Effect of bed size on hydrodynamics in 3‐D gas–solid fluidized beds

Verma, Vikrant; Padding, JT Johan; Deen, NG Niels; Kuipers, J.A.M.

doi:10.1002/aic.14738

Cited by 32 publications

(14 citation statements)

References 36 publications

(55 reference statements)

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“…In a computational study, Wang et al 6 investigated the effect of nonspherical particles on the bubbling behavior in a bed of aspect ratio 12 and concluded that bubbles move with higher degree of fluctuation compared with those in a bed of spherical particles. Using CFD computations, Verma et al 7 found that bubble size increases only within a certain range of different bed diameters and then remains constant. An increase in a bed diameter at a constant bed height indicates a decrease in the aspect ratio.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

Agu

Ugwu

Pfeifer

et al. 2019

Ind. Eng. Chem. Res.

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Deep bubbling fluidized beds have some advantages that make them attractive for industrial applications. Using different powders, this paper investigates the bubbling behavior in deep beds. The results show that bubbles grow faster in the bed of angular/rough particles than in that of round/smooth particles and that the rate of bubble growth increases with increase in the particle size. With an increase in the bed height, the changes in the bubble diameter and solids distribution decrease within the bubbling regime but may vary within the slugging regime due to the chaotic behavior of slug flows. The bubble frequency increases with an increase in the gas velocity only when the bubble diameter is below a certain threshold value; for larger bubbles, the bubble frequency is lower. The maximum bubble frequency indicates the onset of slugging. Correlations for predicting the maximum bubble/slugging frequency averaged over the bed height and the corresponding bubble diameter are proposed.

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Section: Introductionmentioning

confidence: 99%

Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

Agu

Ugwu

Pfeifer

et al. 2019

Ind. Eng. Chem. Res.

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show abstract

“…It should be stressed that, however, the sizes of the used fluidized beds, say 30, 50, and 70 mm, are relatively small, which make it easy to produce similar flow patterns in these fluidized beds. For a fluidized bed of larger size, the flow patterns may change, 38 and therefore the generality needs to be further checked.…”

Section: Generality Of the Trained Modelmentioning

confidence: 99%

A machine learning approach for electrical capacitance tomography measurement of gas–solid fluidized beds

Guo

Yang

et al. 2019

AIChE Journal

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Electrical capacitance tomography has been widely used to obtain key hydrodynamic parameters of gas-solid fluidized beds, which is normally realized by first reconstructing images and then by analyzing these images. This indirect approach is time-consuming and hence difficult for on-line monitoring. Meanwhile, considering recurrence of similar flow patterns in fluidized beds, most of these calculations are repetitive and should be avoided. Here, we develop a machine learning approach to address these problems. First, superficial gas velocity linear-increasing strategy is used to perform highthroughput experiments to collect a large amount of training samples. These samples are used to train the map from normalized capacitance measurements to key parameters that obtained by an iterative image reconstruction algorithm off-line. The trained model can then be used for on-line monitoring. Preliminary tests revealed that the trained models show good prediction and generality for the estimation of the overall solid concentration and the equivalent bubble diameter.

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“…Despite the advantage, however, only few studies have investigated hydrodynamics in large fluidized beds (e.g. [24][25][26][27]), most of which are limited to 2D simulations. Thus, there is a strong need for fine-grid 3D simulations to rigorously quantify the effect of scale on fluidization hydrodynamics.…”

Section: Introductionmentioning

confidence: 99%

Study of the effect of reactor scale on fluidization hydrodynamics using fine-grid CFD simulations based on the two-fluid model

et al. 2016

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Reliable scale-up of fluidized beds is essential to ensure that analysis and performance optimization at lab-scale can be applied to commercial scales. However, scaling fluidized beds for dynamic similarity continues to be challenging because flow hydrodynamics at lab-scale are largely influenced by bed geometry making extrapolation of conclusions to large-scales infeasible. Therefore, this study is focused on analyzing the effect of bed geometry on the fluidization hydrodynamics using large-scale CFD simulations. The two fluid model (TFM) is employed to describe the solids motion efficiently and simulations are conducted for fluidization of 1150 μm LLDPE and 500 μm glass beads in beds of different sizes (diameter D = 15-70 cm and initial bed height H 0 = 10-75 cm). The hydrodynamics are subsequently investigated qualitatively using time-resolved visualizations, bubble centroid and solids velocity maps as well as quantitatively using detailed bubble statistics and solids circulation metrics. It is shown that as the bed diameter is increased, average bubble sizes decrease although similar-sized bubbles rise faster because of lower wall resistance, both factors contributing to faster solids circulation. On the other hand, fluidization hydrodynamics in 50 cm diameter bed are relatively insensitive to the choice of H 0 and similarities in solids circulation patterns are observed in shallow beds as well as in the lower regions of deep beds. Finally, it is shown that the size and spatial-distribution of bubbles is crucial for maintaining dynamic similarity of bubbling beds. Specifically, the bed dimensions (D, H 0 ) must ensure that (a) bubbles are typically much smaller than the bed diameter and (b) solids circulation patterns are similar across scales of interest. Overall, insights from this study can be used for describing the gas distribution and solids motion more accurately for better design of commercial beds.

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Effect of bed size on hydrodynamics in 3‐D gas–solid fluidized beds

Cited by 32 publications

References 36 publications

Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

A machine learning approach for electrical capacitance tomography measurement of gas–solid fluidized beds

Study of the effect of reactor scale on fluidization hydrodynamics using fine-grid CFD simulations based on the two-fluid model

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