Effect of Solid Particles on the Slurry Bubble Columns Behavior – A Review

Mahmoudi, Sadra; Hlawitschka, Mario

doi:10.1002/cben.202100032

Cited by 11 publications

(4 citation statements)

References 132 publications

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“…The classification of three-phase reactors is based on the particle sizes and density of the material . The key distinction lies between the fluidized bed reactor and slurry bubble column reactor (SBCR).…”

Section: Introductionmentioning

confidence: 99%

“…8,9 The classification of three-phase reactors is based on the particle sizes and density of the material. 10 The key distinction lies between the fluidized bed reactor and slurry bubble column reactor (SBCR). SBCR typically utilizes smaller particles, often less than 100 mm in diameter, and a lower concentration of solid, usually below 10%.…”

Section: Introductionmentioning

confidence: 99%

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Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Dhakane,

Yadav

2024

Ind. Eng. Chem. Res.

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Bubble column reactors (BCRs) find extensive application in the chemical industry for facilitating three-phase reactions where most often solid-phase catalysts are employed alongside gaseous and liquid reactants. In these reactors, accurately identifying the prevailing hydrodynamic behavior is crucial for both design optimization and successful scale-up efforts. This research investigates the flow characteristics of solid−liquid−gas mixtures in cylindrical bubble columns through a comprehensive approach combining computational fluid dynamics (CFD) simulations and a CFD-based machine learning (ML) model. The three-phase Eulerian−Eulerian CFD simulations explored the effects of 5% and 20% solid loading with varying superficial gas velocities (0.1 and 0.2 m/s) on solid distribution. The obtained results align qualitatively with the experimental findings from the literature. A CFD−deep neural network (DNN) model is presented in a parallel study of a three-phase bubble column situation. By using a large data set obtained from CFD simulations, the deep neural network model is trained to accurately predict fluid dynamics closely resembling the CFD simulations. The ML architecture incorporates input data obtained from CFD results, thereby allowing the deep neural network to acquire knowledge and identify patterns in different fluid flow characteristics, such as velocity, pressure, and holdup. The tuning of hyperparameters is crucial for successful ML predictions (with R 2 values 0.97 to 0.99) in the current CFD-DNN model. Additionally, a graphical user interface (GUI) on the basis of CFD-DNN has been developed for easy model handling. The CFD-DNN model showcases its capacity to forecast hydrodynamic parameters that were not used in the training process, hence diminishing computing expenses and simulation duration in comparison with conventional CFD techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Dhakane,

Yadav

2024

Ind. Eng. Chem. Res.

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“…We often encounter gas–solid–liquid three phase flow systems in chemical and energy engineering, for examples, slurry reactors, − deep-sea mining, mineral floatation, and severe accidents in a nuclear reactor. , In these systems, the solid particle behavior is usually influenced by the motion of bubbles in the liquid phase. For the better understanding of the gas–solid–liquid flow systems, several numerical models − have been proposed due to the significant development of computer hardware.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Discrete Element Modeling for a Gas–Solid–Liquid Flow System

Duan

Yamada

et al. 2023

Ind. Eng. Chem. Res.

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Gas−solid−liquid three-phase flow systems are ubiquitous in chemical and energy engineering. To simulate the multiphase systems, the authors' group has developed a numerical method that couples the discrete element method (DEM) and the volume of fluid (VOF) method, where the local volume average technique is employed. When the unresolved DEM-VOF method is applied to industrial systems, there are two critical problems: one is the inflexibility to select the grid size for the gas−liquid−flow simulations, and the other is the heavy computational cost due to a large number of particles. To solve these problems, a novel numerical method is proposed by introducing the refined grid model and coarse-grained model into the unresolved DEM-VOF method. Two types of verification tests are performed to illustrate the adequacy of the new method. First, a water entry problem of solid particles is simulated to verify the combination of the refined grid model and the unresolved DEM-VOF method. Second, a gas−solid−liquid fluidized bed system is computed to verify the combination of the refined grid model, the coarse-grained DEM method, and the unresolved DEM-VOF method. Through the verification tests, it is demonstrated that the new method can flexibly give the grid size for the VOF regardless of the solid particle size. Besides, the new approach makes it possible to reduce the calculation time of the DEM to 4.6% of the original time. The new approach will drastically improve the numerical modeling for gas−solid−liquid flows from the viewpoint of efficiency, accuracy, and flexibility.

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“…Although hydrodynamic studies of three-phase bubble columns can be found, most of them investigated only a small subset of the relevant hydrodynamic observables. Thus, a complete set of data from all three phases is not available [12]. In particular, reliable measurements of the bubble diameter are often missing.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamics in a Bubble Column – Part 1: Two‐Phase Flow

Sommer,

Draw,

Wang

et al. 2023

Chem Eng & Technol

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Multiphase computational fluid dynamics (CFD) simulation is a useful tool to study the hydrodynamics in a bubble column if appropriate closure models are known. Systematic assessment of different models is an ongoing venture that benefits from improved validation data. The present study accumulates a database on two-phase flow experiments in a bubble column. This is achieved by using a combination of particle image velocimetry and shadowgraphy to measure the liquid velocity field and gas dispersion properties simultaneously. This methodology is applied for different needle diameters and gas flow rates. The experimental data are compared with CFD simulations which show good predictions. A systematic investigation of the three-phase flow in the bubble column will appear as a sequel.

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Effect of Solid Particles on the Slurry Bubble Columns Behavior – A Review

Cited by 11 publications

References 132 publications

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Large-Scale Discrete Element Modeling for a Gas–Solid–Liquid Flow System

Hydrodynamics in a Bubble Column – Part 1: Two‐Phase Flow

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