A Conservative Macroscopic Model for Binary-mixture Fluidized Beds

Authors

  • Mohamed Sobhi Alagha
    Affiliation
    Department of Mechanical Engineering, Faculty of Engineering, Kafrelsheikh University, 33516 Kafrelsheikh, El-Giesh Street 5, Egypt
    Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, H-1111 Budapest, 9 Műegyetem rkp., Hungary
  • Pal Szentannai
    Affiliation
    Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, H-1111 Budapest, 9 Műegyetem rkp., Hungary
https://doi.org/10.3311/PPch.17420

Abstract

Two approaches are commonly used for modeling the vertical mixing of binary-mixture fluidized beds, Computational Fluid Dynamics (CFD) and macroscopic modeling. A common realization of the latter one is the Gibiralo–Rowe (G-R) model, which uses the Two-Phase Theory. This macroscopic model obviously overperforms CFDs regarding computational cost; however, determining its coefficients is a still challenging issue. Although several methods were published for solving this, the general problem with most of them remains their neglecting the conservation of mass. In the present new procedure, the mass conservation is applied to correct the values of the G-R model coefficients estimated from known equations. The present model was validated on a wide variety of fluidized bed systems. The results show that this conservative and macroscopic model gives more accurate predictions than the recently published other macroscopic models, and this one is, in general, better than the CFD model from the perspective of prediction accuracy as well.

Keywords:

fluidized beds, mixing, segregation, macroscopic modeling, CFD, validation

Citation data from Crossref and Scopus

Published Online

2021-08-26

How to Cite

Alagha, M. S., Szentannai, P. “A Conservative Macroscopic Model for Binary-mixture Fluidized Beds”, Periodica Polytechnica Chemical Engineering, 65(4), pp. 525–535, 2021. https://doi.org/10.3311/PPch.17420

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