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

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.

Funding source: University of Nottingham Ningbo China (FoSE New Researchers Grant I01210100011), the Faculty Inspiration Grant of University of Nottingham (FIG2019), the Qianjiang Talent Scheme (QJD1803014), the National Key R&D Program of China (Grant: 2018YFC1903500), the Ningbo Science and Technology Innovation 2025 Key Project (Grant 2020Z100) and the Ningbo Municipal Commonweal Key Program (Grant 2019C10033 and 2019C10104).
Related subjects: Production & Supply Chain
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/content/journal2361
2021-07-08
2022-07-06
http://instance.metastore.ingenta.com/content/journal2361
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