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Operating Strategy to Produce Consistent CSD in Combined Antisolvent-Cooling Crystallization Using FBRM Martin Wijaya Hermanto,*,† Pui Shan Chow,† and Reginald B. H. Tan*,†,‡ †

Institute of Chemical and Engineering Sciences, 1 Pesek Road, Jurong Island, Singapore 627833 Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260

‡

ABSTRACT: In this study, an operating strategy which uses focused beam reflectance measurement (FBRM) to achieve consistent product quality in a combined antisolvent-cooling crystallization process is developed. The proposed operating strategy involves rapid generation of seed crystals by antisolvent addition, which is followed by a seed conditioning process through gentle heating. Subsequently, the conditioned seed crystals are grown by combined antisolvent addition and cooling. FBRM statistics were used as indications for switching from one operating region to the subsequent one. The operating strategy is demonstrated for the crystallization of paracetamol in acetone−water solution and glycine in water−ethanol solution. The crystal size distribution (CSD) produced by this operating strategy is significantly more consistent than that produced by the unseeded antisolvent crystallization and slightly more consistent than that produced by seeded antisolvent crystallization. The proposed operating strategy also reduces agglomerations in product crystals substantially and is robust in the presence of initial concentration disturbances. Furthermore, a new implementation of a distance measure is proposed for quantitative assessment of product CSD consistency.

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INTRODUCTION Crystallization is among the oldest unit operation in the chemical industry. It has played important roles as a separation or purification technique in the manufacturing of high valueadded specialty chemicals and pharmaceutical active ingredients.1 The quality of crystallization products is generally determined by the crystals shape, habit, and polymorph, and the crystal size distribution (CSD), particularly the average size and the width of the size distribution. Many factors including but not limited to nucleation rate, growth rate, agglomeration, breakage, vessel size, and presence of impurities influence the product of crystallization process.1−4 As a result, producing consistent product CSD from batch-tobatch is challenging. Inconsistent CSD often leads to reduced efficiency in downstream processing (such as filtration and drying), which translates into the decrease in productivity and a loss of profit.5 In addition, the product effectiveness (e.g., bioavailability, tablet stability) may also be adversely affected. Considerable time and effort have been invested in the development of crystallization processes to obtain uniform and consistent product CSD. Crystallization by seeding has played a major role in producing consistent product CSD by suppressing nucleation and allowing more surface area for crystal growth.6−13 To obtain consistent product CSD using seeding technique, consistent quality, amount, and size distribution of seed crystals need to be used. Seed crystals can be generated either directly from recrystallization or from subsequent particle size reduction processes, such as sifting, screening, sonication, etc. These processes, however, may even promote the uncertainty in the size uniformity of the produced seed crystals.12 Furthermore, there may be cases where seeded crystallization is infeasible, for example, due to safety issue. When the material (solvent/product) involved during crystal© 2012 American Chemical Society

lization is very toxic, safety can be an issue during seed preparation or dosing. The availability of accurate in situ sensors in recent years has opened the possibility of feedback control-based crystallization design and operation. The most commonly used feedback control method is the closed-loop concentration control (Ccontrol) using attenuated total reflectance Fourier transform infrared (ATR-FTIR) for solute concentration measurement.14−21 C-control has been shown to be robust,4,16,22−24 but a recent study by Chew et al.22 shows that for unseeded crystallization systems, inconsistent product CSD was obtained though C-control was employed. It was due to the randomness and irreproducibility of primary nucleation events.25 Furthermore, the implementation of ATR-FTIR in industries is still rather challenging due to the difficulty of its calibration, in particular the manipulation of the impurities concentration in calibration solutions. The concentration range of both impurities and desired solute in calibration solutions must cover the possible fluctuations in commercial crystallizers to achieve a satisfactory measurement precision.26 In addition, from an industrial viewpoint, the vulnerability of the ATR element poses several setbacks. Possibly due to mechanical and/or thermal stress, the short lifetime of the ATR immersion probes is sometimes incompatible with industrial applications,27,28 and encrustation of the probe can easily occur.26,29 Kadam et al.30 investigates that inaccuracies in calculated concentrations by the ATR-FTIR instrument in semi-industrial crystallizer may occur due to uneven temperature distribution Received: Revised: Accepted: Published: 13773

March 21, 2012 September 10, 2012 October 2, 2012 October 2, 2012 dx.doi.org/10.1021/ie301626c | Ind. Eng. Chem. Res. 2012, 51, 13773−13783

Industrial & Engineering Chemistry Research

Article

CSD by combining controlled seeding by impinging jet crystallization with a batch crystallizer operating at a controlled constant growth rate. The main difference between the works considered in former literatures50,51 and the latter52 is the issue being addressed, where the former works focus on producing consistent CSD from batch-to-batch while the latter focuses on the tailoring of product CSD. In our previous antisolvent crystallization study,51 the seed conditioning to achieve consistent product CSD was done by solvent addition, which has a few shortcomings. First, the solvent addition is limited by the volume of the crystallizer. Consequently, the target FBRM statistic during seed conditioning process may not be always achievable. Second, increasing solvent concentration by solvent addition is relatively slow, leading to a long seed conditioning process. Third, solvent addition reduces the final product crystals yield. To alleviate these shortcomings, the current study investigates combined antisolvent-cooling crystallization. Combining antisolvent and cooling crystallizations were shown to give advantages over individual antisolvent or cooling crystallization. For example, Nagy et al.53 presented a modelbased combined technique which simultaneously controls the antisolvent addition rate and the cooling profile in crystallization of the lovastatin drug. It was shown that the resulting product crystals have better yield and higher quality of CSD compared to the individual antisolvent or cooling crystallization. Lindenberg et al.54 obtained optimal cooling and antisolvent addition profiles of the combined antisolventcooling crystallization process based on a population balance model. In their work, ATR-FTIR data were used to estimate nucleation and growth kinetics, and FBRM data were used to indicate the onset of nucleation. The study shows that combining cooling and antisolvent crystallization improves productivity and reduces the formation of fines. Knox et al.55 studied the effect of solvent composition on the yield and metastable zone width in combined antisolvent-crystallization of paracetamol. They concluded that for the model system considered, the yield of the product cyrstals was higher when antisolvent addition was performed first; this was due to the increase in growth rate as more antisolvent was added. Ragab et al.56 studied the advantages of combined antisolvent-cooling crystallization for crystallization of progesterone for pulmonary drug delivery. An increase of 39% in the fine particle fraction (FPF) was demonstrated for some powders produced by combined cooling and antisolvent crystallization. Compared to the aforementioned literatures on combined antisolvent-cooling crystallization, which a majority emphasized on the control of the product crystals size and improvement of yield, the current study emphasizes more on the reproducibility/consistency of product CSD from batch-to-batch. Crystallization of two model systems, namely paracetamol in acetone−water solution and glycine in water−ethanol solution, are investigated in this study. To assess the robustness of the proposed operating strategy, variations in initial solution concentration are introduced to simulate batch-to-batch variations from upstream processes. Furthermore, the implementation of a quantitative distance measure to accurately assess the consistency of final CSD from batch-to-batch is proposed.

along the length of the probe. This uneven temperature distribution may induce changes in the characteristic absorptions as the alignment of the optical component is disturbed by the uneven thermal expansion. Furthermore, the intensity can also be altered due to the differences in the curvature of the fiber optics. In recent years, focused beam reflectance measurement (FBRM) has gained popularity for in situ characterization of high-concentration particulate slurries.31−36 The restoration of CSD from chord length distribution (CLD) obtained by FBRM is not straightforward and requires many assumptions.37−41 Therefore, FBRM data are more often used qualitatively for monitoring the process evolution with time, such as identifying the onset of primary nucleation and detecting attrition and agglomeration. There have been several reports on the use of FBRM signal for feedback control of crystallization processes, some of which are listed. Tadayyon and Rohani31 constructed a feedback control system for a continuous cooling crystallizer using FBRM signals in which the flow rate of fines dissolution stream was adjusted in a real-time manner to increase average crystal size. Doki et al.42 utilized ATR-FTIR and FBRM to selectively crystallize metastable α-form in seeded batch-cooling crystallization. Seed crystals introduced into the crystallizer were grown by cooling. Then, fine particles generated by secondary nucleation were dissolved by heating the crystallizer discontinuously on the way of cooling. In recent literatures,12,43,44 information on nucleation and dissolution provided by FBRM was used to determine their cooling and heating policies to achieve the desired CSD. Specifically, they tried to maintain the desired total counts of CLD measured by FBRM by regulating heating and cooling policies. A similar approach was utilized for antisolvent crystallization, except that antisolvent and solvent additions were used instead of cooling and heating.45 The policy indirectly assumed that nucleation and dissolution are the only major factors affecting the total counts. However, this might not be the case, as many other factors may change the total counts significantly, such as crystal concentration, growth, agglomeration, and breakage and settlement of large crystals at the bottom of crystallizer.46,47 Woo et al.48 improved the robustness of concentration control by adapting the operating curve based on changes in particle counts measured by FBRM. Nagy et al.49 investigated internal fines removal in cooling crystallization using population balance-based control. In their approach, measurements from FBRM and a conductivity meter were used to estimate kinetic parameters in the population balance model. Then, an optimal temperature profile to achieve a monomodal target distribution is obtained by dynamic optimization. Recently, a fully automated technique using FBRM measurements and feedback control was successfully developed for cooling and antisolvent crystallization of paracetamol and glycine.50,51 These works addressed the issue of internal seeding, in particular the in situ conditioning of primary nucleation in unseeded crystallizations, as the critical primary step to achieve product consistency. In the strategy, seed crystals were initially generated through primary nucleation, then the generated seed crystals were conditioned by redissolving the fine crystals, and finally the conditioned seed crystals were grown into final product crystals. FBRM statistics were used to indicate when to move from one operating region to the subsequent one. Through simulation study, Woo et al.52 also consider internal seeding approach, where optimal control strategies are proposed to manufacture crystals with a targeted

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EXPERIMENTAL SECTION Materials. Paracetamol (98%, Alfa Aesar) and glycine (99%, Sigma-Aldrich) were used as received. Analytical reagent grade

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dx.doi.org/10.1021/ie301626c | Ind. Eng. Chem. Res. 2012, 51, 13773−13783


Article

Malvern Mastersizer (Mastersizer 2000, with Hydro S dispersion unit).

acetone and ethanol used in this study were purchased from Fisher Scientific. Experimental Setup. A picture of the experimental setup is shown in Figure 1. Slightly undersaturated (98% saturation)

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STRATEGY FOR ACHIEVING CSD CONSISTENCY IN COMBINED ANTISOLVENT-COOLING CRYSTALLIZATION A typical operating profile of the proposed operating strategy is divided into five operating regions (regions I−V), as shown in Figure 2. In region I, antisolvent (i.e., water in paracetamol−

Figure 1. Experimental setup for all the crystallization experiments.

paracetamol−acetone−water solution (306 g paracetamol/kg solvent in 200 g of 50 wt % acetone 50 wt % water at 23 °C) and glycine−water−ethanol solution (92.6 g glycine/kg solvent in 300 g of 80 wt % water 20 wt % ethanol at 25 °C) were used as the model solution systems. DI water is used as antisolvent in the former, while ethanol solution (80 wt %) is used in the latter. The crystallization experiments were performed in a 1 L flatbottomed glass crystallizer with an inner diameter of 100 mm. It has four baffles which enhance the mixing properties. A stainless-steel marine-type impeller with a diameter of 42 mm driven by a variable speed overhead stirrer motor was utilized to agitate the system at 500 rpm. The temperature was controlled by a water circulator (Julabo FP50) equipped with a thermocouple (Julabo Pt100). A variable speed peristaltic pump (Masterflex 7550 with Easy-Load II) was used to add the antisolvent. The injection point was about 5 mm above the impeller tip to accelerate dispersion. FBRM probe (Mettler-Toledo, model D600L) was inserted into the turbulent zone of the suspension. The CLD was obtained every 30 s using the Control Interface Software, version 6.7. Data acquired were analyzed using the Data Review Software, version 6.7, which displays CLD and the corresponding statistics. For each experiment, the dissolution of paracetamol/glycine crystals was carried out by heating the system to 35 °C and maintaining at this temperature for at least 20 min before cooling it to the desired initial temperature (23 °C for paracetamol−acetone−water system and 25 °C for glycine− water−ethanol system). The experimental procedure using the proposed operating strategy to achieve consistent product CSD is elaborated in the next section. For comparison studies, uncontrolled (both unseeded and seeded) antisolvent crystallization was also performed, where a total of 250 g of antisolvent was added at 4 g/min. For seeded experiments, 0.25 g of paracetamol/glycine seed crystals in the sieve range of 106−150 μm was introduced into the solution after 5 min of antisolvent addition. The seed crystals are obtained by milling and sieving. As discussed in the Results and Discussion section, a better seed preparation strategy is possible to produce better product quality. After each run, the crystal products were filtered and dried overnight at ambient temperature (∼23 °C). The CSD of the dried products was then obtained using

Figure 2. Operating profile of the proposed operating strategy. The solid and dashed lines indicate the solvent composition (i.e., acetone composition in paracetamol−acetone−water system) and the temperature, respectively.

acetone−water system) was added to generate seed crystals. Then, these seed crystals are conditioned by heating in region II until the desired seed crystals property (determined from FBRM statistics) is achieved. In regions III and IV, the conditioned seed crystals were grown into the desired product crystals by antisolvent addition and cooling, respectively. Alternatively, these two regions (regions III and IV) can be replaced by other feedback control strategies (e.g., concentration control). Finally, the system is allowed to reach equilibrium in region V, which is indicated by negligible changes in FBRM statistics. Please note that depending on the model systems, seed crystals generation (region I) can also be initiated by cooling. Solvent composition which favors crystal growth needs to be determined to choose between cooling or antisolvent addition for seed crystals generation. For the case of paracetamol crystallization, it was shown in the literature55 that a solution with a high mass fraction of water creates an environment that is more favorable for crystal growth. Therefore, for the paracetamol−acetone−water system, it will be more beneficial for crystal growth when antisolvent addition is used during seed generation to enrich the solution with water. In this study, fine counts measured by FBRM are defined as counts with a chord length less than 50 μm, while coarse counts include all other chord lengths. Coarse-to-fine ratio (CTFR) is defined as the ratio of the coarse counts to the fine counts. A more detailed description of each operating region is elaborated below. Region I. To generate seed crystals, antisolvent is injected at a constant rate while monitoring the coarse counts measured by FBRM. To facilitate comparison with other crystallization experiments investigated in this study, the antisolvent is injected at 4 g/min, which is the same injection rate as that 13775



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Essentially, seed quality Q is a weighted sum of distances of CTFR and total counts to their corresponding target values. When Q is at its minimum, CTFR and total counts are the closest to their corresponding target values, and therefore the heating needs to be stopped. α is a weighting factor ranging from 0 to 1. Setting α closer to 0 puts more weight on maintaining consistent total counts. In contrast, setting α closer to 1 puts more weight on maintaining consistent CTFR. From our studies, CTFR plays a more important role than total counts in producing consistent CSD. Hence, α ≥ 0.5 is generally used. A preliminary study shows that α = 0.7 is able to maintain consistent CTFR without sacrificing the consistency of total counts. Hence this value is used throughout all the experiments using the proposed operating strategy. CTFRt and TCt are the predetermined target values for CTFR and total counts, respectively. To choose appropriate values for CTFRt and TCt, a preliminary experiment was carried out while monitoring the CTFR and the total counts during a seed conditioning process by heating. Generally, CTFR is increasing and total counts are decreasing when the solution is already undersaturated. Different pairs of CTFR and total counts values after the system is undersaturated can be used as target values in the proposed operating strategy. The effects of choosing different pairs of CTFRt and TCt values are shown in the Results and Discussion section. Typical profiles for CTFR, total counts, and Q in regions II and III are shown in Figure 4. In region II, it can be observed

used in uncontrolled (unseeded and seeded) experiments. When the coarse counts reach a predetermined target value, the antisolvent addition is stopped. The rationale of choosing the coarse counts target value (CCt) is to have sufficient but not excessive generation of seed crystals. The coarse counts profile of a preliminary antisolvent crystallization experiments is shown in Figure 3. Generally, the target value can be chosen just

Figure 3. Coarse counts profile during unseeded antisolvent crystallization with antisolvent addition rate of 4 g/min for paracetamol−acetone−water system.

before the steepest increase in the coarse counts. This can be done by calculating the slope of the coarse counts with respect to time and choosing a point within a small region before the maximum slope is reached as the target value. In Figure 3, this can be interpreted as the range between the two dashed lines A and B (i.e., between 15 and 50 #/s). The effect of choosing the two limiting coarse count values is investigated in the Results and Discussion section. Generating excessive seed crystals (target value above dashed line A) will lengthen the batch time, since the seed conditioning process in the subsequent operating region will be longer, thus lowering the productivity. On the other hand, generating too few seed crystals (target value below dashed line B) may impair the seed conditioning efficacy, as most seed crystals will be dissolved during the conditioning process. Region II. The main function of this region is to condition the seed crystals previously generated by dissolving the fine crystals. The seed conditioning process is done by heating at 0.2 °C/min until the FBRM statistics reach a predetermined target value. To produce consistent product CSD, FBRM statistics which are relevant to product CSD consistency were investigated, and they were used to indicate when to stop the conditioning process. In our previous antisolvent crystallization study,51 CTFR was used as the indicator. In the current study, we have noted the importance of maintaining consistent total counts (TC) in addition to maintaining consistent CTFR. It was observed through preliminary experiments that having consistent CTFR and total counts simultaneously generally produces more consistent product CSD than having consistent CTFR alone. Therefore, a new seed quality parameter which calculates the trade-off between maintaining the consistency of CTFR and the total counts is used as the indicator and is calculated as follows Q=α

CTFR − CTFR t TC − TCt + (1 − α) CTFR t TCt

Figure 4. Typical profiles for CTFR, total counts, and parameter Q (α = 0.7) in regions II and III for paracetamol−acetone−water system. The vertical line indicates the time when heating is stopped.

that initially (t ≤ 47 min) the total counts increase before reaching a plateau, indicating that nucleation and crystal growth continue to occur as the solution is still supersaturated. The second increase (t ∼ 52 min) in total counts is possibly due to the loosening of agglomerates (which indeed may be the case, as confirmed by microscopic images of product crystals shown in the Results and Discussion section), and it also indicates that the solution starts to become undersaturated. At the same time, the CTFR also increases, indicating that the majority individual crystals which compose the agglomerates are from coarse chord length region (>50 μm). After reaching the maximum point, the total counts decrease due to the dissolution of fine crystals. CTFR is generally increasing in region II. A shoulder occurring at t ∼ 52 min coincides with the aforementioned second increase in total counts, which is an indication that the solution becomes undersaturated. The seed quality Q is generally decreasing in region II, indicating that both CTFR and total

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reach a plateau and then increases slightly during cooling in region IV due to crystal growth. Measure of Variations in CSD. Assessing CSD consistency is usually done qualitatively through visual observation of the CSD plot. However, for more accurate assessment, it is important to have a quantitative measure of the CSD consistency. There are many distance measures in literatures57−60 (e.g., city block L1-norm, Euclidean L2-norm, Minkowski Lp-norm, Chebyshev L∞-norm, etc.) which can be used for measuring distance between histograms, but not all are suitable to measure distance between CSD. For example, the conventional distance measures L1- and L2-norm are not suitable because they are shuffling invariant60 (i.e., the distance does not change when the levels are shuffled), whereas CSD is ordinal variable and is inherently shuffling variant. A distance measure which takes into account the correlation among levels and is suitable for CSD is DMDPA. It is a distance measure based on minimum difference of pair assignment (MDPA).61 The DMDPA between two CSD can be calculated as follows

counts are getting closer to the target values. Finally, when Q passes its minimum value, the heating is stopped. Regions III and IV. After the seed crystals are conditioned, they are grown into the desired product crystals in regions III and IV. In region III, antisolvent is added at 4 g/min. The amount of antisolvent addition can be determined by the desired final yield or by a predetermined total amount of antisolvent utilized (i.e., the total amount of antisolvent used in regions I and III). In this study, to give a fair comparison of the uncontrolled (unseeded and seeded) experiments, the latter option is selected, and the same total amount of antisolvent (i.e., 250 g) is used. In region IV, the system is cooled back to the initial temperature at 0.1 °C/min. Please note that it is possible to cool the system to below the initial temperature to improve product crystals yield. However, to accommodate comparison studies with other antisolvent crystallization experiments, it is desirable to have the same product crystals yield. Hence, the system is cooled back to the initial temperature in this study. Please note that the main purpose of regions III and IV is to grow the seed crystals, therefore other feedback control strategies (e.g., concentration control) may be implemented here if desired. To summarize, the total counts profile throughout the operating regions of the proposed operating strategy is shown in Figure 5. At the beginning of antisolvent addition in region I,

b

DMDPA =

i

∑ ∑ (CSD1j − CSD2j ) i=1

(2)

j=1

where b is the total number of bins in each CSD and CSD1j and CSD2j are the frequency values at the jth bin of the first and second CSD, respectively. To accommodate more than two CSD, the above DMDPA is calculated for every possible CSD pairs, and the arithmetic average (DMDPA,ave) is taken as the variation index. In this study, DMDPA,ave is utilized to measure the extent of CSD variations obtained from different batches of the same experimental method.

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RESULTS AND DISCUSSION In this section, the experimental results for both paracetamol− acetone−water and glycine−water−ethanol systems obtained from the proposed operating strategy are compared to those obtained from standard unseeded and seeded antisolvent crystallizations. Then, for the paracetamol−acetone−water system, the robustness of each method is assessed in the presence of initial concentration disturbances. Experimental Results Obtained from Different Methods of Crystallization. The operation settings (i.e., each setting consists of target values CCt, CTFRt, and TCt) used in the proposed operating strategy for both paracetamol− acetone−water and glycine−water−ethanol systems are shown in Table 1. For the paracetamol−acetone−water system, three different settings (settings 1−3) are studied. From setting 1 to 2, CCt is decreased while maintaining CTFRt and TCt. In other words, the seed generation period is shortened from setting 1 to 2. From setting 2 to 3, CCt is maintained while increasing CTFRt and decreasing TCt. This implies that the seed conditioning process in setting 3 is longer than that in

Figure 5. Total counts profile during in a crystallization experiment using the proposed operating strategy.

the total counts are closed to 0, indicating that the solution is homogeneous. Nearing the end of region I (t ∼ 35 min), the total counts start to increase, due to the simultaneous nucleation and growth of crystals. The antisolvent addition is then stopped, and the system is heated in region II. Since the solution is still supersaturated, the total counts keep increasing in the beginning of region II, before reaching a plateau. Then, the total counts increase again at t ∼ 52 min, possibly due to the loosening of agglomerates (which indeed may be the case, as confirmed by microscopic images of product crystals shown in the Results and Discussion section). After reaching maximum, the total counts are decreasing, indicating the dissolution of fine crystals. Once the desired seed crystals property is reached, the temperature is kept constant, and the antisolvent addition is continued in region III. This transition is marked by the appearance of a shoulder in the total counts at the beginning of region III, after which the total counts continue to decrease. The decrease in the total counts in this region can be attributed to the dilution of the slurry during antisolvent addition. At the end of region III, the total counts

Table 1. Settings Used in the Proposed Operating Strategy for Paracetamol−Acetone−Water and Glycine−Water− Ethanol Systems paracetamol−acetone−water

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glycine−water−ethanol

targets

setting 1

setting 2

setting 3

setting 1

CCt (#/s) CTFRt TCt (#/s)

50 0.45 500

15 0.45 500

15 0.60 300

50 0.50 500



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Figure 6. CSD of product crystals from five repeated crystallization experiments for paracetamol−acetone−water system: (a) unseeded and (b) seeded experiments and (c) proposed operating strategy (setting 1).

The CSD statistics resulting from unseeded and seeded experiments and the proposed operating strategies (settings 1− 3) for paracetamol−acetone−water system are tabulated in Table 2. As observed, the mean sizes of the CSD obtained using

setting 2. For the glycine−water−ethanol system, one operation setting (setting 1) is studied. The CSD resulting from five repeated crystallization experiments for paracetamol−acetone−water system using unseeded and seeded experiments and the proposed operating strategy (with setting 1) are shown in Figure 6a−c. It can be observed that the CSD obtained from the unseeded experiments (Figure 6a) has the largest variations, despite the same experimental procedure being followed for each experiment. These variations are most likely due to the stochastic nature of nucleation, which is affected by impurities (e.g., dust particles) or other external factors. Much lesser variations are found in the CSD produced by the seeded experiments (Figure 6b). This result indeed corroborates with the findings in the literatures,6−13,50,51 that seeding suppresses nucleation and allows more surface area for crystal growth, thus increasing the consistency in product CSD. It is also observed from Figure 6c that the proposed operating strategy using setting 1 successfully produces very consistent CSD from batch-to-batch. In fact, comparing Figure 6b,c, the proposed operating strategy produces slightly more consistent CSD than the seeded experiments. In addition, secondary nucleation is suppressed using the proposed operating strategy, while it is still apparent in the seeded experiments as indicated by a small peak around 50 μm in Figure 6b. Please note that the product CSD consistency in the seeded crystallization might be improved further if better seed preparations were carried out. For example, Aamir et al.62 reported that crystallized−sieved seed crystals or milled−washed−sieved seed crystals resulted in less surface breeding and agglomeration compared to milled−sieved seed crystals.

Table 2. CSD Statistics Obtained from Different Crystallization Methods for Paracetamol−Acetone−Water Systema CSD statistics

unseeded

seeded

setting 1

setting 2

setting 3

mean std dev DMDPA,ave

392.66 203.10 117.36

372.42 136.38 27.81

480.18 194.37 16.34

472.76 195.20 17.57

551.46 217.62 22.33

a

Each value shows the average from five experiments.

the proposed operating strategies are larger than that obtained using the unseeded experiments (i.e., 20−40% increase in mean size), while the standard deviations are similar. The increase in the mean size can be explained by the seed conditioning process, where fine crystals are redissolved, allowing more solute for the remaining larger crystals to grow in the subsequent antisolvent addition and cooling processes. Investigating the effects of different target values in the proposed operating strategy, it can be seen that decreasing CCt while maintaining CTFRt and TCt (from setting 1 to 2) has minimal effect on the final mean size and standard deviation. The reason is that many seed crystals generated during the seed generation process are redissolved again in the subsequent seed conditioning process which have the same CTFRt and TCt. As a result, the conditioned seed crystals CSD produced by both settings is similar. However, from setting 2 to 3, the target values for the seed conditioning process are changed (i.e., 13778



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Figure 7. CSD of product crystals from five repeated crystallization experiments for glycine−water−ethanol system: (a) unseeded and (b) seeded experiments and (c) proposed operating strategy (setting 1).

CTFRt is increased and TCt is decreased), resulting in larger mean size and standard deviation. Longer seed conditioning process generally leads to more redissolution of seed crystals, leaving smaller crystal concentration in the slurry. On one hand, this leads to the availability of more solute for the growth of the fewer remaining crystals, resulting in larger mean size. On the other hand, the supersaturation may exceed the metastable zone during the subsequent antisolvent addition and/or cooling processes which induces additional nucleation, thus resulting in larger standard deviation. From CSD consistency point of view, the DMDPA,ave is able to confirm the previous visual observation of the CSD plots (Figure 6a−c), that the unseeded experiments produce the largest variations in CSD (indicated by the largest DMDPA,ave value), followed by the seeded experiments, and the proposed operating strategy produces the most consistent CSD. For glycine−water−ethanol system, the CSD resulting from five repeated crystallization experiments using unseeded and seeded experiments and the proposed operating strategy are shown in Figure 7a−c. Their corresponding CSD statistics are tabulated in Table 3. Similar to the previous system, the unseeded experiments produce CSD with the largest variations (Figure 7a), and seeding is again shown to be fairly robust in producing consistent CSD (Figure 7b). It is also observed that

the proposed operating strategy consistently produces the most consistent CSD among all the three methods (Figure 7c), as confirmed by the DMDPA,ave values in Table 3. In addition to producing more consistent CSD, the proposed operating strategy also produces larger crystals (i.e., 18% increase in mean size) than the unseeded experiments. The resulting microscopic images of paracetamol and glycine crystals obtained from the three methods are shown in Figures 8 and 9, respectively. As shown, the product crystals produced by both unseeded (Figure 8a,b) and seeded (Figure 9a,b) experiments have a considerable degree of agglomerations, especially for the paracetamol−acetone−water system. In contrast, through process conditioning process, the proposed operating strategy successfully reduces the agglomerations to a large extent. In addition, if individual crystals are concerned, the proposed operating strategy seems to result in product crystals with the most uniform size distribution. However, this observation at first seems contradictory with the Mastersizer data (Tables 2 and 3), which show that the CSD obtained from the proposed operating strategy has a similar standard deviation to that obtained from the unseeded experiments. This is because Mastersizer measurement does not distinguish agglomerates from individual crystals, considering agglomerates as individual crystals with larger size. Consequently, the measurements give impression that the product crystals obtained from unseeded experiments are as uniform as those obtained from the proposed operating strategy, while the fact is that the proposed operating strategy gives superior product crystals uniformity. In summary, the proposed operating strategy has been successfully demonstrated in two pharmaceutical systems with rather different crystallization characteristics. These differences

Table 3. CSD Statistics Obtained from Different Crystallization Methods for Glycine−Water−Ethanol System CSD statistics

unseeded

seeded

setting 1


367.41 230.16 124.1

398.87 237.19 73.476

435.32 238.86 26.944 13779



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Figure 8. Microscopic images of paracetamol crystals obtained from (a) unseeded and (b) seeded experiments and (c) proposed operating strategy.

Figure 9. Microscopic images of glycine crystals obtained from (a) unseeded and (b) seeded experiments and (c) proposed operating strategy.

Figure 10. CSD of product crystals from nine repeated crystallization experiments for paracetamol−water−ethanol system (four with initial concentration disturbances): (a) unseeded and (b) seeded experiments and (c) proposed operating strategy (setting 1).

can be observed by comparing the results between glycine− water−ethanol and paracetamol−acetone−water systems. From microscopic images of crystals produced by unseeded experiments (Figures 8a and 9a), the shape of the paracetamol

crystals is closer to spherical than that of glycine crystals. Meanwhile, glycine crystals have more elongated shape. Hence, breakages during crystallization (e.g., due to collision with impeller) are expected to be more severe with glycine crystals. 13780



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From the same figures, it can also be seen that paracetamol crystals are more prone to agglomeration than glycine crystals. Furthermore, from the product CSD figures for unseeded experiments (Figures 6a and 7a), it can be seen that the product CSD of glycine system contains more randomness than that of paracetamol system, which may be attributed to different aforementioned characteristics, stochastic nature of primary nucleation, and other factors (e.g., growth kinetic). Crystallization Experiments of Paracetamol−Acetone−Water System with Initial Concentration Disturbances. It is possible to have initial concentration variations from batch-to-batch in crystallization process, as the feed usually results from upstream processes. Hence, it is important to assess the robustness of crystallization process in the presence of initial concentration disturbances. In this study, the robustness of the three crystallization methods for paracetamol−acetone−water system are assessed by varying the initial concentration by ±5%. The CSD of the product crystals from nine repeated experiments (four with initial concentration disturbances) using the unseeded and seeded experiments, and the proposed operating strategy (setting 1) are shown in Figure 10a−c. Their corresponding CSD statistics are tabulated in Table 4.

consistent CSD, while the unseeded and seeded experiments produce less consistent CSD. Seeded experiments with −5% initial concentration disturbance result in a large CSD deviation, due to the dissolution of the seed crystals. This shows that external seeding does not always ensure consistent product CSD, since operators may unknowingly introduce seed crystals at the wrong time, causing significantly different product CSD due to seed crystals dissolution. Furthermore, the product CSD of seeded crystallization also depends largely on the seed crystals quality and preparation methods, which may introduce another layer of inconsistency. Comparing the microscopic images of the product crystals also show that the proposed operating strategy produces less agglomeration than the standard unseeded and seeded experiments. This study has also shown the implementation of DMDPA,ave for more accurate assessment of variations in CSD obtained from different batches of the same experimental method.

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*E-mail: [email protected]; reginald_tan@ ices.a-star.edu.sg. Notes

The authors declare no competing financial interest.

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Table 4. CSD Statistics Obtained from Different Crystallization Methods for Paracetamol−Acetone−Water System (with ±5% Initial Concentration Disturbances) CSD statistics

unseeded

seeded

setting 1


394.87 200.31 85.78

395.65 177.01 91.13

479.36 195.01 29.14

AUTHOR INFORMATION

Corresponding Author

ACKNOWLEDGMENTS The authors thank Agency for Science, Technology, and Research (A*STAR) for funding this research. The authors also thank Agnes Phua for carrying out some parts of the experiments used in this study.

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As expected, the unseeded experiments are not robust in the presence of initial concentration disturbances, as shown by the large variations in CSD (Figure 10a). Interestingly for the seeded experiments (Figure 10b), the two CSD obtained with −5% disturbance deviate rather far from the rest, rendering higher DMDPA,ave value than the unseeded experiments (Table 4). Investigating the FBRM data for these experiments, it was discovered that a majority of the seed crystals were dissolved shortly after seeding. This shows a situation where seeding does not always ensure product CSD consistency, since operators may unknowingly introduce seeds at the wrong time which causes significantly different product CSD due to seed crystals dissolution. On the other hand, the proposed operating strategy is still robust in the presence of initial concentration disturbances, producing consistent CSD (Figure 10c) without adjusting the experimental procedure or operation settings.

REFERENCES

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CONCLUSIONS In this study, an operating strategy which utilizes FBRM to achieve consistent product CSD in combined antisolventcooling crystallization has been developed and implemented in two model systems, namely paracetamol−acetone−water and glycine−water−ethanol systems. Standard unseeded and seeded antisolvent crystallization experiments are also performed to provide comparison studies. It is shown that the proposed operating strategy produces the most consistent CSD. Furthermore, to assess the robustness of each method, crystallization experiments in the presence of initial concentration disturbances are also investigated. The results show that the proposed operating strategy is still robust in producing 13781



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