Modeling and Simulation of the Motion and Gasification Behaviors of Superellipsoidal Biomass Particles in an Entrained-Flow Reactor

Biomass entrained-flow gasification has aroused lots of interest because of its great potential in syngas production. For simplification purposes, traditional computational fluid dynamics simulations of biomass gasification normally assume that the fuel particles have a spherical shape. However, bec...

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Veröffentlicht in:Energy & fuels 2021-01, Vol.35 (2), p.1488-1502
Hauptverfasser: Zhang, Runhui, Ku, Xiaoke, Yang, Shuna, Wang, Jin, Fan, Liwu
Format: Artikel
Sprache:eng
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Zusammenfassung:Biomass entrained-flow gasification has aroused lots of interest because of its great potential in syngas production. For simplification purposes, traditional computational fluid dynamics simulations of biomass gasification normally assume that the fuel particles have a spherical shape. However, because of the fibrous structure, the ground biomass particles are mostly nonspherical. Thus, in this work, a superellipsoidal model is adopted to describe biomass particles within the Eulerian–Lagrangian framework. The drag, lift, and torque exerted on the particles and particle-wall collisions are all resolved. Moreover, key processes during biomass gasification (e.g., particle dynamics, heat/mass transfer, and chemical reactions) are all considered. Besides the validation, the effects of five particle-related parameters (i.e., particle aspect ratio, shape factor, volume, initial velocity, and shrinking factor) on the motion and gasification behaviors of nonspherical biomass particles in an entrained-flow reactor are explored. Results reveal that the prolate particles are more scattered inside the reactor due to the orientation-related drag and lift forces; small-volume and prolate particles have a higher heating rate and carbon conversion; the particle shape factor and initial velocity do not significantly affect the gasification process within the tested parameter range; and considering the particle shrinkage predicts a lower conversion rate.
ISSN:0887-0624
1520-5029
DOI:10.1021/acs.energyfuels.0c03440