A New Correlation For The Prediction of Minimum

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 23 (2014) pp. 21561-21573 © Research India Publications http://www.ripublication.com

A New Correlation For The Prediction of Minimum Fluidization of Sand And Irregularly Shape Biomass Mixtures In A Bubbling Fluidized Bed AndriCahyo Kumoro1, DedyAlharis Nasution2, Adi Cifriadi3, Aprilina Purbasari1 and AsronFerdian Falaah3 1

Department of Chemical Engineering, Faculty of Engineering, Diponegoro University, Jl. Prof. H. Soedarto, SH No. 1, Semarang, Indonesia 54275 2 Indonesian Center for Agricultural Engineering Research and Development, TromolPos 2, Serpong, Indonesia 15310 3 Indonesian Rubber Research Institute, Jl. Salak No. 1A Bogor, Indonesia 16151

Abstract This work aimed to determine the minimum fluidization velocity of biomass and river sand particles mixtures at various biomass mass fractions in the range of 0 to 1. Mixtures of river sand and rice husk or corn cob particles were chosen to represent mixtures of particles with different size, density and shape. Based on the experimental data obtained from the present work and those found in the literatures, a new correlation was developed to predict the minimum fluidization velocity for biomass and sand mixtures in terms of sphericity, Reynolds and Archimedes numbers.Further, it was found that the minimum fluidization velocity based on Archimedes number of biomass and sand particles mixture depends on the average sphericity of particles mixturesand the corresponding Reynolds number.The sphericity of the biomass and sand particles mixture affects Archimedes number of the mixture significantly in the laminar region, but does not affect the Archimedes number in the intermediate region. The predictions agree fairly well with the reported experimental and literatures data in the range of 1Re50, which cover both laminar and intermediate flows regions. This newly developed correlation is also applicable for the prediction of minimum fluidization velocity of inert or biomass particles only by setting the mass fraction to be 0 or 1 accordingly. Keywords: minimum fluidization velocity;binary mixture; rice husk;corn cob; sphericity; voidage

21562

AndriCahyo Kumoro et al

Introduction The excellent heat and mass transfer, and enhanced chemical reaction rate between particles and the fluid medium have attracted huge interest from researchers to explore the implementationfluidized bed technology in various fields[1]. In the last few decades, this technology has been used extensively for conversion of biomass into various products through combustion, pyrolysis, and gasification. High amorphous silica content in the rice husk ash obtained from rice husk combustion in fluidized bed combustor has driven the formulation of silica products for various applications, such as food and drug adsorbent, filler, synthetic zeolite, chromatographic materials, etc. [2]. However, rice husk particles are very difficult to fluidize which hinder their combustion in fluidized bed combustor. This low fluidization quality is mainly caused by rice husk particle’ssmall in size, low density, non-granular shape and flaky nature [3-4]. Rice husk particles have a boat-like shape consisting of two interlocking halves with rough surface which results in large interparticlefriction. In order to avoid excessive elutriation losses and to achieve reasonable combustion efficiency, the fluidization characteristics of such peculiar particles need to be understood. Fluidization behavior of cohesiveparticles may be improved by mixingthem with other solid particles to form a binary or multisolid system [4]. Inert materials, such as sand particles are usually added to rice husk particles to improve the overall fluidization quality, mixing and heat and mass transfer of rice husk particles in the fluidized bed combustor[5].The minimum fluidization velocity (Umf) of a single, binary and multisolid system is one of the most important parameters required for the designing of a fluidized bedsystem [6]. This parameter reflects the lower limit of the fluidizing gas flow rate required for fluidization and is necessary for modeling of fluidized systems [7]. A large number of works have been done to investigate theUmf of inert particles such as sand, glass bead, and alumina [8]. However, only a few studies have beencarried out to determine the Umfof biomassor their mixture with inert particles. Abdullah et al. studied the Umf for various biomass residues with different sizes and densities including sawdust, coal bottom ash, coconut shell, rice husk, and palm fiber [9]. They concluded that bulk density and voidage are the two main factors contributing to the fluidizing quality. In other occasion, Wohet al. studied the effect of particle size on Umf of palm kernel cake particles in a 4.6 cm ID fluidized bed [10]. They concluded that Umf increased with particle size. Zhonget al. studied the fluidization of biomass particles and binary mixtures of biomass and inert particles [11].The biomass particles included wood chips, mung beans; millet, and cotton stalk, while the inert particles included silica sand, CFB cinder, and aluminum oxide. They found that the Umf of long thin biomass types such as corn stalk and cotton stalk increases with increasing diameter and length-to-diameter, and the Umf of binary mixtures increases with an increasing weight percentage of biomass, and diameter and density of inert particles. The tendencies of increasing Umf with the increasing mass percentage of biomass were also reported for rice husk and saw dust [12], corn grain and wood stick [13] and corn cob [6]. In contrary, the Umf of sweet sorghum bagasse and sand particles mixtures decreased with increasing biomass percentage [14]. This

A New Correlation for the Prediction of Minimum Fluidization

21563

difference is most likely due to the high irregularity of the biomass particles. Based on their laboratory investigation on the fluidization of 500-600 μm walnut and corn cob particles, Escedero and Heindelreported thatUmfvalues were independent of bed heights, but increased with particles density [15].Aznar et al. suggested that Umf of binary mixture should be estimated from the physical properties of this mixture, such as composition, particle density, and particle diameter, instead of measuring Umfof each component [16]. Rao and Bheemarasetti proposed a new correlation to predict minimum fluidization velocity for biomass/sand mixtures based on effective density and effective diameter of the mixture [12]. The proposed correlation satisfactorily agreed with the experimental Umf values up to 15% weight of biomass particles in mixtures. Raoand Reddy studied the fluidization characteristics for binary mixtures of three biomass particles (i.e. rice husk, saw dust, and groundnut shells) with sand particles [17]. They comparedUmfvalues obtained from experimental investigation and theoretical Umf values calculated using correlations for biomass/sand mixtures available in the literatures. The results showed that the correlation proposed by Todes is most suitable to predict Umffor those three biomass fuels [18]. Si and Guo determined Umfof biomass/sand mixtures in a 5.3 cm ID Perspex column and studied the effect of mass percentage of biomass ranging from 20 to 50% masson the Umf values [19]. They found that when the weight percentage of biomass was greater than 50%, there were large relative errors on Umf between predicted values and experimental data.Paudel and Fengincluded biomass weight percentage in their proposed empirical correlation to predict minimum fluidization velocityand obtained a satisfactorily predictions [6]. In this study, a 10 cm IDPerspex fluidization columnwas used to study the fluidization of biomass particles, inert particles, and the mixture of biomass and inert particles. River sand particles were used as inert particles, while the biomass particles include rice husk and corn cob. The biomass massfractionsin the biomass and inert particles mixtures ranged from 0 to 1. Based on those Umfdata, a new correlation for mixture of inert particles and biomass particles was developed. The accuracy of the proposed correlation was then compared with experimental data and some wellknown correlations as tabulated in Table 1.

21564

AndriCahyo Kumoro et al Table 1: The minimum fluidization prediction models used in this study

Models

Equations (

Cheung et al. [20]

=

Rao&Bheem arasetti[12] =

)

. . − 1650.

= .

+ +

Eq. No (1)

.

=

(2)

.

(2a) .

+

. (2b)

= 20.

+ 0.36 .

.

= {30.28 + [0.046(1 − ) + 0.108 − 30.28 1 = +

Paudel&Fen g[6]

=

.

. .

.

+ +

.

]

}

.

(3) (3a) (3b)

. .

Modeling of Minimum Fluidization Velocity Assuming negligible wall friction and acceleration effects, the dynamic pressure gradient across a fluidized bed in the range of 10-2Remf 2×10-3 can be satisfactorily correlated usingErgun's equation [21]. ∆

= 150.

.

. ∅.

+ 1.75.

. ( ∅.

.

)

(4)

The Ergun's equation [21], which uses the Sauter mean diameter as the average particle size was proved to be the most suitable for the prediction of pressure drop through unit length of packed bed of non-spherical particles in both fixed bed regime and at the point of the minimum fluidization [22]. As the fluid velocity increases, the drag exerted by the upward flowing fluid on the particles and the void fraction gradually diminishes the gravitational force acting on the bed. When the pressure drop becomes equal to weight of the bed per unit crosssectional, the bed begins to expand and for such conditions,  = mf and U = Umf. 1−

.

−

. . =∆

(5)

Therefore, after re-arrangement, Eq. (4) is 1−

.

−

.

= 150.

Multiplication each side by:

. .

. ∅.

+ 1.75.

. ( ∅.

.

)

(6)

and expressing the equation in

dimensionless numbers (Archimedes and Reynolds) should obtain:

A New Correlation for the Prediction of Minimum Fluidization = 150. where

.

∅ . .

=

+ ∅. . .

.

and

. =

21565 (7)

.

.

If values of mf,∅ and dp are known, the minimum fluidization velocity Umf can be calculated theoretically. Unfortunately, is its very difficult to experimentally measure the bed voidage at minimum fluidization velocity (mf), especially when beds of irregular shape and coarse particles are handled [23]. Therefore, a new correlation without the inclusion of mffor the prediction of Umfis necessary to be proposed. However, the shape factor (∅) is kept in the correlation to facilitate the prediction of Umffor irregularly shape particles.This effort also accommodates Aznar et al. suggestion to use physical properties of the particles to predict Umf either formonodisperse or mixture of particles [16]. Eq. (7) can be rewritten as: = .∅

.

+ .∅

.

(8)

whereA, B, C and D are the adjustable parameters of the equation. Inclusion of biomass mass fraction in Eq. (8) based on correlation of Paudel and Zeng results the following equation [6]: = . (1 −

). ∅

.

+ .

.∅

.

(9)

The density of the sand-biomass mixtures can be calculated usingRao andBheemarasetti correlation according to Eq. (2a) [12].However, the correlation proposed Rao and Bheemarasetti to determine the average particle diameter was not suitable in this study [12]. This is because the correlation is not applicable to calculate the average particle density when w1= 0. Therefore, the average particle diameter was calculated using Eq. (3b) as previously developed by Paudel&Feng[6]. The mass mean sphericityof the particles mixture can be calculated from the sphericity data of irregular particles of different sizes using the equation suggested by Jena et al. [22]: ∅ =∑

(

.∅ )

(10)

Experimental Apparatus The experimental setup consisted of a cold fluidized bed column, cyclone separator, air flowmeter, blower, silica gel bed and manometers. The cold fluidized bed column was a 10 cm ID and 100 cm long Perspex tube fitted with a perforated platewith 0.5 mm holes diameter as a distributor having 2 % opening area. The experimental set-up is schematically shown in Figure 1.

21566

AndriCahyo Kumoro et al

Figure 1: Schematic diagram of the experimental set-up

Materials The biomass materials used in the present studywere rice husk and corn cob, while theinert material used weretwo types of river sand particles differently in their size and density. Due to their non-granular shape, the sphericityfor rice husk and corn cob particles was measured using a computer-based digital image analysis system. The average diameters of sand and corn cobparticles were determined by sieving them through ASTM E-11 Standard Screens according to ASTM C136- 06 followed by calculating their Sauter mean diameters [24].The physical properties of those materials are presented in Table 2. Table 2: Physical Properties of River Sands, Rice Husk and Corn Cob

Materials

River Sand 1 River Sand 2 [6] Rice Husk Corn Cob [6]

Particle Bulk Voidage Density Density (-) (kg/m3) (kg/m3) 2450 2630

1428 1560

0.35 0.406

Mean Particle Size (µm) 350 241

635 1080

152 520

0.86 0.52

1560 1040

Shape

Sphericity (-)

Granular Granular

0.94 0.94

Non granular Angular

0.18 0.71

A New Correlation for the Prediction of Minimum Fluidization

21567

Variables studied The biomass was taken on mass basis, which can be measured more accurately than on the basis of bulk volume. The mass fractions of the biomass in the mixture studied were ranging from 0 to1. In every set of experiment, the total mass of the biomass material and sand in the fluidized bed was kept at 0.5 kg. Procedure The sand and biomass were initially thoroughly mixed in a small bucket and fedinto the fluidization columnfrom to form sand and biomass bed mixture. The sand and biomass particles were supported by a perforated plate. Prior to fluidization study, the fluidizing air was firstly dried through a silica gel bed and metered using an air flowmeter. The study began with passing the dry air at low flowrates to the sand and biomass bed mixture in the fluidization column, and the pressure drop across the bed was measured using a U-tube manometer for each flowrate. The air flowrates were increased incrementally until constant pressure drop values were obtained. The experiment was followed by measuring pressure dropsof the fluidization system fordecreasing air flow ratesuntil the bed defluidized. The pressure drop values obtained were plotted against the fluidizing air velocities. The minimum fluidization velocity, Umf, was obtained from the intersection of the extrapolations from the fixed bed and fluidized bed regions. However, the value of Umfwas taken from the plot of decreasing flow as the value obtained from the increasing flow was susceptible to the hysteresis loops.

Results and Discussion Many biomass fuels from agricultural wastescannot be easily fluidized because of their peculiar shapes, sizes, and densities. Therefore, the use of an inert particle (e.g. river sand of the present work) is usually added to fluidize those biomass fuels [13]. To represent those types of biomass, rice husk particles, which are classified as Geldart D particles, were used in this study [9]. While corn cob particles was also used in this study as the representative of biomass of Geldart B particles. Both river sand 1 and river sand 2 particles used as inert particles in this study are simply classified as Geldart B particles. However, in actual biomass processing, where inert particles are mixed with biomass particles, the particle mixing in the bed becomes even more complex. Therefore, the correlation developed for inert particles or biomass particles only may not be suitable to predict the Umfof the mixture. As expected for Geldart’s Group B and Group D particles, the bed transfers from the fixed bed into a bubbling fluidized bed when the superficial air velocity is increased beyond the minimum fluidization velocity of the system.The Umfof river sands, rice husk and corn cob particles were obtained from the intersection of the extrapolations of pressure drop vs. superficial air velocitydiagram of the fixed bed and fluidized bed regions[25]. The same procedure was applied for the determination of Umffor river sand and biomass particles mixture. However, the determination of Umffor rice husk particles was more difficult than the other type of materials in this study. With 10 cm bed ID, formation of slugs during rice husk fluidization was

21568

AndriCahyo Kumoro et al

observed. Similar report was reported by Paudel and Abuadalasuggesting that slug flow tends to occur during fluidization of low sphericity particles in small diameter or narrow beds [26]. This phenomenon is, however, not observed for more spherical (∅ ≥ 0.85) and smooth particles like river sand. Rice husk particles (∅ = 0.18) have longer particle-particle friction than granular sand particles for two reasons: (i) Particleparticle surface contact area per unit mass of particles is much larger for low shape factor particles. (ii) Particle-particle friction coefficient between rice husk particles is very high due to exceptionally rough textures of the outer surface of husk particles. On the other hand, this friction coefficient is close to zero for smooth granular sand particles. This causes rice husk particles interlock and often form bridges until they collapse giving rise to channels. The bridge formed is supported by the pressure drop across the slug through which some air percolates[27]. The Umfvalues were 0.1643, 0.074, 0.64 and 0.61 m/s for river sand 1, river sand 2, rice husk and corn cob particles, respectively. It is clear that the Umf is lower for river sand with smaller mean diameter and, hence, it is more suitable for fluidization of biomass particles. The Umf of rice husk obtained in this study is almost doubled of that obtained by Abdullah et al.,which were only 0.37 m/s [9]. However, this resultis close to that reported by Rao and Reddy[17] and Fang et al. [28], which were 0.53 m/s and 0.6 m/s, respectively. The reasons for the variation in results could be due to the difference in mass ratio of rice husk to sand, particle size distribution, type of distributors, and static bed height [17]. A very good agreement was observed for the values of Umf of corn cob and sands obtained in this study and those reported by Paudel and Feng[6] and Oliveira et al. [14]. Therefore, the Umf values of corn cob and sand particles mixtures at various corn cob mass fractions obtained by Paudel and Feng were then used in this study[6]. The experimental data obtained in this study was treatedusing the relationship between the Reynolds numbers at the minimum fluidization velocity and the Archimedes number of the binary mixturesaccording to Eq. 2a, 3b, 9 and 10. The optimum parameters of Eq. (10) were found to be 914.2, 2, 14.838 and 0, for A, B, C and D, respectively. Substitution of these parameters into Eq. (10) formsa new equation as shown below: = 914.2∅ .

+ 14.838

(11)

Eq. (11) shows that the effect of sphericity of the biomass and sand particles mixture on the Archimedes number of the mixture is very pronounced in the laminar region, but no effect is found to the Archimedes number in the intermediate region. The Umfobtained from experiment and calculations using Eq. (11), Cheung et al. [20],Rao and Bheemarasetti[12] and Paudel&Feng[6]correlations are presented in Figure 2

Umf, (m/s)

A New Correlation for the Prediction of Minimum Fluidization 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

21569

Exp. Data Equation 11 Equation12 Cheung [20] Paudel & Feng [6] Rao and Bheemarasetti [12]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Biomass mass fraction, x2

Umf, (m/s)

Figure 2: Minimum fluidization velocity of river sand and rice husk particles mixtures 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Exp. Data Equation 11 Equation12 Cheung [20] Paudel & Feng [6] Rao and Bheemarasetti [12]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Biomass mass fraction, x2

Figure 3: Minimum fluidization velocity of river sand and corn cobs particles mixtures

21570

AndriCahyo Kumoro et al

and Figure 3. The Umfvalues predicted using models proposed by Cheung et al. [20] and Rao and Bheemarasetti are in large deviations from those obtained from experiment [12]. Paudel&Feng[6]correlation shows its superiority over other models when used to predict Umffor mixtures of sand and corn cob particles. However, this model is very poor when it is used to predict Umffor mixtures of sand and rice husk particles. The model proposed by Cheung et al. is only suitable for binarysystems with particles of similar density and withparticle size ratios of less than 3 [20].The equation was later found to be also applicable forbinary systems with particles of different densities[29], but the particle size ratio restriction of 3 stillapplies. This restriction is not fulfilled by the systems in this study, where the particle size ratio were 4.45 and 4.31 for river sand and rice husk particle mixture and river sand and corn cob mixtures, respectively. Although Rao and Bheemarasetticlaimed that their proposed correlation quite satisfactorily predicts the Umfvalues up to about a 10 wt% of the biomass in the mixtures, but their correlation resulted underestimate values of Umf up to 40 wt% of the biomass in the mixtures for the systems in this study [12]. However, for a higher weightpercentage of biomass (50 wt% and above), it is found to predict higher values of Umf. This could be due to thehigh volume ratio of the biomass in the bed corresponding to the higher weight percentage ofthe solids. As clearly shown in Figure 2 and Figure 3, Eq. (11) also does not fit well the experimental data. Therefore, an effort was carried out to improve the accuracy of Eq. (11) by introducing biomass mass fraction from which another new equation was obtained: = 1176. (1 −

). ∅ .

+ 22.432

(12)

The values ofUmfpredicted using Eq. (12) vs. biomass mass fractions for rice husk and corn cob are also plotted in Figure 2 and Figure 3, respectively. It is observed that the predictions of Umfusing the proposed correlation are consistently in good agreement with the experimental values and the difference is below 10% for most cases.

Conclusions The minimum fluidization velocity of binary mixture of river sand and biomass (rice husk or corn cob) mixtures at different mass fractionsof biomass ranging from 0 to 1 hasbeen experimentally determined. An improved empirical correlation has been developed to satisfactorily predict minimum fluidization velocity for biomass and sand particles mixtures based on twenty-one sets of experimental data collected in the present work and those found in the literature. The correlation includes physical properties of the involving particles in term of Reynolds number atminimum fluidization velocity (Remf), Archimedes number (Ar), sphericityand biomass mass fraction. Based on the correlation, a more pronounced effect ofsphericity of the biomass and sand particles mixture on the Archimedes number of the mixture was found in the laminar region, but no effectwas observed in the intermediate region. This newcorrelation could provide better predictions of the minimum fluidization velocityfor biomass related mixtures.

A New Correlation for the Prediction of Minimum Fluidization

21571

Acknowledgement The authors would like to express their gratitude to the Indonesian Agency for Agricultural Research and Development (IAARD) for its financial support through KKP3N Research Grant 2013 with contract No.: 734/LB.620/I.1/2/2013. List of Symbols Ar dp dp1 dp2 dpe

: : : : :

g L Remf U Umf U1 U2 w1 w2 x2 x2

: : : : : : : : : : :

Greek symbols P  mf µf f p pe

: : : : : : :

p1 p2 ∅ ∅ ∅

: : : : :

Archimedes number diameter of particles diameter of river sand particles diameter of biomass particles effective diameter of river sand and biomass particles mixture gravitational acceleration constant bed length Reynolds number at minimum fluidization velocity superficial air velocity minimum fluidization velocity of particles minimum fluidization velocity of river sand particles minimum fluidization velocity of biomass particles mass of sand particles mass of sand particles mass fraction of biomass mass fraction of biomass

(-) (m) (m) (m) (m)

pressure drop along the bed of particles bed voidage bed voidage at minimum fluidization velocity dynamic viscosity of fluidizing air density of fluidizing air density of particles effective density of river sand and biomass particles mixture density of river sand particles density of biomass particles sphericity of river sand particles sphericity of biomass particles sphericity of river sand and biomass particles mixture

(kg/m.s2) (-) (-) (kg/m.s) (kg/m3) (kg/m3) (kg/m3)

(m/s2) (m) (-) (m/s) (m/s) (m/s) (m/s) (kg) (kg) (-) (-)

(kg/m3) (kg/m3) (-) (-) (-)

21572

AndriCahyo Kumoro et al

References [1]

[2]

[3] [4] [5]

[6]

[7]

[8]

[9]

[10]

[11]

[12] [13]

[14]

[15]

Cui, H. and Grace, J. R., 2007, “Fluidization of Biomass Particles: A Review Of Experimental Multiphase Flow Aspects”, Chem. Eng. Sci., 62(Compendex), pp. 45-55. McDonald, R. H., 1991, “Silica in Nova Scotia”. Information Circular ME 14, Second Edition, Nova Scotia Department of Natural Resources, Mineral Resources Branch, Government of Nova Scotia. Geldart, D., 1973, “Types of gas fluidization”, Powder Technol., 7, pp. 285–292. Sen, R. and Ghosh, D. N., 1992, “Fluidization and Combustion Characteristics of Rice Husk”, Indian Chem. Eng.34(4), pp. 206-211. Yang, W. C., 2003, “Bubbling Fluidized Beds”, in: Wen-Ching Yang (Ed.), Handbook of Fluidization and Fluid-Particle Systems, Marcel Dekker, Inc., New York. Paudel, B. and Feng, Z. G., 2013, “Prediction of Minimum Fluidization Velocity for Binary Mixtures of Biomass and Inert Particles”, Powder Technol., 237, pp. 134-140. Hilal, N., Ghannam, M. T. and Anabtawi, M. Z., 2001, “Effect of Bed Diameter, Distributor and Inserts on Minimum Fluidization Velocity”, Chem. Eng. Technol., 24, pp.161-165. Fana, X., Parkera, D. J., Yang, Z., Seville, J. P. K. and Baeyens, J., 2008, “The Effect Of Bed Materials On The Solid/Bubble Motion In A Fluidised Bed”, Chem. Eng. Sci., 63, pp. 943-950. Abdullah, M. Z., Husain, Z. and Pong, S. L. Y., 2003, “Analysis of Cold Flow Fluidization Test Results For Various Biomass Fuels”, Biomass and Bioenergy, 24, pp. 487-494. Woh, F. C., Janaun, J., Prabhakar, A. and Krishnaiah, K., 2007, “Minimum fluidization Velocity of Palm Kernel Cake Particles in Fluidized Bed Fermenter”, J. Appl. Sci., 7, pp. 2183-2187. Zhong, W., Jin, B., Zhang, Y., Wang, X. and Xiao, R., 2008, “Fluidization of Biomass Particles in A Gas–Solid Fluidized Bed”, Energy & Fuels, 22, pp. 4170-4176. Rao, T. R. and Bheemarasetti, J. V. R., 2001, “Minimum Fluidization Velocities of Mixtures of Biomass and Sands”, Energy, 26, pp. 633-644. Shao, Y., Ren, B., Jin, B., Zhong, W., Hu, H., Chen, X., Sha, C., 2013, “Experimental Flow Behaviors of Irregular Particles With Silica Sand in Solid Waste Fluidized Bed”, Powder Technol., 234, pp. 67-75. Oliveira, T. J. P., Cardoso, C. R., and Ataíde, C. H., 2013, “Bubbling Fluidization of Biomass And Sand Binary Mixtures: Minimum Fluidization Velocity and Particle Segregation”, Chem. Eng. Process., 72, pp. 113-121. Escudero, D. and Heindel, T. J., 2010, “Bed Height and Material Density Effects on Minimum Fluidization Velocity in a Cylindrical Fluidized

A New Correlation for the Prediction of Minimum Fluidization

[16]

[17]

[18] [19]

[20]

[21] [22]

[23] [24]

[25]

[26]

[27] [28]

[29]

21573

Bed”, Proc. 7th International Conference on Multiphase Flow ICMF 2010, Tampa, Florida, United States, (COLL-1674). Aznar, M. P., Gracia-Gorria, F. A. and Corella, J., 1992, “Minimum and Maximum Velocities for Fluidization for Mixtures of Agricultural and Forest Residues with Second Fluidized Solid. I. Preliminary Data and Results with Sand-Sawdust Mixtures”, Int. Chem. Eng., 32, pp. 95-102. Rao, K. V. N. S. and Reddy, G. V., 2010, Cold Flow Studies of Rice Husk, Saw Dust, and Groundnut Shell Fuels in a Fluidized Bed, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32:18, 17011711. Todes, O. M., 1965, “Applications of Fluidized Beds in the Chemical Industry”, Part 11, Zonanie, Leningrad, pp. 4-27. Si, C. and Guo, Q., 2008, “Fluidization Characteristics of Binary Mixtures of Biomass and Quartz Sand in an Acoustic Fluidized Bed”, Ind. Eng. Chem. Res., 47 (23), pp. 9773-9782. Cheung, L., Nienow, A. W. and Rowe, P. N., 1974, “Minimum Fluidization Velocity of a Binary Mixture of Different Sized Particles”, Chem. Eng. Sci., 24, pp. 1301-1303. Ergun, S., 1952, “Fluid Flow Through Packed Columns”, Chem. Eng. Prog., 48, pp. 89-94. Jena, H. M., Roy, G. K. and Biswal, K. C., 2008, “Studies on Pressure Drop and Minimum Fluidization Velocity of Gas–Solid Fluidization of Homogeneous Well-Mixed Ternary Mixtures in Un-Promoted and Promoted Square Bed”, Chem. Eng. J., 145 (1), pp. 16-24. Coltters, R. and Rivas, A. L., 2004, “Minimum Fluidation Velocity Correlations in Particulate Systems”, Powder Technol., 147, pp. 34-48. A.S.T.M., ASTM C136 – 06. 2007. Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates, American Association of State Highway and Transportation Officials. Li, Z., Kobayashi, N., Nishimura, A. and Hasatani, M., 2007, A Method To Predict the Minimum Fluidization Velocity of Binary Mixtures Based on Particle Packing Properties, Chem. Eng. Commun., 192 (7), 918-932. Paudel, B. and Abuadala, A., 2008, “Fluidization characteristics of rice husk in a circulating fluidized bed,” Circulating Fluidized Bed Technology IX (CFB-9). Leon, M. A. and Dutta, A., 2010, “Fluidization Characteristics of Rice Husk in a Bubbling Fluidized Bed”, Can. J. Chem. Eng., 88, pp. 18-22. Fang, M., Yang, L., Chen, G., Shi, Z., Luo, Z., and Cen, K. 2004. Experimental study on rice husk combustion in a circulating fluidized bed. Fuel Proc.Tech., 85:1273–1282. Chiba, S., Chiba, T., Nienow, A. W., and Kobayashi H., 1979, “TheMinimum Fluidization Velocity, Bed Expansion and Pressure-Drop Profile of Binary Particle Mixtures”, Powder Technol., 22, pp. 255-269.

21574

AndriCahyo Kumoro et al