Determination of Liquid Radial Spreading Coefficients of Some Highly Effective Packings

Dzhonova-Atanasova, Daniela; Kolev, Nikolai; Nakov, Svetoslav

doi:10.1002/ceat.200600327

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…where ϕ is the sphericity of the packing. Dzhonova‐Atanasova et al 7 observed similar results regarding the effect of the packing size on the liquid dispersion from their measurements of liquid spreading in a column of 470 mm in diameter packed with several highly effective packings in the absence of gas flow.…”

Section: Introductionmentioning

confidence: 61%

“…As one of the key parameters affecting the hydrodynamics of trickle‐bed reactors (TBR), dispersion of the liquid across the packed bed usually results from several different mechanisms 1, such as the mechanical dispersion induced by the variation of the microscopic velocity profiles in the porous structures, the capillary dispersion due to capillary pressure gradients, and the interaction forces between different phases 2. The above mechanisms make liquid dispersion a rather complex process, and many variables should be considered in the analysis of liquid dispersion in TBR, such as the gas and liquid velocities 3, 4, the liquid properties 5, the packing shape and size 6, 7, the packing orientation 8, the ratio of the column diameter to the particle diameter 9, the reactor pressure 10, the bed height 11, and so on.…”

Section: Introductionmentioning

confidence: 99%

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A Stochastic Model for Liquid Dispersion in a Trickle‐Bed Reactor

et al. 2014

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A stochastic model was developed to describe the liquid radial dispersion in a trickle-bed reactor, which has conventionally been modeled by the effective diffusion model. To establish the stochastic model, the column was divided into some layers of particles in the axial direction and several annulus rings in each layer in the radial direction. The liquid spreading was regarded as a fluid transport process and the matrix of transfer probability was derived based on the coordinates of the particle network in the cross-section of the column. With this transfer probability matrix and the initial liquid distribution, the liquid distribution in the downstream was predicted. In order to evaluate the model parameters, experimental data of the liquid dispersion were obtained from electrical capacitance tomography images and tracer injection. Good agreement was obtained between the stochastic model and the experimental liquid distribution data.

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Section: Introductionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

A Stochastic Model for Liquid Dispersion in a Trickle‐Bed Reactor

et al. 2014

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“…The authors have not found other attempts to apply the presented approach for modeling the liquid distribution in a layer of random RSRM packing, which is characterized with "open to flow structure". As reported in Dzhonova et al (2007), these types of packings have many lamellas and distribute liquid flow randomly, on the contrary to the previous generations of older types of packings like Raschig rings, which radial redistribution ability is higher than RSRM.…”

Section: Discussionmentioning

confidence: 79%

Liquid Distribution in a Semi-Industrial Packed Column - Experimental and Theory

Dzhonova-Atanasova

Semkov

Petrova

et al. 2018

FSAB

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The scientific interest in the efficiency of packed bed columns is part of the world-wide pursuit of sustainability of processes. The maldistribution of the phases in the apparatus reduces the efficiency and makes difficult the prediction of process performance and scaling up. In the present work the operation of liquid distribution devices and high performance packings are investigated addressing the reasons for hydrodynamic non-uniformity of the liquid phase, including the formation and development of wall flow. Data are obtained from semi-industrial size experimental studies and mathematical modelling of the liquid flow through a layer of random Raschig Super-Ring packing. The effect of measures for ensuring uniform initial liquid distribution in the column apparatus is evaluated and the parameters in the mathematical model are identified. Practical applications: Packed columns are typical apparatuses for absorption, desorption, rectification and direct heat transfer with applications in power industry, biofuel technologies, and food production.

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“…In the case of unknown value of D for the packing considered, another approach (single jet method) is developed, which is explained in detail in [2], and needs experimental data for radial liquid distribution, measured after relatively short packing layer and at central point initial irrigation. Fortunately, such data was available and we applied the single jet method to determine D for a packing RMSR 70-5.…”

Section: Methodsmentioning

confidence: 99%

“…In the last 10 years, there has only been a limited number of studies about the 4 th generation random packings except our research in this field [3-4, 8-9, 11], which investigate experimentally and model the radial liquid distribution and maldistribution [3][4]9] in those packings. Moreover, there is insufficient information about the radial spreading ability and wall flow effects in these packings [2,7], especially in the column diameters greater than 0.5 m. In contrast, the older type of packings are widely examined in the literature. Therefore, the motivation of our work is to fill the gap and test our models in industrial scale area, based on the existing experimental data for highperformance random packings.…”

Section: Introductionmentioning

confidence: 99%

Flow Simulation and Identification of Important Model Parameters in Industrial Packed Beds for High-Performance Random Packings

Petrova¹,

Dzhonova-Atanasova²

2019

J. Ecol. Eng.

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The goals of this work were: first, to simulate the liquid flow distribution in a large diameter (1.2 m) packed column with an RMSR 70-5 high performance random packing (layer height up to 3 m), by a dispersion model. Second, to find and estimate the important model parameters and flow maldistribution factor, using experimental data and two different optimization approaches. A three-parameter dispersion model for prediction of radial liquid distribution and two different approaches to determine some of the model parameters from experimental data were used. In parallel, a two-parameter optimization procedure for model parameters identification was performed based on the minimum of residual variance between model and experimental liquid velocities over a column crosssection. The simulated and experimental flow maldistribution, were estimated by means of an integral estimation-a maldistribution factor. The comparison between the model and experimental liquid distribution and respective maldistribution factors at packing heights H = 1 m and H = 2.5 m for liquid load 16.6•10-3 m 3 /m 2 •s showed very good agreement, even for a high packing layer. In conclusion, the presented model predictions and estimations about RSRM 70-packing 5 characteristics and behaviour will complement the information about its efficiency and operation in industrial processes.

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Determination of Liquid Radial Spreading Coefficients of Some Highly Effective Packings

Cited by 14 publications

References 6 publications

A Stochastic Model for Liquid Dispersion in a Trickle‐Bed Reactor

A Stochastic Model for Liquid Dispersion in a Trickle‐Bed Reactor

Liquid Distribution in a Semi-Industrial Packed Column - Experimental and Theory

Flow Simulation and Identification of Important Model Parameters in Industrial Packed Beds for High-Performance Random Packings

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