kim, Seok Jun & Choi, Yun Sung & Lee, Chung Geon & Cho, La Hoon & Kim, Dae Hyun, 2019. "Development and validation of mass reduction model to optimize torrefaction for agricultural byproduct biomass," Renewable Energy, Elsevier, vol. 139(C), pages 988-999.
    2. Chen, Wei-Hsin & Lin, Bo-Jhih & Colin, Baptiste & Chang, Jo-Shu & Pétrissans, Anélie & Bi, Xiaotao & Pétrissans, Mathieu, 2018. "Hygroscopic transformation of woody biomass torrefaction for carbon storage," Applied Energy, Elsevier, vol. 231(C), pages 768-776.
    3. Oluoti, Kehinde & Doddapaneni, Tharaka Rama K.C. & Richards, Tobias, 2018. "Investigating the kinetics and biofuel properties of Alstonia congensis and Ceiba pentandra via torrefaction," Energy, Elsevier, vol. 150(C), pages 134-141.
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    2. Silveira, Edgar A. & Macedo, Lucélia A. & Rousset, Patrick & Candelier, Kevin & Galvão, Luiz Gustavo O. & Chaves, Bruno S. & Commandré, Jean-Michel, 2022. "A potassium responsive numerical path to model catalytic torrefaction kinetics," Energy, Elsevier, vol. 239(PB).
    3. Zhang, Congyu & Chen, Wei-Hsin & Zhang, Ying & Ho, Shih-Hsin, 2023. "Influence of microorganisms on the variation of raw and oxidatively torrefied microalgal biomass properties," Energy, Elsevier, vol. 276(C).
    4. Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.
    5. Antonios Nazos & Dorothea Politi & Georgios Giakoumakis & Dimitrios Sidiras, 2022. "Simulation and Optimization of Lignocellulosic Biomass Wet- and Dry-Torrefaction Process for Energy, Fuels and Materials Production: A Review," Energies, MDPI, vol. 15(23), pages 1-35, November.
    6. Sunyong Park & Seok Jun Kim & Kwang Cheol Oh & La Hoon Cho & DaeHyun Kim, 2023. "Developing a Proximate Component Prediction Model of Biomass Based on Element Analysis," Energies, MDPI, vol. 16(1), pages 1-14, January.

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