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Distributed activation energy model for lignocellulosic biomass torrefaction kinetics with combined heating program

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  • Feng, Yipeng
  • Qiu, Keying
  • Zhang, Zhiping
  • Li, Chong
  • Rahman, Md. Maksudur
  • Cai, Junmeng

Abstract

Torrefaction kinetics is fundamental for the theoretical investigation and industrial application of torrefaction processes. Most of biomass torrefaction kinetic studies focused on kinetic modelling under either isothermal or linear heating programs with one or several activation energies, which couldn't accurately reflect its reaction mechanisms. A distributed activation energy model (DAEM) was proposed to analyze logging residue torrefaction kinetics with a combined heating program at different temperatures. The model parameters were efficiently optimized by using the pattern search method. The results showed that the DAEM could excellently describe the experimental data of logging residue torrefaction at various conditions. The obtained activation energy distributions for logging residue torrefaction with the combined heating program at final temperatures of 240, 270 and 300 °C lay in the range of 154–172 kJ mol−1, 160–177 kJ mol−1 and 165–185 kJ mol−1, respectively. These findings indicated that major reactions occurring during torrefaction were the devolatilization and carbonization of biomass's hemicellulose constituents and partial decomposition of biomass's cellulose constituents. The experimental kinetic data of mesocarp fiber torrefaction at final temperatures of 220, 250 and 270 °C from the literature was also successfully described by the DAEM.

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  • Feng, Yipeng & Qiu, Keying & Zhang, Zhiping & Li, Chong & Rahman, Md. Maksudur & Cai, Junmeng, 2022. "Distributed activation energy model for lignocellulosic biomass torrefaction kinetics with combined heating program," Energy, Elsevier, vol. 239(PC).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024762
    DOI: 10.1016/j.energy.2021.122228
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    3. Abdul Waheed & Salman Raza Naqvi & Imtiaz Ali, 2022. "Co-Torrefaction Progress of Biomass Residue/Waste Obtained for High-Value Bio-Solid Products," Energies, MDPI, vol. 15(21), pages 1-20, November.
    4. Sun Yong Park & Kwang Cheol Oh & Seok Jun Kim & La Hoon Cho & Young Kwang Jeon & DaeHyun Kim, 2023. "Development of a Biomass Component Prediction Model Based on Elemental and Proximate Analyses," Energies, MDPI, vol. 16(14), pages 1-17, July.
    5. Chen, Rui & Cai, Jun & Li, Xinli & Lyu, Qinggang & Qi, Xiaobin, 2023. "Modelling of large biomass and coal particle based on a novel C-DAEM: A numerical study on heat transfer and pyrolysis behavior," Energy, Elsevier, vol. 283(C).
    6. Li, Yu & Tan, Zhiwu & Zhu, Youjian & Zhang, Wennan & Du, Zhenyi & Shao, Jingai & Jiang, Long & Yang, Haiping & Chen, Hanping, 2022. "Effects of P-based additives on agricultural biomass torrefaction and particulate matter emissions from fuel combustion," Renewable Energy, Elsevier, vol. 190(C), pages 66-77.

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