Forces acting on a single introduced particle in a solid–liquid fluidised bed

Peng, Zhengbiao; Ghatage, Swapnil V.; Doroodchi, Elham; Joshi, Jyeshtharaj B.; Evans, Geoffrey M.; Moghtaderi, Behdad

doi:10.1016/j.ces.2014.04.040

Cited by 44 publications

(10 citation statements)

References 59 publications

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“…Therefore, a constant solid concentration of ( ϕ m1 + ϕ m2 )/2 along the bed height is obtained. However, in the real liquid fluidized beds that operate under superficial liquid velocities smaller than the particle terminal settling velocity, the complete mixing status of the particle species might be hardly observed due to the local turbulence of the liquid flow and the resultant large liquid bubbles under high superficial liquid velocities …”

Section: Resultsmentioning

confidence: 99%

A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

2016

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A simple mathematical model for predicting the solid concentration profile in binary‐solid liquid fluidized beds is presented. The main assumption is that the solid concentration distribution follows the logistic function, which is supported by the literature. Various equations have been derived to solve key system quantities (e.g., bed expansion height, length and position of the transition zone). In contrast to previous models that often involve adjustable parameters and strongly rely on the availability of experimental data, the present model only requires inputs of fluid and particle properties, operating conditions, and correlations for dispersion and slip velocity. The results showed that the model is applicable to different binary‐solid systems that have size and/or density differences. The model's capability of predicting the layer inversion phenomenon has also been demonstrated. The model is simple but proves capable of accurately predicting key information for the design, operation and scale up of liquid fluidized beds. © 2016 American Institute of Chemical Engineers AIChE J, 63: 469–484, 2017

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Section: Resultsmentioning

confidence: 99%

A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

2016

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“…Moreover, they were able to successfully reproduce the particle velocity distributions. Alternatively, Peng et al [124] studied the forces acting on a foreign particle that was introduced into a fluidized bed containing particles with a lower density. Employing their validated CFD-DEM model, they evaluated this system based on the foreign particle's collision frequency and classification velocity.…”

Section: Fluidized Bedsmentioning

confidence: 99%

A Novel Approach For Analyzing Mixing Quality In Solid-Liquid Stirred Tanks Via Coupled Computational Fluid Dynamics - Discrete Element Method And Electrical Resistance Tomography

Tran¹

2021

Preprint

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The mixing quality of a solid-liquid stirred tank operating in the turbulent regime was investigated, numerically and to an extent experimentally. Simulations were performed by coupling Computational Fluid Dynamics (CFD) and the Discrete Element Method (DEM). The results were evaluated against experimental data obtained using Electrical Resistance Tomography (ERT). This facilitated a novel and more rigorous assessment of CFD-DEM coupling – i.e. based on the spatial distribution of particle concentrations. Furthermore, a new mixing index definition was developed to quantify suspension quality to work in tandem with existing dispersion mixing indexes. This provides a more complete interpretation of mixing quality. In this work, it was found that the model underestimated suspension and dispersion due to model limitations associated with mesh size and fluid-particle interaction models. Furthermore, the predicted mixing quality was sensitive to changes in the drag model, including other fluid-particle interaction forces in simulations, and variations in certain particle properties

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“…To avoid the incorrect void fractions, several approaches have been applied. Peng et al [21] used a particle meshing method (PMM) in which the particle is meshed to the small parts before each sub-part is mapped to cells according to the location of their centroids. In the offset method, used by Alobaid et al [60] with a momentum and void fraction grid, the void fraction is defined with the Eulerian cell displacements and averaged across the calculated void fractions.…”

Section: Nomenclaturementioning

confidence: 99%

“…Computational fluid dynamics-discrete element method (CFD-DEM), also referred to as a combined continuum and discrete model (CCDM) ( [19] and [17]), is commonly used to obtain detailed information of the flow fields and the particle-particle interactions in gas-solid fluidized beds and pneumatic conveying ( [20]). Liquid fluidized beds have been also studied with good agreement ( [21], [22], [23], [24] and [25]). In the resent years, CFD-DEM has also been used to study particle batch settling ( [26] and [27]), sediment transport ( [28], [29], [30] and [31]), solid-liquid mixing ( [32]) and slurry transport in pipes ( [33], [34] and [35]).…”

Section: Introductionmentioning

confidence: 99%

CFD-DEM simulations of hydrodynamics of combined ion exchange-membrane filtration

Naukkarinen

Nikku

Turunen-Saaresti

2019

Chemical Engineering Science

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The combined ion exchange-membrane filtration system, whereby an active filtration layer is formed using a slurry flow of resin beads and water, has the potential to reduce energy usage and increase the quality of purified wastewater. The hydrodynamic design of these systems is in its infancy and there is a need for numerical methods to analyze such systems. In this work, computational fluid dynamics coupled with the discrete element method (CFD-DEM) is utilized to analyse the hydrodynamics of the combined ion exchange-membrane filtration system, and the formation of the filtrating bed and the parameters affecting it are studied by varying the flow conditions. As a result, the details of the bed formation with insights on the parameters affecting these phenomena and suggestions for an improved hydrodynamic design of the combined ion exchange-membrane filtration system are presented.

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Forces acting on a single introduced particle in a solid–liquid fluidised bed

Cited by 44 publications

References 59 publications

A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

A simple model for predicting solid concentration distribution in binary‐solid liquid fluidized beds

A Novel Approach For Analyzing Mixing Quality In Solid-Liquid Stirred Tanks Via Coupled Computational Fluid Dynamics - Discrete Element Method And Electrical Resistance Tomography

CFD-DEM simulations of hydrodynamics of combined ion exchange-membrane filtration

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