Design and Development of a Catalytic Fixed-Bed Reactor for Gasification of Banana Biomass in Hydrogen Production

Valorization of biomass through gasification for green hydrogen generation: A comprehensive review

Green and sustainable hydrogen from biomass gasification processes is one of the promising ways to alternate fossil fuels-based hydrogen production. First off, an overview of green hydrogen generation from biomass gasification processes is presented and the corresponding possible gasification reactions and the effect of respective experimental criteria are explained in detail. In addition, a comprehensive explanation of the catalytic effect on tar reduction and hydrogen generation via catalytic gasification is presented regarding the functional mechanisms of various types of catalysts. Furthermore, the commercialization aspects, the associated technical challenges and barriers, and the prospects of a biomass gasification process for green hydrogen generation are discussed. Finally, this comprehensive review provides the related advancements, challenges, and great insight of biomass gasification for the green hydrogen generation to realize a sustainable hydrogen society via biomass valorization.

Research on a New Drag Force Model for Cylindrical Particles in Fixed Bed Reactors

Fixed bed reactors play an important role in converting solid wastes to high-quality products. The solid wastes, as well as the corresponding catalysts, are often made into cylindrical particles. However, research on the drag force for cylindrical particles is still rarely reported. In this work, the fixed bed porosity was firstly predicted with the unresolved CFD-DEM method and validated against experimental data. Then, the Ergun model, Di Felice model, and Ganser model were evaluated against the reported pressure drop data for both the spherical and cylindrical particles, so that a more solid drag force theory could be selected as a candidate for cylindrical particles. Finally, a new Ganser model was proposed for cylindrical particle drag force prediction based on the reported experimental results and validated by other experimental data. It was found that, for the spherical particle bed, the relative prediction errors of the Di Felice model are approximately 10%, while those of the Ergun model are approximately 15%. For the cylindrical particle bed, the relative prediction errors of the Ganser model are approximately 10%, while those of the Di Felice model are much higher than 10%. With the new Ganser model proposed in this work, the maximum error between the predicted pressure drop and the experimental data can be lowered to approximately 5%. The research is of reference value for drag force model selection when simulating similar FBRs with cylindrical particles.