Effects of flow velocity and bubble size distribution on oxygen mass transfer in bubble column reactors—A critical evaluation of the computational fluid dynamics‐population balance model

Khalil, Ahmed E.E.; Rosso, Diego; DeGroot, Christopher T.

doi:10.1002/wer.1604

Cited by 9 publications

(11 citation statements)

References 69 publications

(110 reference statements)

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“…Collision efficiency is modeled with a comparison of t ij and actual contact time during the collision (τ ij ). This model has been shown to produce accurate results for coalescence in Euler-Euler two-fluid model with an integrated population balance model [59] but may sometimes over-predict coalescence, [60] which may impact mass transfer calculations.…”

Section: 𝜕(𝛼mentioning

confidence: 99%

CFD evaluation of hydrophobic feedstock bench‐scale fermenters for efficient high agitation volumetric mass transfer

Marx,

Liu,

Yoon

et al. 2024

Biotechnology Journal

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A new biomanufacturing platform combining intracellular metabolic engineering of the oleaginous yeast Yarrowia lipolytica and extracellular bioreaction engineering provides efficient bioconversion of plant oils/animal fats into high‐value products. However, predicting the hydrodynamics and mass transfer parameters is difficult due to the high agitation and sparging required to create dispersed oil droplets in an aqueous medium for efficient yeast fermentation. In the current study, commercial computational fluid dynamic (CFD) solver Ansys CFX coupled with the MUSIG model first predicts two‐phase system (oil/water and air/water) mixing dynamics and their particle size distributions. Then, a three‐phase model (oil, air, and water) utilizing dispersed air bubbles and a polydispersed oil phase was implemented to explore fermenter mixing, gas dispersion efficiency, and volumetric mass transfer coefficient estimations (kLa). The study analyzed the effect of the impeller type, agitation speed, and power input on the tank's flow field and revealed that upward‐pumping pitched blade impellers (PBI) in the top two positions (compared to Rushton‐type) provided advantageous oil phase homogeneity and similar estimated kLa values with reduced power. These results show good agreement with the experimental mixing and kLa data.

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Section: 𝜕(𝛼mentioning

confidence: 99%

CFD evaluation of hydrophobic feedstock bench‐scale fermenters for efficient high agitation volumetric mass transfer

Marx,

Liu,

Yoon

et al. 2024

Biotechnology Journal

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show abstract

“…However, the accuracy cannot be easily confirmed due to large variation (5-fold range) of reported mass transfer values. Khalil et al 271 performed CFD-PBM simulation to obtain the size distribution of bubbles in a column. Figure 14 shows that the significance of bubble size distribution (BSD) on mass transfer coefficient.…”

Section: Determination Of Mass Transfer Coefficientmentioning

confidence: 99%

“…The Schiller and Naumann model can be used in both the gas bubbling reactor ,,,,, and the spray tower. , The drag force is described by eq for the bubbling reactor. The drag force is described by eq for the spray tower with the drag coefficient described by eq , which is similar with eq . f = C italicD Re 24 C italicD = { true 24 Re ( 1 + 0.15 × Re 0.687 ) Re ≤ 1000 0.44 Re > 1000 …”

Section: Two/three Phase Flowmentioning

confidence: 99%

Computational Fluid Dynamics Based Modeling of Gas Absorption Process: A State-of-the-Art Review

Liu,

Wang,

Cai

et al. 2024

Ind. Eng. Chem. Res.

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The effective removal of gas phase species relies on effective gas liquid hydrodynamic contact coupling with fast chemical reaction rates. To promote mass transfer rate and the development of reagents, computational fluid dynamic modeling has long been recognized as an effective methodology for the quantification of physical and chemical parameters. The simulation of the gas liquid process involves the coupling of models including multiphase flow, turbulence, interfacial forces, mass transfer, and chemical reactions. The selection of suitable models according to practical applications has been challenging. In terms of multiphase flow, volume of fraction, Euler−Euler, and Euler−Lagrange approaches are discussed. With regard to turbulence, direct numerical simulation, large eddy simulation, and Reynolds averaged Navier−Stokes approach are compared. With respect to interfacial forces, the dominance of drag, lift, virtual mass, wall lubrication, and turbulent dispersion forces for different situations are obtained. In terms of mass transfer, the development and applications of film model, dimensionless analysis, penetration model, and surface renewal model are discussed. Different methodologies to enhance mass transfer include improvement of equipment structure, addition of nanofluid, and exposure to an energy field are summarized. To accelerate the computational process, four methods including the space−time conservation element and solution element model, compartmental model, reduction in dimension, and multiscale approach are discussed. This paper intends to provide a thorough review on the applications of different models to different situations and, in particular, to reveal the restrictions of different models. Overall, this study lays the foundation for future studies on the optimization of scrubbers for large scale practical applications. It also allows a deeper understanding of the interactions of fluid dynamics, mass transfer, and chemical reactions.

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“…Catalytic synthesis of gas and liquid phases often occurs in chemical bubbling column reactors [1]. To evaluate the reaction process and improve the reaction efficiency, it is necessary to accurately detect the characteristics of multiphase flow in the column reactor, such as the flow pattern, velocity and phase holdup [2,3].…”

Section: Introductionmentioning

confidence: 99%

Direct estimation of gas holdup in gas–liquid bubble column reactors using ultrasonic transmission tomography and artificial neural processing

Li¹,

Hu²,

Wang³

et al. 2022

Meas. Sci. Technol.

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Ultrasonic transmission tomography is an effective non-intrusive method for detecting gas–liquid two-phase flow patterns. A specific interest is the many processes whose reaction utilizes a bubble column, where the fast estimation of cross-sectional gas-holdup ratio is important for monitoring and control. In this study reference indirect image-based estimates were obtained from reconstructed tomographic data. Direct (non-image) estimation of the gas holdup ratio was also obtained using trained neural processing networks. Two forms were trialled: a generalized regression neural network; and a long short-term memory network. Comparison trials were carried out for single-bubble, dual-bubble, circulation and laminar flows. Relative cross-sectional gas holdup error was selected for evaluation. For the image-based indirect trials the Tikhonov regularization algorithm had the lowest error range: 2.15%–15.64%. For direct methods the long short-term memory network had the lowest error range: 0.41%–9.63%, giving better performance than the image-based methods. The experimental data were used to verify the effectiveness of the network. The root-mean-square error of the test metrics for generalized regression neural network and long short-term memory network were 6.4260 and 5.4282, respectively, indicating that long short-term memory network has higher performance in processing the data in this paper.

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Effects of flow velocity and bubble size distribution on oxygen mass transfer in bubble column reactors—A critical evaluation of the computational fluid dynamics‐population balance model

Cited by 9 publications

References 69 publications

CFD evaluation of hydrophobic feedstock bench‐scale fermenters for efficient high agitation volumetric mass transfer

CFD evaluation of hydrophobic feedstock bench‐scale fermenters for efficient high agitation volumetric mass transfer

Computational Fluid Dynamics Based Modeling of Gas Absorption Process: A State-of-the-Art Review

Direct estimation of gas holdup in gas–liquid bubble column reactors using ultrasonic transmission tomography and artificial neural processing

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