- Email: [email protected]
Scaleup of wet granulation processes: science not art
Powder Technology 130 (2003) 35 – 40 www.elsevier.com/locate/powtec
Scaleup of wet granulation processes: science not art J.D. Litster Particle and Process Design Centre, Department of Chemical Engineering, University of Queensland, St. Lucia, Queensland 4072, Australia
Abstract Significant advances have been made in the last decade to quantify the process of wet granulation. The attributes of product granules from the granulation process are controlled by a combination of three groups of processes occurring in the granulator: (1) wetting and nucleation, (2) growth and consolidation and (3) breakage and attrition. For the first two of these processes, the key controlling dimensionless groups are defined and regime maps are presented and validated with data from tumbling and mixer granulators. Granulation is an example of particle design. For quantitative analysis, both careful characterisation of the feed formulation and knowledge of operating parameters are required. A key thesis of this paper is that the design, scaleup and operation of granulation processes can now be considered as quantitative engineering rather than a black art. D 2002 Elsevier Science B.V. All rights reserved. Re´sume´ La pre´ce´dente de´cennie a connu des avance´es significatives dans le domaine de la quantification des proce´de´s de granulation humide. Les caracte´ristiques des granule´s obtenus par ce proce´de´ sont controle´es par une combinaison de trois ope´rations a` l’inte´rieur du granulateur: (1) mouillage et nucle´ation, (2) croissance et consolidation, (3) fractionnement et attrition. Les groupes adimensionnels controˆlant les deux premie`res ope´rations ont e´te´ de´finis et des diagrammes repre´sentant les diffe´rents re´gimes sont pre´sente´s et valide´s a` partir de donne´es obtenues sur deux types de granulateurs (tambour et me´langeur). La granulation est un exemple de ‘‘conception de particule’’ (particle design). Une analyse quantitative requiert a` la fois une caracte´risation soigne´e de la formulation en entre´e et la connaissance des parame`tres ope´ratoires. La the`se soutenue dans le pre´sent article est que la conception, le changement d’e´chelle et la mise en oeuvre des proce´de´s de granulation peuvent de´sormais eˆtre conside´re´s comme relevant de l’inge´nierie quantitative plutoˆt que d’une quelconque alchimie. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Wetting; Attrition; Particle design
1. Introduction At about the time the last World Congress on Agglomeration was held in 1993, papers from our group (and others) would often start with the phrase ‘‘Granulation is more of an art than a science.’’ However, over the past decade, there have been substantial gains in our understanding of the granulation process. A key thesis of this paper is that the design, scaleup and operation of granulation processes can now be considered as quantitative engineering rather than a black art. There are two key elements that contribute to this improved quantitative understanding. Firstly, granulation must be recognised as an example of particle design, i.e., granules of controlled attributes are produced by a combination of both formulation development and process engi-
E-mail address: [email protected] (J.D. Litster).
neering. The corollary to this is that high-quality formulation characterisation and good understanding of the behaviour of the powder in the process equipment is required. Secondly, the analysis of granulation processes needs to take place at many levels of scrutiny from the level of particle –particle interactions inside a granule to the level of fully integrated granulation plant and the understanding at each level transferred to the next. This is exactly analogous to the process used by chemists and chemical engineers in reactor engineering and reactor design. Consider granulation at the scale of a granule inside the granulator. Three key classes of rate processes control the density and size distribution of the granules formed [1,2] (see Fig. 1): 1. Wetting and nucleation 2. Growth and consolidation 3. Attrition and breakage
0032-5910/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 3 2 - 5 9 1 0 ( 0 2 ) 0 0 2 2 2 - X
36
J.D. Litster / Powder Technology 130 (2003) 35–40
Fig. 1. The rate processes of granulation.
To predict the granule attributes from the process, the role of each of these rate processes must be quantified and understood separately. Our approach has been to define the controlling groups and develop regime maps for each rate process. A summary of the approach for (1) wetting and nucleation and (2) consolidation and growth are given below. Implications for the modelling of granulation circuits and design of new granulators are then briefly discussed.
2. Wetting and nucleation The initial step in all wet granulation processes is the distribution of a liquid binder through the powder. As powder passed through the spray zone, spray drops impact the powder bed (see Fig. 1). These drops penetrate the powder bed due to capillary forces to form nuclei granules. Ideal nucleation occurs when each drop forms a single nucleus granule. We call this drop-controlled nucleation. For this to occur, two conditions must be met. Firstly, the drops must not substantially overlap on the powder surface. This will cause agglomerate granules to occur and ultimately caking of the bed surface. Secondly, the drops must penetrate quickly into the powder bed before coalescing with other drops on the powder surface. If these conditions are not met, the liquid will be poorly distributed
Fig. 2. Nuclei size distributions from ex-granulator experiments at different powder bed velocities (lactose powder with water spray) [4].
in the spray zone and liquid distribution by mechanical dispersion is necessary. Drop overlap is controlled by the dimensionless spray flux, which is the ratio of the projected area of drops from the spray nozzle to the area flux of powder surface through the spray zone. Thus, Wa ¼
3V˙ ˙ d 2Ad
ð1Þ
where the powder surface is traversing the spray zone with a flux A˙ (m2/s), V˙ is the volumetric spray rate and dd is the drop diameter. Fig. 2 shows the impact of varying spray flux by changing the velocity of the powder through the spray zone. At high powder velocity, a narrow nuclei distribution is achieved. At lower velocities, the nuclei size distribution broadens substantially due to agglomerates formed on the basis of drop overlap on the powder surface. From statistical considerations, we can relate the fraction of agglomerates formed directly to the dimensionless spray flux by a very simple equation [3]: fagglom ¼ 1 expð4Wa Þ
ð2Þ
Fig. 3 shows remarkable agreement between experimental results and Eq. (2) for both ex-granulator and in-granulator nucleation experiments.
Fig. 3. Agglomerate formation for mixer granulator and ex-granulator nucleation experiments (25 l Fielder granulator) [3].
J.D. Litster / Powder Technology 130 (2003) 35–40
37
The drop penetration time can be predicted from consideration of the capillary pressure driven flow of liquid into pores in the powder bed [5]: 2=3
tp ¼ 1:35
Vo l e2eff Reff cLV cosh
ð3Þ
where tp is the drop penetration time, Vo is the total drop volume, eeff is the effective bed porosity, cLV is the liquid surface tension, l is the liquid viscosity, h is the solid – liquid contact angle and Reff is the effective pore radius. Hapgood and Litster [5] showed that for fine powders Reff and eeff cannot be reasonably estimated using the Kozeny Carmen approach, because the presence of macrovoids in the bed could not be neglected. Thus, Reff and eeff were calculated as: eeff ¼ etap ð1 emacrovoids Þ ¼ etap ð1 e þ etap Þ Reff ¼
/d32 eeff 3 ð1 eeff Þ
ð4Þ
Fig. 5. Proposed nucleation regime map. For ideal nucleation in the dropcontrolled regime, the two conditions must be satisfied: (1) low Wa and (2) low tp. In the mechanical dispersion regime, one or both of these conditions are not met, and good binder dispersion requires good mechanical mixing [6].
ð5Þ
where the specific surface particle diameter is d32 and / is the particle sphericity. e and etap are the loose packed and tapped porosity, respectively. Fig. 4 shows that Eq. (3) gives reasonable agreement with experimental data over a wide range of fluid – powder combinations. The regimes for wetting and nucleation can be conveniently shown in a regime map. The map is shown conceptually in Fig. 5 and an example of validation of the map for mixer granulation is shown in Fig. 6. Note that narrow granule size distributions are achieved only in the drop-
controlled regime (bottom left corner) when both the dimensionless spray flux and the drop penetration time are small. This regime also emphasises granulation as particle design. The drop penetration time is largely a function of formulation properties and can be varied by formulation design. The dimensionless spray flux is largely a function of operating parameters and is influenced by the choice of nozzle(s), liquid flow rate, impeller speed, etc. During scaleup, the dimensionless spray flux should be kept constant.
Fig. 4. Experimental drop penetration times versus the times predicted by the improved theory with eeff and Reff in Eq. (3). Solid line is the equality line and the dashed lines show F1 s [5].
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J.D. Litster / Powder Technology 130 (2003) 35–40
Fig. 7. Regime map for drum and mixer granulation experiments with glass ballotini, iron ore, chalcopyrite and sodium sulfate powders [8].
Fig. 6. Nucleation regime map in 25 l Fielder mixer after 2 min. Contour lines estimate the relative spread of the granule size distribution. Lactose powder with water and HPC solution [6].
Our own experience is that most industrial granulators do not operate in the drop-controlled regime. Hence, greater attention to wetting and nucleation could lead to substantial improvements in control of granule attributes.
3. Growth and consolidation If wetting and nucleation is well controlled, then the extent of granule consolidation and coalescence will affect the key granule attributes of density and size, respectively. Granule consolidation depends on the extent of granule deformation during impact collisions in the granulator. Granule deformation is a function of the mechanical properties of the granule matrix and energy of collisions. To a first approximation, the deformation is given by the Stokes deformation number [7,8]: Stdef ¼
qg Uc2 2Yg
can lead to successful coalescence. This is the steady growth regime. Granules grow steadily immediately after addition of the liquid binder. For both these growth regimes, the rate of growth is very sensitive to the granule liquid content, represented by the granule saturation smax: smax ¼
wqs ð1 emin Þ ql emin
ð7Þ
where qs and ql are the solid and liquid densities and emin is the minimum granule porosity. At low liquid saturation values, nuclei granules can be formed but grow no further (nucleation regime). As for wetting and nucleation, we can summarise the growth regimes on a regime map in terms of these two dimensionless groups. Fig. 7 shows that there is remarkably good agreement between data for a range of materials and several scales of granulator for drum granulation.
ð6Þ
where qg is the granule density, Uc is the effective collision velocity in the granulator and Yg is the dynamic yield stress of the granule matrix. Deformation also influences the growth of granules by coalescence. At low deformation number, granules do not deform when they collide in the granulator and will only coalesce if they are surface wet. This is the induction growth regime. There is no substantial growth until granules consolidate and liquid is squeezed to the granule surface. For high deformation number, granules deform substantially during collision and establish a significant contact area that
Fig. 8. Peak flow stress versus strain rate for pellets made with glass ballotini (x32 = 34.5 Am) and water, glycerol or silicone oil binders. Pellets 25 mm long and 20 mm diameter [9].
J.D. Litster / Powder Technology 130 (2003) 35–40
39
Fig. 11. Stable cycling of granule liquid content and size in a continuous drum granulation pilot plant [16]. Fig. 9. Dimensionless strength versus capillary number for pellets made with glass ballotini (x32 = 34.5 Am) and water, glycerol or silicone oil binders. Pellets 25 mm long and 20 mm diameter [9].
However, the data for mixer granulators do not fall correctly on this map. There are two possible reasons for this. Firstly, granule flow patterns in mixer granulators are very complex with a very wide distribution of collision velocities, so that the choice of Uc is problematic. Secondly, the dynamic yield stress is a function of impact velocity (strain rate). The higher impact velocities in mixer granulators may mean higher yield stresses than are predicted from low or medium strain rate mechanical testing. Recent work by Iveson et al. [9] using an Instron dynamite testing machine shows the strain rate dependence of granule yield stress (see Fig. 8). Note that not only the magnitude of the yield stress but also the ranking of different formulations can vary with strain rate. Initial attempts by the workers to nondimensionalise the strength strain rate relationship to take account of viscous dissipation in the binder fluid are very promising (see Fig. 9). In summary, granule growth behaviour is a strong function of the mechanical properties of the granule matrix
and the granule saturation. With careful characterisation, the granulation growth regime is predictable.
4. Modelling, simulation and control of granulation processes The regime map approach proposed above is very useful in formulation design and scaleup but does not predict granule attributes, particularly granule size distributions and other properties. For more sophisticated optimisation and control of granulation processes, models to predict these distributions are necessary. Such models can be built into a population balance framework and are valuable provided that the rate expressions incorporate directly knowledge of the physics governing the granulation processes. An example of such an approach is the successful dynamic simulation of fertiliser granulation circuits [10, 11] in which the granule growth kernel accounts for variations in fertiliser viscosity using the collision model
Fig. 10. Modelling coalescence of surface wet, deformable granules: (a) probability of coalescence and (b) comparison of experimental data with model fit for batch drum granulation [14].
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J.D. Litster / Powder Technology 130 (2003) 35–40
of Ennis et al. [12]. Our improved understanding of the role of granule deformation is leading to models for coalescence of deformable granules [13] and population balances that incorporate this knowledge [14] (see Fig. 10). Increased understanding shows that granule coalescence probability depends not only on granule size but also on porosity and liquid content. This leads to multidimensional distributions. Recently, Wauters [15] developed a multidimensional population balance model to follow distributions of granule size, porosity and liquid content. These models will only be valuable if they incorporate good understanding of wetting and nucleation, which establish the initial liquid distribution and effectively set the initial conditions for the growth models. Wet granulation is a controllable process. Fig. 11 shows an example from the UQ continuous granulation pilot plant where granule liquid content and size distribution were varied by step changes in the liquid flow rate to the granulator. The process is very repeatable. The apparently stochastic behaviour and hysteresis sometimes seen in pilot plant and full-scale granulation are probably due to insufficient control of input powder and liquid flow rates and properties and variability introduced due to poor control of wetting and nucleation. The corollary to this is that there are many opportunities for the use of model-based control and optimisation. These opportunities are rarely grasped at present.
5. Concluding comments There has been considerable progress in understanding of granulation processes in the last decade:
The key formulation properties and processes parameters that control granule attributes are largely known. Regime maps for granule nucleation and growth have been developed and, at least partly, validated. New techniques for measuring granule mechanical properties are established. Granulation models are incorporating the new physical understanding. To complete the establishment of granulation as a ‘‘text book’’ unit operation, further work is required in better quantitative understanding of
Granule mechanics including better constituitive models Wet powder flow in granulators Granule breakage, especially in mixer granulators.
Nevertheless, we are now in a position to achieve (1) rational scaleup based on an understanding of controlling dimensionless groups for granulation processes and (2) rational particle design based on careful formulation char-
acterisation and manipulation. Granulation has completed the transition from art to quantitative engineering.
Acknowledgements This presentation has been an overview of work to which many people have contributed. I would like to acknowledge colleagues and students at UQ (Anthony Adetayo, Simon Iveson, Karen Hapgood, Lian Liu and Hans Wildeboer) as well as collaborations with Neil Page and Simon Iveson (Newcastle University, NSW), Brian Scarlett, Gabrie Meesters and Flip Wauters (Delft University of Technology, the Netherlands), Brian Ennis (E&G Associates), Jim Michaels and colleagues (Merck and Co.) and Paul Mort (Procter & Gamble).
References [1] S.M. Iveson, J.D. Litster, K. Hapgood, B.J. Ennis, Nucleation, growth and breakage in agitated wet agglomeration processes: A review, Powder Technol. 117 (2001) 3 – 39. [2] R.H. Snow, T. Allen, B.J. Ennis, J.D. Litster, Size reduction and size enlargement, in: D. Green (Ed.), Perry’s Chemical Engineers Handbook, Section 20, McGraw-Hill, New York, 1997, 98 pp. [3] K.P. Hapgood, Nucleation and binder dispersion in wet granulation, PhD thesis, University of Queensland, 2000. [4] J.D. Litster, K.P. Hapgood, S.K. Kamineni, T. Hsu, A. Sims, M. Roberts, J. Michaels, Liquid distribution in wet granulation. Dimensionless spray flux, Powder Technol. 114 (2001) 32 – 39. [5] K.P. Hapgood, J.D. Litster, S.R. Biggs, T. Howes, Drop penetration into loose packed powder beds, submitted to J. Colloid Interface Sci., 253 (2002) 353 – 366. [6] K.P. Hapgood, J.D. Litster, Nucleation regime map for wet granulation, submitted to AIChE J. (in press). [7] S.J. Iveson, J.D. Litster, A growth regime map for liquid bound granules, AIChE J. 44 (1998) 1510 – 1518. [8] S.M. Iveson, P.A.L. Wauters, S. Forrest, J.D. Litster, G.M.H. Meesters, B. Scarlett, Growth regime map for liquid bound granules: Further developments and experimental validation, Powder Technol. 117 (2001) 83 – 87. [9] S.M. Iveson, J.A. Beathe, N.W. Page, The dynamic strength of wet powder compacts at varying strain rates, Powder Technol. 127 (2002) 149 – 161. [10] J. Zhang, J.D. Litster, F.Y. Wang, I.T. Cameron, Evaluation of control strategies for fertiliser granulation circuits using dynamic simulation, Powder Technol. 108 (2000) 122 – 129. [11] A.A. Adetayo, J.D. Litster, I.T. Cameron, Steady state modelling and simulation of a fertilizer granulation circuit, Comput. Chem. Eng. 19 (1995) 383 – 393. [12] B.J. Ennis, G.I. Tardos, R. Pfeffer, Powder Technol. 65 (1991) 257 – 272. [13] L.X. Liu, S.M. Iveson, J.D. Litster, B.J. Ennis, Coalescence of deformable granules in wet granulation processes, AIChE J. 46 (2000), 529 – 539. [14] L.X. Liu, J.D. Litster, Population balance modeling of granulation with a physically based coalescence kernel, submitted to Chem. Eng. Sci., 57 (2002) 2183 – 2191. [15] Wauters, P.A.L. Modelling and mechanisms of granulation, PhD thesis, Delft Technical University, 2001. [16] T. Friedrich, M. Eng. Thesis, Delft Technical University, 2000.
Scaleup of wet granulation processes: science not art J.D. Litster Particle and Process Design Centre, Department of Chemical Engineering, University of Queensland, St. Lucia, Queensland 4072, Australia
Abstract Significant advances have been made in the last decade to quantify the process of wet granulation. The attributes of product granules from the granulation process are controlled by a combination of three groups of processes occurring in the granulator: (1) wetting and nucleation, (2) growth and consolidation and (3) breakage and attrition. For the first two of these processes, the key controlling dimensionless groups are defined and regime maps are presented and validated with data from tumbling and mixer granulators. Granulation is an example of particle design. For quantitative analysis, both careful characterisation of the feed formulation and knowledge of operating parameters are required. A key thesis of this paper is that the design, scaleup and operation of granulation processes can now be considered as quantitative engineering rather than a black art. D 2002 Elsevier Science B.V. All rights reserved. Re´sume´ La pre´ce´dente de´cennie a connu des avance´es significatives dans le domaine de la quantification des proce´de´s de granulation humide. Les caracte´ristiques des granule´s obtenus par ce proce´de´ sont controle´es par une combinaison de trois ope´rations a` l’inte´rieur du granulateur: (1) mouillage et nucle´ation, (2) croissance et consolidation, (3) fractionnement et attrition. Les groupes adimensionnels controˆlant les deux premie`res ope´rations ont e´te´ de´finis et des diagrammes repre´sentant les diffe´rents re´gimes sont pre´sente´s et valide´s a` partir de donne´es obtenues sur deux types de granulateurs (tambour et me´langeur). La granulation est un exemple de ‘‘conception de particule’’ (particle design). Une analyse quantitative requiert a` la fois une caracte´risation soigne´e de la formulation en entre´e et la connaissance des parame`tres ope´ratoires. La the`se soutenue dans le pre´sent article est que la conception, le changement d’e´chelle et la mise en oeuvre des proce´de´s de granulation peuvent de´sormais eˆtre conside´re´s comme relevant de l’inge´nierie quantitative plutoˆt que d’une quelconque alchimie. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Wetting; Attrition; Particle design
1. Introduction At about the time the last World Congress on Agglomeration was held in 1993, papers from our group (and others) would often start with the phrase ‘‘Granulation is more of an art than a science.’’ However, over the past decade, there have been substantial gains in our understanding of the granulation process. A key thesis of this paper is that the design, scaleup and operation of granulation processes can now be considered as quantitative engineering rather than a black art. There are two key elements that contribute to this improved quantitative understanding. Firstly, granulation must be recognised as an example of particle design, i.e., granules of controlled attributes are produced by a combination of both formulation development and process engi-
E-mail address: [email protected] (J.D. Litster).
neering. The corollary to this is that high-quality formulation characterisation and good understanding of the behaviour of the powder in the process equipment is required. Secondly, the analysis of granulation processes needs to take place at many levels of scrutiny from the level of particle –particle interactions inside a granule to the level of fully integrated granulation plant and the understanding at each level transferred to the next. This is exactly analogous to the process used by chemists and chemical engineers in reactor engineering and reactor design. Consider granulation at the scale of a granule inside the granulator. Three key classes of rate processes control the density and size distribution of the granules formed [1,2] (see Fig. 1): 1. Wetting and nucleation 2. Growth and consolidation 3. Attrition and breakage
0032-5910/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 3 2 - 5 9 1 0 ( 0 2 ) 0 0 2 2 2 - X
36
J.D. Litster / Powder Technology 130 (2003) 35–40
Fig. 1. The rate processes of granulation.
To predict the granule attributes from the process, the role of each of these rate processes must be quantified and understood separately. Our approach has been to define the controlling groups and develop regime maps for each rate process. A summary of the approach for (1) wetting and nucleation and (2) consolidation and growth are given below. Implications for the modelling of granulation circuits and design of new granulators are then briefly discussed.
2. Wetting and nucleation The initial step in all wet granulation processes is the distribution of a liquid binder through the powder. As powder passed through the spray zone, spray drops impact the powder bed (see Fig. 1). These drops penetrate the powder bed due to capillary forces to form nuclei granules. Ideal nucleation occurs when each drop forms a single nucleus granule. We call this drop-controlled nucleation. For this to occur, two conditions must be met. Firstly, the drops must not substantially overlap on the powder surface. This will cause agglomerate granules to occur and ultimately caking of the bed surface. Secondly, the drops must penetrate quickly into the powder bed before coalescing with other drops on the powder surface. If these conditions are not met, the liquid will be poorly distributed
Fig. 2. Nuclei size distributions from ex-granulator experiments at different powder bed velocities (lactose powder with water spray) [4].
in the spray zone and liquid distribution by mechanical dispersion is necessary. Drop overlap is controlled by the dimensionless spray flux, which is the ratio of the projected area of drops from the spray nozzle to the area flux of powder surface through the spray zone. Thus, Wa ¼
3V˙ ˙ d 2Ad
ð1Þ
where the powder surface is traversing the spray zone with a flux A˙ (m2/s), V˙ is the volumetric spray rate and dd is the drop diameter. Fig. 2 shows the impact of varying spray flux by changing the velocity of the powder through the spray zone. At high powder velocity, a narrow nuclei distribution is achieved. At lower velocities, the nuclei size distribution broadens substantially due to agglomerates formed on the basis of drop overlap on the powder surface. From statistical considerations, we can relate the fraction of agglomerates formed directly to the dimensionless spray flux by a very simple equation [3]: fagglom ¼ 1 expð4Wa Þ
ð2Þ
Fig. 3 shows remarkable agreement between experimental results and Eq. (2) for both ex-granulator and in-granulator nucleation experiments.
Fig. 3. Agglomerate formation for mixer granulator and ex-granulator nucleation experiments (25 l Fielder granulator) [3].
J.D. Litster / Powder Technology 130 (2003) 35–40
37
The drop penetration time can be predicted from consideration of the capillary pressure driven flow of liquid into pores in the powder bed [5]: 2=3
tp ¼ 1:35
Vo l e2eff Reff cLV cosh
ð3Þ
where tp is the drop penetration time, Vo is the total drop volume, eeff is the effective bed porosity, cLV is the liquid surface tension, l is the liquid viscosity, h is the solid – liquid contact angle and Reff is the effective pore radius. Hapgood and Litster [5] showed that for fine powders Reff and eeff cannot be reasonably estimated using the Kozeny Carmen approach, because the presence of macrovoids in the bed could not be neglected. Thus, Reff and eeff were calculated as: eeff ¼ etap ð1 emacrovoids Þ ¼ etap ð1 e þ etap Þ Reff ¼
/d32 eeff 3 ð1 eeff Þ
ð4Þ
Fig. 5. Proposed nucleation regime map. For ideal nucleation in the dropcontrolled regime, the two conditions must be satisfied: (1) low Wa and (2) low tp. In the mechanical dispersion regime, one or both of these conditions are not met, and good binder dispersion requires good mechanical mixing [6].
ð5Þ
where the specific surface particle diameter is d32 and / is the particle sphericity. e and etap are the loose packed and tapped porosity, respectively. Fig. 4 shows that Eq. (3) gives reasonable agreement with experimental data over a wide range of fluid – powder combinations. The regimes for wetting and nucleation can be conveniently shown in a regime map. The map is shown conceptually in Fig. 5 and an example of validation of the map for mixer granulation is shown in Fig. 6. Note that narrow granule size distributions are achieved only in the drop-
controlled regime (bottom left corner) when both the dimensionless spray flux and the drop penetration time are small. This regime also emphasises granulation as particle design. The drop penetration time is largely a function of formulation properties and can be varied by formulation design. The dimensionless spray flux is largely a function of operating parameters and is influenced by the choice of nozzle(s), liquid flow rate, impeller speed, etc. During scaleup, the dimensionless spray flux should be kept constant.
Fig. 4. Experimental drop penetration times versus the times predicted by the improved theory with eeff and Reff in Eq. (3). Solid line is the equality line and the dashed lines show F1 s [5].
38
J.D. Litster / Powder Technology 130 (2003) 35–40
Fig. 7. Regime map for drum and mixer granulation experiments with glass ballotini, iron ore, chalcopyrite and sodium sulfate powders [8].
Fig. 6. Nucleation regime map in 25 l Fielder mixer after 2 min. Contour lines estimate the relative spread of the granule size distribution. Lactose powder with water and HPC solution [6].
Our own experience is that most industrial granulators do not operate in the drop-controlled regime. Hence, greater attention to wetting and nucleation could lead to substantial improvements in control of granule attributes.
3. Growth and consolidation If wetting and nucleation is well controlled, then the extent of granule consolidation and coalescence will affect the key granule attributes of density and size, respectively. Granule consolidation depends on the extent of granule deformation during impact collisions in the granulator. Granule deformation is a function of the mechanical properties of the granule matrix and energy of collisions. To a first approximation, the deformation is given by the Stokes deformation number [7,8]: Stdef ¼
qg Uc2 2Yg
can lead to successful coalescence. This is the steady growth regime. Granules grow steadily immediately after addition of the liquid binder. For both these growth regimes, the rate of growth is very sensitive to the granule liquid content, represented by the granule saturation smax: smax ¼
wqs ð1 emin Þ ql emin
ð7Þ
where qs and ql are the solid and liquid densities and emin is the minimum granule porosity. At low liquid saturation values, nuclei granules can be formed but grow no further (nucleation regime). As for wetting and nucleation, we can summarise the growth regimes on a regime map in terms of these two dimensionless groups. Fig. 7 shows that there is remarkably good agreement between data for a range of materials and several scales of granulator for drum granulation.
ð6Þ
where qg is the granule density, Uc is the effective collision velocity in the granulator and Yg is the dynamic yield stress of the granule matrix. Deformation also influences the growth of granules by coalescence. At low deformation number, granules do not deform when they collide in the granulator and will only coalesce if they are surface wet. This is the induction growth regime. There is no substantial growth until granules consolidate and liquid is squeezed to the granule surface. For high deformation number, granules deform substantially during collision and establish a significant contact area that
Fig. 8. Peak flow stress versus strain rate for pellets made with glass ballotini (x32 = 34.5 Am) and water, glycerol or silicone oil binders. Pellets 25 mm long and 20 mm diameter [9].
J.D. Litster / Powder Technology 130 (2003) 35–40
39
Fig. 11. Stable cycling of granule liquid content and size in a continuous drum granulation pilot plant [16]. Fig. 9. Dimensionless strength versus capillary number for pellets made with glass ballotini (x32 = 34.5 Am) and water, glycerol or silicone oil binders. Pellets 25 mm long and 20 mm diameter [9].
However, the data for mixer granulators do not fall correctly on this map. There are two possible reasons for this. Firstly, granule flow patterns in mixer granulators are very complex with a very wide distribution of collision velocities, so that the choice of Uc is problematic. Secondly, the dynamic yield stress is a function of impact velocity (strain rate). The higher impact velocities in mixer granulators may mean higher yield stresses than are predicted from low or medium strain rate mechanical testing. Recent work by Iveson et al. [9] using an Instron dynamite testing machine shows the strain rate dependence of granule yield stress (see Fig. 8). Note that not only the magnitude of the yield stress but also the ranking of different formulations can vary with strain rate. Initial attempts by the workers to nondimensionalise the strength strain rate relationship to take account of viscous dissipation in the binder fluid are very promising (see Fig. 9). In summary, granule growth behaviour is a strong function of the mechanical properties of the granule matrix
and the granule saturation. With careful characterisation, the granulation growth regime is predictable.
4. Modelling, simulation and control of granulation processes The regime map approach proposed above is very useful in formulation design and scaleup but does not predict granule attributes, particularly granule size distributions and other properties. For more sophisticated optimisation and control of granulation processes, models to predict these distributions are necessary. Such models can be built into a population balance framework and are valuable provided that the rate expressions incorporate directly knowledge of the physics governing the granulation processes. An example of such an approach is the successful dynamic simulation of fertiliser granulation circuits [10, 11] in which the granule growth kernel accounts for variations in fertiliser viscosity using the collision model
Fig. 10. Modelling coalescence of surface wet, deformable granules: (a) probability of coalescence and (b) comparison of experimental data with model fit for batch drum granulation [14].
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J.D. Litster / Powder Technology 130 (2003) 35–40
of Ennis et al. [12]. Our improved understanding of the role of granule deformation is leading to models for coalescence of deformable granules [13] and population balances that incorporate this knowledge [14] (see Fig. 10). Increased understanding shows that granule coalescence probability depends not only on granule size but also on porosity and liquid content. This leads to multidimensional distributions. Recently, Wauters [15] developed a multidimensional population balance model to follow distributions of granule size, porosity and liquid content. These models will only be valuable if they incorporate good understanding of wetting and nucleation, which establish the initial liquid distribution and effectively set the initial conditions for the growth models. Wet granulation is a controllable process. Fig. 11 shows an example from the UQ continuous granulation pilot plant where granule liquid content and size distribution were varied by step changes in the liquid flow rate to the granulator. The process is very repeatable. The apparently stochastic behaviour and hysteresis sometimes seen in pilot plant and full-scale granulation are probably due to insufficient control of input powder and liquid flow rates and properties and variability introduced due to poor control of wetting and nucleation. The corollary to this is that there are many opportunities for the use of model-based control and optimisation. These opportunities are rarely grasped at present.
5. Concluding comments There has been considerable progress in understanding of granulation processes in the last decade:
The key formulation properties and processes parameters that control granule attributes are largely known. Regime maps for granule nucleation and growth have been developed and, at least partly, validated. New techniques for measuring granule mechanical properties are established. Granulation models are incorporating the new physical understanding. To complete the establishment of granulation as a ‘‘text book’’ unit operation, further work is required in better quantitative understanding of
Granule mechanics including better constituitive models Wet powder flow in granulators Granule breakage, especially in mixer granulators.
Nevertheless, we are now in a position to achieve (1) rational scaleup based on an understanding of controlling dimensionless groups for granulation processes and (2) rational particle design based on careful formulation char-
acterisation and manipulation. Granulation has completed the transition from art to quantitative engineering.
Acknowledgements This presentation has been an overview of work to which many people have contributed. I would like to acknowledge colleagues and students at UQ (Anthony Adetayo, Simon Iveson, Karen Hapgood, Lian Liu and Hans Wildeboer) as well as collaborations with Neil Page and Simon Iveson (Newcastle University, NSW), Brian Scarlett, Gabrie Meesters and Flip Wauters (Delft University of Technology, the Netherlands), Brian Ennis (E&G Associates), Jim Michaels and colleagues (Merck and Co.) and Paul Mort (Procter & Gamble).
References [1] S.M. Iveson, J.D. Litster, K. Hapgood, B.J. Ennis, Nucleation, growth and breakage in agitated wet agglomeration processes: A review, Powder Technol. 117 (2001) 3 – 39. [2] R.H. Snow, T. Allen, B.J. Ennis, J.D. Litster, Size reduction and size enlargement, in: D. Green (Ed.), Perry’s Chemical Engineers Handbook, Section 20, McGraw-Hill, New York, 1997, 98 pp. [3] K.P. Hapgood, Nucleation and binder dispersion in wet granulation, PhD thesis, University of Queensland, 2000. [4] J.D. Litster, K.P. Hapgood, S.K. Kamineni, T. Hsu, A. Sims, M. Roberts, J. Michaels, Liquid distribution in wet granulation. Dimensionless spray flux, Powder Technol. 114 (2001) 32 – 39. [5] K.P. Hapgood, J.D. Litster, S.R. Biggs, T. Howes, Drop penetration into loose packed powder beds, submitted to J. Colloid Interface Sci., 253 (2002) 353 – 366. [6] K.P. Hapgood, J.D. Litster, Nucleation regime map for wet granulation, submitted to AIChE J. (in press). [7] S.J. Iveson, J.D. Litster, A growth regime map for liquid bound granules, AIChE J. 44 (1998) 1510 – 1518. [8] S.M. Iveson, P.A.L. Wauters, S. Forrest, J.D. Litster, G.M.H. Meesters, B. Scarlett, Growth regime map for liquid bound granules: Further developments and experimental validation, Powder Technol. 117 (2001) 83 – 87. [9] S.M. Iveson, J.A. Beathe, N.W. Page, The dynamic strength of wet powder compacts at varying strain rates, Powder Technol. 127 (2002) 149 – 161. [10] J. Zhang, J.D. Litster, F.Y. Wang, I.T. Cameron, Evaluation of control strategies for fertiliser granulation circuits using dynamic simulation, Powder Technol. 108 (2000) 122 – 129. [11] A.A. Adetayo, J.D. Litster, I.T. Cameron, Steady state modelling and simulation of a fertilizer granulation circuit, Comput. Chem. Eng. 19 (1995) 383 – 393. [12] B.J. Ennis, G.I. Tardos, R. Pfeffer, Powder Technol. 65 (1991) 257 – 272. [13] L.X. Liu, S.M. Iveson, J.D. Litster, B.J. Ennis, Coalescence of deformable granules in wet granulation processes, AIChE J. 46 (2000), 529 – 539. [14] L.X. Liu, J.D. Litster, Population balance modeling of granulation with a physically based coalescence kernel, submitted to Chem. Eng. Sci., 57 (2002) 2183 – 2191. [15] Wauters, P.A.L. Modelling and mechanisms of granulation, PhD thesis, Delft Technical University, 2001. [16] T. Friedrich, M. Eng. Thesis, Delft Technical University, 2000.