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Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach

Author

Listed:
  • Zheng Lian

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Yixiao Wang

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Xiyue Zhang

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Abubakar Yusuf

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Lord Famiyeh

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • David Murindababisha

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Huan Jin

    (School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China)

  • Yiyang Liu

    (Department of Chemistry, University College London (UCL), 20 Gordon Street, London WC1H 0AJ, UK)

  • Jun He

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China)

  • Yunshan Wang

    (National Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Gang Yang

    (National Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Yong Sun

    (Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
    School of Engineering, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia)

Abstract

The current hydrogen generation technologies, especially biomass gasification using fluidized bed reactors (FBRs), were rigorously reviewed. There are involute operational parameters in a fluidized bed gasifier that determine the anticipated outcomes for hydrogen production purposes. However, limited reviews are present that link these parametric conditions with the corresponding performances based on experimental data collection. Using the constructed artificial neural networks (ANNs) as the supervised machine learning algorithm for data training, the operational parameters from 52 literature reports were utilized to perform both the qualitative and quantitative assessments of the performance, such as the hydrogen yield (HY), hydrogen content (HC) and carbon conversion efficiency (CCE). Seven types of operational parameters, including the steam-to-biomass ratio (SBR), equivalent ratio (ER), temperature, particle size of the feedstock, residence time, lower heating value (LHV) and carbon content (CC), were closely investigated. Six binary parameters have been identified to be statistically significant to the performance parameters (hydrogen yield (HY)), hydrogen content (HC) and carbon conversion efficiency (CCE) by analysis of variance (ANOVA). The optimal operational conditions derived from the machine leaning were recommended according to the needs of the outcomes. This review may provide helpful insights for researchers to comprehensively consider the operational conditions in order to achieve high hydrogen production using fluidized bed reactors during biomass gasification.

Suggested Citation

  • Zheng Lian & Yixiao Wang & Xiyue Zhang & Abubakar Yusuf & Lord Famiyeh & David Murindababisha & Huan Jin & Yiyang Liu & Jun He & Yunshan Wang & Gang Yang & Yong Sun, 2021. "Hydrogen Production by Fluidized Bed Reactors: A Quantitative Perspective Using the Supervised Machine Learning Approach," J, MDPI, vol. 4(3), pages 1-22, July.
    • Handle: RePEc:gam:jjopen:v:4:y:2021:i:3:p:22-287:d:589982
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    References listed on IDEAS

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