Experimental and numerical research for fluidization behaviors in a gas–solid acoustic fluidized bed

Chen, Cao; Dong, Shuqin; Zhao, Yanan; Guo, Qingjie

doi:10.1002/aic.12115

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…These variations again are attributed to the fact that the sound energy produced by the acoustic field maintains smaller bubble sizes which create a better and more uniform distribution between the gas and solid particles throughout the bed, especially in the center region of the fluidized bed. Similar results were observed by (Cao et al, 2010) using experimental and numerical methods in a gas-solid fluidized bed of FCC particles. .11 shows the variations of the local time-average gas holdup for ground walnut shell at h = 0.25D and U g = 3U mf .…”

Section: Quantitative Results Of the Effects Of Acoustics On The Timesupporting

confidence: 87%

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Characterization of the hydrodynamic structure of a 3D acoustic fluidized bed

Guevara¹,

Roder²

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Section: Quantitative Results Of the Effects Of Acoustics On The Timesupporting

confidence: 87%

“…Cao et al (Cao et al, 2010) modified the Syamlal-O'Brien drag model to include acoustic behavior; using CFD software they obtained a good agreement between the simulations and the experimental data. They determined that the radial volume fraction increases with an increase in the sound pressure level.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the hydrodynamic structure of a 3D acoustic fluidized bed

Guevara¹,

Roder²

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“…Gas is set as ideal gas, and liquid density changed with the pressure based on the following equation: where p is the local pressure, p 0 is the normal pressure, ρ is the local liquid density, ρ 0 is the liquid density at normal pressure, and E is the elasticity coefficient of liquid. Dynamic mesh method and changing Schmidt number method were integrated into the developed CFD model to simulate ultrasound‐stimulated mass transfer in the hairy root culture 29–31. To reduce the calculating error produced by grid, the grid size near the vibration surface was 0.0001 × 0.002 m 2 and the grid size in other regions was 0.002 × 0.002 m 2 (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Computational fluid dynamics modeling of mass‐transfer behavior in a bioreactor for hairy root culture. II. Analysis of ultrasound‐intensified process

Sun

Liu

2011

Biotechnology Progress

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Recently, cichoric acid production from hairy roots of Echinacea purpurea was significantly improved by ultrasound stimulation in an airlift bioreactor. In this article, the possible mechanism on ultrasound-intensified hairy root culture of E. purpurea in the bioreactor was elucidated with the help of computational fluid dynamics (CFD) simulation, membrane permeability detection, dissolved oxygen concentration detection, confocal laser-scanning microscopy (LSM) observation, and phenylalanine ammonium lyase (PAL) activity analysis. The CFD model developed in Part I was used to simulate the hydrodynamics and oxygen mass transfer in hairy root bioreactor culture stimulated by ultrasound. A dynamic mesh model combined with a changing Schmidt number method was used for the simulation of the ultrasound field. Simulation results and experimental data illustrated that ultrasound intensified oxygen mass transfer in the hairy root clump, which subsequently stimulated root growth and cichoric acid biosynthesis. Ultrasound increased the hairy root membrane permeability, and a high root membrane permeability of 0.359 h(-1) was observed at the bottom region in the bioreactor. LSM observation showed that the change in the membrane permeability recovered to normal in the further culture after ultrasound stimulation. PAL activity in the hairy roots was stimulated by ultrasound increase and was correlated well to cichoric acid accumulation in the hairy roots of E. purpurea.

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“…Savari et al developed an online method for monitoring the mean particle size in a fluidized bed using pressure fluctuations and AE signals using a recurrence plot and recurrence quantification analysis. Cao et al examined the influence of acoustic assistance on the fluidization behavior and established a modified version of the Syamlal–O’Brien resistance model. The fluidization parameters obtained using the model were consistent with those obtained via experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Savari et al 13 developed an online method for monitoring the mean particle size in a fluidized bed using pressure fluctuations and AE signals using a recurrence plot and recurrence quantification analysis. Cao et al 14 In previous studies, fluidized-bed agglomeration alerts were achieved using the support vector data description (SVDD) method. 15 In previous research, 16 different types of sensors (acoustic sensors, accelerometers, microphones, and dynamic pressure transducers) and acoustic signals have been used in different frequency bands (infrasound, audible acoustic wave, and highfrequency AE) to detect the fluidized states of FBRs for different purposes (such as drying, fluidization, coating, and mixing).…”

Section: Introductionmentioning

confidence: 99%

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

Chen

Jing

et al. 2019

Ind. Eng. Chem. Res.

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The acoustic method is sensitive to agglomerations in fluidized bed reactors; however, a single acoustic sensor is not sufficient for industrial-scale reactors with regard to size, environmental noise, and signal pollution. Therefore, a multiacoustic sensor-based monitoring approach is investigated in this study. Different agglomeration warning models are established on the basis of each separate sensor, and then information-fusion technology is introduced. In the modeling process, an early warning method for industrial applications is designed. First, singular value decomposition is introduced for feature extraction to simplify the signal preprocessing and reduce the number of preset parameters. Thereafter, as agglomeration samples are lacking in industrial data, support vector data description (SVDD), an unsupervised learning method, is employed to establish the agglomeration warning model according to samples under normal conditions. As the Boolean outputs of SVDD are not suitable for information fusion, the output probability estimation method for SVDD is studied. In the estimation method, additional sigmoid functions are incorporated into the SVDD models, and the maximum likelihood algorithm is combined with the expectation−maximization algorithm to optimize the parameters of the sigmoid functions. Finally, the Dempster−Shafer evidence theory is applied for information fusion. Experiments conducted on a pilot plant prove that the proposed early agglomeration detection method is more stable than the S statistics of acoustic signals. The multisensor-based warning method can provide better detection of agglomeration than the temperature−pressure method, and for industrial applications, it provides more reliable detection results than single-sensor systems.

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Experimental and numerical research for fluidization behaviors in a gas–solid acoustic fluidized bed

Cited by 11 publications

References 30 publications

Characterization of the hydrodynamic structure of a 3D acoustic fluidized bed

Characterization of the hydrodynamic structure of a 3D acoustic fluidized bed

Computational fluid dynamics modeling of mass‐transfer behavior in a bioreactor for hairy root culture. II. Analysis of ultrasound‐intensified process

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

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