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Effect of fluidized bed particle properties and agglomerate shape on the stability of agglomerates in a fluidized bed
Powder Technology 237 (2013) 46–52
Contents lists available at SciVerse ScienceDirect
Powder Technology journal homepage: www.elsevier.com/locate/powtec
Effect of fluidized bed particle properties and agglomerate shape on the stability of agglomerates in a fluidized bed Flora Parveen a, Franco Berruti a, Cedric Briens a,⁎, Jennifer McMillan b a b
Institute for Chemicals and Fuels from Alternative Resources (ICFAR), The University of Western Ontario, London, Ontario, Canada N6A 5B9 Edmonton Research Centre, Syncrude Canada Ltd., Edmonton, AB, Canada T6N 1H4
a r t i c l e
i n f o
Article history: Received 25 January 2012 Received in revised form 2 November 2012 Accepted 29 December 2012 Available online 8 January 2013 Keywords: Fluidized bed Agglomerates Breakage Stability Wet agglomerates
a b s t r a c t In particulate operations, agglomeration is a vital process as it is inevitable in the pharmaceutical, food, and detergent industries. It can be used to create particles of specific shapes and sizes, stabilize particulate mixtures and reduce dust emissions. However, agglomeration increases reactor fouling in fluid coking systems. The goal of this study is to understand the effect of fluidized bed particles and agglomerate shape on agglomerate stability, i.e. the resistance of agglomerates to breakage. Artificial agglomerates were made of polyurethane foam, magnets and radio frequency identification (RFID) tags. Artificial agglomerates greatly improve the reproducibility and ease of the agglomerate stability measurements, and agglomerate breakage anywhere in the bed is detected with an RFID reader. The important findings for the effect of bed particles on the stability of agglomerates are that at low excess fluidization velocities, if the density of the bed particles is increased, the agglomerates spend more time in the surface region of bed, where bubbles are larger. At high velocity, the effect is negligible. An empirical correlation is developed to predict the agglomeration breakage time. The bed particle size and shape have a negligible effect on the agglomerate stability. Spherical agglomerates are more stable than cylindrical agglomerate as these agglomerates are exposed to less shear from bed turbulence. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Many industries use gas–solid fluidized beds where liquid feed that is injected into the bed causes solid particles to form wet agglomerates. With detergents, foods, agricultural chemicals and pharmaceutical products, agglomeration is used to improve flow properties and compactability and to reduce dustiness and caking. In these cases, agglomerate breakage must be avoided. In contrast, agglomerate breakage must be promoted in the fluid coking process since wet agglomerates lead to serious fouling, create impediments on heat and mass transfer processes as well as contribute to lower yields and premature shutdowns [1]. The objective of this paper is to study the effect of the properties of the fluidized bed particles and of the agglomerate shape on agglomerate stability in a fluidized bed. The effect of fluidization gas velocity, liquid flow rate and various physicochemical properties on the coating and granulation of solid particles in pharmaceutical industries were investigated by Hemati et al. [2]. Weber et al. [3] determined the effects of liquid concentration, fluidizing velocity and liquid viscosity on agglomerate stability in a fluidized bed. They made wet agglomerates by mixing different solid particles (silica sand, fluid coke, and glass beads) with different liquids (water, sugar–water mixtures, and biodiesel). These agglomerates ⁎ Corresponding author. E-mail address: [email protected] (C. Briens). 0032-5910/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.powtec.2012.12.057
were introduced into a fluidized bed and the bed was defluidized after a set time; the size of the surviving agglomerates was then measured. Weber [4] also proposed a modeling tool to predict agglomerate breakage behavior. Rajniak et al. [5] studied the binder property effects on granule morphology. They found that for a lower concentration, a longer coating period was required for granule growth inside the fluidized bed. House et al. [1] showed that by improving the spray stability and redistributing the liquid droplets to the solid-rich region of the jet, binder–solid contact was improved, which helps to prevent the formation of large agglomerates. Their investigation emphasized the effects of different spray nozzle configurations to determine the quality of liquid–solid contacting. Many researchers have studied the strength of wet granules in fluidized beds. Fu et al. [6] describe the effect of the impact velocity of agglomerates on their breakage behavior in high shear mixers, using a simple granule test. Liu et al. [7] investigated the effects of physical properties such as binder viscosity, surface tension, binder saturation, granule porosity, particle shape and particle size on the breakage of wet granules in high shear mixers. Schubert et al. [8] studied the tensile strength of agglomerates. Iveson et al. [9] described the effect of liquid properties on the strength of agglomerates. Parveen et al. [10] developed a new method which uses model agglomerates of well defined properties to simulate wet agglomerates, as several agglomerate properties, such as liquid concentration, change with time in the fluidized bed, which makes it difficult to determine their impact on agglomerate breakage. A non-invasive
F. Parveen et al. / Powder Technology 237 (2013) 46–52
47
Table 1 Agglomerate properties. Features
“P” agglomerate
“Q” agglomerate
Density (kg/m3) Height (m) Diameter (m) Critical shear force (N)
870 0.025 0.02 ≈0.09
1200 0.025 0.02 ≈0.1
Fig. 1. View of one half of a model spherical agglomerate.
method was developed to determine the exact time at which an agglomerate broke in a fluidized bed. The model agglomerates were fabricated from polyurethane foam, magnets and two radio frequency identification (RFID) tags. The two halves of a model agglomerate were held together by the magnets and the force required to break up the agglomerate was set by adjusting the magnets distance. This force was accurately measured through separate experiments before introducing the model agglomerate into the fluidized bed. Agglomerate breakage anywhere in the bed is detected with an RFID reader: as long as the agglomerate halves were held together, the two RFID signals canceled each other: a signal could be detected as soon as the two halves came apart. The use of artificial agglomerates was validated by comparing their breakage rate to that of real liquid– solid agglomerates. This study uses the methods from Parveen et al. [10] to measure the effect of fluidized bed particle properties and agglomerate shape on agglomerate stability in a fluidized bed.
2.3. Fluidized bed experiments In each run, an agglomerate was introduced in an acrylic fluidization column with an internal diameter of 0.10 m and with a fluidized bed that was about 0.15 m high. The procedure used to obtain the breakage time of agglomeration inside the fluidized bed can be found in Ref. [10]. Table 2 describes the bed particle properties. The bed was fluidized at the specified superficial gas velocity, which was varied between 0.22 and 0.35 m/s; the fluidization air flowrate was controlled with a calibrated sonic orifice and a pressure regulator. For each set of conditions, 30 runs were conducted to get the probability distribution of the breakage time.
2.4. Agglomerate location measurement 2. Equipment and experimental procedures This section describes the procedures and equipment that were used to measure the stability of model agglomerates in fluidized beds. Agglomerate location in the fluidized bed was also measured to help interpret the agglomerate stability results.
The procedure used to detect where agglomerates are present inside the fluidized bed was described in Ref. [11]. It provides the fraction of the time during which an agglomerate is within the antenna detection zone. 8 runs of 5 min each were conducted for each set of conditions to determine the probability of an agglomerate being inside the antenna detection zone.
2.1. Model agglomerate fabrication
2.5. Effect of shape experiments
Cylindrical agglomerates of different densities were made. The fabrication procedure for model agglomerates was described in Ref. [10]. Table 1 gives the features of the two model agglomerates that were used in this study.
Experiments were conducted with spherical and cylindrical agglomerates to determine the effect of shape on agglomerate stability. Polyurethane foam and polypropylene hollow balls were used to make spherical agglomerates. The plastic hollow balls were cut into two identical halves. Polyurethane foam and epoxy glue were inserted in the half spherical hollow plastic ball to adjust the agglomerate density. The two halves of each spherical agglomerate were held together by magnets. A radio frequency tag was placed on the top of magnet. Fig. 1 is showing one part of a spherical agglomerate. Two cylindrical (X and Y) and two spherical agglomerates (A and B) were made, with characteristics shown in Table 3. Critical shear force measurements for spherical agglomerates were performed using the instrument described in Ref. [10].
2.2. Critical shear force measurement A shear force measurement device was developed to accurately measure the force required to split an agglomerate, which was also described in Ref. [10]. In this study, the agglomerate magnets were adjusted so that the agglomerate critical shear force was always about 0.1 N.
Table 2 Properties of bed particles. Bed particles Fluid coke Silica sand Glass beads
Sauter mean diameter (μm)
Minimum fluidization velocity (m/s)
900
121
0.008
1590
198
0.03
1500
172
0.022
Minimum fluidizing bed density (ρmf) (kg/m3)
Table 3 Agglomerates features. Features
Cylindrical agglomerate (X)
Spherical agglomerate (A)
Cylindrical agglomerate (Y)
Spherical agglomerate (B)
Density (kg/m3) Height (m) Diameter (m) Critical shear force (N) Volume (m3)
870 0.025 0.02 0.09 ± 0.011 7.85 × 10−6
870 – 0.024 0.124 ± 0.008 7.3 × 10−6
840 0.02 0.015 0.052 ± 0.006 3.5 × 10−6
840 – 0.02 0.09 ± 0.007 4 × 10−6
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F. Parveen et al. / Powder Technology 237 (2013) 46–52 Table 4 Median time t50 (corresponding to 50% probability of breakage) and slope value for standard linear regression (error corresponds to 90% confidence interval). Different agglomerate
Velocity (m/s)
t50 ± error (s)
c ± error (s)
“P” agglomerate in fluid coke bed
0.35 0.29 0.22 0.16 0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22
18.3 ± 1 29.2 ± 0.8 55.2 ± 0.9 139 ± 8.5 19 ± 1.3 26.4 ± 1.2 33.9 ± 1.7 29.2 ± 2 46.6 ± 2 80.9 ± 2.9 31.4 ± 2.4 38.9 ± 1.1 88.9 ± 3.9
3.9 ± 0.3 7.4 ± 0.6 10.2 ± .5 32.6 ± 6 6.2 ± 0.8 9.3 ± .7 10.5 ± 1.1 10.5 ± 1 11.2 ± 1.1 21.6 ± 1.8 11.1 ± 1.5 11 ± 0.7 26.2 ± 2.5
“P” agglomerate in silica sand bed
“Q” agglomerate in silica sand bed
“Q” agglomerate in glass bead bed
3. Results and discussion 3.1. Effect of different fluidized bed particles 3.1.1. Fluidized bed experiments with model agglomerates Fluid coke, silica sand and glass beads were used as bed particles to investigate the effect of bed particles on agglomerate stability. 30 replicate experiments were performed for each type of bed particles, at fluidization velocities of 0.22, 0.29 and 0.35 m/s. With fluid coke bed particles, the “P” agglomerate was studied at 0.16 m/s and 15 replicates were performed for that case. Parveen et al. [10] showed that the logistic function gave a good representation of the variation with time of the probability of breakage of an agglomerate in a fluidized bed. The logistic function was, therefore, used to obtain the median breakage time from the experimental data, i.e., the time at which the agglomerate has a 50% probability of breakage. p¼
a 1 þ exp − t−b c
Fig. 2. Probability distribution of the breakage time for the “P” agglomerate in silica sand bed (the line corresponds to the logistic equation that was fitted to the experimental data).
Each agglomerate was inserted in the fluidized bed at three different fluidization velocities (0.22, 0.29 and 0.35 m/s) to determine its breakage time. 30 replicates were performed for each case.
Fig. 3. Fraction of time in antenna detection zone for different conditions.
ð1Þ
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Fig. 5. Fraction of time spent near the top of the fluidized bed for different agglomerates at different fluidization velocities (95% confidence interval).
Parveen et al. [10] found that the logistic function gave a good fit of the experimental data. Fig. 2 shows the fit for “P” agglomerate for all the experimental conditions utilized in this work. The intercept
Fig. 4. (a) Effect of agglomerate properties. (b) Effect of bed particle density,.(c) Effect of the size and shape of the bed particles, on agglomerate stability (90% confidence interval).
where p is the cumulative probability of breakage and t is the time. The equation can be rearranged to give: t i ¼ b−c ln
1 −1 : pi
ð2Þ
Fig. 6. Effect of ρagglo / ρmf on agglomeration stability (90% confidence interval).
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Fig. 8. Model accuracy measurement by comparing the value of t50 measured vs. t50 calculated with Eq. (3). Fig. 7. Effect of u / umf on agglomeration stability (90% confidence interval).
b, which is the median time t50, and the slope c could therefore be obtained by linear regression of the experimental data. The standard linear regression also provided the means of evaluating the 90% confidence interval of the median breakage time. All the experimental results are provided in Table 4. 3.1.2. Agglomerate location test 8 replicate runs were conducted with each model agglomerate and for each set of conditions. Fig. 3 shows the fraction of the time that the agglomerate spent near the bed surface, in the antenna detection zone, at the lowest velocity (0.22 m/s). The agglomerates spent less time near the bed surface at this velocity in contrast to experiments conducted at higher velocities. 3.1.3. Discussion Fig. 4 shows that, with all agglomerates and fluidized bed particles, the median breakage time of agglomerates decreased gradually as the fluidization velocity increased. This confirms the results reported by Hemati et al. [2], Subero et al. [12] and Weber et al. [3]. Fig. 4a shows that the agglomerate “P”, which has a lower density than agglomerate “Q”, broke more easily than agglomerate “Q”, although it had the same critical shear force. According to Fig. 5a, agglomerate “P” spent more time in the upper region of the bed, where gas bubbles are larger and where agglomerates are exposed to more intense shear. Fig. 4b shows the effect of the density of the bed particles on agglomerate stability. At low excess fluidization velocities, the agglomerate stability was significantly higher in the bed of coke particles than in the bed of sand. At high excess fluidization velocities, there was no significant effect of the bed particles on the agglomerate stability. When immersed in a bed of silica sand, the “P” agglomerate displays a low density ratio (ρagglo / ρmf = 0.55) in contrast with the experiments when it is introduced in a bed of coke (ρagglo / ρmf =
0.96). Therefore, a “P” agglomerate is expected to stay in the upper region of a silica sand bed where it faces significant turbulence generated by bubble breakage as shown in Fig. 5. On the other hand, in a fluid coke bed this agglomerate is comparatively heavier. To investigate the behavior of heavier agglomerates in the silica sand bed, the “Q” agglomerate (ρagglo / ρmf = 0.75) was fluidized. The experimental results show that it takes much longer to break a “Q” agglomerate in the silica sand bed than to break the “P” agglomerate in a bed of fluid coke. Since all the bed particles belong to Geldart's group B, the excess fluidization velocity is proportional to the bubble gas flowrate. Fig. 5 shows that, at low fluidization velocities, the agglomerate spent a much larger fraction of its time in the upper bed region when it was in the sand bed, while, at higher fluidization velocities, there was no significant difference between the sand and coke beds. Here, again, there is a strong correlation between the breakage time and the fraction of the time spent in the more turbulent upper bed region. Fig. 4c shows the effect of the size and shape of the bed particles on agglomerate stability. The sand and glass beads have similar densities and sizes, but the sand particles are more angular (Table 2). Fig. 4c indicates that the effect of particle shape on agglomerate stability is negligible at all excess fluidization velocities. Fig. 6 shows that the ratio of agglomerate density to the bed density at minimum fluidization cannot solely explain the observed differences in agglomerate stability. Similarly, Fig. 7 shows that the ratio of the fluidization velocity to the minimum fluidization velocity cannot solely account for the observed differences in agglomerate stability. Therefore, it is possible to expect that agglomerate stability maybe correlated with a combination of these two factors, as discussed in the following section. 3.1.4. Development of correlation to predict the median breakage time The following correlation predicts the mean agglomerate breakage time from the ratio of the fluidization velocity to the minimum fluidization velocity and from the ratio of the agglomerate density to the bed density: 2 ρagglo 0:5 u d −1 t 50 ¼ a þ b ln þc þ ρ : agglo umf ρmf
Table 5 Coefficient values for the correlation. Coefficients
Values
a b c d
1.22 0.009 −1.07 −0.24
ð3Þ
ρmf
The coefficients values are shown in Table 5. Fig. 8 shows that the correlation gives very good predictions of the mean agglomerate breakage time. At this stage, this correlation
F. Parveen et al. / Powder Technology 237 (2013) 46–52
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Table 6 Median time t50 (corresponding to 50% probability of breakage) and slope value for standard linear regression (error corresponds to 90% confidence interval). Agglomerate type
Velocity (m/s)
t50 ± error (s)
c ± error (s)
“A” spherical agglomerate
0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22
34.9 ± 0.8 74.1 ± 2.1 101.2 ± 5.1 25.7 ± 1.7 44 ± 2 76.3 ± 2.5 18.3 ± 1 29.2 ± 0.8 55.2 ± 0.9 14.3 ± 1 25.6 ± 1 33.4 ± 1.5
10.4 ± 0.5 22.7 ± 1.4 28.9 ± 3.2 8.2 ± 1.1 13.1 ± 1.3 15.9 ± 1.6 3.9 ± 0.3 7.4 ± 0.6 10.2 ± .5 4.7 ± 0.7 7.4 ± 0.6 9.6 ± 1
“B” spherical agglomerate
“X” cylindrical agglomerate
“Y” cylindrical agglomerate
3.2. Effect of agglomerate shape on its stability 3.2.1. Fluidized bed experiments Two spherical and two cylindrical model agglomerates were used to check the effect of shape of agglomerates on their stability in a fluidized bed of fluid coke. 30 replicates were performed for each type of agglomerate at fluidization velocities of 0.22, 0.29 and 0.35 m/s. Fig. 9 shows that the logistic equation gave a good fit of the experimental data for the spherical agglomerate “A” under all experimental conditions. The standard linear regression also provided the means of evaluating the 90% confidence interval of the median breakage time. All the experimental results are provided in Table 6. 3.2.2. Discussion Fig. 10 shows that spherical agglomerates take a much longer time to break up in a fluidized bed than cylindrical agglomerates, over the whole range of fluidization velocities. Cylindrical and spherical agglomerates both are broken by the shear forces created by gas bubbles within the fluidized bed. The torque acting on an agglomerate can be obtained for the following equation: τ ¼ r F sinθ
ð4Þ
where F is the force and r is the distance between where the force is applied and where the torque is measured. For cylindrical agglomerates,
Fig. 9. Probability distribution of the breakage time for the spherical agglomerate “A” (the line corresponds to the logistic equation that was fitted to the experimental data).
applies to a fluidized bed of coke particles with a Sauter-mean diameter of around 150 μm and further experiments would be required to demonstrate its applicability to other particles.
Fig. 10. Median breakage time dependence on different shape of agglomerates.
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F. Parveen et al. / Powder Technology 237 (2013) 46–52
Fig. 11. Shear force working on cylindrical and spherical agglomerates.
most of the acting forces create 90° angles with the breakage plane, while for spherical agglomerates, the forces do not create 90° angles (Fig. 11). This reduces the magnitude of the torque acting on the breakage plane of spherical agglomerates. This explains why spherical agglomerates were more stable.
ρmf Minimum fluidizing bed density, kg/m 3 a, b, c, and d Coefficients.
Acknowledgments 4. Conclusions Several conclusions can be made from this study: 1) At low excess fluidization velocities, if the density of the bed particles is increased, the agglomerates spend more time in the surface region of bed, where bubbles are larger: therefore, agglomerates are exposed to a more intense shear force and are more likely to break. At high excess fluidization velocities, the time the agglomerates spend in the upper bed region is nearly independent of the density of the bed particles and, consequently, there is little effect of the particle density on agglomerate stability. 2) The shape and size of the bed particles have a negligible effect on agglomerate stability, when operating at the same excess fluidization velocity. 3) Spherical agglomerates are more stable than cylindrical agglomerate as these agglomerates are exposed to less shear from bed turbulence. Notation
p P t τ r F θ t50 u umf ρagglo
Cumulative probability of agglomeration breakage Total probability for model agglomerate breakage Time, s Magnitude of torque (N-m) Length of the arm (distance between where the torque is measured to where the force is applied), m Magnitude of force, N Angle between the force vector with the level arm vector. Median breakage time, s Fluidization gas velocity, m/s Minimum fluidization gas velocity, m/s Density of agglomerate, kg/m 3
The authors gratefully acknowledge funding and help from Syncrude Canada Ltd. Financial assistance was also provided by the Natural Sciences and Engineering Research Council of Canada (NSERC).
References [1] P.K. House, M. Saberian, C.L. Briens, F. Berruti, E. Chan, Injection of a liquid spray into a fluidized bed: particle–liquid mixing and impact on fluid coker yields, Industrial and Engineering Chemistry Research 43 (2004) 5663–5669. [2] M. Hemati, R. Cherif, K. Saleh, V. Pont, Fluidized bed coating and granulation: influence of process-related variables and physicochemical properties on the growth kinetics, Powder Technology 130 (2003) 18–34. [3] Sarah Weber, Cedric Briens, Franco Berruti, Edward Chan, Murray Gray, Effect of agglomerate properties on agglomerate stability in fluidized beds, Chemical Engineering Science 63 (2008) 4245–4256. [4] S.C. Weber, Agglomerate stability in fluidized beds, PhD thesis, University of Western Ontario, London, Ontario, Canada, 2009. [5] P. Rajniak, C. Mancinell, R.T. Chern, F. Stepanek, L. Farber, B.T. Hill, Experimental study of wet granulation in fluidized bed: impact of the binder properties on the granule morphology, International Journal of Pharmaceutics 334 (2007) 92–102. [6] J. Fu, G.K. Reynolds, M.J. Adams, M.J. Hounslow, A.D. Salman, An experimental study of the impact breakage of wet granules, Chemical Engineering Science 60 (2005) 4005–4018. [7] L.X. Liu, R. Smith, J.D. Litster, Wet granulation breakage in a breakage only high-shear mixer: effect of formulation properties on breakage behavior, Powder Technology 189 (2009) 158–164. [8] H. Schubert, Tensile strength of agglomerates, Powder Technology 11 (1975) 107–119. [9] S.M. Iveson, J.A. Beathe, N.W. Page, The dynamic strength of partially saturated powder compacts: the effect of liquid properties, Powder Technology 127 (2002) 149–161. [10] F. Parveen, S. Josset, C. Briens, F. Berruti, Effect of size and density on agglomerates breakage in a fluidized bed, Powder Technology 231 (2012) 102–111. [11] F. Parveen, C. Briens, F. Berruti, Effect of particle size, liquid content and location on the stability of agglomerates in a fluidized bed, Powder Technology 231 (2012) 102–111. [12] Jesus Subero, Mojtaba Ghadiri, Breakage patterns of agglomerates, Powder Technology 120 (2001) 232–243.
Contents lists available at SciVerse ScienceDirect
Powder Technology journal homepage: www.elsevier.com/locate/powtec
Effect of fluidized bed particle properties and agglomerate shape on the stability of agglomerates in a fluidized bed Flora Parveen a, Franco Berruti a, Cedric Briens a,⁎, Jennifer McMillan b a b
Institute for Chemicals and Fuels from Alternative Resources (ICFAR), The University of Western Ontario, London, Ontario, Canada N6A 5B9 Edmonton Research Centre, Syncrude Canada Ltd., Edmonton, AB, Canada T6N 1H4
a r t i c l e
i n f o
Article history: Received 25 January 2012 Received in revised form 2 November 2012 Accepted 29 December 2012 Available online 8 January 2013 Keywords: Fluidized bed Agglomerates Breakage Stability Wet agglomerates
a b s t r a c t In particulate operations, agglomeration is a vital process as it is inevitable in the pharmaceutical, food, and detergent industries. It can be used to create particles of specific shapes and sizes, stabilize particulate mixtures and reduce dust emissions. However, agglomeration increases reactor fouling in fluid coking systems. The goal of this study is to understand the effect of fluidized bed particles and agglomerate shape on agglomerate stability, i.e. the resistance of agglomerates to breakage. Artificial agglomerates were made of polyurethane foam, magnets and radio frequency identification (RFID) tags. Artificial agglomerates greatly improve the reproducibility and ease of the agglomerate stability measurements, and agglomerate breakage anywhere in the bed is detected with an RFID reader. The important findings for the effect of bed particles on the stability of agglomerates are that at low excess fluidization velocities, if the density of the bed particles is increased, the agglomerates spend more time in the surface region of bed, where bubbles are larger. At high velocity, the effect is negligible. An empirical correlation is developed to predict the agglomeration breakage time. The bed particle size and shape have a negligible effect on the agglomerate stability. Spherical agglomerates are more stable than cylindrical agglomerate as these agglomerates are exposed to less shear from bed turbulence. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Many industries use gas–solid fluidized beds where liquid feed that is injected into the bed causes solid particles to form wet agglomerates. With detergents, foods, agricultural chemicals and pharmaceutical products, agglomeration is used to improve flow properties and compactability and to reduce dustiness and caking. In these cases, agglomerate breakage must be avoided. In contrast, agglomerate breakage must be promoted in the fluid coking process since wet agglomerates lead to serious fouling, create impediments on heat and mass transfer processes as well as contribute to lower yields and premature shutdowns [1]. The objective of this paper is to study the effect of the properties of the fluidized bed particles and of the agglomerate shape on agglomerate stability in a fluidized bed. The effect of fluidization gas velocity, liquid flow rate and various physicochemical properties on the coating and granulation of solid particles in pharmaceutical industries were investigated by Hemati et al. [2]. Weber et al. [3] determined the effects of liquid concentration, fluidizing velocity and liquid viscosity on agglomerate stability in a fluidized bed. They made wet agglomerates by mixing different solid particles (silica sand, fluid coke, and glass beads) with different liquids (water, sugar–water mixtures, and biodiesel). These agglomerates ⁎ Corresponding author. E-mail address: [email protected] (C. Briens). 0032-5910/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.powtec.2012.12.057
were introduced into a fluidized bed and the bed was defluidized after a set time; the size of the surviving agglomerates was then measured. Weber [4] also proposed a modeling tool to predict agglomerate breakage behavior. Rajniak et al. [5] studied the binder property effects on granule morphology. They found that for a lower concentration, a longer coating period was required for granule growth inside the fluidized bed. House et al. [1] showed that by improving the spray stability and redistributing the liquid droplets to the solid-rich region of the jet, binder–solid contact was improved, which helps to prevent the formation of large agglomerates. Their investigation emphasized the effects of different spray nozzle configurations to determine the quality of liquid–solid contacting. Many researchers have studied the strength of wet granules in fluidized beds. Fu et al. [6] describe the effect of the impact velocity of agglomerates on their breakage behavior in high shear mixers, using a simple granule test. Liu et al. [7] investigated the effects of physical properties such as binder viscosity, surface tension, binder saturation, granule porosity, particle shape and particle size on the breakage of wet granules in high shear mixers. Schubert et al. [8] studied the tensile strength of agglomerates. Iveson et al. [9] described the effect of liquid properties on the strength of agglomerates. Parveen et al. [10] developed a new method which uses model agglomerates of well defined properties to simulate wet agglomerates, as several agglomerate properties, such as liquid concentration, change with time in the fluidized bed, which makes it difficult to determine their impact on agglomerate breakage. A non-invasive
F. Parveen et al. / Powder Technology 237 (2013) 46–52
47
Table 1 Agglomerate properties. Features
“P” agglomerate
“Q” agglomerate
Density (kg/m3) Height (m) Diameter (m) Critical shear force (N)
870 0.025 0.02 ≈0.09
1200 0.025 0.02 ≈0.1
Fig. 1. View of one half of a model spherical agglomerate.
method was developed to determine the exact time at which an agglomerate broke in a fluidized bed. The model agglomerates were fabricated from polyurethane foam, magnets and two radio frequency identification (RFID) tags. The two halves of a model agglomerate were held together by the magnets and the force required to break up the agglomerate was set by adjusting the magnets distance. This force was accurately measured through separate experiments before introducing the model agglomerate into the fluidized bed. Agglomerate breakage anywhere in the bed is detected with an RFID reader: as long as the agglomerate halves were held together, the two RFID signals canceled each other: a signal could be detected as soon as the two halves came apart. The use of artificial agglomerates was validated by comparing their breakage rate to that of real liquid– solid agglomerates. This study uses the methods from Parveen et al. [10] to measure the effect of fluidized bed particle properties and agglomerate shape on agglomerate stability in a fluidized bed.
2.3. Fluidized bed experiments In each run, an agglomerate was introduced in an acrylic fluidization column with an internal diameter of 0.10 m and with a fluidized bed that was about 0.15 m high. The procedure used to obtain the breakage time of agglomeration inside the fluidized bed can be found in Ref. [10]. Table 2 describes the bed particle properties. The bed was fluidized at the specified superficial gas velocity, which was varied between 0.22 and 0.35 m/s; the fluidization air flowrate was controlled with a calibrated sonic orifice and a pressure regulator. For each set of conditions, 30 runs were conducted to get the probability distribution of the breakage time.
2.4. Agglomerate location measurement 2. Equipment and experimental procedures This section describes the procedures and equipment that were used to measure the stability of model agglomerates in fluidized beds. Agglomerate location in the fluidized bed was also measured to help interpret the agglomerate stability results.
The procedure used to detect where agglomerates are present inside the fluidized bed was described in Ref. [11]. It provides the fraction of the time during which an agglomerate is within the antenna detection zone. 8 runs of 5 min each were conducted for each set of conditions to determine the probability of an agglomerate being inside the antenna detection zone.
2.1. Model agglomerate fabrication
2.5. Effect of shape experiments
Cylindrical agglomerates of different densities were made. The fabrication procedure for model agglomerates was described in Ref. [10]. Table 1 gives the features of the two model agglomerates that were used in this study.
Experiments were conducted with spherical and cylindrical agglomerates to determine the effect of shape on agglomerate stability. Polyurethane foam and polypropylene hollow balls were used to make spherical agglomerates. The plastic hollow balls were cut into two identical halves. Polyurethane foam and epoxy glue were inserted in the half spherical hollow plastic ball to adjust the agglomerate density. The two halves of each spherical agglomerate were held together by magnets. A radio frequency tag was placed on the top of magnet. Fig. 1 is showing one part of a spherical agglomerate. Two cylindrical (X and Y) and two spherical agglomerates (A and B) were made, with characteristics shown in Table 3. Critical shear force measurements for spherical agglomerates were performed using the instrument described in Ref. [10].
2.2. Critical shear force measurement A shear force measurement device was developed to accurately measure the force required to split an agglomerate, which was also described in Ref. [10]. In this study, the agglomerate magnets were adjusted so that the agglomerate critical shear force was always about 0.1 N.
Table 2 Properties of bed particles. Bed particles Fluid coke Silica sand Glass beads
Sauter mean diameter (μm)
Minimum fluidization velocity (m/s)
900
121
0.008
1590
198
0.03
1500
172
0.022
Minimum fluidizing bed density (ρmf) (kg/m3)
Table 3 Agglomerates features. Features
Cylindrical agglomerate (X)
Spherical agglomerate (A)
Cylindrical agglomerate (Y)
Spherical agglomerate (B)
Density (kg/m3) Height (m) Diameter (m) Critical shear force (N) Volume (m3)
870 0.025 0.02 0.09 ± 0.011 7.85 × 10−6
870 – 0.024 0.124 ± 0.008 7.3 × 10−6
840 0.02 0.015 0.052 ± 0.006 3.5 × 10−6
840 – 0.02 0.09 ± 0.007 4 × 10−6
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F. Parveen et al. / Powder Technology 237 (2013) 46–52 Table 4 Median time t50 (corresponding to 50% probability of breakage) and slope value for standard linear regression (error corresponds to 90% confidence interval). Different agglomerate
Velocity (m/s)
t50 ± error (s)
c ± error (s)
“P” agglomerate in fluid coke bed
0.35 0.29 0.22 0.16 0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22
18.3 ± 1 29.2 ± 0.8 55.2 ± 0.9 139 ± 8.5 19 ± 1.3 26.4 ± 1.2 33.9 ± 1.7 29.2 ± 2 46.6 ± 2 80.9 ± 2.9 31.4 ± 2.4 38.9 ± 1.1 88.9 ± 3.9
3.9 ± 0.3 7.4 ± 0.6 10.2 ± .5 32.6 ± 6 6.2 ± 0.8 9.3 ± .7 10.5 ± 1.1 10.5 ± 1 11.2 ± 1.1 21.6 ± 1.8 11.1 ± 1.5 11 ± 0.7 26.2 ± 2.5
“P” agglomerate in silica sand bed
“Q” agglomerate in silica sand bed
“Q” agglomerate in glass bead bed
3. Results and discussion 3.1. Effect of different fluidized bed particles 3.1.1. Fluidized bed experiments with model agglomerates Fluid coke, silica sand and glass beads were used as bed particles to investigate the effect of bed particles on agglomerate stability. 30 replicate experiments were performed for each type of bed particles, at fluidization velocities of 0.22, 0.29 and 0.35 m/s. With fluid coke bed particles, the “P” agglomerate was studied at 0.16 m/s and 15 replicates were performed for that case. Parveen et al. [10] showed that the logistic function gave a good representation of the variation with time of the probability of breakage of an agglomerate in a fluidized bed. The logistic function was, therefore, used to obtain the median breakage time from the experimental data, i.e., the time at which the agglomerate has a 50% probability of breakage. p¼
a 1 þ exp − t−b c
Fig. 2. Probability distribution of the breakage time for the “P” agglomerate in silica sand bed (the line corresponds to the logistic equation that was fitted to the experimental data).
Each agglomerate was inserted in the fluidized bed at three different fluidization velocities (0.22, 0.29 and 0.35 m/s) to determine its breakage time. 30 replicates were performed for each case.
Fig. 3. Fraction of time in antenna detection zone for different conditions.
ð1Þ
F. Parveen et al. / Powder Technology 237 (2013) 46–52
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Fig. 5. Fraction of time spent near the top of the fluidized bed for different agglomerates at different fluidization velocities (95% confidence interval).
Parveen et al. [10] found that the logistic function gave a good fit of the experimental data. Fig. 2 shows the fit for “P” agglomerate for all the experimental conditions utilized in this work. The intercept
Fig. 4. (a) Effect of agglomerate properties. (b) Effect of bed particle density,.(c) Effect of the size and shape of the bed particles, on agglomerate stability (90% confidence interval).
where p is the cumulative probability of breakage and t is the time. The equation can be rearranged to give: t i ¼ b−c ln
1 −1 : pi
ð2Þ
Fig. 6. Effect of ρagglo / ρmf on agglomeration stability (90% confidence interval).
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F. Parveen et al. / Powder Technology 237 (2013) 46–52
Fig. 8. Model accuracy measurement by comparing the value of t50 measured vs. t50 calculated with Eq. (3). Fig. 7. Effect of u / umf on agglomeration stability (90% confidence interval).
b, which is the median time t50, and the slope c could therefore be obtained by linear regression of the experimental data. The standard linear regression also provided the means of evaluating the 90% confidence interval of the median breakage time. All the experimental results are provided in Table 4. 3.1.2. Agglomerate location test 8 replicate runs were conducted with each model agglomerate and for each set of conditions. Fig. 3 shows the fraction of the time that the agglomerate spent near the bed surface, in the antenna detection zone, at the lowest velocity (0.22 m/s). The agglomerates spent less time near the bed surface at this velocity in contrast to experiments conducted at higher velocities. 3.1.3. Discussion Fig. 4 shows that, with all agglomerates and fluidized bed particles, the median breakage time of agglomerates decreased gradually as the fluidization velocity increased. This confirms the results reported by Hemati et al. [2], Subero et al. [12] and Weber et al. [3]. Fig. 4a shows that the agglomerate “P”, which has a lower density than agglomerate “Q”, broke more easily than agglomerate “Q”, although it had the same critical shear force. According to Fig. 5a, agglomerate “P” spent more time in the upper region of the bed, where gas bubbles are larger and where agglomerates are exposed to more intense shear. Fig. 4b shows the effect of the density of the bed particles on agglomerate stability. At low excess fluidization velocities, the agglomerate stability was significantly higher in the bed of coke particles than in the bed of sand. At high excess fluidization velocities, there was no significant effect of the bed particles on the agglomerate stability. When immersed in a bed of silica sand, the “P” agglomerate displays a low density ratio (ρagglo / ρmf = 0.55) in contrast with the experiments when it is introduced in a bed of coke (ρagglo / ρmf =
0.96). Therefore, a “P” agglomerate is expected to stay in the upper region of a silica sand bed where it faces significant turbulence generated by bubble breakage as shown in Fig. 5. On the other hand, in a fluid coke bed this agglomerate is comparatively heavier. To investigate the behavior of heavier agglomerates in the silica sand bed, the “Q” agglomerate (ρagglo / ρmf = 0.75) was fluidized. The experimental results show that it takes much longer to break a “Q” agglomerate in the silica sand bed than to break the “P” agglomerate in a bed of fluid coke. Since all the bed particles belong to Geldart's group B, the excess fluidization velocity is proportional to the bubble gas flowrate. Fig. 5 shows that, at low fluidization velocities, the agglomerate spent a much larger fraction of its time in the upper bed region when it was in the sand bed, while, at higher fluidization velocities, there was no significant difference between the sand and coke beds. Here, again, there is a strong correlation between the breakage time and the fraction of the time spent in the more turbulent upper bed region. Fig. 4c shows the effect of the size and shape of the bed particles on agglomerate stability. The sand and glass beads have similar densities and sizes, but the sand particles are more angular (Table 2). Fig. 4c indicates that the effect of particle shape on agglomerate stability is negligible at all excess fluidization velocities. Fig. 6 shows that the ratio of agglomerate density to the bed density at minimum fluidization cannot solely explain the observed differences in agglomerate stability. Similarly, Fig. 7 shows that the ratio of the fluidization velocity to the minimum fluidization velocity cannot solely account for the observed differences in agglomerate stability. Therefore, it is possible to expect that agglomerate stability maybe correlated with a combination of these two factors, as discussed in the following section. 3.1.4. Development of correlation to predict the median breakage time The following correlation predicts the mean agglomerate breakage time from the ratio of the fluidization velocity to the minimum fluidization velocity and from the ratio of the agglomerate density to the bed density: 2 ρagglo 0:5 u d −1 t 50 ¼ a þ b ln þc þ ρ : agglo umf ρmf
Table 5 Coefficient values for the correlation. Coefficients
Values
a b c d
1.22 0.009 −1.07 −0.24
ð3Þ
ρmf
The coefficients values are shown in Table 5. Fig. 8 shows that the correlation gives very good predictions of the mean agglomerate breakage time. At this stage, this correlation
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Table 6 Median time t50 (corresponding to 50% probability of breakage) and slope value for standard linear regression (error corresponds to 90% confidence interval). Agglomerate type
Velocity (m/s)
t50 ± error (s)
c ± error (s)
“A” spherical agglomerate
0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22 0.35 0.29 0.22
34.9 ± 0.8 74.1 ± 2.1 101.2 ± 5.1 25.7 ± 1.7 44 ± 2 76.3 ± 2.5 18.3 ± 1 29.2 ± 0.8 55.2 ± 0.9 14.3 ± 1 25.6 ± 1 33.4 ± 1.5
10.4 ± 0.5 22.7 ± 1.4 28.9 ± 3.2 8.2 ± 1.1 13.1 ± 1.3 15.9 ± 1.6 3.9 ± 0.3 7.4 ± 0.6 10.2 ± .5 4.7 ± 0.7 7.4 ± 0.6 9.6 ± 1
“B” spherical agglomerate
“X” cylindrical agglomerate
“Y” cylindrical agglomerate
3.2. Effect of agglomerate shape on its stability 3.2.1. Fluidized bed experiments Two spherical and two cylindrical model agglomerates were used to check the effect of shape of agglomerates on their stability in a fluidized bed of fluid coke. 30 replicates were performed for each type of agglomerate at fluidization velocities of 0.22, 0.29 and 0.35 m/s. Fig. 9 shows that the logistic equation gave a good fit of the experimental data for the spherical agglomerate “A” under all experimental conditions. The standard linear regression also provided the means of evaluating the 90% confidence interval of the median breakage time. All the experimental results are provided in Table 6. 3.2.2. Discussion Fig. 10 shows that spherical agglomerates take a much longer time to break up in a fluidized bed than cylindrical agglomerates, over the whole range of fluidization velocities. Cylindrical and spherical agglomerates both are broken by the shear forces created by gas bubbles within the fluidized bed. The torque acting on an agglomerate can be obtained for the following equation: τ ¼ r F sinθ
ð4Þ
where F is the force and r is the distance between where the force is applied and where the torque is measured. For cylindrical agglomerates,
Fig. 9. Probability distribution of the breakage time for the spherical agglomerate “A” (the line corresponds to the logistic equation that was fitted to the experimental data).
applies to a fluidized bed of coke particles with a Sauter-mean diameter of around 150 μm and further experiments would be required to demonstrate its applicability to other particles.
Fig. 10. Median breakage time dependence on different shape of agglomerates.
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Fig. 11. Shear force working on cylindrical and spherical agglomerates.
most of the acting forces create 90° angles with the breakage plane, while for spherical agglomerates, the forces do not create 90° angles (Fig. 11). This reduces the magnitude of the torque acting on the breakage plane of spherical agglomerates. This explains why spherical agglomerates were more stable.
ρmf Minimum fluidizing bed density, kg/m 3 a, b, c, and d Coefficients.
Acknowledgments 4. Conclusions Several conclusions can be made from this study: 1) At low excess fluidization velocities, if the density of the bed particles is increased, the agglomerates spend more time in the surface region of bed, where bubbles are larger: therefore, agglomerates are exposed to a more intense shear force and are more likely to break. At high excess fluidization velocities, the time the agglomerates spend in the upper bed region is nearly independent of the density of the bed particles and, consequently, there is little effect of the particle density on agglomerate stability. 2) The shape and size of the bed particles have a negligible effect on agglomerate stability, when operating at the same excess fluidization velocity. 3) Spherical agglomerates are more stable than cylindrical agglomerate as these agglomerates are exposed to less shear from bed turbulence. Notation
p P t τ r F θ t50 u umf ρagglo
Cumulative probability of agglomeration breakage Total probability for model agglomerate breakage Time, s Magnitude of torque (N-m) Length of the arm (distance between where the torque is measured to where the force is applied), m Magnitude of force, N Angle between the force vector with the level arm vector. Median breakage time, s Fluidization gas velocity, m/s Minimum fluidization gas velocity, m/s Density of agglomerate, kg/m 3
The authors gratefully acknowledge funding and help from Syncrude Canada Ltd. Financial assistance was also provided by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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