Effect of material type and particle size distribution on pressure drop in packed beds of large particles: Extending the Ergun equation

Koekemoer, Andrei; Luckos, Adam

doi:10.1016/j.fuel.2015.05.036

Cited by 117 publications

(50 citation statements)

References 21 publications

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“…Relationships to predict Forchheimer coefficients a and b are based on these two parameters (e.g., Ergun 1952;Macdonald et al 1979;Kovács 1981;Kadlec and Knight 1996;Sidiropoulou et al 2007). However, numerical simulations, as well as experimental studies, showed that the geometry of the pore structure have a large influence on flow behavior (e.g., Hill and Koch 2002;Li and Ma 2011;Allen et al 2013;Koekemoer and Luckos 2015). Here, we investigated the effect of nonlinear flow behavior empirically for granular porous media (filter sand) with various grain size distributions for a given reference median grain size (d 50 ) and compaction grade.…”

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confidence: 99%

The Effect of Grain Size Distribution on Nonlinear Flow Behavior in Sandy Porous Media

et al. 2017

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The current study provides new experimental data on nonlinear flow behavior in various uniformly graded granular materials (20 samples) ranging from medium sands (d 50 > 0.39 mm) to gravel (d 50 = 6.3 mm). Generally, theoretical equations relate the Forchheimer parameters a and b to the porosity, as well as the characteristic pore length, which is assumed to be the median grain size (d 50 ) of the porous medium. However, numerical and experimental studies show that flow resistance in porous media is largely determined by the geometry of the pore structure. In this study, the effect of the grain size distribution was analyzed using subangular-subrounded sands and approximately equal compaction grades. We have used a reference dataset of 11 uniformly graded filter sands. Mixtures of filter sands were used to obtain a slightly more well-graded composite sand (increased C u values by a factor of 1.19 up to 2.32) with respect to its associated reference sand at equal median grain size (d 50 ) and porosity. For all composite sands, the observed flow resistance was higher than in the corresponding reference sand at equal d 50 , resulting in increased a coefficients by factors up to 1.68, as well as increased b coefficients by factors up to 1.44. A modified Ergun relationship with Ergun constants of 139.1 for A and 2.2 for B, as well as the use of d m − σ as characteristic pore length predicted the coefficients a and b accurately.

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confidence: 99%

The Effect of Grain Size Distribution on Nonlinear Flow Behavior in Sandy Porous Media

et al. 2017

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“…It was observed that the variables of highest influence on the drying chamber pressure and consequently in pellet bed permeability was the diameter standard deviation, followed by pellet production and pellet sphericity. Particle size distribution, which has a direct relationship with the standard deviation of the pellet diameter, has a main role in bed permeability, since smaller pellets occupy interstitial sites among bigger pellets, decreasing bed voiding (Koekemoer and Luckos, 2015). In his way, a wider diameter distribution and/ or the presence of fines block the air flow through the bed (Yazdanpanah et al, 2010), causing a drying deficiency on the opposite side.…”

Section: Resultsmentioning

confidence: 99%

“…The gas flow is dependent on the strength and gas permeability of the bed, and highly influenced by the physical consistency of the pellets. The most adopted equation to solve gas flow through packed beds is Ergun's equation (Hinkley et al, 1994;Luckos and Bunt, 2011;Trahana et al, 2014;Koekemoer and Luckos, 2015;Erdin et al, 2015):…”

Section: Introductionmentioning

confidence: 99%

Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

et al. 2017

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The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.

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“…For example, Kunii and Levenspiel (1991) reported that Ergun equation resulted in under-prediction (greater than 25 % error) of pressure drop in packed bed that contains particles with sizes ranging between 4.75 mm and 37.5 mm. Similarly, Koekemoer and Luckos (2015) obtained 29 % pressure drop prediction error for a bed that contains coal, char and ash particles. Dolejs and Machac (1995) obtained 72.6 % and 24.9 % pressure drop prediction error for packed beds of polyhedral and cubes respectively while Gunarathne et al (2014) obtained 35 % error from predicting pressure drop in biomass pellet (cylindrical) packed bed.…”

Section: Introductionmentioning

confidence: 96%

“…There has been substantial progress made in quantifying the properties of biomass feedstock (Oginni et al, 2016;Olatunde et al, 2016) but the pressure drop and minimum fluidization velocity correlations developed for nonbiomass materials are still currently used for biomass feedstocks. This has resulted in difficulties in sizing and designing equipment and reactors for fluidizing biomass feedstocks (Allen et al, 2013;Koekemoer and Luckos, 2015;Kunii and Levenspiel, 1991). Therefore the main focus of this study is to develop reactor pressure drop and U mf equations that utilize the properties of and are suitable for biomass feedstocks.…”

Section: Introductionmentioning

confidence: 99%

Modified Ergun Equation for Airflow through Packed Bed of Loblolly Pine Grinds

Olatunde¹,

Fasina

2019

KONA

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Biomass grinds are typically non-spherical and are composed of particles with wide range of sizes that may vary up to 10× between the smallest and largest particle. Since fluidized bed system is often used to convert biomass into fuels, chemicals and products, the viscous and kinetic energy losses' coefficients in the Ergun equation were determined to incorporate these unique characteristics of biomass grinds. The revised Ergun's equation, validated using loblolly pine wood grinds, and data from other published work resulted in estimated Ergun's K 1 and K 2 coefficients of 201 and 2.7 respectively. In addition, the relative mean deviation between experimental and predicted pressure drop was in general better with the modified Ergun's equation when compared to the original Ergun's equation.

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Effect of material type and particle size distribution on pressure drop in packed beds of large particles: Extending the Ergun equation

Cited by 117 publications

References 21 publications

The Effect of Grain Size Distribution on Nonlinear Flow Behavior in Sandy Porous Media

The Effect of Grain Size Distribution on Nonlinear Flow Behavior in Sandy Porous Media

Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

Modified Ergun Equation for Airflow through Packed Bed of Loblolly Pine Grinds

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