Implementation of Focused Beam Reflectance Measurement (FBRM

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DOI: 10.1021/cg100533n

Implementation of Focused Beam Reflectance Measurement (FBRM) in Antisolvent Crystallization to Achieve Consistent Product Quality

2010, Vol. 10 3668–3674

Martin Wijaya Hermanto,*,† Pui Shan Chow,† and Reginald B. H. Tan*,†,‡ †

Institute of Chemical & Engineering Sciences, A*STAR (Agency for Science, Technology and Research), 1 Pesek Road, Jurong Island, Singapore 627833, and ‡Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260 Received April 22, 2010; Revised Manuscript Received June 4, 2010

ABSTRACT: In this study, focused beam reflectance measurement (FBRM) is used to achieve batch-to-batch product quality consistency in terms of crystal size distribution (CSD) in unseeded antisolvent crystallization. The method comprises three regions. Initially, sufficient internal seed crystals are generated through primary nucleation, then the seed crystals generated are conditioned by redissolving the fine crystals, and finally the conditioned seed crystals are grown into final product crystals. Results showed that the product CSD consistency from the proposed control method was much better than that from unseeded antisolvent crystallization, while it was as good as that from externally seeded antisolvent crystallization. The robustness of the proposed control method was also demonstrated by performing experiments with initial concentration disturbances.

Introduction The quality of crystallization products is usually determined by the crystals shape, size, and crystal size distribution (CSD), particularly, the average size and the width of the size distribution. Despite the long history and the widespread application of crystallization, there are still a disproportionate number of problems associated with the understanding and control of the process. The major concern in the pharmaceutical industry is to optimize consistency and quality of the final product while improving productivity. Pharmaceutical manufacturing has traditionally been a recipebased operation, and the resulting recipe is usually not robust in the presence of impurities. As a result, this issue has motivated research in crystallization control. 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 (C-control) using attenuated total reflectance Fourier transform infrared (ATR-FTIR) for solute concentration measurement.1-8 In this feedback control strategy, a predetermined concentration-temperature profile is followed during the crystallization process. C-control has been shown to be robust,1,9-12 but a recent study by Chew et al.10 shows that for unseeded crystallization systems, inconsistent product crystals were obtained though C-control was employed. This could be due to the randomness and irreproducibility of primary nucleation events.13 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.14 In addition, from an industrial viewpoint, *To whom correspondence should be addressed. E-mail: martin_hermanto@ ices.a-star.edu.sg (M.W.H.); [email protected] (R.B.H.T.). Phone: þ65-67963841. Fax: þ65-63166183. pubs.acs.org/crystal

Published on Web 06/25/2010

the vulnerability of the ATR element poses several setbacks. Mechanical damage or chemical deterioration of the ATR element immediately affects the accuracy of the calibration, and encrustation of the probe can easily occur. In recent years, focused beam reflectance measurement (FBRM) has gained popularity for in situ characterization of high concentration particulate slurries.15-20 The FBRM probe utilizes laser light backscattering technology to measure the chord length distribution (CLD) of the particles. Though CLD is related to the crystal size distribution (CSD), the restoration of CSD from CLD based on first principles is extremely difficult and requires many assumptions.21-25 In addition, their algorithms are only applicable to well-defined systems with known shape and optical properties and may not extend to systems in general. Therefore, FBRM data are more often used qualitatively for monitoring the process evolution with time such as identifying the onset of primary nucleation, detecting attrition and agglomeration. Because of the complicated relationship between CLD and CSD, there have been few reports on the use of FBRM signals for feedback control despite the widespread use of FBRM in process monitoring. Tadayyon and Rohani15 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 the average crystal size. Doki et al.26 utilized ATR-FTIR and FBRM to selectively crystallize metastable R-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. Abu Bakar et al.27,28 used information on nucleation and dissolution, provided by FBRM, 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 addition were used instead of cooling and heating.29 The policy indirectly assumed that nucleation and dissolution are the r 2010 American Chemical Society

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Figure 1. Experimental setup for antisolvent crystallization experiments.

only major factors affecting the total counts. However, this might not be the case, as many other factors may cause the change in the total counts significantly, such as the crystals concentration, growth, agglomerations, breakage, and settlement of large crystals at the bottom of the crystallizer.30,31 Woo et al.32 improved the robustness of concentration control by adapting the operating curve based on changes in particle counts measured by FBRM. Recently, Chew et al.33 demonstrated that a fully automated technique using FBRM measurements and feedback control of temperature was successful in achieving consistent CSD in repeated batch cooling crystallizations of glycine and paracetamol. That work 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. The strategy employed was to manipulate the system temperature according to the FBRM statistics to refine the size distribution of the initial seeds internally generated through primary nucleation. Compared to the standard unseeded experiments, it was shown that the proposed seed conditioning method successfully produced significantly more consistent crystals distributions in several repeat experiments. In the current work, this strategy33 is refined and extended to antisolvent crystallization, which is the most common technique for the separation and purification of many heat-sensitive pharmaceuticals and fine chemicals.2,34 To automate the process control, in-house software has been developed using Visual Basic .NET. The CSD of the product crystals were obtained by Malvern Mastersizer. Furthermore, in view that the initial condition of crystallization processes may result from upstream processes, the initial solution concentration of the crystallization processes may contain batch-to-batch variations. Hence, in this work the robustness of the proposed method is assessed by artificially introducing variations in initial solution concentration.

Figure 2. A typical operating profile of the proposed method. outlet of the dip-tube, which caused clogging due to significant nucleation and growth of crystals there. The antisolvent crystallization experiments were performed in a 1-L flat-bottomed glass crystallizer with an inner diameter of 100 mm. It has four glass 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. Under our present experimental conditions, agitation speed of 500 rpm was sufficient to effectively disperse the solvent and antisolvent and to suspend the product crystals. The system temperature was controlled at 25 °C using a circulator (Thermo Haake, model C50P) and measured every 2 s using a stainless steel Pt100 thermocouple. Two variable speed peristaltic pumps (Masterflex 7550 with EasyLoad II) were used to add the solvent and antisolvent, respectively. The injection points were 5 mm above the impeller tip to accelerate dispersion. FBRM probe (Lasentec, model D600X) was inserted into the turbulent zone of the suspension. The CLD was obtained every 30 s using the Control Interface Software, version 6.0b16. Data acquired were analyzed using the Data Review Software, version 6.0b16, which displays CLD and the corresponding statistics. For each experiment, the dissolution of glycine is carried out by heating the system to 50 °C, and maintaining at this temperature for at least 30 min before cooling it to 25 °C. For the uncontrolled experiments (both unseeded and seeded), a total of 250 g of antisolvent was pumped into the system at 2 g/min. For seeded experiments, 0.069 g (0.4% seed loading) of glycine seed crystals in the sieve range of 90-106 μm was added after 5 min of antisolvent addition. After every run, the crystal products were filtered and dried overnight in a vacuum oven at 35 °C. The CSD of the dried products were then obtained by using Malvern Mastersizer (Mastersizer 2000, with Hydro S dispersion unit), where ethanol was used as a dispersant medium. The pump/stir speed was set at 2500 rpm and ultrasonics treatment at 40% tip displacement was performed for 30 s before each CSD measurement.

Experimental Section

Achieving CSD Consistency in Antisolvent Crystallization by Using FBRM

A schematic diagram of the experimental setup is shown in Figure 1. Glycine powder (g99% supplied by Sigma-Aldrich Co.), ethanol (Fisher-scientific, analytical reagent grade), and deionized (DI) water were used to prepare the solutions. The initial solution for each experiment was saturated solution (unless otherwise stated) of glycine, which was prepared by dissolving 28.44 g of glycine in 300 g of ethanol-water mixture (20 wt % ethanol) at 25 °C. DI-water was used as solvent (during seed conditioning in the proposed method) and 80 wt % ethanol was used as antisolvent. 80 wt % ethanol was used instead of pure ethanol to avoid very high supersaturation at the

The typical operating profile of the proposed method is shown in Figure 2. It consists of three operating regions AB, BC, and CD. Briefly, the seed crystals were generated in region AB and they were conditioned by redissolving the fine crystals in region BC. Finally, the conditioned seed crystals were grown into the desired product crystals in region CD. Please note that in this work, the fine counts measured by FBRM are defined as counts of chord length less than 30 μm, while coarse counts include all other chord lengths. Moving-average filter

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Figure 3. Coarse counts profile of crystallization with antisolvent addition of 2 g/min.

with three moving-average length was implemented in Lasentec FBRM acquisition software. A detailed description of each region is as follows: Region AB. Antisolvent was pumped at a constant rate of 2 g/min. Coarse counts were used to indicate whether sufficient seed crystals were generated through primary nucleation. Coarse counts were utilized instead of total counts because the former are less sensitive to FBRM probe fouling, which usually affects chord length measurements below 30 μm. In addition, it is relatively less important to include the fine counts at this stage, because the fine crystals are redissolved in the next region. A general idea to determine the coarse counts set point is to have sufficient but not too much generation of seed crystals. If too few crystals are generated, it is likely that the majority of the seed crystals will be redissolved in region BC because most of the crystals are still in the fine region. This limits the improvement that can be made in the seed conditioning region (region BC). In contrast, if too many seed crystals are generated, more crystals need to be redissolved in the seed conditioning region, resulting in a longer batch time and poorer productivity. A preliminary crystallization experiment was conducted to determine an appropriate set point for the coarse counts. Figure 3 shows the coarse counts profile for antisolvent crystallization when antisolvent was added at 2 g/min antisolvent in an uncontrolled experiment. To obtain sufficient but not too many seed crystals, the antisolvent addition was stopped just before the steepest increase in coarse counts. In this case, the coarse counts in question were about 50 #/s. Region BC. The seed crystals generated in region AB were conditioned by adding solvent at 1 g/min. In the previous work by Chew at al.,33 coefficient of variation (i.e., CV, defined as ratio of standard deviation to mean) calculated from CLD measurement by FBRM was used as the quality indicator. The decreasing of CV implied the narrowing of CSD during seed conditioning. However, in this work it was found that CV was rather noisy despite the implementation of data filtering (Figure 4). This may lead to false indication regarding when the predetermined quality is reached so as to stop the solvent addition. Therefore, a more robust indicator with less noise is required. In this work, coarse-to-fine ratio (CTFR), which is defined as the ratio of coarse counts to fine counts, is utilized.

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Figure 4. A typical CV profile in region BC.

Figure 5. A typical CTFR profile in region BC.

A typical CTFR profile in region BC is shown in Figure 5. In this run, solvent addition occurred at about 26 min. It can be observed that the CTFR profile is relatively less noisy than the CV profile (Figure 4), and it consists of two distinct sections. In the first section, the CTFR increased only slightly from 26 to 50 min, where the solution was still supersaturated. Therefore, this increase was not due to fine crystals dissolution, but rather due to the growth of fine crystals into coarse crystals. At about 37 min, the anomalous sharp drop in CTFR was due to probe fouling. This suggests that FBRM has the same encrustation problem as ATRFTIR and many other in situ measurement devices. Probe fouling could also be observed by the sudden appearance of an additional peak in the CLD, usually in the small chord length measurement. However, after withdrawal of the probe, rinsing with DI-water and reinsertion, the CTFR readings reverted to the correct trend. The duration of the cleaning process was rather short (no longer than 1 min), and hence the result of the experiment was not compromised in any way. At about 50 min, the CTFR began to increase monotonically and at a faster rate due to fine crystals dissolution. The transition point between the two sections serves as a good indicator of the point at which the solution becomes undersaturated. Finally, when the predetermined CTFR set point was reached (i.e., CTFR set point = 2.4 for

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Figure 6. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated unseeded experiments.

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Figure 7. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated seeded experiments.

this case), the solvent addition was stopped. The impact of choosing different CTFR set points is discussed later. Region CD. After the desired quality of the seed crystals was achieved, antisolvent was added at 2 g/min to grow the conditioned seed crystals. 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 AB and CD). In this work, the latter option was selected. Note that the main purpose of this region is to grow the seed crystals; therefore, other feedback control strategies (e.g., concentration control) may be implemented here. Results and Discussion In this section, the experimental results from the proposed method are compared to those from standard unseeded and seeded antisolvent crystallization. The results are presented as both square-weighted CLD (from FBRM statistics) and measured CSD. Several workers have shown that squareweighted CLD often corresponds well with CSD.28,30,31 Then the robustness of each approach is assessed by giving disturbances in initial solution concentration. Finally, the relationship between the statistics from square-weighted CLD and those from CSD are discussed. Performance Comparison of the Proposed Method and the Standard Unseeded and Seeded Crystallization. Figures 6 and 7 show the square-weighted CLD (top figures) and CSD (bottom figures) for five repeated unseeded and seeded experiments, respectively. Large variations in both squareweighted CLD and CSD were observed for the unseeded experiments, despite the same experimental procedure being followed for each experiment. These variations were most likely due to the presence of impurities (e.g., dust particles) or other external factors which may have affected the extent or rate of primary nucleation. On the other hand, the seeded experiments resulted in excellent batch-to-batch consistency in both square-weighted CLD and CSD. The consistency in the seeded experiments corroborate with that observed in cooling crystallization.33 For the proposed method, three different CTFR set points, CTFR = 2.0, 2.4, and 2.8, were investigated. The square-weighted CLD and CSD for the proposed method are

Figure 8. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated experiments using proposed method with CTFR set point = 2.0.

shown in Figures 8-10 for CTFR set points = 2.0, 2.4, and 2.8, respectively. It is obvious that the proposed method resulted in significantly more consistent product crystals than those from unseeded experiments and as consistent as those from seeded experiments. From these figures, we can also observe that the consistency in square-weighted CLD generally leads to consistency in CSD. Although it is reported that samples with the same CSD but with different crystal shapes can result in different CLD measurements by FBRM,35 it is of less concern in the proposed method, since the objective of the proposed method is to obtain consistent product quality of the same system in every batch. Hence, if there were changes in crystal habits during the batch, the changes would be more or less consistent from batch-tobatch, since the operating condition did not change significantly, and the changes in the crystal habits would be reflected in the CLD. Therefore, CLD measured by FBRM can be used reliably to monitor the consistency of the CSD of product crystals. The batch-to-batch variations between different repeat experiments can be compared fairly (i.e., regardless the

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differences in mean size) by the CV of mean size and CV of standard deviation, as compiled in Table 1. It is obvious that the unseeded experiments perform the worst (i.e., produce the largest CV of mean size and CV of standard deviation). On the other hand, the proposed method with CTFR set point = 2.8 performs the best and the proposed method with CTFR set point = 2.4 performs comparatively close to the seeded experiments. It is also noted that in the proposed method, the mean product crystal size increases and batchto-batch variations decrease as the CTFR set point increases. This observation is reasonable because as the target CTFR

Figure 9. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated experiments using the proposed method with CTFR set point = 2.4.

Figure 10. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated experiments using proposed method with CTFR set point = 2.8.

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increases, more fine crystals which cause the majority of the variations are redissolved. It also implies that the number of internally generated seeds decreases. Hence, with the same amount of supersaturation consumption, the mean product crystal size increases with CTFR. As for the seeded experiments, the CTFR value after seeding is about 1, which is lower than those experiments produced via the proposed method. This explains the smaller mean product crystal size of the seeded experiments. Robustness Assessment. It is not uncommon that the initial condition of crystallization processes may change due to the output of upstream processes. In this case, batch-to-batch variations in initial condition of crystallization processes may occur. To simulate these variations, experiments with disturbed initial solution concentration, comprising -10%, -5%, 0%, þ5%, and þ10% variations with respect to the nominal concentration value (i.e., saturation concentration) were performed. The resulting square-weighted CLD and CSD of the product crystals for the unseeded, seeded, and the proposed method with CTFR set point = 2.4 are shown in Figures 11-13, respectively. CTFR set point = 2.4 was chosen for the proposed method because it performed similarly to the seeded crystallization in experiments without disturbances. Our results show that the unseeded experiments are the least robust to disturbances, producing even larger batch-to-batch variations than those in experiments without disturbances. On the other hand, the seeded experiments and the proposed method show significantly better robustness to disturbances. In fact, it can be seen from Table 2 that indeed both seeded and the proposed method give similar CV of mean size, while the proposed method performs slightly better since the CV of standard deviation is smaller.

Figure 11. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated unseeded experiments (with initial concentration disturbances).

Table 1. CSD Statistics for the Product Crystals from Various Experiments without Initial Concentration Disturbances experiments

mean size [μm]

CV of mean size

std dev [μm]

CV of std dev

unseeded seeded proposed (CTFR = 2.0) proposed (CTFR = 2.4) proposed (CTFR = 2.8)

447.19 352.73 553.76 619.09 744.56

0.1108 0.0347 0.0657 0.0372 0.0214

303.93 198.40 351.83 382.63 403.52

0.1124 0.0143 0.0585 0.0208 0.0186

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Relationship between Mean and Standard Deviation from Square-weighted CLD and Those from CSD. As observed earlier, batch-to-batch consistency in square-weighted CLD generally leads to batch-to-batch consistency in CSD. On the other hand, it is more challenging to find the relationship between the two distributions quantitatively. To gain a better understanding of this relationship, the square-weighted CLD and CSD statistics from all experiments used in this study were compiled. The mean and standard deviation from square-weighted CLD versus those from CSD are shown in Figures 14 and 15, respectively. From the trends of the

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experimental data, both the mean and standard deviation between square-weighted CLD and CSD exhibit good linearity over the size range studied (350-750 μm). The gradient and slope of the linear curve fitted to the mean data are 3.1931 and -46.824, respectively, while those fitted to the standard deviation data are 2.1829 and 37.784, respectively. The standard error of prediction for the mean and standard deviation data are 39.361 and 33.544 μm, as represented by the shaded regions in the respective figures. In other words,

Figure 12. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated seeded experiments (with initial concentration disturbances).

Figure 14. Mean from square-weighted CLD versus mean from CSD. Note that the data points are experimental data, the straight line is the best linear fit of the experimental data, and the shaded region is the standard error of prediction.

Figure 13. Square-weighted CLD (top) and CSD (bottom) of product crystals from five repeated experiments using proposed method with CTFR set point = 2.4 (with initial concentration disturbances).

Figure 15. Standard deviation from square-weighted CLD versus standard deviation from CSD. Note that the data points are experimental data, the straight line is the best linear fit of the experimental data, and the shaded region is the standard error of prediction.

Table 2. CSD Statistics for the Product Crystals from Various Experiments with Initial Concentration Disturbances experiments

mean size [μm]

CV of mean size

Std. dev. [μm]

CV of std. dev.

unseeded seeded proposed (CTFR = 2.4)

455.39 345.03 596.66

0.1352 0.0417 0.0447

314.70 191.52 367.33

0.1421 0.0731 0.0406

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statistically at least 50% of all future measurement data should lie inside the shaded regions. Conclusions In this study, we have successfully achieved batch-to-batch product quality consistency in antisolvent crystallization of glycine. The method was a refinement and extension of the previously proposed method by our group that was applied for cooling crystallization.33 The robustness of the proposed method was also compared to the unseeded and seeded antisolvent crystallization, by giving initial concentration disturbances. From these experiments, it was shown that the proposed method was much more robust than the unseeded crystallization and was as robust as the seeded crystallization. Furthermore, we have also shown that the FBRM can be used reliably to monitor the consistency of CSD, as it was shown that consistency in square-weighted CLD led to consistency in CSD, at least for the system considered here. Finally, we also observe that the mean and standard deviation from squareweighted CLD and those from CSD exhibit linear relationship within the size range studied here (350-750 μm). Acknowledgment. The authors thank Dr. Zaiqun Yu for his technical assistance and Agnes Phua for help with obtaining the CSD data by Malvern Mastersizer. This work was supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology, and Research).

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