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Features of the processes of heat and mass transfer when drying a large thickness layer of wood biomass

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  • Kuznetsov, G.V.
  • Syrodoy, S.V.
  • Nigay, N.A.
  • Maksimov, V.I.
  • Gutareva, N.Yu.

Abstract

The article presents the results of the experimental studies of heat transfer in a layer of moist wood biomass under conditions of its dehydration when heated in a high-temperature gas environment.

Suggested Citation

  • Kuznetsov, G.V. & Syrodoy, S.V. & Nigay, N.A. & Maksimov, V.I. & Gutareva, N.Yu., 2021. "Features of the processes of heat and mass transfer when drying a large thickness layer of wood biomass," Renewable Energy, Elsevier, vol. 169(C), pages 498-511.
    • Handle: RePEc:eee:renene:v:169:y:2021:i:c:p:498-511
    DOI: 10.1016/j.renene.2020.12.137
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    1. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    2. Saidur, R. & Abdelaziz, E.A. & Demirbas, A. & Hossain, M.S. & Mekhilef, S., 2011. "A review on biomass as a fuel for boilers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2262-2289, June.
    3. Thakur, Amit & Canter, Christina E. & Kumar, Amit, 2014. "Life-cycle energy and emission analysis of power generation from forest biomass," Applied Energy, Elsevier, vol. 128(C), pages 246-253.
    4. Chen, Wei-Hsin & Kuo, Po-Chih, 2011. "Torrefaction and co-torrefaction characterization of hemicellulose, cellulose and lignin as well as torrefaction of some basic constituents in biomass," Energy, Elsevier, vol. 36(2), pages 803-811.
    5. Dogan, Eyup & Inglesi-Lotz, Roula, 2017. "Analyzing the effects of real income and biomass energy consumption on carbon dioxide (CO2) emissions: Empirical evidence from the panel of biomass-consuming countries," Energy, Elsevier, vol. 138(C), pages 721-727.
    6. Moiseyev, Alexander & Solberg, Birger & Kallio, A. Maarit I. & Lindner, Marcus, 2011. "An economic analysis of the potential contribution of forest biomass to the EU RES target and its implications for the EU forest industries," Journal of Forest Economics, Elsevier, vol. 17(2), pages 197-213, April.
    7. Meng, Fanbin & Wang, Donghai, 2020. "Effects of vacuum freeze drying pretreatment on biomass and biochar properties," Renewable Energy, Elsevier, vol. 155(C), pages 1-9.
    8. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    9. Madhu Khanna & Jürgen Scheffran & David Zilberman (ed.), 2010. "Handbook of Bioenergy Economics and Policy," Natural Resource Management and Policy, Springer, number 978-1-4419-0369-3, March.
    10. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
    11. Nabavi, Vahid & Azizi, Majid & Tarmian, Asghar & Ray, Charles David, 2020. "Feasibility study on the production and consumption of wood pellets in Iran to meet return-on-investment and greenhouse gas emissions targets," Renewable Energy, Elsevier, vol. 151(C), pages 1-20.
    12. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    13. Verma, Munna & Loha, Chanchal & Sinha, Amar Nath & Chatterjee, Pradip Kumar, 2017. "Drying of biomass for utilising in co-firing with coal and its impact on environment – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 732-741.
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    Cited by:

    1. Kuznetsov, G.V. & Nigay, N.A. & Syrodoy, S.V. & Gutareva, N. Yu & Malyshev, D. Yu, 2022. "A comparative analysis of the characteristics of the water removal processes in preparation for incineration of typical wood waste and forest combustible materials," Energy, Elsevier, vol. 239(PE).
    2. Syrodoy, S.V. & Kuznetsov, G.V. & Nigay, N.A. & Purin, M.V. & Kostoreva, Zh.A., 2023. "The effect of compaction of the dispersed wood biomass layer on its drying efficiency," Renewable Energy, Elsevier, vol. 211(C), pages 64-75.
    3. Weronika Tulej & Szymon Głowacki & Andrzej Bryś & Mariusz Sojak & Piotr Wichowski & Krzysztof Górnicki, 2021. "Research on Determination of Water Diffusion Coefficient in Single Particles of Wood Biomass Dried Using Convective Drying Method," Energies, MDPI, vol. 14(4), pages 1-12, February.

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