Effects of heterogeneous flow on carbon conversion in gas-solid circulating fluidized beds

Nikku, Markku; Bordbar, Hadi; Myöhänen, Kari; Hyppänen, Timo

doi:10.1016/j.fuel.2020.118623

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Gas–solid fluidized bed technology is widely employed in fluidized catalytic cracking, circulating fluidized bed boilers, biomass pyrolysis, coal gasification, and other applications due to its outstanding heat and mass transfer performance . The complicated mesoscale structures in gas–solid flows, such as bubble-like voids, streamers, and clusters, are considered to have a significant impact on interphase momentum, heat, and mass transfer. − …”

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of the Mesoscale Drag Force Model Based on Machine Learning Methods

Zhang,

Yu,

Chen

et al. 2024

Ind. Eng. Chem. Res.

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The presence of mesoscale structures in gas−solid flows significantly complicates the constitutive relationship of the gas−solid drag force in coarse-grid simulations. This study employs artificial neural networks to evaluate the performance of various filtered quantities in predicting the mesoscale drag force. Our findings indicate that the drag model solely relying on local filtered quantities, such as solid volume fraction, slip velocity, or gas pressure gradient force, is unable to achieve the desired level of accuracy. The consideration of neighboring solid volume fractions significantly enhances the performance of the drag model, particularly in the dilute regions. In two-dimensional systems, the solid volume fractions at the eight grids closest to the considered grid are used. Additionally, the filtered solid volume fraction gradient and the filtered solid volume fraction at a second scale present a viable alternative to replace the eight neighboring solid volume fractions. Such an alternative offers an acceptable level of accuracy and increased simplicity when functional drag models are pursued.

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Section: Introductionmentioning

confidence: 99%

Construction and Analysis of the Mesoscale Drag Force Model Based on Machine Learning Methods

Zhang,

Yu,

Chen

et al. 2024

Ind. Eng. Chem. Res.

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Effective lateral dispersion of momentum, heat and mass in bubbling fluidized beds

Gustafsson,

Castilla,

Pallarès

et al. 2024

Front. Chem. Sci. Eng.

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The lateral dispersion of bed material in a bubbling fluidized bed is a key parameter in the prediction of the effective in-bed heat transfer and transport of heterogenous reactants, properties important for the successful design and scale-up of thermal and/or chemical processes. Computational fluid dynamics simulations offer means to investigate such beds in silico and derive effective parameters for reduced-order models. In this work, we use the Eulerian-Eulerian two-fluid model with the kinetic theory of granular flow to perform numerical simulations of solids mixing and heat transfer in bubbling fluidized beds. We extract the lateral solids dispersion coefficient using four different methods: by fitting the transient response of the bed to (1) an ideal heat or (2) mass transfer problem, (3) by extracting the time-averaged heat transfer behavior and (4) through a momentum transfer approach in an analogy with single-phase turbulence. The method (2) fitting against a mass transfer problem is found to produce robust results at a reasonable computational cost when assessed against experiments. Furthermore, the gas inlet boundary condition is shown to have a significant effect on the prediction, indicating a need to account for nozzle characteristics when simulating industrial cases.

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Evaluation of mixing of a secondary solid phase in a circulating fluidized bed riser

Nikku

Myöhänen

Ritvanen

et al. 2023

Chemical Engineering Science

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Effects of heterogeneous flow on carbon conversion in gas-solid circulating fluidized beds

Cited by 5 publications

References 39 publications

Construction and Analysis of the Mesoscale Drag Force Model Based on Machine Learning Methods

Construction and Analysis of the Mesoscale Drag Force Model Based on Machine Learning Methods

Effective lateral dispersion of momentum, heat and mass in bubbling fluidized beds

Evaluation of mixing of a secondary solid phase in a circulating fluidized bed riser

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