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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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    References listed on IDEAS

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    Cited by:

    1. Hu, Chenshu & Guo, Xiaolin & Dai, Yuyang & Zhu, Jian & Cheng, Wen & Xu, Hongbo & Zeng, Lingfang, 2024. "A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor," Renewable Energy, Elsevier, vol. 225(C).
    2. Song, Yueyao & Hu, Jinwen & Evrendilek, Fatih & Buyukada, Musa & Liang, Guanjie & Huang, Wenxiao & Liu, Jingyong, 2021. "Reaction mechanisms and product patterns of Pteris vittata pyrolysis for cleaner energy," Renewable Energy, Elsevier, vol. 167(C), pages 600-612.
    3. 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).
    4. Zhang, Shanhong & Yu, Guanghui & Guo, Yu & Wang, Yang, 2023. "Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning," Energy, Elsevier, vol. 276(C).
    5. Kabir, Faryal & Gulfraz, Muhammad & Raja, Ghazala Kaukab & Inam-ul-Haq, Muhammad & Awais, Muhammad & Mustafa, Muhammad Salman & Khan, Sami Ullah & Tlili, Iskander & Shadloo, Mostafa Safdari, 2020. "Screening of native hyper-lipid producing microalgae strains for biomass and lipid production," Renewable Energy, Elsevier, vol. 160(C), pages 1295-1307.

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