Agglomeration of Paracetamol during Crystallization in Pure and

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Ind. Eng. Chem. Res. 2004, 43, 629-637

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GENERAL RESEARCH Agglomeration of Paracetamol during Crystallization in Pure and Mixed Solvents Eva M. Ålander, Marketta S. Uusi-Penttila1 , and Åke C. Rasmuson* Department of Chemical Engineering and Technology, Royal Institute of Technology, SE-100 44 Stockholm, Sweden

The agglomeration of paracetamol during crystallization has been investigated. It is shown that the agglomeration behavior depends on the solvent composition. The following solvent systems were used in isothermal desupersaturation experiments: five different acetone-toluene-water mixtures and the pure solvents acetone, 2-propanol, acetic acid, and ethylene glycol. Sieving, image analysis processed by principal component analysis, and agglomerate strength measurements were used to characterize the product particles. Mixtures with a high concentration of acetone were found to produce a highly agglomerated product with strong agglomerates. In contrast, products from crystallization in ethylene glycol, 2-propanol, acetic acid, and acetonetoluene-water mixtures having a high concentration of water contained not only agglomerates but also a significant fraction of single crystals. Furthermore, the agglomerates formed in these solvents were much weaker than those produced in mixtures with a high content of acetone. The results were correlated with the polarity and the viscosity of the solvents. Introduction Crystallization is an important process for separation and purification in the chemical, pharmaceutical, and food industries. Crystallization involves several separate mechanisms: nucleation, crystal growth, agglomeration, aging, and ripening. Aside from crystal nucleation and growth, agglomeration is often the most important mechanism controlling the particle size distribution and morphology of the product. Agglomeration denotes the process in which small crystals adhere and grow together to form larger solid bodies. Crystal agglomeration occurs in three steps: (i) crystals collide, (ii) attractive forces keep the crystals together in aggregates, and (iii) the aggregates are strengthened into agglomerates by the growth of crystalline bridges. Hydrodynamics and crystal-crystal adhesion forces determine the time available for the formation of crystalline bridges. Agglomeration can be desirable or undesirable depending on the downstream processing and application of the product. Agglomerates can be more difficult to wash because mother liquor and impurities can become entrapped inside the solid particles, whereas on the other hand, an increased overall particle dimension makes the product easier to handle in solid-liquid separations. Agglomeration can improve the flowability and compaction properties of the crystalline product. This is true for spherical agglomerates, whereas irregularly shaped agglomerates might be worse in these respects in comparison to single crystals. A number of parameters influence crystal agglomeration. Among the most important parameters are super* To whom correspondence should be addressed. E-mail: [email protected]. Tel.: 46-8-7908227. Fax: +46-8-105228.

saturation, particle concentration, particle size, and agitation rate. Increased supersaturation increases the nucleation rate and hence the total number of small crystals per unit volume, and it increases the growth rate of crystalline bridges. Consequently, the rate and degree of agglomeration increases with increasing supersaturation.1-5 A higher particle concentration leads to a higher collision frequency and thus to a higher degree of agglomeration,1,5 unless the formed agglomerates are weak and break down as a result of collisions.4 Small crystals agglomerate more easily than large crystals, and agglomeration is an important phenomenon especially for primary particle sizes ranging from 1 µm to several tens of micrometers.6 David et al.7 distinguish particles as being larger or smaller than the Kolmogorov microscale (about 20-40 µm in agitated tanks). Larger particles collide because of the fluctuating velocity field, whereas smaller particles collide because of viscous laminar microshear stresses within turbulent eddies. In 1917, Smoluchowski developed a theory to predict collisions of particles8 that has been shown experimentally to be valid for colloidal systems. For the agglomeration of larger particles, the Smoluchowski theory has to be modified because only some collisions lead to agglomerates.9 Recently, it has been shown that the extent of crystal agglomeration can be related to the shape and micromeretic properties of the primary crystals.10 If the particles are of the same size, spherical particles stick together more easily than elongated particles, which have to be aligned after they collide. The rate or degree of agglomeration usually decreases with increasing agitation.4,11,12 In a few studies, the opposite trend has been observed,3,13 and occasionally no influence has been found.1 The maxi-

10.1021/ie0301280 CCC: $27.50 © 2004 American Chemical Society Published on Web 12/13/2003

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Table 1. Product Size Distributions

solventa composition atw 100-0-0 atw 70-30-0 atw 30-0-70 atw 68-29-3 atw 92-5-3 atw 97-0-3 ethylene glycol 2-propanol acetic acid

particle mass fraction (%) weightb 125-280 280-450 450-630 mean size CVc (µm) µm µm µm (%) 11.9 17.1 8.7 12.4 7.7 6.2 58.8 40.9 55.9

57.7 52.8 34.0 39.3 59.5 42.6 12.8 44.7 40.6

24.2 28.5 36.7 27.8 23.7 38.3 4.6 4.9 1.2

410 393 497 474 433 472 225 329 278

36 39 43 49 37 37 64 52 53

a atw ) wt % of acetone-toluene-water on a solute-free basis. Based on weight and arithmetic mean value of the lower and upper nominal sieve size of each sieve fraction: 0-125, 125-280, 280-450, 450-630, 630-800, and 800-1000 µm. c Coefficient of variation calculated from the mean size cumulative weight percentages (16, 50, and 86%).

b

mum agglomerate size usually decreases with increasing agitation.14,15 The choice of solvent can influence crystal nucleation, growth, and morphology by modifying the solution properties (such as density, viscosity, or component diffusivities) and the solute solubility, as well as the solvent-surface interactions. Useful reviews on solvent effects in crystallization processes can be found in works by Davey,16 Klug,17 and Lahav and Leiserowitz.18 However, essentially no work has focused on the influence of the solvent composition on agglomeration. Obviously most important parameters influencing crystal agglomeration are influenced by solvent composition and in a rather complex way. Furthermore, the characterization of an agglomerated product is quite complicated. It is reasonably straightforward to determine the size distribution. However, to increase the understanding, a more detailed characterization has to be performed. The introductory work in implementing microscopy particle characterization, using image analysis and the application of principal component analysis (PCA), as well as agglomerate strength testing, has already been described.19 The present paper focuses on the agglomeration behavior of paracetamol crystallized in pure and mixed solvents. The properties of the solid product are determined by sieving, image analysis, and strength measurements. The results of a number of experiments at a constant crystallization temperature (20 °C) with equal initial supersaturations and with similar magma densities (particle concentrations) allows us to discuss the possible effects of the solvent on agglomeration. Experimental Details Crystallization Experiments. Paracetamol was crystallized in four pure solvents, namely, acetone, 2-propanol, acetic acid, and ethylene glycol, and in five different mixtures within the one-phase region of the acetone-toluene-water system.20 See Table 1. The batch experiments were performed in a 1-L jacketed crystallizer with baffles and a three-blade propeller at equal initial supersaturations (C/C*)1.4), at a constant temperature (20 °C), and at an agitation rate of 350 rpm. Nucleation was induced by the addition of a few seeds. All experiments were continued until saturation equilibrium was reached. By selecting solvent compositions within a certain solubility range (100-200 g of

paracetamol per kilogram of solvent at 30 °C), the particle concentration (magma density) was confined to some extent. To accomplish nucleation in the ethylene glycol solution, additional seed crystals, an increased agitation rate (600 rpm), and a lower crystallization temperature (15 °C) were required. Details of the isothermal desupersaturation experiments were provided in an earlier publication.19 Methods of Characterization. The dry solid product from each crystallization experiment was sieved. From each experiment the particles of three sieve fractions, namely, 125-280, 280-450, and 450-630 µm, together representing 75% or more of the total weight of the product (see Table 1), were further examined. The particles were examined under a microscope to determine the degree of agglomeration. First, image analysis was used to measure image descriptors for the product particles. Then, principal component analysis (PCA) was applied to the measured image descriptors. Details of this method are described in an earlier publication.19 As the PCA results showed correlations with the degree of agglomeration, a calibration model was established to classify and quantify the degrees of agglomeration of particles produced at different solvent compositions. This evaluation of the image analysis data with a calibration model is new and is therefore described here. A calibration set consisting of 56 particles, single crystals, and crystal agglomerates was handpicked. The particles of the calibration set were allocated into four groups, as shown in Figure 1. According to visual observations, the four groups can be described as single crystals, agglomerates with 2-5 crystals, agglomerates with 5-10 crystals, and agglomerates with more than 10 crystals. Figure 2 shows the PCA score plot based on four image descriptors (perimeter ratio, fractal dimension, heterogeneity, and clumpiness) for the calibration set. Each score represents a characterization of one particle and thus considers four descriptor measurements from the particle image. In Figure 2, the PC1 axis is intentionally placed to cross the PC2 axis at -2.5. With this coordinate system and the positive PC1 axis defined as 0°, the particle scores are found at angles between 40° and 130°, moving counterclockwise. A comparison of the score plot in Figure 2 with the loading plot in Figure 3 reveals that the scores of highly agglomerated particles variate more in direction of descriptors describing the countour of the particle (perimeter ratio and fractal dimension) than in direction of descriptors describing variations in gray scale (heterogeneity and clumpiness). On the other hand, the opposite is observed for the single crystal scores. The calibration set was validated using a test set of 120 particles (produced in acetic acid, 280-450 µm) classified visually into the four groups of the calibration particle set. The PCA score plot in Figure 4 shows the validation result. The developed PCA method exhibits good reliability, classifying 105 out of 120 particles (88%) correctly into the four groups. It is clear from the score plot in Figure 2 that there is a gradual change from single crystals to agglomerates, with many small crystals in the agglomerate between 40° and 130°. Thus, the principal component (PC) ratio [PC1/(PC2 + 2.5)] of a particle score represents a measure of the number of crystals in the agglomerate. We define the number of crystals in a particle (agglomerate) as the C/A number. A C/A number equal to 1

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Figure 1. Calibration particle set (280-450 µm): (a) single crystals, (b) agglomerates with 2-5 crystals, (c) agglomerates with 5-10 crystals, (d) agglomerates with more than 10 crystals.

Figure 2. Score plot of the calibration particle set. Symbols: particles classified according to visual observations.

Figure 3. Loading plot of image descriptors for the calibration particle set. PC1 and PC2 correspond to 74 and 22% of the variance, respectively.

indicates that the particle is a single crystal, and a C/A number equal to or greater than 2 indicates an agglomerated particle. Particles from the calibration set were examined visually for their C/A numbers, which were then used to create a correlation between the C/A number and the PC ratio of the particle score (see Figure 5). Image descriptor data of particle samples from the crystallization experiments were then processed by PCA together with the corresponding data of the calibration particles in such a way that the loading plot was equal to that of the calibration set itself. Hence, for each particle, a PC ratio was obtained that had a fixed reference (independent of sieve fraction and solvent composition) in the calibration set. The correlation function obtained in Figure 5 was then used to translate the PC ratio of a particle score into an estimate of the

C/A number. The number of particles within different ranges of C/A values (0-2, 2-4, 4-6, etc., crystals per particle) was used to present the C/A number distribution for each sample. In addition, the strengths of the agglomerates were determined by compressing single agglomerates until first breakage. This method has been presented previously.19 Results Particle Size. The particle weight mean size and the corresponding coefficient of variation (CV) for particles crystallized in different solvent compositions are given in Table 1. The mean size ranges from 225 to 497 µm, and the coefficient of variation ranges from 36 to 64%. The particles crystallized in ethylene glycol differ from

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Figure 4. Score plot of a test set (120 particles). Symbols: particles classified according to visual observations.

Figure 5. Correlation between C/A number (visually observed) and PC ratio of the particle score.

the other particles by having a lower weight mean size and, in relative terms, a wider distribution. Also, the particles crystallized in acetic acid and 2-propanol are smaller than those crystallized in pure acetone and in the acetone-toluene-water mixtures. Particle Morphology. The characteristics of the particles vary not only between different solvent composition, but also between different sieve fractions for a particular solvent composition. This is shown in Figure 6 with particles from the 280-450- and 450630-µm sieve fractions crystallized in three different solvent compositions. Particles from the 125-280-µm sieve fraction visually appeared similar to those from the 280-450-µm sieve fraction. To begin, the crystals have different shapes depending on the solvent composition. In Figure 6, crystals produced in the acetone-toluene (70-30 wt %) mixture and in acetic acid have a rhombic shape, whereas crystals from the acetone-water (30-70 wt %) mixture are more rounded. Crystals formed in pure acetone, 2-propanol, and solvent mixtures with high contents of acetone have rhombic shapes, whereas ethylene glycol crystals are round in shape (not shown in Figure 6). Second, the solid product contains different amounts of single crystals and agglomerates, and the number and

size distributions of crystals within the agglomerates vary. For example, focusing on particles in the 280450-µm sieve fraction, the particles produced in the acetone-toluene mixture are agglomerates consisting of many small crystals. The solid products produced in the acetone-water mixture and in acetic acid, on the other hand, contain both single crystals and agglomerates consisting of small or relatively small crystals. Particles in the 450-630-µm sieve fraction crystallized in the acetone-toluene mixture and in the acetonewater mixture contain an increased number of single crystals when compared to the 280-450-µm sieve fraction. The agglomerates consist of either one large crystal with a few smaller crystals attached or two to three larger crystals in the agglomerate. Furthermore, hardly any single crystals are observed in the 450-630-µm sieve fraction when crystallized in acetic acid; instead, agglomerates with many small crystals are formed. Product particles from crystallization in pure acetone and in solvent mixtures with high contents of acetone look similar to the particles formed in the acetonetoluene mixture. Particles of the 450-630-µm sieve fraction produced in 2-propanol and ethylene glycol look similar to those produced in acetic acid (see Figure 6). The differences that can be observed visually are quantified in the C/A number distributions. The C/A number distribution for particles crystallized in different acetone-toluene-water mixtures are graphically illustrated in Figure 7. As can be seen, particles formed in the acetone-water (30-70 wt %) mixture are clearly less agglomerated, with a peak in the C/A number range of 2-4. All other products exhibit their peak C/A number value at 6-8. A significant fraction of toluene (about 30 wt %) in the solvent mixture seems to give somewhat less agglomerated particles, as indicated by a slightly broader distribution. Furthermore, a small amount of water (3 wt %) in the solvent mixture has no influence on the agglomeration behavior of the crystals. The agglomeration behavior of particles crystallized in four different pure solvents is quantified in the C/A number distributions of Figure 8. The particles crystallized in acetone exhibit the narrowest distribution with a peak at C/A numbers of 6-8. Agglomerates with a high number of crystals per agglomerate dominate the product. Products having broader distributions and agglomerates containing fewer crystals are obtained for 2-propanol, acetic acid, and ethylene glycol. Particles crystallized in ethylene glycol differ from particles produced in 2-propanol and acetic acid by an increased fraction of agglomerates with 6-8 crystals. The C/A number distributions are in good agreement with visual observations of particles characteristics from all four solvents. For example, the significant fraction of single crystals that can be observed in Figure 6 for acetic acid crystallization is supported by a large fraction of particles with C/A numbers of 0-2. Figure 9 shows the C/A number distributions for different sieve fractions of particles crystallized in acetone and 2-propanol. It illustrates the change from highly agglomerated to less agglomerated particles with increasing size of particles crystallized in acetone, whereas the opposite behavior is observed for particles produced in 2-propanol. Furthermore, Figure 9 confirms that particles from the 125-280 and 280-450-µm sieve fractions are very similar in appearance. It is important to note that the changes in visual characteristics with sieve fraction is of greater signifi-

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Figure 6. Images of particles crystallized in (a) acetone-toluene (70-30 wt %), (b) acetic acid, (c) acetone-water (30-70 wt %). Two sieve fractions (different magnifications): 280-450 µm (left) and 450-630 µm (right).

cance for products formed in pure acetone and solvent mixtures containing acetone, as the particles of the 450-630-µm sieve fraction amounts to more than 20% of the total product weight. In contrast, the 450-630µm sieve fraction amounts to 5% of the total product weight for ethylene glycol and 2-propanol and only 1% of the total mass for acetic acid (see Table 1). Agglomerate Strength. Table 2 gives for each solvent composition the mean agglomerate strength and the corresponding coefficient of variation (CV) for the three dominating sieve fractions (i.e., agglomerate sizes). In general, the variation in strength among the agglomerates in each sample is quite broad. This is illustrated in Figure 10 by the cumulative distributions of the agglomerate strength for six different solvent compositions. The required breakage force increases with particle size for agglomerates formed in pure acetone and in the acetone-toluene mixture. The strengths of agglomerates produced in the acetone-water mixture, in ethyl-

ene glycol, and in 2-propanol are independent of agglomerate size and exhibit larger variations. Agglomerates of the 280-450-µm sieve fraction formed in acetic acid reveal a significantly higher force value than agglomerates from the other sieve fractions in the same solvent. Agglomerates from pure acetone, the acetone-toluene mixture, and all acetone-toluene-water mixtures with a small amount of water (3 wt %) have higher strengths than agglomerates from ethylene glycol, 2-propanol, and the acetone-water mixture (30-70 wt %). For the 280-450-µm sieve fraction, acetic acid belongs to the former group, whereas it is closer to the latter group for the other sieve fractions. Discussion Experimental. The crystal agglomeration process is influenced by a number of parameters, e.g., supersaturation, particle concentration, particle size, particle

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Figure 7. C/A number distributions of particles crystallized in different acetone-toluene-water mixtures. Sieve fraction 280450 µm. Based on 300 particles from each solvent composition.

Figure 9. C/A number distribution for different sieve fractions of particles produced in (a) acetone and (b) 2-propanol. Based on 300 particles from each sieve fraction. Table 2. Agglomerate Strengtha

Figure 8. C/A number distributions of particles crystallized in pure solvents. Sieve fraction 280-450 µm. Based on 300 particles from each solvent.

shape and hydrodynamics. These parameters are influenced by the choice of the solvent composition. Furthermore, supersaturation and solvent composition influence nucleation and crystal face growth rates, and all this together makes it experimentally difficult to study solvent effects on crystal agglomeration separate from other effects on the solid product such as crystal shape and crystal size distribution. To overcome some of these difficulties, batch crystallization experiments were carried out at equal initial supersaturations and at a constant temperature. The particle concentration (magma density) was confined to some extent. The same impeller position and same agitation rate were used as far as possible, but hydrodynamics vary depending on solvent composition because of differences in physical properties. In addition, as agglomerates are complex solid structures, the crystallization products exhibit a complex variation in particle properties. In the present experimental results, the particle size distributions are fairly wide (see Table 1), and there is a significant spread in

125-280 µm

280-450 µm

450-630 µm

solvent compositionb

force (N)

CV (%)

force (N)

CV (%)

force (N)

CV (%)

atw 100-0-0 atw 70-30-0 atw 30-0-70 atw 68-29-3 atw 92-5-3 atw 97-0-3 ethylene glycol 2-propanol acetic acid

0.20 0.20

52 46

50 49 80

93 75 62

45 46 70 59 42 58 61 100 64

0.88 0.65 0.19

0.10 0.09 0.12

0.39 0.36 0.17 0.33 0.32 0.29 0.16 0.15 0.32

0.08 0.12 0.13

51 76 100

a Each mean value is based on measurements of 50 single agglomerates. b atw ) wt % of acetone-toluene-water on a solutefree basis.

the properties of the particles within a particular sieve fraction, as shown in Figure 6. The agglomerate shape is far from spherical, and there is, of course, a shape distribution. Furthermore, as shown in Figures 7-9, there is a C/A number distribution within a sieve fraction, which means that the number and size of crystals in the agglomerates vary. Hence, it is not surprising that there is a quite broad distribution in strength among the agglomerates belonging to the same sieve fraction, as shown in Figure 10. The agglomerate strength depends on the particle orientation during the measurement and the area of breakage, which, in turn, depends on the internal structure. The breakage force is very different depending on whether the first rupture is a complete particle fracture or just a small fragment loss. The agglomerate strength also depends on the crystal growth rate and growth time.

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Figure 10. Strength distributions of agglomerates formed in different solvent compositions, sieve fraction 280-450 µm. Based on 50 particles from each solvent composition.

The compression strength determined in the present work might not be a realistic reflection of the breakage strength of agglomerates exposed to hydrodynamic shear stress and collisions. Compression of a bed of agglomerates might provide a better representation of the agglomerate strength, as the contacts between the agglomerates then cause the failure to be in oblique shear.21 Adams et al.22 have shown, however, that the single agglomerate compression strength can be related to the average shear strength of single agglomerates obtained by experiments on a bed of agglomerates. However, a more detailed analysis of the measured strength is outside the scope of the present work, and hence, the values given will not be examined and compared with full detail but should rather be taken as rough estimates of a resistance to breakage. Evaluation of Results. In this first attempt to evaluate and explain our results, we do not consider the details of the distributions of different properties, but rather focus on the mean and median values for a particular sieve fraction and on the overall degree of agglomeration in the sample. From this perspective, the experimental results reveal that solvent mixtures with high contents of acetone favor crystal agglomeration, whereas crystallization in ethylene glycol, 2-propanol, acetic acid, or solvent mixtures with high contents of water lead to a product containing not only agglomerates, but also a significant fraction of single crystals. In Figure 11, the fraction of particles in a sample having a C/A number less than 3 is plotted versus the solvent polarity of the solvent composition. This fraction could be seen as a rough measure of the degree of agglomeration, where a low value corresponds to a high degree of agglomeration. The solvent polarity is given as the EN(T) value, which describes the interaction of a solvent molecule with the phenoxide group in the pyridinium N-phenoxide betaine dye as observed in the UV/vis spectrum.23 As can be seen in Figure 11, the fraction of particles having a low C/A number increases with solvent polarity, i.e., increasing polarity of the solvent seems to reduce crystal agglomeration. A similar correlation is obtained if the degree of agglomeration is described by the median C/A number of each sample. Somewhat similarly to the C/A number, the strength of agglomerates formed in the crystallization also depends on the solvent composition. As shown in Table

Figure 11. Degree of agglomeration (sieve fraction 280-450 µm). Solvent polarities from Richardt,23 Novaki and Seound,24 and Mancini et al.27 Symbols: functional group of solvent molecule of highest concentration in the solution. ATW ) wt % of acetonetoluene-water on a solute-free basis.

2 and Figure 10, the agglomerate strength essentially decreases with increasing polarity. The strength is significantly higher for agglomerates formed in solvent mixtures with high contents of acetone than for agglomerates formed in mixtures with high contents of water, as well as those formed in pure alcohols. The acetic acid results are more complex. Depending on the sieve fraction, agglomerates formed in acetic acid exhibit a low strength or a high strength (see Table 2), and the distribution for the sieve fraction shown in Figure 10 is comparatively wide. The “polarity” of a solvent is not a precisely defined property. The polarity parameter EN(T) is well-defined, but whether this parameter really captures the properties of the solvent that influence agglomeration is not clear. In general, the results can be interpreted as follows: A higher polarity of the solvent leads to a stronger interaction of solvent molecules with the polar faces of the crystals, and this reduces crystalline bridging. However, much of the polarity in the present systems relates to hydrogen bonding. It is thus attractive to explain the results by a mechanism where the solvent molecules that are able to form hydrogen bonds interact with the functional groups exposed at a crystal surface. This will either reduce the growth rate of particular faces or reduce the work of adhesion between different faces or both. A reduced crystal growth rate will reduce the rate at which crystalline bridges are established between crystals and hence reduce the agglomeration. A reduced work of adhesion leads to a reduced time for the establishment of crystalline bridges. A common feature of the solvent compositions in which agglomeration is moderate or low is that solvent molecules contain hydroxyl or carboxyl groups, i.e., exhibit both hydrogen-bond-donating and hydrogenbond-accepting capabilities. Agglomeration is more pronounced in solvent mixtures with high contents of acetone, and acetone exhibits only a hydrogen-bondaccepting capability. The paracetamol molecule consists of two nonpolar groups (a benzene ring that is somewhat electronegative and a methyl group that is somewhat electropositive) and two polar groups (a hydroxyl group and an amide group). The hydroxyl group is both hydrogen-bond donating and accepting. The hydrogen

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enced by the viscosity. At these conditions, the collision rate is proportional to the shear rate,7 γ˘ , which is estimated from

γ˘ )

xFµ

(1)

where F is the density. Hence, the collision rate increases with decreasing dynamic viscosity µ, which might promote the formation of agglomerates. In addition, a lower viscosity also leads to decreased hydrodynamic shear stress, τ

τ ) µγ˘

Figure 12. Degree of agglomeration (sieve fraction 280-450 µm). Viscosities from Baldauf.28 Symbols and abbreviations are the same as for Figure 11.

in the amide group is as positive as the hydrogen of the hydroxyl group, and the carbonyl oxygen is strongly electronegative;25 hence, the amide group also exhibits strong hydrogen-bond-donating and -accepting capabilities. The paracetamol crystal normally exposes several faces, with different molecular arrangements. Green and Meenan26 have shown that single crystals grown in pure water and in pure acetone exhibit very much the same dominant faces and that these faces are both hydrogen-bond accepting and donating. Assuming that these faces are the faces actually growing together in agglomerates, the experimental results can be explained. Ethylene glycol, 2-propanol, acetic acid, and water can form hydrogen bonds with both donating and accepting groups at a crystal surface and, hence, interact with paracetamol crystals more strongly than acetone, which can only bind to donating groups. As shown in Figures 7 and 8, the median C/A number is higher for particles produced in solvent mixtures having a high concentration of acetone. Because these distributions represent a dominant part of the products (see Table 1, particles of sieve fractions 125-280 and 280-450 µm look the same), this necessarily means that the individual crystals are smaller and that the crystal number concentration in the solution originally was probably higher in acetone-rich solvent mixtures. This is, of course, a result of the crystallization process, and as such, it is also an effect of the choice of the solvent composition. However, in general, the degree of agglomeration is expected to increase with decreasing crystal size and increasing crystal concentration, and hence, this can also contribute to explain why agglomeration is more pronounced in solvent mixtures with a high content of acetone. Another aspect is that different solvent compositions exhibit different viscosities. Figure 12 shows the fraction of the particles in a sample having a C/A number less than 3 versus solvent viscosity. As can be seen, the crystal agglomeration increases with decreasing solvent viscosity. In the present study, the agitation Reynolds number varies from 5200 to 36 800, and hence, the agitation is from near turbulent to fully turbulent. The corresponding Kolmogorov microscale (i.e., the smallest eddies) based on the average turbulent energy dissipation rate, , varies from 40 to 170 µm for the data in Figure 12. Particles smaller than the microscale eddies are engulfed and are exposed to laminar shear influ-

(2)

and hence, the opportunity for crystals to be able to cement together into agglomerates increases with decreasing viscosity. Consequently, for small particles, decreasing viscosity is expected to increase agglomeration. However, the particles shown in Figures 1 and 6 do not clearly suggest that the agglomerates are formed only when crystals are below the Kolmogorov microscale. Many of the agglomerates contain fairly large crystals, and often, the individual crystals can be rather clearly distinguished in the agglomerates, i.e., they are not overgrown. Hence, the formation of the agglomerates might very well also include larger crystals. However, so far, we are not aware of a theory supporting the possibility that the agglomeration of larger crystals increases with decreasing viscosity. Conclusions This work shows that the degree of agglomeration of the product and the strength of the agglomerates depend on the solvent composition. Agglomeration and formation of agglomerates with high strength is favored by a high concentration of acetone in acetone-toluenewater mixtures. Crystallization in ethylene glycol, 2-propanol, acetic acid, and in acetone-toluene-water mixtures having a high concentration of water produces a product containing a significant fraction of single crystals, and the agglomerates are much weaker than those produced in mixtures with high contents of acetone. In summary, the product is less agglomerated and the agglomerates are weaker when the crystallization is carried out in more polar solvents. A strong association of solvent molecules to crystal surfaces can reduce the face growth rate, as well as the work of adhesion between surfaces, and hence reduce the formation of crystalline bridges. In the systems studied, a high polarity coincides with hydrogen-bond-donating and -accepting capabilities. These solvents can interact strongly with paracetamol crystal surfaces that exhibit both donating and accepting sites. In less polar mixtures dominated by acetone, only donating sites on the surface can be occupied. Depending on the solvent composition, the viscosity also differs, which might contribute to the influence on the agglomeration. Acknowledgment The financial support of the Swedish Research Council for Engineering Science (TFR) is gratefully acknowledged. Literature Cited (1) David, R.; Marchal, P.; Klein, J.-P.; Villermaux, J. Crystallization and Precipitation Engineering-III. A Discrete Formulation of the Agglomeration Rate of Crystals in a Crystallization Process. Chem. Eng. Sci. 1991, 46(1), 205.

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Received for review February 12, 2003 Revised manuscript received October 6, 2003 Accepted October 15, 2003 IE0301280