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CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network

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  • Zhong, Hanbin
  • Xiong, Qingang
  • Yin, Lina
  • Zhang, Juntao
  • Zhu, Yuqin
  • Liang, Shengrong
  • Niu, Ben
  • Zhang, Xinyu

Abstract

In order to reduce the computational effort of design and optimization for biomass fast pyrolysis reactor, the reduced-order modeling technology was applied to develop reduced-order models (ROMs) based on the CFD data from multi-fluid model (MFM) simulation of biomass fast pyrolysis in a bubbling fluidized bed reactor. The CFD simulations at nine different pyrolysis temperatures were performed, and the product yields and the influence of temperature on product yields were in a good agreement with experiments, which fully validated the CFD approach. The back-propagation (BP) artificial neural network (ANN) was used to map the species mass fraction data of CFD simulation to pyrolysis temperature and coordinates of each computational node in the reactor. The number of neurons and active function in the ANN was optimized. The ability of the developed ROMs to predict the species distributions at both training and testing temperature was investigated. The influence of sample method and number of outputs was also studied.

Suggested Citation

  • Zhong, Hanbin & Xiong, Qingang & Yin, Lina & Zhang, Juntao & Zhu, Yuqin & Liang, Shengrong & Niu, Ben & Zhang, Xinyu, 2020. "CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network," Renewable Energy, Elsevier, vol. 152(C), pages 613-626.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:613-626
    DOI: 10.1016/j.renene.2020.01.057
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    1. Shahbaz, Muhammad & Taqvi, Syed A. & Minh Loy, Adrian Chun & Inayat, Abrar & Uddin, Fahim & Bokhari, Awais & Naqvi, Salman Raza, 2019. "Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO," Renewable Energy, Elsevier, vol. 132(C), pages 243-254.
    2. Zheng, Ji-Lu & Zhu, Ya-Hong & Zhu, Ming-Qiang & Wu, Hai-Tang & Sun, Run-Cang, 2018. "Bio-oil gasification using air - Steam as gasifying agents in an entrained flow gasifier," Energy, Elsevier, vol. 142(C), pages 426-435.
    3. Sansaniwal, S.K. & Pal, K. & Rosen, M.A. & Tyagi, S.K., 2017. "Recent advances in the development of biomass gasification technology: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 363-384.
    4. Zhong, Hanbin & Xu, Fei & Zhang, Juntao & Zhu, Yuqin & Liang, Shengrong & Niu, Ben & Zhang, Xinyu, 2019. "Variation of Geldart classification in MFM simulation of biomass fast pyrolysis considering the decrease of particle density and diameter," Renewable Energy, Elsevier, vol. 135(C), pages 208-217.
    5. Zhong, Hanbin & Xiong, Qingang & Zhu, Yuqin & Liang, Shengrong & Zhang, Juntao & Niu, Ben & Zhang, Xinyu, 2019. "CFD modeling of the effects of particle shrinkage and intra-particle heat conduction on biomass fast pyrolysis," Renewable Energy, Elsevier, vol. 141(C), pages 236-245.
    6. Feng, Ping & Lin, Weigang & Jensen, Peter Arendt & Song, Wenli & Hao, Lifang & Raffelt, Klaus & Dam-Johansen, Kim, 2016. "Entrained flow gasification of coal/bio-oil slurries," Energy, Elsevier, vol. 111(C), pages 793-802.
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    Cited by:

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    2. Farrell, C.C. & Osman, A.I. & Doherty, R. & Saad, M. & Zhang, X. & Murphy, A. & Harrison, J. & Vennard, A.S.M. & Kumaravel, V. & Al-Muhtaseb, A.H. & Rooney, D.W., 2020. "Technical challenges and opportunities in realising a circular economy for waste photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
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