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Particle Size Control in Batch Crystallization of Pyrazinamide at Different Scales Zai Qun YU, Alvin Yeoh, Pui Shan Chow, and Reginald B. H. Tan Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.6b00327 • Publication Date (Web): 17 Nov 2016 Downloaded from http://pubs.acs.org on November 20, 2016

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Organic Process Research & Development

Particle Size Control in Batch Crystallization of Pyrazinamide at Different Scales Zai-Qun YU*,†, Alvin YEOH†, Pui Shan CHOW†, Reginald B. H. TAN*,†,‡ †

Institute of Chemical& Engineering Sciences, A*STAR (Agency for Science, Technology and Research), 1 Pesek Road, Jurong Island, Singapore 627833 ‡

Department of Chemical & Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260

* Corresponding authors: [email protected], [email protected]

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Seed 500 mL Scaleup 1 Scaleup 2

6 4 2 0 1

10

100 Particle Size, µm

1000

PSD of seeds and crystal products on 500 mL scale and 10-L scale. The stirrer speed in 500 mL experiment was 450 rpm. It was 305 rpm in scale-up 1 and 200 rpm in scale-up 2.


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Abstract: A methodology to achieve consistent particle size in seeded batch crystallization at two 0.5 and 10 L scales was demonstrated using pyrazinamide as a model compound. An empirical process model was established from a Design of Experiment (DoE) study on 500 mL scale and subsequently verified by correlating D50 of crystals with stirrer speed, seed size and seed loading. Experiments on 10 L scale showed that the criterion of equivalent impeller tip speed produced larger D50 than that of equivalent input energy. The process model derived from 0.5 L scale was demonstrated to work well for process parameter adjustment to obtain target product size at 10 L scale. Some practical issues in crystallization development were discussed. Key Words: particle size control, scale-up, batch crystallization, empirical process model, operational variability, seeding



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1. Introduction Requirements on particle size in crystallization processes vary. The ease of filtration and drying may be the only consideration in some cases, wherein a relatively narrow population without excessive fines will suffice. In other cases, crystal products are intended for direct tableting together with excipients without further size reduction. Consequently, stringent specifications have to be imposed on particle size in order to achieve desired performance of tablets. A typical specification is expressed as a range for D50 of the product crystals. It is still a challenge to achieve consistent size control across different scales. The prior knowledge and understanding in crystallization accumulated over the years

1, 2

, when

combined with design of experiment (DoE), afford efficient identification of critical process parameters and determination of design space for tight particle size control in some processes on bench scale.

3, 4

Scale-up effects may be one of the biggest obstacles to achieving consistent

particle size control in crystallization. It is not a surprise that the optimal conditions determined on bench scale, when translated to larger vessels according to some criteria, fail to produce crystals in the target particle size range. A frequently cited cause for scale-up effects is inherent irreproducibility of mixing conditions across scales. Mixing influences many aspects of crystallization, from suspension and carrying of crystals across the crystallizer, through heat transfer between crystallizer wall and bulk solution, mass transfer between bulk solution and growing crystal surfaces, to the frequency and impact of collisions between crystals and impeller, as well as collisions among crystals themselves. Mixing becomes more heterogeneous with increasing volume of stirred tanks. Turbulence measured by kinetic energy dissipation rate decays rapidly with distance from impeller tips. In a large stirred tank, the energy dissipation rate near free surface is only a hundredth of that near 4 ACS Paragon Plus Environment

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impeller tips.

5

Blend time can increase from a few seconds in a 1-Liter crystallizer to tens of

seconds in a 50-Liter vessel with geometrical similarity at the same tip speed. The slow mass transfer may lead to local high supersaturation levels in some regions. Moreover, the ratio of heat transfer area to liquid volume decreases with increasing scale. Consequently, temperature difference between tank wall and bulk solution grows larger in a bigger crystallizer if the same cooling rate is to be achieved as in a smaller one. Undesired nucleation or encrustation may take place on the sub-cooled wall as a result. In addition, discrepancy in hydrodynamics between scales may lead to rate changes in secondary nucleation, breakage and agglomeration. These scale-up effects on particle size in a crystallization process under development are difficult to predict reliably by process modelling for the time being, even though progresses have been made in computational fluid dynamics (CFD) and discrete element method (DEM) for two-phase flows. 6-9 Parameter estimation in crystallization kinetics, breakage and agglomeration kernels is a challenging task necessary for rigorous process modelling. Most process modelling studies deal with slurries of spherical particles and their interactions with liquid phase are relatively well understood. However, platy and elongated particles are often encountered in pharmaceutical industries whose behaviour in liquid phases is much more complicated and yet to be modelled. Although DEM can provide dynamic details of every particle, its demand on computational resource limits the number of particles that can be simulated in practice. Additionally, in a commercial context, scale-up is often carried out in a given stirred tank with fixed geometry and heating/cooling characteristics. Geometrical similarity with bench scale vessels is often lacking. Stirrer speed may be the only process parameter that can be adjusted. Having all these undetermined scale-up effects and constraints mentioned above, crystallization practitioners are



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often haunted by a question, i.e., how to proceed with bench-scale studies and take advantage of their results in order to reproduce target particle size consistently upon scale-up? The magnitude of mixing effects on crystallization is contingent on the characteristics of crystallizing systems themselves such as solubility dependence on solvent composition or temperature, nucleation kinetics, mechanical properties of crystals, etc. In general, crystallizing system with steeper solubility curve and faster nucleation rate is more sensitive to unevenness in temperature and solution composition. A narrower meta-stable zone width (MSZW) tends to produce more small particles near sub-cooled surfaces or entry point of anti-solvent. Brittle crystals are more susceptible to breakage than hard ones.

Fortunately, many crystallizing

systems that proceed to scale-up stage have characteristics amenable to size control, such as relatively mild solubility dependence, moderate nucleation rate and MSZW, etc. As a matter of fact, these characteristics are predicated on crystallization solvent to a large extent. The solvent or solvent mixture that promotes crystal growth is intentionally chosen during solvent screen. Therefore, mixing effects or scale-up effects are manageable or limited for many compounds,10 and tight particle size control across scales is viable. For these crystallizing systems, it is believed that the relationship between particle size and process parameters obtained from bench-scale study still holds on larger scale, at least qualitatively. Then the relationship can be employed to adjust the settings of some process parameters in scale-up experiments when the first trials miss the target. Adjustment of process parameters may also be necessitated when some of them cannot reach their original set points due to process constraints during manufacture, and the relationship will serve as a guide for adjustment. For example, seeds are prepared in batches and seed size often varies from batch to


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batch. Different seed size will lead to different product size if other process parameters are not adjusted accordingly. The abovementioned approach will be demonstrated by pyrazinamide crystallization in methanol/1,4-dioxane mixture. In the first place, a DoE study was conducted in a 500-mL crystallizer to establish the functional relationship (empirical process model) between particle size (D50) and process parameters. The process model was then verified on the same scale under a few different conditions from the DoE study, and D50 of all verification batches fell in the prediction intervals for new observations. After that, four batches on 10 L scale were carried out. Practical issues and considerations encountered in tight particle size control were discussed. In a previous study,11 the design space for crystallization of δ-form pyrazinamide was determined and verified on bench scale. δ-form is meta-stable and it will transform to α-form given sufficient time. The design space for δ-form is described as follows: (1) Solvent: 1,4-dioxane/methanol mixture with a mass ratio of 1:4 (2) Cooling rate: ≥0.3 °C/min (3) Seed loading: ≥1.0 wt%, δ-form, milled to 65-75 µm by sonication. (4) Stirrer speed 450 rpm (marine propeller with a diameter of 42 mm). This study is an extension of the previous study with particle size chosen as the second critical quality attribute. The effects of three process parameters on particle size will be investigated within the design space for δ-form crystallization, including stirrer speed, seed size and seed loading. Other process parameters were fixed at a certain value in the design space. Cooling rate was set at 0.3 °C/min for all experiments because higher cooling rates may not be achievable at larger scales, or they may lead to large temperature difference across crystallizer wall that



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promotes encrustation. δ-form seeds milled by sonication was used. The two critical quality attributes should be maintained across all scales.

2 Experimental 2.1 Chemicals Pyrazinamide was manufactured by Hangzhou Dayang Chem, methanol (HPLC grade) by J. T. Baker, and 1,4-dioxane (ACS, ISO) by Merck Millipore. 2.2 Geometry of crystallizers of different scales The shape of crystallizers on different scales is shown in Figure 1. The dimensions of crystallizer and internal fittings are listed in Table 1.

Figure 1 Schematic diagrams crystallizers. The volume of crystallizer is 500 mL and 10 L from left to right. The dimension is not exactly to scale. Table 1 Geometry of crystallizers 500 mL 10 L Construction material glass glass Vessel diameter, mm 100 240 Impeller diameter, mm 42 96 Impeller type Marine Marine propeller propeller Baffle shape and width, mm Round, 8 Round, 10 Number of baffles 4 4 Actual solvent mass, g 350 7700 Actual solvent volume, L 0.42 9.24 Actual Level, mm 64 235 8 ACS Paragon Plus Environment

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Liquid in jacket side

De-ionized water

Julabo bath fluid HL80

There is no strict geometrical similarity between these two crystallizers. The 500 mL crystallizer has a flat bottom with an inner diameter of 100 mm. It is equipped with a marine-type propeller for agitation with a diameter of 42 mm. There are four built-in glass baffles with a width of 8 mm. The 10L crystallizer has a round bottom with an inner diameter of 240 mm. A marine-type propeller is fitted to it with a diameter of 96 mm. Four built-in baffles are each 10 mm wide. The impellers were positioned at around one third of the liquid level from the bottom in their respective crystallizers. Focused beam reflectance measurement (FBRM, Mettler-Toledo, Model G400) was used to monitor the process. 2.3 Procedures Pyrazinamide powder was added to the crystallizer, followed by a proportionate amount of methanol/1,4-dioxane mixture (mass ratio of 4:1). The mixture was heated to 48.0 °C for dissolution under agitation. After all solids have dissolved, the solution was cooled to the saturation temperature of 45.0 °C. Then a cooling ramp of 0.3 °C/min was applied until the final temperature of 5.0 °C. Seeds were applied between 42.0 and 43.0 °C. At the end of each batch, slurry samples were taken, filtered and air dried in fumehood. Dried samples were sent for powder X-ray diffraction analysis (Bruker AXS GmbH, Germany), with Cu Kα radiation (λ=1.54056 Å). The voltage and current applied were 35 kV and 40 mA, respectively. The samples were scanned within a range of 2 to 50° 2θ at a scan rate of 2.0 °/min. Particle size measurement was conducted using Malvern MasterSizer (2000). Hydro2000 wet sampling unit was used. 2.4 Seeds preparation



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Dry seeds were used in this study. A sonicator (Sonics & Materials Inc., Model: VCX750) was used to reduce seed size by wet milling. Ethanol was chosen as the medium for wet milling because pyrazinamide has a very low solubility in it. Suspension of δ-form crystals was contained in a jacketed vessel with a sonication probe in it. The suspension was agitated by a magnetic stirrer and the jacket temperature was controlled at 5.0 °C. Excessive heat can be produced during sonication, so cooling was necessary. The final size of seeds was affected by power output, sonication time, the size and position of probe in the suspension of σ-form crystals. In this study, sonication time was used to manipulate final size while other parameters were kept constant. The resulting slurries were filtered and the filter cake was transferred from the filtration funnel to an evaporation dish for drying in a fume hood at room temperature. The filter cake must be stirred loose before drying to avoid agglomeration. In Figure 2 are seeds in different states, i.e., slurry (Figure 2a), dried without stirring of filter cake beforehand (Figure 2b) and dried after stirring of filter cake (Figure 2c). It can be seen that seeds are dispersed well in slurry after sonication. Serious agglomeration was observed in the dried seeds without stirring beforehand and the resulting seeds were not usable.

(a)

(a)

(b) 10 ACS Paragon Plus Environment

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(c) Figure 2 Morphology of milled seeds by ultrasound. (a) seeds slurry after sonication (b) agglomerated dried seeds (c) well-dispersed dried seeds (76.3 µm). The scale bar represents 200 µm

3 Results and Discussion 3.1 DoE study and regression analysis Stirrer speed, seed loading and seed size were three process parameters to be investigated. Facecentred Central Composite Design (CCD) was employed with three replicates at the center. However, there was difficulty in obtaining seeds of the exact sizes that fit CCD. Three batches of seeds were prepared with a size of 65.5, 71.1 and 83.7 µm, respectively. It can be seen that the intermediate size was not exactly the average of the remaining two sizes. Consequently, the DoE was not orthogonal anymore. It will be shown that this deviation from orthogonality does not affect the regression analysis significantly except for changes in covariance matrix of estimated coefficients and prediction intervals. Experimental conditions and results for DoE study on 500mL scale are shown in Table 2. Table 2 experimental conditions and results of DoE study on 500-mL scale No. N, Ls, Cs , Experimental Fitted Residual, rpm µm wt% D50, µm D50, µm µm 1 300 65.5 2 408.1 420.2 -12.1 2 300 65.5 4 315.9 321.9 -6.0 3 300 83.7 2 456.1 447.1 9.0 11 ACS Paragon Plus Environment


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No.

N, Ls, Cs , Experimental rpm µm wt% D50, µm 4 300 83.7 4 411.1 5 600* 65.5 2 NA 6 600 65.5 4 255.9 7 600 83.7 2 371.4 8 600 83.7 4 338.3 9 600 71.1 3 308.8 10 300 71.1 3 403.1 11 450 83.7 3 379.4 12 450 65.5 3 317.1 13 450 71.1 4 320.7 14 450 76.3 3 374.3 15 450 71.1 3 363.8 16 450 71.1 3 344.4 17 450 71.1 3 360.8 N: stirrer speed; Ls: seed size; Cs: seed loading *α-Form crystals were harvested from Batch 5

Fitted D50, µm 409.2 NA 251.0 376.1 338.2 317.7 388.6 392.7 335.6 313.3 369.4 353.1 353.1 353.1

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Residual, µm 1.9 NA 4.9 -4.7 0.1 -8.9 14.5 -13.3 -18.5 7.4 4.9 10.7 -8.7 7.7

17 batches were conducted in total. In all experiments except for Batch 5, δ-form crystals were obtained with no detectible amount of ɑ-form. Batch 5 produced α-form crystals. Batch 5 was not included in regression analysis. Stepwise regression was performed on the data in Table 2 using Matlab based on the following empirical process model: ‫ܦ‬ହ଴ = ߚ଴ + ߚଵ ܰ + ߚଶ ‫ܮ‬௦ + ߚଷ ‫ܥ‬௦ + ߚସ ‫ܮ‬௦ ‫ܥ‬௦

(1)

Estimates of regression coefficients and their statistics are given in Table 3. Table 3 Stepwise regression results Estimate SE t-Statistics P-value β0 581.42

125.6

4.629

7.295E-04

β1 -0.2603

0.02750 -9.466

1.276E-06

β2 -0.2734

1.663

-0.1644

0.8724

β3 -119.4

39.09

-3.055

0.01096

β4 1.229

0.5122

2.399

0.03532

SE: standard error. The R2 and R2 adjusted of regression is 0.956 and 0.941 respectively.


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The normal probability plot of residuals is displayed in Figure 3. It can be seen that the assumption of normal distribution of errors is not violated. The empirical model provides an unbiased estimate of conditional response mean. 100 Normal %Probability

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10

1 -20

-10

0 10 Residual of D50, µm Figure 3 Normal probability plot of residuals

20

3.2 Effects of process parameters It has been known that particle size distribution (PSD) of crystals is defined by a series of events including nucleation, crystal growth, breakage and agglomeration. Nucleation and breakage generate small particles, while growth and agglomeration shift PSD towards bigger size. Various process parameters can affect one or more of these events and consequently modify PSD. The empirical model reveals that higher stirrer speed produces smaller D50 (negative regression coefficient of the stirrer speed term, β1).

This is because crystal breakage and secondary

nucleation is enhanced under intensified agitation, leading to smaller particle size. The model also shows that D50 goes down with more seeds. Seeds provide surfaces for solute molecules to deposit on. Each seed gets less solute to deposit on with a higher seed loading, resulting in a smaller average product size. 12



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Table 3 shows that there is a significant interaction between Ls and Cs as reflected by the value of coefficient, β4. This is expected because the total surface area of seeds rises with increasing seed loading and decreasing seed size. At the same time, the P-value for β2 is very large (0.872), indicating that the effects of Ls can be mostly explained by the interaction term β4, and β2 can be removed from the empirical model. 3.3 Risk of polymorphic transformation α-Form was collected at the end of Batch 5 of DoE study. Batch 5 was repeated twice and αForm was collected in both repeat runs as well. Thus it was confirmed that polymorphic transformation did not occur randomly. The seeds used in Batch 5 were from the same source as in batches 1, 2, 6 and 12. The operating conditions of Batch 5 seemed to be conducive for conversion. There are two possible factors favouring polymorphic transformation that took place in this batch, i.e., high stirrer speed and small seed size. Polymorphic transformation involves two steps, i.e., dissolution of meta-stable form and growth of the stable form.

13

Either step can

be the limiting step, contingent on the properties and operating conditions of system. The dissolution step can be boosted by higher stirrer speed and smaller seed size. The polymorphic transformation can be detected by FBRM. In Figure 4, the curve of total counts versus time in Batches 5, 13 and 17 are given. The curve in Batch 5 underwent a sharp increase, while those in batches 13 and 17 climbed almost linearly with time. As the inset crystal images in Figure 4 shows, α-Form crystals have a thin needle shape, while δ-form crystals are platy. Needle-like crystals generate more counts of chord lengths. Trending of FBRM can be used as a warning sign of undesired conversion. Remedial measure can be taken once this kind of fingerprint appears.


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16000 14000 Total Counts, #/mea

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12000

DoE 5 DoE 13 DoE 17

10000 8000 6000 4000 2000 0 0:00:00

0:30:00

1:00:00

1:30:00

2:00:00

2:30:00

Time, hh:mm:ss Figure 4 Total counts of chord lengths in three batches. δ-form was obtained in Batches 13 and 17, while α-form was harvested in Batch 5. 3.4 Model verification and application Verification was conducted at three sets of conditions as shown in Table 4. Stirrer speed was set at 450 rpm in all verification runs. In the first set of conditions, seed size was 76.3 µm and seed loading, 2.0 wt%. In the second set of conditions, seed size was changed to 90.5 µm and seed loading to 3.5 wt%. In the last set, 90.5-µm seeds were used at a seed loading of 4.0 wt%. Predicted D50 by the empirical model and the prediction intervals at 95% confidence level for the three runs are listed on the right side of the table. Lb is the lower bound of prediction interval (PI) and Ub the upper bound. Data in Table 4 shows that D50 of all verification runs falls in their respective prediction intervals and the empirical model was thus verified. Table 4 Results of verification experiments and prediction intervals N, Ls, Cs, D50, Predicted D50, µm rpm µm wt% µm Lb Mean Ub 1 450 76.3 2.0 389.0 363.3 392.1 420.8 2 450 90.5 3.5 381.4 378.2 410.8 443.4 3 450 90.5 4.0 378.8 370.3 406.7 443.0 N: stirrer speed; Ls: seed size; Cs: seed loading. 15 ACS Paragon Plus Environment


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Lb is the lower bound of prediction intervals and Ub the upper bound. The empirical model obtained in DoE study can be used to determine the settings of process parameters for a target mean product size (D50). Scale-up experiments will then be conducted around these settings according to some criterion. If the first scale-up experiment deviates from the target, the empirical model can be employed to adjust the settings of process parameters to get close to it. It is true that the empirical model may not hold on larger scale as accurately as on bench scale. However, the qualitative relationship between quality attributes and process parameters reflected in it still applies, pointing to the direction of parameter adjustment. 3.3 Variations in D50 stemming from operational variability The prediction intervals in Table 4 indicate the ranges wherein actual D50 may appear at the specified conditions. They are related to the residuals that cannot be explained by the empirical model. The prediction intervals in Table 4 do not include the uncertainty stemming from operational variability. On bench scale, process parameters differ from their set points marginally. The difference will become more pronounced with increasing scale. For example, D50 of seeds may change from batch to batch due to uncontrollable parameters in preparation. Some seeds may fall on the surfaces of internal fittings instead of the slurry, resulting in an actual seed loading lower than the target value. Propagation of operational variability in seed size and seed loading to D50 of crystal products can be derived analytically for the linear empirical model as follows: ݀(‫ܦ‬ହ଴ ) = (ߚଶ + ߚସ ‫ܥ‬௦ )݀(‫ܮ‬௦ ) + (ߚଷ + ߚସ ‫ܮ‬௦ )݀(‫ܥ‬௦ )

(2)

Where d(X) stands for changes in X. Equation (2) can be visualized as in Figure 5.


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Figure 5 Changes in D50 due to operational variability in seed size (Ls) and seed loading (Cs) Suppose actual seed size deviates 5 µm from its set point (76.3 µm) and seed loading does 0.05 wt% from its set point (2.0 wt%). Then D50 will stray 9.6 µm away from its target. This will add to the prediction intervals in Table 4. It follows that the actual size range of crystals is determined by uncertainty in empirical process models (prediction interval) and operational variability. It is not a surprise that the size range specified by formulation studies may not be achievable based on existing process models. As a result, either more experiments may be needed to cover extra process parameters to improve the process model, or the target size range has to be revised to accommodate fluctuations. Thus specification of particle size is often an iterative process involving both crystallization development and formulation studies.



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3.4 Scale-up to 10 L crystallizer Scale-up rule for stirrer speed. A critical parameter to be determined for scale-up is stirrer speed. There are different scale-up rules catering for different mixing requirements.

14

Two scale-up

rules for stirrer speed are in wide use for crystallization. One is equivalent input energy per unit volume, and the other equivalent tip speed of impeller. These two rules will be compared. Energy input to suspensions in this project is calculated using the following empirical equation 5: ܲ = ܲ଴ ߩܰ ଷ ‫ܦ‬ହ

(3)

where P0 stands for power number of impeller, and it takes the value of 0.34 for both scales. ρ is the density of solvent (kg/m3), and N the stirrer speed (rps) and D the impeller diameter (m). Presence of solids and lack of strict geometrical similarity is not taken into account in Equation (3). A more rigorous method to calculate the stirrer speed for equivalent energy input is by computational fluid dynamics, but that is beyond the scope of this work. The stirrer speed of 450 rpm in the 500 ml crystallizer corresponds to 305 rpm in the 10 L crystallizer when the criterion of equivalent energy input is applied. With equivalent tip speed, it corresponds to 196 rpm (rounded to 200 rpm) in the 10 L crystallizer. The first two sets of verification conditions in Table 4 were chosen to scale up. Four batches were conducted in the 10-L crystallizer as shown in Table 5. Table 5 Scale-up experiments in 10 L crystallizer No. 1 2 3 4

N, rpm 305 200 200 200

Ls, µm 90.5 90.5 76.3 82.8

Cs, Actual wt% D50, µm 3.5 377.2 3.5 400.8 2.0 406.4 2.81 393.7

Predicted D50, µm Lb Mean Ub 378.2

410.8 443.4

363.3 363.6

392.1 420.8 392.0 420.4


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In the first two batches seed size and seed loading were kept the same, i.e., 90.5 µm and 3.5 wt%, respectively. Stirrer speed was set at 305 rpm in Batch 1 (equivalent kinetic energy input). The resulting D50 was 377.2 µm, which was lower than the predicted mean value and falling out of the lower limit of prediction interval. In Batch 2, the stirrer speed was set at 200 rpm (equivalent tip speed) while seed size and seed loading were kept constant. D50 of crystal products increased to 400.8 µm, which is in the prediction interval and closer to the predicted mean value compared with Batch 1. It can be seen that D50 decreases with increasing stirrer speed, in line with the relationship established on bench scale. 8 Seed

7 Volume percentage, %

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500 mL 6

Scaleup 1

5

Scaleup 2

4 3 2 1 0 1

10


1000

Figure 6 PSD of seeds and crystal products on 500 mL scale and 10-L scale. The stirrer speed in 500 mL experiment was 450 rpm. It was 305 rpm in scale-up 1 and 200 rpm in scale-up 2. A close look at the PSD of crystals displayed in Figure 6 reveals that the general shape of PSD curve was maintained across 500 mL and 10 L scales with the criterion of equivalent kinetic energy input. There is a shoulder in the left half of PSD curve (around 100 µm) from 500 mL scale experiment and it was retained in Scale-up 1 (equivalent kinetic energy input). The shoulder disappeared in the PSD of crystals from Scale-up 2 (equivalent tip speed), indicating that there were few small particles. The decision on stirrer speed in scale-up is contingent on the 19 ACS Paragon Plus Environment


requirements of particle size control. When the shape of PSD is more important than a single statistics of PSD such as D50, the criterion of equivalent kinetic energy input will be a better choice. Adjustment of process parameter for target product size. In the Batch 3, the seed size and seed loading were changed to 76.3 µm and 2.0 wt%, and the stirrer speed was 200 rpm (equivalent tip speed). The resulting D50 was close to that from 500 mL scale experiment and fell within the prediction interval. Likewise, the shoulder in the left half of PSD curve disappeared in scale-up experiment (Figure 7). 8 Seed

7 Volume percentage, %

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Figure 7 PSD of seeds and crystal products on 500 mL and 10-L scale experiments respectively with equivalent tip speed of impeller. Now the target D50 for Batch 4 was set at 392.0 µm, the same as in last batch, but the seeds of 76.3 µm have run out. A new lot of seeds was prepared with a size of 82.8 µm. Based on the empirical model, the seed loading must be adjusted to 2.81 wt% as shown in Table 5. The actual D50 of crystals from this batch was 393.7 µm, very close to the target value. The PSD of seeds and product for Batch 4 is not shown here.


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Optimal settings of process parameters. According to the empirical process model, many combinations of stirrer speed and seed loading can meet the target of 392.0 µm when seed size is fixed at 82.8 µm. The combination that has the narrowest prediction interval should be chosen for scale-up. The prediction intervals of various combinations on 500 mL scale were calculated and are displayed in Figure 8. The stirrer speed of 450 rpm on 500 mL scale corresponds to the stirrer speed of 200 rpm on 10 L scale. It be seen that the process parameters in the 4th batch had almost the tightest prediction intervals. The width of prediction intervals should be considered in process parameter setting.

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(b) Figure 8 Prediction interval at (a) different stirrer speed, and (b) different seed loading for a target D50 of 392.0 µm when seed size is fixed at 82.8 µm. The red line stands for the target D50 of 392.0 µm. The blue lines are the upper and lower boundaries of the interval at 95% confidence level. 450 rpm on 500 mL scale is equivalent to 200 rpm on 10 L scale. 3.5 Thermal response of 500 mL and 10 L crystallizers Thermal responses of the 500 mL crystallizer and the 10 L crystallizer were recorded during experiment as shown in Figure 9. Suspension temperature was the controlled variable and the cooling rate was set at 0.3 °C/min.


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50 Setpoint Circulator Temperature Suspension Temperature

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Figure 9 Thermal response curve of (a) 500 mL and (b) 10 L crystallizer. Cooling rate was 0.3 °C/min for both crystallizers. There are three curves in Figure 9, i.e., set point of suspension temperature, actual suspension temperature and circulator temperature (close to jacket temperature). The actual suspension temperature was measured in the middle between stirrer shaft and crystallizer wall. Figure 9(a) reveals that suspension temperature could track its set point very intimately in the 500 mL



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crystallizer (less than 0.5 ºC difference). The difference between suspension and circulator temperature (related with temperature difference across the crystallizer wall) was less than 1.0 °C. Figure 9(b) shows that while a cooling rate of 0.3 °C/min could be achieved in the 10 L crystallizer. The actual suspension temperature deviated from its set point by less than 1.5 ºC and the difference between suspension and circulator temperature was around 8.0 °C. Fortunately, the sub-cooled crystal wall did not cause any problem for polymorph and particle size control.

4 Conclusion Particle size control in batch crystallization of pyrazinamide has been studied at two different scales. An empirical process model was derived from a DoE study on 500 mL scale that correlated D50 with stirrer speed, seed size and seed loading, enabling determination of process parameters for target product size. Deviations of product D50 from predicted size due to model uncertainty and operational variability on larger scale were calculated. We anticipate that the target particle size range should accommodate the possible fluctuations at commercial scale. Optimal process parameters should be chosen to narrow the prediction intervals, which help bring the actual particle size to the target size. The process was scaled up to 10-L scale and the criterion of equivalent tip speed of impeller produced crystals with D50 that agreed with the empirical process model, while the criterion of equivalent input energy per unit volume produced smaller D50. Furthermore, the empirical model enabled ad hoc adjustment of process parameters at 10 L scale to achieve target particle size, allowing operational flexibility within the design space. In addition, consistent particle size and solid from could be achieved at different scales. Our study outlines a feasible experimental and modelling strategy to ensure consistent crystal product quality from laboratory process development to mini-production scale. A broadly similar approach could be useful for emerging technologies such as continuous crystallization15-17. 24 ACS Paragon Plus Environment

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References (1) Mullin, J. W., Crystallization. 4 ed.; Butterworth-Heinemann: Oxford, 2001. (2) Myerson, A. S., Handbook of Industrial Crystallization. 2 ed.; Butterworth-Heinemann New York, 2002. (3) Yu, Z. Q.; Chow, P. S.; Tan, R. B. H., Design Space for Polymorphic Co-crystallization: Incorporating Process Model Uncertainty and Operational Variability. Crystal Growth & Design 2014, 14 (8), 3949-3957. (4) Yu, Z. Q.; Chow, P. S.; Tan, R. B. H.; Ang, W. H., PAT-Enabled Determination of Design Space for Seeded Cooling Crystallization. Organic Process Research & Development 2013, 17 (3), 549-556. (5) Paul, E. L.; Atiemo-Obeng, V.; Kresta, S. M., Handbook of Industrial Mixing: Science and Practice. John Wiley & Sons: New York, 2003. (6) Ali, B. A.; Boerner, M.; Peglow, M.; Janiga, G.; Seidel-Morgenstern, A.; Thevenin, D., Coupled Computational Fluid Dynamics-Discrete Element Method Simulations of a Pilot-Scale Batch Crystallizer. Crystal Growth & Design 2015, 15 (1), 145-155. (7) Rane, C. V.; Ekambara, K.; Joshi, J. B.; Ramkrishna, D., Effect of Impeller Design and Power Consumption on Crystal Size Distribution. Aiche Journal 2014, 60 (10), 3596-3613. (8) Derksen, J. J., Long-time solids suspension simulations by means of a large-eddy approach. Chemical Engineering Research & Design 2006, 84 (A1), 38-46. (9) Nikolic, D. D.; Frawley, P. J., Application of the Lagrangian meshfree approach to modelling of batch crystallisation: Part I-Modelling of stirred tank hydrodynamics. Chemical Engineering Science 2016, 145, 317-328. (10) Fakatselis, T. E., Residence time optimization in continuous crystallizers. Crystal Growth & Design 2002, 2 (5), 375-379. (11) Hermanto, M. W.; Yeoh, A.; Soh, B.; Chow, P. S.; Tan, R. B. H., Robust Crystallization Process Development for the Metastable delta-form of Pyrazinamide. Organic Process Research & Development 2015, 19 (12), 1987-1996. (12) Kubota, N.; Doki, N.; Yokota, M.; Jagadesh, D., Seeding effect on product crystal size in batch crystallization. J. Chem. Eng. Jpn. 2002, 35 (11), 1063-1071. (13) Beckmann, W., Seeding the desired polymorph: Background, possibilities, limitations, and case studies. Organic Process Research & Development 2000, 4 (5), 372-383. (14) Zauner, R.; Jones, A. G., Scale-up of continuous and semibatch precipitation processes. Industrial & Engineering Chemistry Research 2000, 39 (7), 2392-2403. (15) Eder, R. J. P.; Schrank, S.; Besenhard, M. O.; Roblegg, E.; Gruber-Woelfler, H.; Khinast, J. G., Continuous Sonocrystallization of Acetylsalicylic Acid (ASA): Control of Crystal Size. Crystal Growth & Design 2012, 12 (10), 4733-4738. (16) Jiang, M.; Zhu, Z.; Jimenez, E.; Papageorgiou, C. D.; Waetzig, J.; Hardy, A.; Langston, M.; Braatz, R. D., Continuous-Flow Tubular Crystallization in Slugs Spontaneously Induced by Hydrodynamics. Crystal Growth & Design 2014, 14 (2), 851-860. (17) Wong, S. Y.; Tatusko, A. P.; Trout, B. L.; Myerson, A. S., Development of Continuous Crystallization Processes Using a Single-Stage Mixed-Suspension, Mixed-Product Removal Crystallizer with Recycle. Crystal Growth & Design 2012, 12 (11), 5701-5707.


Particle Size Control in Batch Crystallization of Pyrazinamide on

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