Recent Advances for Seeding
a Crystallization Process
A Review of Modern Techniques
New Technologies for
Crystallization Development
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.
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
Introduction
The desire to produce powders with a specific particle size distribution and powder
handling properties continues to drive crystallzation 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 designing 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 practice as well as looking at
more recent advanced seeding techniques which are implemented with real time feedback control using FBRM® and PVM® to grow large, well defined crystals.
2
However, the seeding process itself introduces additional questions (process parameters)
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.
3
much seed should be
“ How
added?
“
size should the seed
“ What
particles be and at what
temperature should the
seed be added?
“
“
Will the seeding protocol
be consistent upon
scale-up?
“
Crystal Size
#s-1m-3, µms-1, µm
Ensuring Consistency in Particle Size Distribution Through Seeding
Having 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.
Growth Rate
Nucleation Rate
Supersaturation (g/g)
Figure 1. Plot showing the relationship between
supersaturation, growth and nucleation rate
Concentration
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).
Metastable Zone Limit
Solubility
Temperature
Solubility curve and MSZW (Metastable Zone Width)
Concentration
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
Barrett, M. et al, Chemical Engineering Research
and Design1
can be seen from Figure 2. This data is for an organic compound being crystallized 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.
a. No Solids Present (MSZW = 15°C)
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
MSZW = 15°C
particles has significantly narrowed the MSZ. This is an effect
which should be taken into account when designing the seeding
Nucleation
Cooling
temperature otherwise the addition of the seed particles may
induce nucleation as a result of being added too close to the MSZ,
Metastable Zone Limit
which has narrowed because of their presence in the solution.
Solubility
Temperature
b. Solids Present (MSZW = 9°C)
MSZW = 9°C
Concentration
This effect could also be of particular importance if the crystallization 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.
Nucleation
Cooling
Metastable Zone Limit
Solubility
Temperature
Figure 2. Influence of a few seed particles on the
MetaStable Zone Width as measured with FBRM®
4
Batch A (25°C Seeding)
Counts (1-5µm range)
8000
Batch B (22.5°C Seeding)
Batch C (20°C Seeding)
7000
Batch C has significantly higher
concentration of fines
6000
5000
4000
Batch C
solids present
before seeding
3000
2000
1000
0
00:00
03:00
06:00
09:00
12:00 15:00
18:00
Time (hr:min)
21:00
24:00
27:00
30:00
Figure 3. FBRM® identifying the presence of solids
prior to seeding during a production crystallization
Concentration
18000
16000
14000
20°C 22°C 25°C
Cooling
MSZW
Solubility
12000
Temperature
10000
8000
6000
4000
2000
0
00:00
01:00
02:00
03:00
04:00
05:00
Time (hr:min)
06:00
07:00
08:00
Figure 4. Influence of the seeding temperature
on the relative rate of nucleation
20°C Seeding
12
Concentration
14
100µm
20°C 22°C 25°C
Cooling
MSZW
Solubility
22.5°C Seeding
10
Counts (Sq. Weight)
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, seeding 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 distributions 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 demonstrates 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.
9000
Counts (0-20µm range)
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.
Temperature
8
100µm
6
25°C Seeding
4
2
100µm
0
0
10
100
1000
Chord Length (µm)
5
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).
Identifying Seed Loading and Seed Size Distribution
The 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.
Median, #Weight 1-1000µm
Counts/Sec, 1-5µm
Counts (Sq. Weight)
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, agglomerated structure,
with many particles growing in multiple
directions from a single seed crystal and
400
eventually resulting in breakage and
350
secondary nucleation of smaller crystals.
Increase in Fines
This is an undesired growth mechanism
300
which may present additional problems
downstream during filtration and drying
250
as well as poor powder handling properInital Seed Dispersion
ties. One way to avoid this type of growth
200
behavior in milled seed is to ‘heal’ the seed
150
particles beforehand. This is done by suspending the seed particles in a saturated
100
Decrease in Coarse
solution of mother liquor and mixing for a
period of 12 to 24 hours. During this time
50
Seeds Added
the fractured faces on the crystals will be
0
repaired through the Ostwald ripening
00:00
02:00
04:00
process, resulting in seed particles with a
smooth crystal surface which will promote
uniform particle growth.
Figure 6. PVM® image of milled seed with surface
imperfections resulting in an undesired dendritic
growth mechanism
It is also good practice to implement an isothermal hold period after seed addition.
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.
6
Counts/Sec, 100-250µm
Seed Dimension Reaches a
Steady State (i.e. Fully Dispersed)
06:00
Time (hh:mm)
08:00
10:00
12:00
Figure 7. FBRM® tracking the dispersion of seed
particles in a large scale crystallizer
a. Inconsistency
b. Consistency
Batch B
Batch C
Batch C
Batch D
Batch D
Batch E
Batch E
Counts
Batch A
Batch B
Counts
Batch A
Chord Length (µm)
Chord Length (µm)
Figure 8. FBRM® endpoint distributions for consistent and in-consistent processes
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).
7
Video: Crystallization
Process Transfer
See how chemists and chemical engineers
avoid batch inconsistencies, improve
filtration and eliminate batch failure. Learn
how GlaxoSmithKline (GSK) investigated
the root cause of the batch inconsistences
during crystallization scale-up.
Click here to view this video
100
110
100
Process Cycle (hr)
80
80
Process Cycle
70
Product de
70
60
60
Diastereometric Excess de (%)
90
90
50
50
40
40
15
25
35
Nucleation Temperature (°C)
45
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.
Batch
Seed Amount (wt%)
Agitation (RPMs)
Filt. Time (min)
1
0.2
131
10.5
2
2.0
131
6.5
3
2.0
234
5
4
2.0
131
4
5
2.0
234
4
6
0.2
131
11
50
37500
Batch Temp (Exp. 5)
Count/Sec (Exp. 5)
Batch Temp (Exp. 6)
30000
Count/Sec (Exp. 6)
Exp. 5
30
22500
TN = 43.5°C
ees = 99.1%
Count/Second
40
Temperature (°C)
Seeding to Control Purity and Optimize
Downstream Separations
Mousaw et al.3 described the importance of the seeding step
during the production crystallization of a diastereomeric intermediate compound. The material was occasionally failing 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 temperature 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 supersaturation, 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).
15000
20
Exp. 6
10
7500
TN = 18°C
ees = 85.7%
tfilt = 150sec
0
50
100
150
200
250
Time (min)
300
350
0
400
Figure 10. The effect of seed loading on the
temperature of secondary nucleation
8
65
55
Temperature (°C)
50
9
45
40
D
35
30
B C
25
20
E
15
0
50
100
150
200
Time (min)
250
300
Figure 11. Temperature profile of a typical
crystallization run using the closed loop feedback
control strategy
2
a. Unseeded
1.5
1
0.5
Figure 12 highlights the significant improvement in the final product particle distribution 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.
0
1
2
10
100
Chord Length (µm)
1000
b. Seed bed created with
feedback control loop
1.5
Counts/Second
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.
A
60
Counts/Second
Advanced Seeding Techniques and Feedback Control
Traditionally, 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.
1
0.5
0
1
10
100
Chord Length (µm)
1000
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®
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 principal 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.
Concentration
DNC Operating Profile
Solubility
Temperature
Figure 13. Typical Direct Nucleation Control (DNC)
operating profile
4000
Number of Counts
Target Counts/s
3000
Anti-solvent
16
2000
11
1000
6
1
0
0
20
40
Time (min)
60
b. 2000
80
Fine (<22µm)
Coarse (>250µm)
Number of Counts/s (Fine)
This method now provides a means of targeting a particular par1500
ticle size or particle size distribution as can be seen from Figure
15. If smaller particles are required then the crystallization can be
1000
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
500
®
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
0
it is counting the total number of particles measured by the probe.
0
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
10
Addition Rate (g/min)
21
Solvent
400
300
200
100
Number of Counts/s (Coarse)
a.
Counts/Second
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 crystallization
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.
Metastable Zone Limit
1
20
40
Time (min)
60
80
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
300
Mean Chord Length (sq weighted)
general could be particularly beneficial to contract manufacturers 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 effective means whereby
the desired particle size can be achieved directly from the crystallization process and downstream milling operations could be
eliminated.
200
Recently, Saleemi et al.7 implemented this approach to improve
the final powder properties of a cardiovascular active pharma100
ceutical ingredient (API). The original linear cooling crystallization resulted in agglomerated particles with a strong odor which
was attributed to residual solvent trapped in the agglomerated
0
structure. Larger primary particles were desired by formulations
0
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 crystallization, 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.
Conclusions
The use of a seeding step is a great way to introduce a degree of consistency and repeatability
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.
11
Uncontrolled
4000 #/Sec
2000 #/Sec
20
40
Time (min)
60
80
Figure 15. Comparison of the square-weighted
mean chord length for the un-controlled and DNC
experiments at 2000#/sec and 4000#/sec
Appendix: Focused Beam Reflectance
Measurement (FBRM®)
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. Figure c. Chord Length
Distributions
Laser Source
Laser Return
Optics Module
1
2
3
4
Figure b.
Figure a.
Sapphire Window
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 (Figure a).
A magnified view shows individual 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 measurement of particle dimension) is simply defined as the straight
line distance from one edge of a particle or
12
particle structure to another edge. Thousands of individual chord lengths are typically measured each second to produce
the Chord Length Distribution (CLD)
(Figure 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 techniques,
FBRM® measurement makes no assumption of particle shape. This allows the
fundamental measurement to be used to
directly track changes in the particle dimension, shape, and count.
Figure d. Trended
Statistics
References
1.
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 Smith
[email protected]
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