Recent Advances for Seeding a Crystallization ProcessA Review of Modern Techniques

Brian O’Sullivan, Benjamin Smith and Georgiy Baramidze, Mettler-Toledo AutoChem, Inc.

Seeding has become one of the most critical steps in optimizing crystallization behavior, process efficiency, and product quality. Inconsistent filtration rates, drying times, yields, bulk density, flow properties, and particle size distributions can often be traced back to inconsistent seeding and nucleation. Many parameters must be taken into consideration when designing a seeding strategy such as seed size, seed loading (mass) and seed addition temperature. These parameters are generally optimized based on process kinetics and the desired final particle properties and must remain consistent during scale-up and technology transfer.

Monitoring the seed behavior in situ, during the crystallization itself, provides a significant advantage during the design and scale-up of the seeding strategy. The 15-30 minutes following seed addition are generally the most critical period in ensuring the effectiveness of the seed. Mapping the seed behavior inline is often critical to ensure the batch proceeds as expected, and understanding seeding parameters means less experiments are necessary to optimize a crystallization.

More recently advanced seeding techniques have been investigated as possible alternatives to the traditional

approach. One such method is to create the seed bed in-process through nucleation and growth while controlling the particle size using inline particle characterization - such as with FBRM® technology. This approach can offer significant advantages for processes where safety is a primary concern.

This white paper describes a best practice approach for designing a seeding strategy and discusses recent publications on advanced seeding techniques with real time feedback control of the particle size distribution.

New Technologies for Crystallization Development

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Contents

Ensuring Consistency in Particle Size Distribution through Seeding 3

Choosing the Seeding Conditions (Supersaturation) 4

Identifying Seed Loading and Seed Size Distribution 6

Seeding to Control Purity and Optimize Downstream Separations 8

Advanced Seeding Techniques and Feedback Control 9

Conclusions 11

Appendix: Focused Beam Reflectance Measurement (FBRM®) 12

IntroductionThe desire to produce powders with a specific particle size distribution and pow-der handling properties continues to drive crystallization research, from industry and academia. The need for a particular particle size differs depending on the requirements of the producer. In certain bulk chemical processes large particles are produced to ensure fast separation times - maximizing the economic efficiency of the process. On the other hand in some pharmaceutical processes small particles are required to improve the bioavailability of the active ingredient. In either case there is a requirement to target a pre-defined particle size that if not met can result in additional processing costs or, in extreme cases, loss of product. Fundamental understanding of crystallization has improved dramatically over the past 30 years yet this single unit operation still poses some of the greatest challenges when design-ing and developing new chemical processes. The introduction of advanced in situ monitoring techniques such as FBRM® (Focused Beam Reflectance Measurement), PVM® (Particle Vision and Measurement), ATR-FTIR and Raman provide the ability to monitor and control crystallization processes in real time. This has significantly improved consistency and repeatability in manufacturing. These in situ technologies enable critical steps during the process to be closely monitored and understood so process deviations or upsets can be observed in real time and corrected, as opposed to the traditional approach of sampling at the end of the process when it is too late to do so.

In more recent years, seeding has become one of the most critical steps in optimizing crystallization behavior and ensuring the final particle size. Seed crystals with the correct particle size, mass and crystal form introduced at the right point in the process can change a poorly behaved, inconsistent crystallization process, to one that is repeatable and produces particles with the required particle size specification. This white paper discusses general seeding theory and best practices as well as looking at more recent advanced seeding techniques which are implemented with real time feed-back control using FBRM® and PVM® to grow large, well defined crystals.

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Ensuring Consistency in Particle Size Distribution Through SeedingHaving a consistent, robust, well behaved crystallization process is necessary in the large scale production of any chemical process. Batch crystallization is often the biggest bottleneck at production scale and can result in a product that takes excessively long times to filter and dry. If the targeted particle size specification is not met, the product may need to be re-crystallized or perhaps an additional unit operation such as milling may be introduced to achieve the desired particle size. Such inconsistencies and irregularities at the crystallization step can significantly increase the cost of processing through higher energy and personnel costs and lower product throughputs. A common way of introducing some level of consistency into batch crystallization processes is through the introduction of a seeding step. A seeding step provides the opportunity to add the same mass, size and polymorphic form of seed crystals at the exact same temperature or timepoint in a batch. Therefore, in essence through seeding, scientists can ensure the same starting point from one batch to the next providing higher repeatability to the process.

However, the seeding process itself introduces additional questions (process param-eters) that previously were not considered: ‘how much seed should be added?’, ‘what size should the seed particles be and at what temperature should the seed be added?’. To answer these questions the function of seed particles in a crystallization process must be considered. In very simple terms, seed particles are added to replace the nuclei/particles that would otherwise be created through spontaneous nucleation. The seed particles provide the surface on which the dissolved solute can grow. Ideally, after seeding, the particle population would remain constant throughout the batch (assuming no breakage or agglomeration occurs in the process). For example, if 1000 seed particles were added, it would be expected to have 1000 particles at the end of the crystallization. These particles would of course by this time be larger in size. In reality the nucleation process, which is a function of supersaturation as is particle growth, can never be fully quenched (see Figure 1). Therefore there will always be some level of nucleation even in the presence of seed particles. The level of this nucleation is a function of the rate of supersaturation generation during the process and becomes a critical parameter that must be optimized with the seeding step itself.

So, how do scientists identify if a process would benefit from a seeding step? In numerous cases this can be relatively easy. Processes with inconsistencies in the final particle size distribution often result in inefficiency with downstream processing such as filtration or drying. In these cases, the seed distribution and the point of nucleation is typically not the same from one batch to the next. The introduction of a seeding step is critical in optimizing these batch crystallization processes. Polymorphic crystallization processes are also prime candidates for seeding because the addition of seed crystals with the desired polymorphic form greatly increases the probability of avoiding undesired forms.

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How much seed should be added?

What size should the seed particles be and at what temperature should the seed be added?

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Choosing the Seeding Conditions (Supersaturation)Having decided that a process should be seeded, the next step is to identify at what temperature or point in time should the seed be added. This is where solubility/MSZW (MetaStable Zone Width) data can be of significant benefit. A general rule of thumb would be to add the seed particles midway between the solubility curve and the metastable zone. One consideration that needs to be taken into account, especially when scaling-up, is that solubility is a thermodynamic function and is scale independent. The MSZ on the other hand is a kinetic function which is scale dependent and is influenced by the rate of supersaturation generation, mass transfer, heat transfer and the presence of solids or impurities (it should also be noted that dissolved impurities can have a significant impact on the solubility).

This means that the MSZW observed at lab scale may not be the same as that observed at production scale and can result in problems during scale-up. A simple example of this can be seen from Figure 2. This data is for an organic compound being crystal-lized from water. Cooling is at a fixed linear rate. Figure 2a shows a MSZW of 15°C when cooling a perfectly clean, particle free solution. In the case of Figure 2b, 10 large seed crystals (weighing less than 0.001g) were added at the point of saturation. The point of nucleation in this case was detected 6°C earlier than that observed for Figure 2a, resulting in a MSZW of 9°C as compared to 15°C. In this case, the presence of only a few seed particles has significantly narrowed the MSZ. This is an effect which should be taken into account when designing the seeding temperature otherwise the addition of the seed particles may induce nucleation as a result of being added too close to the MSZ, which has narrowed because of their presence in the solution.

This effect could also be of particular importance if the crys-tallization vessel is not fully washed down between batches. Hang up or encrustation on the walls of the vessel from the previous batch may inadvertently ‘seed’ the new solution, thereby narrowing the MSZW to that observed previously or, in a worst case scenario, even inducing nucleation which would result in a significant variation in the batch. Figure 3 from Pfizer2 compares FBRM® fine counts between 1-5µm for three production batches. Batch C has resulted in significantly more fine particles compared to A and B. Closer examination of the FBRM® trend identifies that Batch C has particles present in the solution prior to the addition of the seed crystals. These particles could be the result of an un-expected spontaneous nucleation event or from particles left on the walls of the vessel from earlier batches. In either case the presence of these particles resulted in the batch producing many more fine particles than expected and consequently failing particle size specification.

Solubility curve and MSZW (Metastable Zone Width)

Barrett, M. et al, Chemical Engineering Research and Design1

Figure 2. Influence of a few seed particles on the MetaStable Zone Width as measured with FBRM®

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The point in the MSZ where the seed is added (seeding temperature) also has a big impact on the relative rates of nucleation and growth as can be seen from Figures 4 and 5. FBRM® results from three seeding experiments are compared where 0.25g of seed were added at different temperatures in the MSZ (25°C, 22.5°C and 20°C). The seed particles were all the same particle size distribution and this was characterized by FBRM® by comparing the chord length distribution a few minutes after seed addition for each experiment.

Figure 4 compares FBRM® fine counts between 1-20µm, which would be indicative of the relative nucleation rate. Seeding close to the MSZ (20°C) and therefore at the highest supersaturation, results in the fastest rate of nucleation and therefore the greatest number of fine particles. On the other hand, seed-ing close to the solubility curve and a low supersaturation level has resulted in a slow nucleation rate and the least number of fine particles. Figure 5 shows a comparison of the endpoint distribu-tions for each of the experiments along with PVM® images at the isolation point (20°C). Seeding close to the MSZ (20°C) results in the least particle growth while seeding close to the solubility curve (25°C) produces the highest relative growth rate and the greatest number of large particles. This data demon-strates that the seed temperature can be manipulated to have a growth or nucleation dominated process which in turn will influence the final particle size distribution.

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Figure 3. FBRM® identifying the presence of solids prior to seeding during a production crystallization

Figure 4. Influence of the seeding temperature on the relative rate of nucleation

Figure 5. Endpoint chord length distributions and PVM® images showing the increase in particle size (growth) at higher seed temperatures. All data shown at isolation point (20°C).

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Identifying Seed Loading and Seed Size DistributionThe next point to consider is ‘how much seed mass to add?’ and ‘what size should it be?’. This really depends on the final particle size required, polymorphic form, or if there are impurity concerns associated with the crystallization. The seed is generally being added to provide the necessary surface area for particle growth (This is not always the case. Sometimes a small amount of seed is added close to the MSZ to induce nucleation). The size and number of seed particles added determines this available surface area. Generally seed particles are small as this increases the number of ‘points’ for crystal growth. If large particles are required then less seed may be added therefore getting a fewer number of particles to grow very large in size. On the other hand if small particles are required then a larger seed loading is necessary so that the growth is spread across a greater number of particles, consequently growing each particle only by some small amount. Typical seed loadings can vary from as little as 0.1 wt% to as much as 5 wt% (and even more) depending on the requirements. An important point to note at this stage is that the size and number of seed particles is not the only parameter that needs to be considered. Following seed addition the rate of supersaturation generation (cooling rate or anti-solvent addition rate) should map the growth kinetics of the process as much as possible. This will ensure that a low level of supersaturation is maintained throughout which promotes particle growth while minimizing nucleation.

Seed particles are often milled to achieve the correct particle size distribution. These milled particles will have fractured edges and faces which may in turn act as points for growth, therefore resulting in multiple growth points along the surface of a single seed particle. This can sometimes result in an undesired growth mechanism which leads to agglomerated particle structures (as seen in Figure 6). Ten minutes after the addition of a milled seed the particles are growing into a dendritic, agglomer-ated structure, with many particles growing in multiple directions from a single seed crystal and eventually resulting in breakage and secondary nucleation of smaller crystals. This is an undesired growth mechanism which may present additional problems downstream during filtration and drying as well as poor powder handling properties. One way to avoid this type of growth behavior in milled seed is to ‘heal’ the seed particles beforehand. This is done by suspending the seed particles in a saturated solution of mother liquor and mixing for a period of 12 to 24 hours. During this time the fractured faces on the crystals will be repaired through the Ostwald ripening process, resulting in seed particles with a smooth crystal surface which will pro-mote uniform particle growth.

It is also good practice to implement an isothermal hold period after seed addi-tion. The function of this hold period is to allow an appropriate time for the seed to properly disperse and to consume the initial solution supersaturation through growth. Usually these hold periods are quantified during the development of the process at lab scale.

Figure 6. PVM® image of milled seed with surface imperfections resulting in an undesired dendritic growth mechanism

Figure 7. FBRM® tracking the dispersion of seed particles in a large scale crystallizer

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However on scale-up the seed dispersion kinetics can be significantly different to those observed at lab scale. The addition of a dry powder seed to a solution will often result in the powder clumping together due to electrostatic interactions and cohesive forces. Over time these loose adhesive forces will be broken down through wetting and shear forces as a result of mixing. On scaling-up the mixing speed may be reduced resulting in a different hydrodynamic environment and dispersion kinetics to that observed at the development scale. Figure 7 shows FBRM® data monitoring the dispersion of seed in a large scale crystallizer. As can be seen in this case it takes the seed three hours to properly disperse. Therefore an isothemal hold period of at least three hours is neccessary before moving to the next step of the process. Conversely, at lab scale the seed dispersed in less than one hour. This is another great example of how real time monitoring ensures that the process is continually optimized and improved even during scale-up.

Seed mass is usually determined by weighing the seeds on a balance. Mass is of course insensitive to size, count, or surface area. The seed size distribution is often measured offline with laser diffraction or sieving which are less sensitive to the fines fraction and surface area. In the lab this is not usually a problem since the seed source is small and only a few grams of milled material are added to a vessel. However this becomes a bigger source of inconsistency on scale-up. In manufacturing, the seed source may be stored in a larger container and several hundred grams or kilograms may be added to a vessel. Segregation of the seed source may result in an inconsistent seed size. This can be validated by inline particle size measurement tracking the number distribution a few minutes post seeding. If the initial number of seeds is consistent and the seed dimension and chord length distribution is consistent, then the endpoint particle size distribution has a much greater chance for batch-to-batch consistency (Figure 8).

Figure 8. FBRM® endpoint distributions for consis-tent and in-consistent processes

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Seeding to Control Purity and Optimize Downstream SeparationsMousaw et al.3 described the importance of the seeding step during the production crystallization of a diastereomeric intermediate compound. The material was occasionally fail-ing optical purity specifications during manufacturing due to the presence of the un-desired diastereomer (R-isomer). The failed batches also had significantly longer filtration and drying times compared to good batches. Seeding with the desired diastereomer (S-isomer) was a critical step of the crystallization process as it was known to significantly improve the kinetic resolution. However, in the original process, the seeding step was followed by a large secondary nucleation event as observed through FBRM® measurements at laboratory scale and turbidity measurements at production scale. Results from an entire production campaign identified that the temperature of this secondary nucleation event could be directly correlated with the diastereomeric excess – the higher the temperature of secondary nucleation the higher the diastereomeric excess (product purity). It was also observed that higher secondary nucleation temperatures significantly improved the filtration and drying rates, resulting in shorter process cycle times (Figure 9). Consequently this secondary nucleation tempera-ture was identified as a critical parameter that needed to be controlled. Through experimentation at both lab and pilot scales, mixing speed and seed surface area were identified as being critical in determining the temperature of secondary nucleation. It was shown that seed loadings with larger seed surface areas resulted in higher temperatures of secondary nucleation. Figure 10 compares lab FBRM® data for a high (2 wt%) and low (0.2 wt%) seed loading. For the high seed loading the temperature of secondary nucleation is seen at 43°C which corresponds to low supersaturation and therefore a relatively small nucleation event. The low seed loading on the other hand resulted in a temperature of secondary nucleation at 18°C, which is a point in the process with much higher supersatura-tion, therefore resulting in a much greater and rapid nucleation event, with many more fine particles crystallizing from solution as is evident from the FBRM® counts. This batch also had a much lower diastereomeric excess and longer filtration time. On implementing the re-designed process with higher seed loading and faster agitation rates at production scale no batch failures were observed over a campaign of 39 batches and the average centrifugation time had decreased from 7.5 hours (old process) to 2.2 hours (new process).

Figure 9. Process cycle time and diastereomeric excess (product purity) plotted against the temperature of secondary nucleation. A high nucleation temperature corresponds to a short cycle time and a high diastereomeric excess.

Figure 10. The effect of seed loading on the temperature of secondary nucleation

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Advanced Seeding Techniques and Feedback ControlTraditionally, seed particles have been added from an external source, generally as a powder but sometimes as a slurry in a saturated solution of mother liquor or in an appropriate anti-solvent. However for some processes there is still reluctance to seed primarily due to safety issues especially when working with high potency materials. Seeding from an external source also increases the probability of introducing some unexpected impurities into the process. These concerns can be addressed by creating the seed particles internally in the process and using FBRM® to ensure a consistent seed bed, in terms of particle dimension and number. Chew et al4 introduced an automated closed loop feedback control technique using FBRM® for unseeded cooling crystallizations for ensuring consistent and repeatable crystal product quality. A typical temperature profile for this process can be seen in Figure 11. This technique involved the use of FBRM® to detect the initial point of spontaneous nucleation during cooling (B). Nucleation was deemed to have occurred when four successive increases in the total counts (#/sec, 1-1000µm) were observed. At this point the controller was triggered to stop cooling and the temperature was held isothermally for 15 minutes, by which time primary nucleation was complete (C). The temperature was then increased at a fixed rate while using the coefficient of variance (C.V.) calculated from FBRM® statistics as {the standard deviation/mean} to monitor the change in particle size distribution. Once the pre-defined C.V. set point was achieved, the target seed size distribution was reached and the controller actuated the final cooling phase (Figure 11: D-E), which was a cool down at a pre-defined constant rate. The C.V. set point was chosen based on a typical PSD (Particle Size Distribution) of external seed particles. A point to note is that the rate of heating (Figure 11: C-D) had a significant impact on the batch time. A slow heating rate gave very tight control but a significantly longer batch time while a faster heating rate resulted in a poorer level of control but a much shorter batch time. As a result a heating rate of 0.3°C/min was chosen as this was deemed an appropriate trade-off between tightness of control and batch time.

Figure 12 highlights the significant improvement in the final product particle dis-tribution consistency when this feedback control approach is used compared to the standard unseeded cooling crystallization for the glycine-water system. Similar results were observed for the paracetamol–water system which has a signicantly lower solubility than glycine in water. More recently, Hermanto et al5, extended and refined this feed back control strategy for the anti-solvent cyrstallization of the glycine-water system with similar success.

A limitation of this technique is that some prior knowledge of the process kinetics are necessary to identify the appropriate hold period after nucleation (Figure 11: B-C) and the final rate of cooling (Figure 11: D-E). Creating a uniform and consistent seed bed is very important as a starting point, however the rate of cooling after seeding must be mapped to the growth kinetics or excessive fines could be generated through secondary nucleation if the cooling rate is too quick. This technique also cannot account for or eliminate fine particles that may be generated through mechanical means such as attrition and breakage, but it is a very simple and straightforward way to create the seed bed in process therefore eliminating any safety concerns associated with traditional seeding techniques.

Figure 11. Temperature profile of a typical crystallization run using the closed loop feedback control strategy

Figure 12. Square-weighted CLD’s (Chord Length Distribution) for the endpoints of five glycine-water cooling crystallizations from (a) unseeded and (b) seed bed created internally through a feedback control loop using FBRM®

a. Unseeded

b. Seed bed created with feedback control loop

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An advancement on this methodology was proposed by Abu Bakar et al.6 through their feedback control technique called ‘Direct Nucleation Control’ (DNC). This is again a model-free approach in which the number of counts measured by FBRM® is a directly controlled region. The advantage of this technique, over that proposed by Chew et al.4, is that it does not use pre-determined heating or cooling profiles. The temperature profiles are automatically generated continuously during the course of the crystallization process in response to the number of particles generated by the nucleation events. (The same principle applies for anti-solvent crystallizations). No prior knowledge of the model, process kinetics or the metastable zone width is necessary as this approach will automatically determine the optimal operating profile by continuously detecting the metastable zone limit in real time using a feedback control strategy. Figure 13 represents a typical operating profile for this approach which automatically switches between heating and cooling (or solvent and anti-solvent addition) to generate nucleation or dissolution of fine particles to maintain a preset desired number of FBRM® counts. As a result fines are continually being generated and dissolved to continuously maintain the total number of particles counted (counts/second) within the pre-defined range.

This approach was initially implemented for the anti-solvent crystallization of the glycine water system. Figure 14a shows a typical profile where the total FBRM® counts are controlled at a pre-defined value of 2000 particles counted (counts/second) During the initial stage of the process there is some over-shoot and under-shoot of the counts before the system finally reaches the pre-defined number of counts. This initial overshoot is attributed to the very fast increase in counts associated with the primary nucleation event. Subsequent nucleation events (second nucleation events) do not have the same rapid increase in counts and therefore it is easier to control and maintain at the preset number of counts. Figure 14b shows that the number of fine counts is held relatively constant throughout the crystal-lization while there is a steady increase in the number of coarse particles. This clearly indicates that growth is the dominant mechanism during this controlled crystallization process.

This method now provides a means of targeting a particular particle size or particle size distribution as can be seen from Figure 15. If smaller particles are required then the crystalliza-tion can be controlled at a higher number of counts (e.g. 4000/sec) or if larger particles are required then a smaller number of counts (e.g. 2000/sec) should be implemented as the control set point. One thing to note is that in this example the FBRM® total counts is used as the control set point. This may not always be the best statistic to use as it is counting the total number of par-ticles measured by the probe. It may be more appropriate to tune the statistic to the size and shape of the particle system under investigation – for example using the counts between 0-20µm to control the nucleation and dissolution of fine particles. Also a combination of statistics may provide a more robust control set point than just using a single value. The use of the Mean Square Weight or co-efficient of variance as used by Chew et al4 would introduce some size information into the control set point and this could result in a more robust control strategy. However this control approach in

Figure 13. Typical Direct Nucleation Control (DNC) operating profile

Figure 14. Profiles for (a) the total number of FBRM® counts/sec and the anti-solvent/solvent addition rates and (b) the FBRM® fine and coarse counts generated during the experiment

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general could be particularly beneficial to contract manufac-turers who routinely have to provide products with different particle size specifications to meet the requirements of their customers. This technique could also provide a cost effec-tive means whereby the desired particle size can be achieved directly from the crystallization process and downstream mill-ing operations could be eliminated.

Recently, Saleemi et al.7 implemented this approach to improve the final powder properties of a cardiovascular active pharmaceutical ingredient (API). The original linear cool-ing crystallization resulted in agglomerated particles with a strong odor which was attributed to residual solvent trapped in the agglomerated structure. Larger primary particles were desired by formulations as they were found to have better flow and compression characteristics when making the tablets and this would also eliminate the odor from trapped residual solvent. Although the odor and particle size challenges could be resolved using a temperature-cycling regime post crystal-lization, the number of cycles required as well as upper and lower temperatures and heating/cooling rates were all determined through a series of trial and error experimentation. The automated direct nucleation control approach automatically determined the required number of temperature cycles during the crystallization itself from a single experiment, and compared to the traditional linear cooling process resulted in a product of superior quality in terms of the particle size and agglomeration (no agglomeration and therefore no trapped residual solvent which caused the odor). Therefore this approach could significantly reduce development time by eliminating lengthy trial-and-error experimentation that would otherwise be needed for process optimization.

ConclusionsThe use of a seeding step is a great way to introduce a degree of consistency and repeat-ability in batch crystallization processes. Several seed variables need to be considered during the design of the seeding step to ensure its successful implementation. The 15-30 minutes following seed addition is generally the most critical period in ensuring the effectiveness of the seed. Because this period is often at an elevated temperature and supersaturation where sampling is difficult,in situ monitoring of seed behavior is often of critical importance during this period. This can be especially important during the scale-up of new processes where differences in fluid hydrodynamics compared to that observed at lab scale can influence seed dispersion kinetics as well as nucleation and growth rates. Mapping seed behavior through scale-up significantly increases the probability of success and minimizes the need for additional scale down experiments for improved understanding and optimization due to unexpected process behavior. Monitoring particle dimension and count in real time enables the seed bed to be created in process and allows for direct feedback control of the seed bed to target a final product of a specific particle size distribution with desired downstream and powder properties.

Figure 15. Comparison of the square-weighted mean chord length for the un-controlled and DNC experiments at 2000#/sec and 4000#/sec

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Measurement for optimization in real time – FBRM® is a precise and

sensitive technology which tracks changes to particle dimension,

particle shape, and particle count. Over a wide detection range

from 0.5 to 2000µm, measurements are acquired in real time while

particles are forming and can still be modified enabling process

optimization and control. No sampling or sample preparation is

required – even in highly concentrated (70% and higher) and

opaque suspensions. 

structure to another edge. Thousands of individual chord lengths are typically measured each second to produce the Chord Length Distribution (CLD) (Fig-ure c). The CLD is a “fingerprint” of the particle system, and provides statistics to detect and monitor changes in particle dimension and particle count in real time (Figure d).

Unlike other particle analysis tech-niques, FBRM® measurement makes no assumption of particle shape. This al-lows the fundamental measurement to be used to directly track changes in the particle dimension, shape, and count.

How does FBRM® work?The FBRM® probe is immersed into a dilute or concentrated flowing slurry, droplet emulsion, or fluidized particle system. A laser is focused to a fine spot at the sapphire window interface (Fig-ure a). A magnified view shows individ-ual particle structures will backscatter the laser light back to the probe (Figure b). These pulses of backscattered light are detected by the probe and translated into Chord Lengths based on the simple calculation of the scan speed (velocity) multiplied by the pulse width (time). A chord length (a fundamental measure-ment of particle dimension) is simply defined as the straight line distance from one edge of a particle or particle

1 2 3 4

Figure b.

Figure c. Chord Length Distributions

Figure d. Trended Statistics

Laser Source

Laser Return

Optics Module

Sapphire Window

Figure a.

Appendix: Focused Beam Reflectance Measurement (FBRM®)

Internet: http://www.mt.com/autochem

Subject to technical changes©10/2012 Mettler-Toledo AutoChem, Inc.7075 Samuel Morse DriveColumbia, MD 21046 USATelephone +1 410 910 8500Fax +1 410 910 8600 Email autochem@mt.com

References1. Barrett, P. and B. Glennon, Characterizing the metastable zone width and solubility

curve using Lasentec FBRM and PVM, Chemical Engineering Research & Design 80 (A7): 799-805 (2002).

2. Chen, C-S. & Timmermans, J., 2006. Crystallization Process Monitoring in Pharmaceutical Manufacturing, Mettler-Toledo Users Conference.

3. Mousaw, P., Saranteas, K. & Prytko, B., 2008. Crystallization Improvements of a Diastereomeric Kinetic Resolution through Understanding of Secondary Nucleation. Organic Process Research & Development, 12(2), 243-248.

4. Chew, J.W., Chow, P.S. & Tan, R.B.H., 2007. Automated In-line Technique Using FBRM to Achieve Consistent Product Quality in Cooling Crystallization. Crystal Growth & Design, 7(8), 1416–1422.

5. Hermanto, M.W., Chow, P.S. & Tan, R.B.H., 2010. Implementation of Focused Beam Reflectance Measurement (FBRM) in Antisolvent Crystallization to Achieve Consistent Product Quality. Crystal Growth & Design, 10(8), 3668-3674.

6. Abu Bakar, M. R., 2009. The Impact of Direct Nucleation Control on Crystal Size Distribution in Pharmaceutical Crystallization Processes. Crystal Growth, 9(3), 1378-1384.

7. Saleemi, A.N., Steele, G., Pedge, N.I., Freeman, A. & Nagy, Z.K., 2012. Enhancing the Crystalline Properties of a Cardiovascular Active Pharmaceutical Ingredient Using a Process Analytical Technology Based Crystallization Feedback Control Strategy. International Journal of Pharmaceutics, 430, 56-64

8. Bakar, M.R.A., Nagy, Z.K. & Rielly, C.D., 2009. Seeded Batch Cooling Crystallization with Temperature Cycling for the Control of Size Uniformity and Polymorphic Purity of Sulfathiazole Crystals. Organic Process Research & Development, 13(6),1343–1356.

www.mt.com/crystallization

Benjamin SmithBen_Smith@mt.com

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