ogrady2007-multi-scale characterization of anti-solvent crystallization by
DESCRIPTION
It's a research paper related to the anti solvent crystallization system.TRANSCRIPT
Page 1
Multi-Scale Characterization of Anti-solvent
Crystallization
by
Des O‟Grady, B.E.
A Thesis presented to the National University of Ireland in fulfilment of the
requirements for the degree of Doctor of Philosophy (Ph.D.)
Under the supervision of
Dr. Brian Glennon
December 2007
School of Chemical and Bioprocess Engineering,
College of Engineering, Mathematical and Physical Sciences,
University College Dublin.
ACKNOWLEDGEMENTS
First and foremost I would like to thank my parents Dorothy and Dermot. Their love
and support over the years has been unstinting and in one way or another they have
taught me everything I know. For that I will always be grateful.
Thanks to my wonderful sisters, Caoilfhionn, Fiona and Elva, for constantly
endeavouring to figure out what it was that I actually did during my PhD. They‟re
inquisitive nature has hopefully rubbed off on me over the years.
I would like to thank Dr. Brian Glennon for his talented and dedicated guidance. He
has an uncanny ability to get to the heart of a problem and convey the solution in a
clear and meaningful way. My PhD studies were made possible and have been a
valuable and extremely enjoyable experience thanks to him.
I would like to thank my extended family of aunts, uncles and cousins. A special
thanks to Joe and Teresa for taking such a keen interest in my studies over the years.
Countless friends have made my PhD studies all the more enjoyable. They offered a
unique brand of humour that provided the perfect distraction from the lab. I have
ninety-two reasons to thank Skellig and Marko along with Bud, Miller and Mac. Not
to concentrate on that small group I have to also thank many others from college and
school including Ed, Hutchy, Dots, Robbie, Ale, Karol, Marsha, Nikki, and Sarah.
Thanks to my fellow postgrads for making UCD a fun place to be. Special thanks to
Ale, Brian and Paul who have been were there from the start. Thanks to an influx of
new faces the last couple of years have been very enjoyable. Thanks to Eoin, Jessica,
Kate, Aisling, Barry, and John for starting it off. Thanks to Mark for all his help and
the CFD work in this thesis. Mairtin, good luck!
Thanks to all the lecturers in the School of Chemical and Bioprocess Engineering. A
special thanks to Dermot Malone, Patricia Kieran, Eoin Casey and Frank
MacLoughlin for all their help.
Thanks to everyone else in the school who helped me out along the way, including,
Pat, Liam, Oliver, Jim, Brid, Sinead, Pat and Brian. A special thanks to Aoife.
Thanks to everyone at Mettler Toledo for giving me a great opportunity during my
PhD. A special thanks to Paul Barrett for opening that door.
Finally, I would like to thank Anne for her brilliant friendship and unwavering loyalty
and love. Here‟s to NYC Anneski!!!
TABLE OF CONTENTS
LIST OF PUBLICATIONS 2
ABSTRACT 1
CHAPTER 1: INTRODUCTION 2
CHAPTER 2: LITERATURE REVIEW 8
2.1 ANTI-SOLVENT CRYSTALLIZATION 8
2.1.1 INTRODUCTION 8
2.1.2 VARIABLES TO CONSIDER IN ANTI-SOLVENT CRYSTALLIZATION 9
2.1.2.1 Choice of Anti-solvent 9
2.1.2.2 Anti-solvent Addition Rate 11
2.1.2.3 Agitation Intensity 13
2.1.2.4 Concentration Effects 14
2.1.2.5 Conclusion 17
2.2 FOCUSSED BEAM REFLECTANCE MEASUREMENT (FBRM) 19 2.2.1 INTRODUCTION 19
2.2.2 MODE OF OPERATION 20
2.2.3 VALIDATION OF FBRM TECHNIQUE – INSTRUMENT PARAMETERS 25
2.2.3.1 Probe Position 26
2.2.3.2 Focal Point Position 27
2.2.3.3 Measurement Duration 29
2.2.4 VALIDATION OF FBRM TECHNIQUE - PROCESS PARAMETERS 30
2.2.4.1 Solids Concentration 30
2.2.4.2 Agitation Rate 32
2.2.4.3 Particle Material 33
2.2.4.4 Surrounding Medium 35
2.2.4.5 Temperature 36
2.2.5 CORRELATING FBRM MEASUREMENTS WITH OTHER PARTICLE SIZE ANALYSERS 37
2.2.6 THEORETICAL MODELLING OF FBRM DATA 40
2.2.6.1 Modelling Spherical Particles 41
2.2.6.2 Modelling Non-Spherical Particles 44
2.2.7 FBRM FOR CRYSTALLIZATION CHARACTERIZATION 47
2.2.7.1 Solubility Curve and Metastable Zone Width Determination 49
2.2.7.2 Nucleation Kinetics 50
2.2.7.2 Monitoring Crystal Size 51
2.2.7.3 Temperature Cycling 54
1.2.7.4 Polymorphic Transitions 55
2.2.7.5 Effect of Impurities 56
2.3 PROCESS VIDEO MICROSCOPE (PVM) 57
2.3.1 INTRODUCTION 57
2.3.2 CHARACTERIZATION OF PARTICULATE SYSTEMS USING PVM 61
2.4 ATTENUATED TOTAL REFLECTANCE FOURIER TRANSFORM INFRARED
SPECTROSCOPY (ATR-FTIR) 62 2.4.1 INTRODUCTION 62
2.4.2 INITIAL WORK USING VARIOUS CALIBRATION MODELS 63
2.4.3 THE USE OF ATR-FTIR TO CHARACTERIZE CRYSTALLIZATION PROCESSES 67
2.4.3.1 Seeding 67
2.4.3.2 Oiling Out 68
CHAPTER 3: SOLUBILITY MEASUREMENT FOR AN ANTI-SOLVENT
SYSTEM USING GRAVIMETRIC ANALSYSIS, ATR-FTIR AND FBRM 69
3.1 ABSTRACT 69
3.2 INTRODUCTION 69
3.3 EXPRESSION OF SOLUBILITY 71
3.4 EXPERIMENTAL WORK AND ANALYSIS 74 3.4.1 GRAVIMETRIC ANALYSIS 74
3.4.2 POLYTHERMAL METHOD USING FBRM 77
3.4.3 SOLUBILITY MEASUREMENT USING ATR-FTIR TECHNIQUE 81
3.4.3.1 Calibration of Probe 81
3.4.3.2 ATR-FTIR Solubility Measurement 87
3.4.4 OVERALL SOLUBILITY MEASUREMENT 90
3.4 DISCUSSION 93
CHAPTER 4: THE EFFECT OF MIXING ON THE METASTABLE ZONE
WIDTH IN ANTI-SOLVENT CRYSTALLIZATION 95
4.1 ABSTRACT 95
4.2 INTRODUCTION 96
4.3. EXPERIMENTAL METHODS 98
4.4 RESULTS AND DISCUSSION 100 4.4.1 COMPARISON OF FBRM AND ATR-FTIR FOR THE DETECTION OF NUCLEATION 100
4.4.2 IMPACT OF PROCESS PARAMETERS ON THE MSZW 104
4.4.3 NUCLEATION KINETICS 106
4.5 DISCUSSION 111
CHAPTER 5: THE USE OF FBRM AND ATR-FTIR TO MONITOR ANTI-
SOLVENT CRYSTALLIZATION AND ESTIMATE GROWTH RATE
KINETICS 113
5.1 ABSTRACT 113
5.2 INTRODUCTION 113
5.3 MATERIALS AND METHODS 115
5.4 RESULTS AND DISCUSSION 116 5.4.1 FBRM RESULTS 116
5.4.2 ATR-FTIR RESULTS 123
5.4.3 GROWTH RATE KINETICS ESTIMATION 125
5.5 CONCLUSIONS 130
CHAPTER 6: SCALE-UP OF ANTI-SOLVENT CRYSTALLIZATION 131
6.1 ABSTRACT 131
6.2 INTRODUCTION 132
6.3 MATERIALS AND METHODS 133
6.3 RESULTS AND DISCUSSION 136 6.3.1 FBRM RESULTS 136
6.4 DISCUSSION 145
6.5 CONCLUSION 147
7. THESIS CONCLUSIONS 149
8. NOMENCLATURE 153
9: REFERENCES 155
APPENDIX A 171
1. ABSTRACT 171
2. INTRODUCTION 172
3. COMPUTATION FLUID DYNAMICS MODEL 173
4. RESULTS 174 4.1 500ML SCALE 174
4.2 70 L SCALE 177
6. REFERENCES 179
LIST OF PUBLICATIONS
D. O'Grady & B. Glennon, „Measurement of Nucleation Kinetics in an Anti-solvent
System‟, Conway Institute Conference, Dublin, Ireland, February, 2005
P. Barrett, B. Smith, B O‟Sullivan, J. Worlitschek, V. Bracken, & D. O'Grady, „A Review of
the Use of Process Analytical Technology for the Understanding and optimization of Batch
Crystallization Processes‟, Organic Process Research and Development,9 (3), 348-355, 2005
D. O'Grady & B. Glennon, „Growth and Nucleation Kinetics for an Anti-solvent System
Using In Situ Instrumentation', Industrial Symposium on Industrial Crystallization, Dresden,
Germany, September 2005
D. O'Grady & B. Glennon, 'Characterisation of Anti-solvent Addition Crystallization Using
In-Line Tools', British Association for Crystal Growth Annual Conference, Sheffield, UK,
September 2005
D. O'Grady & B. Glennon, 'Use of In-Situ Instrumentation to Characterise Anti-solvent
Addition Crystallization', AIChE Annual Meeting, Cincinnati, USA, October, 2005
D. O’Grady & B. Glennon, „Comparing a Fast and Slow Crystallization‟ UCD 150
Engineering Exhibition, Dublin, Ireland, January, 2006
M. Barrett, D. O'Grady, B. Glennon, 'Characterisation of Anti-solvent Addition
Crystallization Using In Situ Tools and Computational Fluid Dynamics', 2006 International
Real Time Analytics Users' Conference, Barcelona, Spain, February 2006
D. O'Grady, M. Barrett, E. Casey & B. Glennon, 'PAT and the Crystallization Toolkit',
Pharmaceutical Manufacturing, 5 (6), 44-47, 2006
D. O' Grady, M. Barrett & B. Glennon, 'Scale-up of Anti-Solvent Crystallization Using in
Situ Tools and Computational Fluid Dynamics', AIChE Annual Meeting, San Francisco, USA,
November, 2006
M. Barrett, D. O'Grady, B. Glennon & E. Casey, 'The Application of CFD to the
Multi-Scale Characterization of Anti-Solvent Addition Crystallization', AIChE Annual
Meeting, San Francisco, USA, November, 2006
D. O’Grady & B. Glennon, „Fundamentals of Anti-solvent Crystallization‟, Mettler
Toledo AutoChem Seminar Series – New Jersey, USA, July 2006
D. O'Grady, M. Barrett, E. Casey & B. Glennon, 'The Effect of Mixing on the Metastable
Zone Width and Nucleation Kinetics In the Anti-solvent Crystallization of Benzoic Acid,
Chemical Engineering Research and Design, Transactions IChemE part A, 85 (7) 945-953,
2007
D. O'Grady & B. Glennon, „Solubility Measurement for an Anti-solvent Crystallization
System Using Gravimetric Analysis, ATR-FTIR and FBRM', Crystal Growth and Design, in
press
1
ABSTRACT
The anti-solvent crystallization of benzoic acid from ethanol-water solutions using
water as the anti-solvent, at scales ranging from 500 mL to 70 L, is presented. A
thorough review of the literature focusing on anti-solvent crystallization and the use
of in situ tools for crystallization characterization was undertaken prior to
experimental studies. A novel method for measuring solubility in organic systems,
based on the FBRM technique, is presented. This method has been compared to other
solubility measurement techniques and found to be reliable over a wide range of
concentrations. The observed trends were found to be consistent with the UNIQUAC
liquid activity coefficient model. Using the FBRM technique, novel methods for the
determination of nucleation and growth kinetics were developed. By measuring the
metastable zone width under a range of process conditions, including addition rate,
agitation intensity and feed location, a kinetic expression for nucleation in organic
anti-solvent systems that incorporates the influence of agitation, was developed and is
presented for the first time. A kinetic expression for growth was elicited from growth
rate data, gathered using the FBRM technique, and liquid phase concentration data,
gathered using the ATR-FTIR technique. This novel method serves to highlight the
possibility of using in situ tools to measure growth rate kinetics in process and in real
time. The extensive characterisation work performed at the laboratory scale provided
the basis for an investigation of system performance upon scale-up to a geometrically
dissimilar 70 L pilot plant crystallizer. Suitable operating parameters were chosen
based on the small scale characterization and the scale-up was deemed successful as
product of a similar size and yield was produced at the 70 L scale using the same
cycle time.
2
CHAPTER 1: INTRODUCTION
In recent years the pharmaceutical industry has shown a renewed interest in the field
of crystallization. Crystallization is seen within the industry as a key process
bottleneck during API drug development and manufacture. Through effective
characterization at the laboratory scale, development times can be reduced, effective
and efficient scale-up can be ensured and cycle times can be improved. At the
manufacturing scale, improved crystallization monitoring and optimization can reduce
the number of failed batches, ensure regulatory compliance and enhance downstream
processing operations such as filtration, drying and formulations. Such work
ultimately leads to reduced costs, improved margins and a shorter time to market for
new drugs.
A key element of the process development lifecycle is the scale-up of the
crystallization operation to large-scale, whilst maintaining product and process
compatibility. A major challenge to address is the impact of scale-related effects on
the process. In this work, the characterization of a crystallization process on a variety
of scales is presented. An anti-solvent crystallization process is chosen for study as it
is a technique that has been relatively neglected in the literature, in favour of cooling,
despite its prevalence in API drug development and manufacture. Effective anti-
solvent crystallization scale-up also presents specific mixing-related problems worthy
of investigation.
In situ tools are employed for the measurement of key crystallization parameters such
as crystallization kinetics and supersaturation profile. Focused Beam Reflectance
3
Measurement (FBRM) is used to study the degree and rate of change of the size, and
number of crystals in suspension and Attenuated Total Reflectance – Fourier
Transform Infrared Spectroscopy (ATR-FTIR) is used to measure solution
concentration. Process Video Microscope (PVM) is used to image crystals as they
exist in process. The use of in situ tools has gained much attention within industrial
and academic crystallization groups for their ease of use, ability to minimize or
remove sampling and real time response to changes in the crystallization system.
Chapter 2 of this thesis is a review of the literature in the field of anti-solvent
crystallization and use of in situ tools for crystallization characterization. Attention is
paid to areas of the field that have been neglected, for example anti-solvent addition
location and the use of organic crystallization systems. A complete review of the
FBRM technique is presented, with the aim being to outline its evolution from novel
technology for particulate systems characterization to the industrial standard for in
situ crystallization characterization.
Chapter 3 will focus on the solubility curve for anti-solvent crystallization systems. A
novel method for the determination of solubility in anti-solvent systems was
developed using the FBRM technique. To assess the reliability of this method two
other techniques, gravimetric analysis and ATR-FTIR, were employed to measure the
solubility. The results for each method were consistent over a full range of
concentrations. The solubility data measured using the FBRM technique also proved
consistent with the UNIQUAC liquid activity coefficient model. Solubility data for
anti-solvent systems is typically presented in terms of grams of solute per gram of
solution. This can make interpretation of key crystallization parameters such as the
4
metastable zone width and supersaturation difficult. In this case the solubility is
expressed on an anti-solvent free basis (grams of solute per gram of solvent) making
subsequent characterization of the system simple and effective.
A method for the estimation of nucleation kinetics in antisolvent systems is presented
in Chapter 4. An extensive study aimed at characterizing the impact of mixing
conditions on the metastable zone width was undertaken and focused on the impact of
addition rate, agitation intensity and feed location. As expected, a widening of the
metastable zone width at higher addition rates was observed. The feed location proved
to be an extremely important parameter with extreme variation in the metastable zone
width observed for two different feed point locations. The impact of agitation
intensity on the metastable zone width proved to be heavily dependant on the feed
location chosen. By modifying traditional nucleation kinetics equations for the anti-
solvent system, a kinetic expression for nucleation in organic anti-solvent systems that
accounts for the impact of agitation was developed and is presented here for the first
time. The formulation of such an expression was not possible under certain condition,
specifically a feed location close to the wall of the vessel. This was due to a non-
repeatable point of nucleation and in some cases nucleation prior to the attainment of
bulk solubility. In order to better understand the mechanism by which this occurred
computational fluid dynamics (CFD) was employed to model mixing conditions in the
vessel. Poor incorporation of the anti-solvent into the bulk solution, when it was
added close to the wall, proved the important factor and the results of this
investigation can be found in Appendix A.
5
A quick, efficient and novel method for estimating growth kinetics for organic
crystallization systems is presented in chapter 4. Growth rate kinetics can be
estimated by finding the relationship between the crystal growth rate and its driving
force, supersaturation. ATR-FTIR can be used to monitor supersaturation and FBRM
can be used to measure a relative growth rate, hence the combination of these in situ
tools can, in theory, be used to generate real time, in situ, growth rate information.
For this study a specific set of process conditions is chosen to ensure a low nucleation
rate and repeatable crystallization with consistent yield. The choice of these
conditions is made based on the findings of chapter 3. The experiment is conducted in
triplicate and the results averaged to elicit the desired growth rate information. The
results indicate the ease with which reliable and representative growth rate
information may be estimated, in situ and in real time.
The implementation of such a technique for crystallization control may prove
extremely useful. Much of the research published to date focuses on controlling the
prevailing level of supersaturation in the crystallizer to achieve the desired product
and process performance. While such work is valuable, in order to truly control the
crystallization process the crystal nucleation and growth rates must also be known.
With this information better control can be achieved that allows specific particle size
distributions to be targeted in a specific batch time. The ability to measure the crystal
growth rates in situ may prove extremely useful for tacking this difficult but
potentially rewarding problem.
6
The scale-up of a successful crystallization process from the laboratory to production
is extremely challenging and is tackled in Chapter 6. Increasing the crystallizer size
impacts the mixing regime, heat and mass transfer properties as well as the surface
area to volume ratio and crystal suspension profile. A combination of these factors
can change the physical and chemical characteristics of the system that may have
been established at smaller scales, resulting in a very different process and product.
In the pharmaceutical industry there are added pressures associated with large scale
crystallization operations due to its time consuming nature and expense, in terms of
operational and product costs. In situ tools can play a vital role in addressing these
many and varied challenges. The direct comparison of continuous and representative
data between scales can be used to identify the source of variation and suggest a
suitable course of action.
For this study scale up from the 500mL laboratory scale to a 70L pilot scale is
performed. The ultimate goal of the scaled up process is to produce a similar yield, in
a similar time and for the crystals to be of a similar size. The extensive
characterization work, outlined in chapters 3-5, is used to determine the set of process
conditions that were most likely to achieve this goal with computational fluid
dynamics employed to identify a suitable feed location. The scale-up ultimately
proved successful with the yield, batch time and crystal size being within acceptable
limits.
Despite this, scale-up success the in situ FBRM data raises concerns over the
mechanism by which the results were achieved. The FBRM data clearly indicates that
while the growth and nucleation rates at both scales were similar, the nucleation point
7
is different and the pilot scale batch nucleates prior to the attainment of bulk
saturation. The FBRM data also indicates that there is significant segregation in the
vessel.
8
CHAPTER 2: LITERATURE REVIEW
2.1 ANTI-SOLVENT CRYSTALLIZATION
2.1.1 Introduction
That a solute can have different solubilities in different solvents has long being
recognised. Anti-solvent crystallization exploits this fact to crystallize solid product
from solution. Solution supersaturation is generated by the addition of a second
solvent (anti-solvent) that reduces the solubility of the solute and induces
crystallization. The solvent with the lower solubility is often called an anti-solvent.
The process is often referred to as drowning-out, salting-out, solventing-out or
extractive crystallization.
The industrial use of an anti-solvent for the separation and purification of solid
product was first demonstrated by Gee et al., (1947). Iron impurities were separated
from the desired aluminium sulphate in a 2-ton-per-day continuous pilot plant using
ethyl alcohol as an anti-solvent. This method had apparently first been suggested by
Vittorf (1924). The process relied on the lower solubility of the aluminium sulphate
compared to the iron impurities in alcohol solutions but ultimately proved
uneconomical due to a low cost differential between the crude and finished product.
In the pharmaceutical industry, where the final solid product is extremely valuable
and the product thermal stability is often low, anti-solvent crystallization provides an
important alternative to cooling and evaporative crystallization.
9
As in a cooling or evaporative crystallization there are a number of issues to consider
when designing an anti-solvent crystallization. The size, number and shape of the
particles produced are important, as these factors can impact on downstream
processing operations such as filtration, drying and formulation. The yield of a batch
is important as the solute in the pharmaceutical industry is extremely valuable and it is
desirable to keep losses at a minimum. It is very important to ensure the correct
polymorphic form is crystallized. In an anti-solvent system supersaturation is
generally high which can lead to a non-stable polymorph. Additionally, because a
second solvent is being added to the crystallization system solvate and hydrate issues
are common. Much research has been carried out to investigate how various process
variables can affect these crystallization characteristics.
2.1.2 Variables to Consider in Anti-solvent Crystallization
2.1.2.1 Choice of Anti-solvent
The first step in the design of an anti-solvent crystallization is the identification of a
suitable anti-solvent. Important characteristics include low solute solubility, high
miscibility in the primary solvent, ease of recovery and potential environmental
impact. Apart from the obvious operational constraints associated with choosing an
anti-solvent, outlined above, there are a number of important crystallization
parameters that can be affected.
10
The choice of anti-solvent can affect particle size and population, through the
different mixing conditions associated with the addition of anti-solvents of different
density and viscosity. Takiyama et al., (1998) found that the number of crystals
produced by mixing a saturated aqueous solution and a saturated ethanol solution was
greater, by two orders of magnitude, than that produced by mixing two saturated
ethanol aqueous solutions having different concentrations. The reason proposed was
that pure water and pure ethanol mixtures took far longer to mix fully leading to areas
of high supersaturation and elevated nucleation rates. Barata and Serrano, (1998b)
showed that the choice of anti-solvent had some impact on crystal size, for the
crystallization of potassium dihydrogen phosphate (KDP) using three different
aqueous alcohol solutions, but did not propose a mechanism for this effect. However,
further work by this group, using the same system, showed that nucleation kinetics
(Barata & Serrano, 1996a) and growth kinetics (Barata & Serrano, 1998a) were
affected by the choice of anti-solvent. It appeared the nucleation kinetics were
influenced by the dielectric constant of the solution and the size of the alcohol
molecules in the anti-solvent. The growth kinetics were influenced by the size of the
alcohol molecules, in that, large molecules increase the physical barrier to solute
deposition. However it was noted that larger alcohol molecules could be less adsorbed
on the crystal surface, thus inducing higher growth rates.
Finally, product yield can be improved by choosing an anti-solvent in which the
solute is most insoluble. This has been shown by Oosterhof et al., (1999), for the
crystallization of sodium carbonate from aqueous solutions. Diethylene glycol was
deemed the most suitable anti-solvent as the as it provided the highest recovery of
sodium carbonate from solution.
11
Another important factor to consider when choosing a suitable anti-solvent is phase
separation. Due to the high level of supersaturation typically encountered during anti-
solvent addition the precipitation of a second liquid phase prior to nucleation of the
solute is not uncommon (Barata & Serrano, 1996a; Oosterhof et al., 1999). Care
should be taken to ensure phase separation is not an issue when selecting a suitable
anti-solvent.
2.1.2.2 Anti-solvent Addition Rate
The anti-solvent addition rate is an important process variable that must be controlled
in order to produce a suitable crystal product. High addition rates typically result in a
wider metastable zone and consequently high supersaturation generation rates. This
has been shown and characterized by Pina et al., (2001) for the crystallization of
single and double sulphates from aqueous solutions using methanol as an anti-solvent.
Similarly, high supersaturation levels can be encountered in regions close to the
addition location, especially at elevated addition rates (Takiyama et al., 1998). Thus,
high addition rates often lead to elevated nucleation rates and the formation of fine
crystals. This has been shown by Holmbäck & Rasmuson (1999) for the benzoic acid-
ethanol-water system and Charmoloue and Rousseau (1991) for the crystallization of
L-serine using methanol as an anti-solvent. In both of these cases the crystal
morphology was also influenced by the addition rate. In some cases, very high
addition rates may lead to an increase in the final product size due to elevated
agglomeration rates (Granberg et al., 1999; Jones et al., 1987)
12
Budz et al., (1986) experimentally determined the most suitable anti-solvent addition
rate, for the crystallization of cocarboxylase hydrochloride from its aqueous solution
by the addition of acetone, by correlating the final product size with addition rate. The
same addition rate produced maxima in the average size and bulk density of the
crystals produced via an unseeded process. A similar result was observed for the
seeded process, but the flow rate that produced the result was double that for the
unseeded process. This indicates the beneficial impact seeding may have, by
maintaining low levels of supersaturation even at high addition rates.
The impact of the addition rate on crystallization is not limited to the crystal size and
shape. High supersaturation levels, resulting from elevated addition rates, can lead to
the formation of undesirable solvates, hydrates and solvates. Kitamura & Sugimoto,
(2003) investigated the crystallization of polymorphs of thiazole-derivative (BPT) by
the addition of water to methanol solutions. When a very low addition rate was used,
nucleation of the solvated methanol crystal form (D) occurred preferentially. At high
water addition rates the proportion of the hydrated crystal form (BH) increased.
Beckmann (1999) studied the effect of addition rate on the crystallization of Abecarnil
from isopropyl acetate by the addition of hexane. At slow anti-solvent addition rates
the most stable polymorphic form, form C, was generally crystallized but at very high
addition rates the unstable B form was observed.
In general, high addition rates have a negative impact on the properties of the final
crystal product, either through poor crystal size distribution, incorrect form or both.
However, at the production scale a balance must be struck between implementing a
13
slow enough addition rate to ensure a suitable crystal product and ensuring the batch
time is not excessive.
2.1.2.3 Agitation Intensity
The agitation intensity is an extremely important variable in any crystallization. It has
increased importance for anti-solvent crystallization where two liquid phases must be
mixed to create supersaturation. The influence of agitation intensity on crystallization
processes is often conflicting. Charmoloue & Rousseau (1991) showed that increasing
agitation intensity reduced agglomeration levels and improved the impurity profile,
but this in turn led to elevated breakage rates. Similarly, Budz et al., (1986) showed
that a high agitation intensity prevented scaling on the walls of the crystallizer but this
was at the expense of excessive secondary nucleation.
Crystallization kinetics are also influenced in conflicting ways by the agitation
intensity. Barata & Serrano (1998a) showed that increasing agitation intensity
improved growth rates because more crystals were formed initially, and the surface
area available for growth increased. However, the growth rate was limited at very
high agitation intensities. This may have been due to crystal breakage but was not
proved. It has also been reported that agitation intensity has no impact on growth
kinetics. Jones & Mylardz (1989) studied the crystallization of potassium sulphate
from aqueous and aqueous acetone solutions using acetone and acetone-water
mixtures as an anti-solvent in an MSMPR. Crystal growth rates were largely
independent of agitation intensity. This may indicate that the crystallization was
14
surface diffusion controlled rather than by the bulk transport of the solute molecules
to the crystal.
Agitation intensity has also been shown to affect nucleation kinetics. Barata &
Serrano, (1996b) studying the KDP-alcohol-water system, showed that, between 50
and 400 rpm, induction periods did not depend on the agitation intensity but above
400 rpm the induction periods became smaller. Takiyama et al. (1998) studied the
number of sodium chloride crystals formed by mixing saturated aqueous ethanol
solutions, under different agitation conditions. The number of crystals formed
exhibited a dependence on the agitation condition leading to the conclusion that
nucleation rates were strongly influenced by local mixing.
Further examples of the conflicting influences of agitation intensity, and mixing in
general, have been presented by Paul et al., (2005). For example, increasing the
agitation can improve heat and mass transfer, minimise settling and improve the
purity profile. However the same increase in agitation may cause attrition, secondary
nucleation and entrain gas from the headspace.
2.1.2.4 Concentration Effects
The concentration of the solution to be crystallized is another important variable that
must be considered in an anti-solvent crystallization. Typically the solution
concentration is chosen to maximise the yield per batch; however, there may be
advantages to crystallizing from a solution of different concentration. Crystallizing
from a dilute solution can avoid regions of high supersaturation and improve product
15
quality, as shown by Budz et al., (1986), for the crystallization of cocarboxylase
hydrochloride by the addition of acetone to dilute and concentrated aqueous solutions.
Crystallizing from concentrated solutions typically results in higher supersaturation
and a smaller final crystal product, due to higher nucleation rates. This has been
shown by a number of researchers including, Holmbäck & Rasmuson (1999),
Beckmann (1999) and Kaneko et al., (2002). Kaneko et al., (2002) studied the
crystallization of sodium chloride from aqueous ethanol solutions using ethanol as the
anti-solvent. At high ethanol solution concentrations the supersaturation was reduced
and monodispersed unagglomerated crystals were formed. As the concentration of
solute in the starting solution increased the number of crystals formed increased.
The influence of solution concentration may also be independent of supersaturation.
Jones & Mylardz (1989), studying the crystallization of potassium sulphate from
aqueous and aqueous acetone solutions in an MSMPR, showed that the growth rate
was strongly affected by the acetone concentration in the solution. At high acetone
concentration the growth rate was significantly reduced for the same level of
supersaturation. Similarly, Granberg et al., (2001) showed that for similar
thermodynamic driving forces the induction time, for the nucleation of paracetamol
from acetone-water mixtures using water as the anti-solvent, increased with
increasing water content in the solution.
The solution concentration may also impact on the polymorphic form of the crystal
product and the rate of transformation from a less stable form to a more stable form.
Kitamura & Sugimoto (2003) studied the crystallization and subsequent
transformation of polymorphs of thiazole-derivative (BPT) by the addition of water to
16
methanol solutions of the solute. At low initial concentrations the hydrated crystal
form (BH) crystallized independent of the addition rate. At higher initial
concentrations the solvated methanol form crystal (D) nucleated. This may have been
due to the hindrance of the hydration of the BPT molecule. At low initial
concentration, transformation of the BH form to the other polymorphic form (A) was
observed and the transformation rate appeared to decrease with initial concentration.
At higher initial concentrations both the BH and D forms transformed to the A form.
At yet higher initial concentrations the D form transformed to the BH form.
Anti-solvent addition can sometimes result in the production of a large number of
very fine crystals. This is typically due to elevated nucleation rates, encountered at the
point of primary contact between solution and anti-solvent. In cases where the
formation of these fine crystals is undesirable, dilution of the anti-solvent with the
system solvent can be beneficial. The usefulness of this technique has been shown for
the crystallization of potassium sulphate from aqueous solution using aqueous acetone
as an anti-solvent (Mullin et al., 1989) and for the crystallization of benzoic acid from
ethanol solutions using aqueous ethanol as an anti-solvent (Holmbäck & Rasmuson,
1999). In the latter case the anti-solvent feed concentration also influenced the crystal
morphology. Diluting the anti-solvent with the system solvent has shown its use in
other crystallization systems, at two different scales (Jones et al., 1987), and in a
mixed-suspension, mixed product removal (MSMPR) crystallizer (Jones and Mylardz,
1989). Barata and Serrano (1998b) also showed that anti-solvent concentration
influences the yield, due to a dilution effect, and the growth rate, due to the formation
of smoother crystals and a reduced mass deposition rate.
17
2.1.2.5 Conclusion
It is clear that a wealth of research has been conducted on the factors affecting anti-
solvent crystallization. However there are some areas that warrant further
investigation especially in respect of their application to pharmaceutical
crystallization. Nucleation kinetics for a large number of anti-solvent systems have
been calculated using induction time experiments. However, pharmaceutical
crystallizations are typically performed by continuously generating supersaturation
until nucleation occurs, rather than generating a given level of supersaturation and
then waiting for nucleation to occur. For this reason there is value in measuring the
metastable zone width under various process conditions and applying this data to
calculate nucleation kinetics.
The effect of process conditions, on various crystallization parameters have been
investigated and outlined above. Much of this research has surmised that
supersaturation is a key variable in defining the final crystal product. However, little
research has been done to actually measure the supersaturation and provide an
understanding of the underlying mechanisms involved in the crystallization. The
ATR-FTIR probe provides the opportunity to monitor supersaturation in situ.
One important process variable that has not been characterized adequately is the feed
point location. Since mixing is a vital aspect of the anti-solvent system the position of
the anti-solvent feed warrants investigation.
18
Much of the work characterising the particle size has been performed using off line
techniques such as microscopy. While useful, these methods rely on sampling which
can be time consuming and unrepresentative. With in line techniques such as FBRM
and PVM now available it is possible to characterize anti-solvent crystallization
without the need to sample.
Finally much of the initial work on anti-solvent crystallization used inorganic
compounds as model systems. Pharmaceutical compounds are in the main organic
compounds and it is best to characterize the anti-solvent system using organic
compounds.
Thus the scope for this study is to characterize anti-solvent addition crystallization
using an organic system with a view to applying the data gathered to pharmaceutical
crystallizations. The solubility of the model system will be measured using a number
of techniques. Metastable zone width data will be measured at various process
conditions including varying feed location. Nucleation kinetics will be calculated
from this data. The crystallization will be examined using in-line tools. FBRM and
PVM will be used to monitor the particle size and shape and ATR-FTIR will monitor
supersaturation. Attempts will be made to scale up the process.
19
2.2 FOCUSSED BEAM REFLECTANCE MEASUREMENT (FBRM)
2.2.1 Introduction
In recent years, Focused Beam Reflectance Measurement (FBRM) has emerged as a
powerful technique for the characterization of particulate systems. FBRM is a probe
based instrument that measures a function of the size, shape and population of a
particulate system in-process and in real time. It can measure the degree of change to
these properties as well as the rate of change making it a useful tool for the study of
dynamic particulate systems. Its advantage over other off-line techniques is that it
measures the particles in situ, eliminating the need to sample and ensuring that the
particles are measured under process conditions. Off line techniques rely on a
potentially unrepresentative sampling procedure and for some it is necessary to
prepare the sample before analysis through for example sonication or dilution. Even
in the case of off-line microscopy, preparing a slide may alter the particles
significantly through breakage. In the case of crystallization, nucleation, growth or
dissolution may occur after sampling thus providing unrepresentative data.
There is a growing body of literature outlining the use of FBRM for the
characterization of a diverse array of particulate applications. These include
granulation (Sistare et al., (2005)), polymerisation (Hukkanen, (2003)), flocculation
(Blanco et al., (2002)), biofilm (Choi (2003)), gas hydrate systems (Clarke & Bishnoi,
(2004)), wastewater treatment (de Clercq (2004)) plant cell suspensions (Jeffers et al.,
(2003)), and filamentous organism fermentations (Pearson, (2003)). It is in the field of
crystallization however, that FBRM has received most attention.
20
2.2.2 Mode of Operation
FBRM operates by shining a monochromatic laser beam, of wavelength 790 nm,
generated by a class 1 laser source, via a fibre optic conduit, to an optical assembly
housed within a probe shaft. This optical assembly (Figure 1.1) consists of a lens
mounted eccentrically, and this entire assembly rotates in a circular motion at high
speeds. The circular motion is generated mechanically in the models used for this
work (M400L and S400A) however for use in explosive environments the assembly
can be rotated pneumatically. The user may change the speed of rotation (scan speed)
in the software to values of 2 m s-1
, 4 m s-1
, 6 m s-1
and 8 m s-1
. For the duration of
this work the scan speed was held constant at 2 ms-1
at which the measurement range
is between 0.5 and 1000 µm. At higher scan speeds the measurement range can be
extended at the upper level but in doing so the sensitivity at the lower end of the range
is compromised. As the monochromatic beam passes through the lens it is focused to
a fine point (d ~ 4μm) just inside the probe window. The circular motion of the
assembly traces the fine point of the laser over the circumference of a circle (Figure
1.1).
When the beam comes in contact with a particle, light is reflected in all directions.
Some of the light is reflected back up the probe to a detector. The detector measures
the time duration for which the light is reflected (Figure 1.2). This time duration can
be used to measure a chord length across the particle according to equation 2.1.
l = t × v Eq. 2.1
21
Where l is the chord length (µm), t is the time duration of the backscatter (s) and v is
the speed of rotation of the laser beam (µm s-1
). For a chord to be accepted the
intensity of backscattered light must exceed a certain threshold. Additionally the rise
time of the electronic pulse generated by the backscattered light must be short. The
theory is that only particles that pass directly through the focal point will have a short
rise time. Thus particles that are too far from the focal point and are out of focus will
not be considered (Sparks & Dobbs 1993).
Since the laser rotates at high speed and a chord length is an entirely random
measurement there is the possibility to measure thousands of chord lengths per
second.
22
Figure 2.2: Chord Length Measurement (Mettler Toledo Users Site)
These individual measurements can be combined to produce a chord length
distribution (CLD) that provides a robust fingerprint of the particulate system. This
distribution is sensitive to changes in the size, number and shape of particles in the
system. Importantly it is sensitive not only to the degree of change but also the rate of
change making the FBRM technique extremely powerful for studying particulate
systems.
The measurement duration is the length of time for which the instrument scans before
the data are logged and can be altered using the software, to between 1 s and 1 hr. A
long measurement duration ensures a large number of counts and therefore
statistically robust data. However, if the measurement duration is too long, key
process changes that occur quicker than the measurement duration will be missed.
Mettler Toledo recommends that a measurement duration of 10 s is used in the
23
laboratory and 1 min is used in the plant. This gives a good balance between
sensitivity to process changes and statistically robust data.
A typical FBRM installation consists of a probe, which is connected via a fibre optic
cable to an electronics box known as the field unit. The field unit is connected to a PC
for instrument operation, data acquisition and data review (Figure 1.3). A more
detailed description of how FBRM operates can be found in the literature (Sparks &
Dobbs, 1993; Tadayyon &Rohani, 1998; Barrett & Glennon, 2002).
An important attribute of the FBRM software is its ability to trend important statistics
of the chord length distribution over time (Figure 1.4). For example the number of
particles in a small size range, e.g. 1 – 10 µm, can be trended over time to assess the
change in the fine end of the distribution. Additionally the same can be done for a
large size range, e.g. 100 – 250 µm. In fact it is possible to focus on any size range or
statistic (i.e. mean, median, or mode) of the distribution and trend it over time to
improve understanding of the process at hand. It is also possible to weight the
statistics to provide the most relevant information. This is especially useful for
monitoring a dynamic process such as attrition where there is a decrease in the size of
the coarse particles and an increase in the number of fine particles (Kougoulos
2005c). Suitable statistics for measuring such a process, apart from counts in
individual size ranges, may be the median for fine particles and a square weighted
mean for the coarse particles. By trending suitable statistics over time it is possible to
not only confirm that agglomeration is taking place but also assess the rate at which it
is occurring and when it has stopped.
24
Figure 2.3: Typical FBRM Setup with probe, field unit and computer (Mettler Toledo User‟s Site)
Figure 2.4: Chord Length Distribution Trended over time (Mettler Toledo User‟s Site)
25
The chord length distribution itself can be manipulated to analyse key particle
parameters. A variety of weightings may be applied to the chord length distribution in
order to highlight the fine and coarse end of the distribution. A 1/length weighting is
applied by dividing the number of counts in each size range by the midpoint of that
size range. This emphasises small particles. A square weighting is applied by
multiplying the number of counts in each size range by the square of the midpoint. In
this way the large particles are emphasised. Heath et al., (2002) provides an excellent
overview of the difference in these distributions and how they may be used and
Fujiwara et al., (2002) compares how different weightings can be used to identify the
onset of nucleation in the aqueous batch cooling crystallization of paracetamol.
2.2.3 Validation of FBRM Technique – Instrument Parameters
Much work has been carried out in order to validate the FBRM technique and assess
how process and instrumental parameters affect the chord length measurement.
Instrumental parameters that have been investigated include probe position, focal
point position, scan speed and measurement duration. Process parameters investigated
include solids concentration, agitation intensity, surrounding medium and particle
material.
It should be noted that the FBRM technique existed under a different name in the
1990s. The Par Tec 100 and Par Tec 200 were precursors to the current FBRM probe.
The Par Tec probes operated on the same principle of backscattered light as the
current FBRM probe. It was possible to display the raw chord data, a spherical
equivalent diameter or a volume distribution. A lot of early validation work was
26
performed on the Par Tec probe that still applies to the current FBRM models. For the
following sections all examples are for the current FBRM probe unless otherwise
stated.
2.2.3.1 Probe Position
Probe positioning plays a vital role in ensuring that a representative sample is
presented to the window. Poor probe position can lead to unrepresentative data and
poor process understanding. It is imperative to position the probe so the particles are
flowing into the window. Tadayyon & Rohani (1998) identified this need and also
suggested the probe window should be sufficiently far away from any light reflective
object such as the stirrer blades to avoid the generation of signal noise, especially at
low concentrations. Barrett and Glennon (1999) assessed the effect of probe position,
on the CLD of alkaline frit in water, at two different scales (1.5 L and 70 L stirred
tanks). The most statistically robust data were achieved when the count data were
highest. This was achieved, at both scales, with the probe mounted from the side at an
angle to the dominant flow direction. The manufacturer provides advice on
positioning the probe in a reactor and a pipeline. In both cases the probe must be
placed at an angle to the dominant flow direction (Figure 1.5).
27
Figure 2.5: Suitable probe position (circled) Mettler Toledo Lasentec Users Site
2.2.3.2 Focal Point Position
The choice of focal point is extremely important to ensure representative data is
gathered. If the focal point is set far from the probe window there will be a reduced
sensitivity to fine particles. This is due to the deterioration of the light signal as it
leaves and re-enters the probe. This fact has been identified by a number of
researchers including Worlitschek and Mazzotti (2003), for the study of a single
particle in water and in a dispersion of fine particles and Heath et al. (2002), for the
study of the CLD of aqueous calcite suspensions. Heath et al., (2002) also showed
that setting the focal point far from the window increased sensitivity to coarse
particles. The reason given for this was that large particles are less likely to enter the
28
measurement zone when the focal point is set close the window. Monnier et al.,
(1996) studied the effect of the focal point position on the measured mean size of
latex and orgasol particles in various solvents using the Par Tec device. The mean size
varied depending on the focal point chosen. For small particles the most accurate size
data was achieved when the focal point was set close to window. For large particles
the opposite was true with the optimal focal point being between 1.5 and 2 mm from
the window. Ruf et al., (2000) monitored how the distance of a particle from the focal
point affected the measured chord length of a single particle. They found that as the
particle moved farther and farther from the window the size of the measured chord
length decreased. This was due to the weakening and broadening on the laser beam
that makes it harder for the FBRM signal-processing unit to detect precisely the
boundary of the particle. For the same reason, a number of chord lengths much
smaller that the particle itself were measured. Worlitschek and Mazzotti (2003) found
similar results for the single particle systems and their findings suggested that when
the whole particle size distribution is of interest, the best focal point to detect large
and small particles was at the probe window. Furthermore, they suggest that if a
specific known size range of particles is of interest then the focal point should be
positioned half the diameter of the smallest particle away from the window. This
eliminates the risk of not detecting the smallest particles.
In each of these studies the optimum focal point position for fine material was
determined to be close to the window. Since fine crystals typically are of most interest
in crystallization applications the focal point is usually set at the window. While some
sensitivity to larger crystals is lost, this set-up ensures maximum sensitivity to the
more important fine crystals. Some of the newer laboratory-based FBRM systems
29
(S400 range) have a fixed focal point at the window. In the case where larger particles
are of interest, for example in granulation, a probe with a variable focal point may be
preferable.
2.2.3.3 Measurement Duration
The choice of measurement duration is a balance between ensuring the collection of
statistically robust data and ensuring key process events are not missed. Additionally,
applying a short measurement duration results in a large amount of data that can be
difficult to handle. Pearson (2004) assessed the effect of measurement duration on the
CLD of filamentous bacteria measurement measured offline. Values between 1 and 60
s were investigated and a measurement duration of 30 s was chosen as it showed only
minor variation in all the statistics of the CLD examined and allowed relatively rapid
collection of data. Monnier et al., (1996) investigated the effect of measurement
duration on the size distribution measured using the Par Tec device for glass beads
between 125 and 250 µm (measured by sieving) in water. The cycle time was varied
between 0.2 s and 3.2 s for the same suspension of glass beads. For a measurement
duration of 0.2 s the total count data was too small to allow correct statistical
processing of the data. When the measurement duration was changed to 3.2 s the
reproducibility of the data increased significantly and the standard deviation halved
indicating a more robust measurement.
30
2.2.4 Validation of FBRM Technique - Process Parameters
2.2.4.1 Solids Concentration
An increase in solids concentration leads to an increase in total counts measured by
the FBRM. This is simply due to the presence of more particles in the measurement
zone at higher concentrations. A linear relationship between solids concentration and
counts has been observed by (Pearson et al., 2004), for a filamentous bacterial system,
and, by Monnier (1996), for aqueous suspensions of glass beads. Other studies have
noted a similar linear relationship but at very high concentration count data has been
observed to stop increasing with increasing solids concentration. Barrett and Glennon
(1999) investigated the relationship between the CLD and solids concentration
aqueous alkaline fit suspensions at scales ranging from 500 mL to 70 L. At all scales
the relationship between counts and concentration was linear at low concentration but
tapered off at high concentrations. Similar results were found by Heath et al., (2002)
investigating the change in total counts of aluminium and calcite particles in water
due to an increase in solids concentration.
The reason for this non-linearity is due to an increase in the instrument dead time at
high concentrations. At low concentration only a small fraction of the distance
traversed by the laser is used to measure a particle. However at high concentration a
significant percentage of the distance is used reducing the time available to detect
other particles. This dead time is slightly larger than the time spent traversing the
particle because the reflected light must return to zero for a short period between
particles. Additionally, the dead time is increased at high concentrations because
31
particles outside the viewing zone are measured and then rejected if the rate at which
the signal increases is not fast enough. Furthermore, overlapping particles may be
counted as one large particle exacerbating the problem. It is possible to calculate this
dead time but it is system specific. Heath et al., (2002) measured the value for
aluminium and calcite particles.
Another possible reason for the non-linear relationship between counts and
concentration is that at high concentration, the intensity of light at the focal point can
be reduced by blocking of the laser path by other particles. Therefore, the intensity of
backscattered light may not be sufficient to generate pulses with sufficient amplitude
to be detected by the instrument (Tadayyon & Rohani 1998).
The impact of solids concentration on the size of the particles measured has also being
investigated by a number of researchers. In some cases a strong influence has been
observed but in others the impact has been negligible. Dowding et al., (2001) showed
that increase in solids concentration, of poly(vinyl chloride) beads in water, resulted
in a decrease in length weight mean chord length and an increase in the cube weighted
mean chord length. It was proposed that at high solids concentration the mean chord
length increased due to clustering of the PVC beads and the cube weighted chord
length decreased due to the reduced probability of seeing the largest particles. Law et
al., (1997) analysed the size distribution measured by the Par Tec 100 of various
particles (pollen, sand, algal cells etc) in water at solids concentrations ranging from
10 to 50,000 mg L-1
. As concentration increased the mean and median particle size
remained relatively constant. The mean standard deviation was about 12% and the
median standard deviation was about 5%. A slight trend to a larger mean particle size
32
at higher concentrations was attributed to overlapping of particles leading to a larger
particle size.
Monnier et al., (1996) made similar investigations for glass beads in water using the
Par Tec instrument but found the mean size to be independent of solids concentration
in the range 0 and 450 g L-1
2.2.4.2 Agitation Rate
Effective agitation is critical to ensure that a representative sample of the particulate
system is presented to the window. Heath et al., (2002) noted a reduction in the
average measured size as agitation intensity was reduced. Settling of the particles was
visibly noted confirming that the large particles were falling out of the measurement
zone impacting on the measured size.
Statistical robustness may also be improved by agitating at a sufficient level. Pearson
et al., (2004) examined the effect of agitation rate on the measured CLD of a
filamentous bacteria system. Agitation was varied between 100 and 500 rpm at a
constant solids concentration. As agitation increased the total number of counts
increased as more of the mycelial aggregates were presented to the probe window.
The mean and median remained constant indicating the new particles presented to the
window were no different in size. However by increasing the count data a more robust
measurement was achieved.
Care must also be taken not to entrain air into the system at elevated agitation rates.
Dowding et al. (2001) investigated the influence of agitation intensity on the CLD of
33
PVC in water at a fixed concentration (2.3 w/w%). The average chord length
increased with increasing agitation and further investigation showed that at higher
agitation levels air bubbles were been entrained in the suspension. These bubbles
were measured by the FBRM as particles in the system and the average chord length
increased. Monnier et al., (1996) investigated the effect of agitation intensity on the
size distribution of glass beads measured by the Par Tec 100 instrument in toluene and
water. In water the total counts increased 10-fold when the agitation intensity was
increased from 250rpm to 750 rpm. However, at higher agitation levels (up to
2000rpm) the total counts actually dropped. This was not the case for the toluene
system where total counts increased with increasing agitation from 500 rpm to 1500
rpm. At the highest values the total counts increased significantly. This was put down
to bubbles being entrained at this high level of agitation. The mean size of particles
measured increased with increasing agitation. This may have been due to a more
representative sample being presented to the window.
An increase in agitation intensity does not always result in changes to the chord length
distribution. Law et al., (1997) found no effect on the size distribution of various
particles (pollen, sand, algal cells etc) generated by the Par Tec 100 instrument for a
change in the agitation intensity. The agitation intensity was varied between 500 and
1000 rpm and no effect on the mean or median size was observed.
2.2.4.3 Particle Material
Since the FBRM technique is based on reflection the optical properties of the particles
under investigation are extremely important. Sparks & Dobbs (1993) investigated a
34
number of different materials using the Par Tec instrument. They surmised that
particle shape, or surface characteristics (spherical, flat, porous), absorbing properties
(colour), refractive index (relative to suspension fluid) and high or low reflectivity
may influence the measurement. They found that large transparent particles in general
displayed very low count rates while opaque highly reflective particles displayed the
highest count rates. It has been noted that in some cases FBRM is a poor technique for
measuring extremely reflective particles such as TiO2. This is due to specular
reflection in which light is reflected in every direction except back to the probe. For
most particulate systems, however, this is not a problem
Ruf et al., (2000) showed the importance of the optical properties of the particle
material by measuring a single particle of ceramic material and a single particle of
quartz in toluene. For the ceramic material a single chord length was returned when
the particle was measured at a specific position. However for the quartz particle a
number of chord lengths were measured. This was due to a splitting of the signal due
to different backscattering properties of different faces of the crystal and a weakening
of the signal. This effect was not observed when the particle was measured in air. This
was due to the more favourable refractive index difference between the fluid and the
particle. Heath et al., (2002) compared the CLD generated by various size fractions of
aluminium and calcite particles. These particles were chosen because they had similar
densities and therefore a similar volume fraction when made up by weight leading to
similar flow properties to maintain suspension. The calcite particles showed a larger
tail in the fine end of the unweighted chord length distribution and this was attributed
to abrasion of fine particles from the particles and the finer edges of the particles
producing more fine counts. The aluminium particles were not subject to this abrasion
35
and had more round edges leading to a more symmetrical unweighted distribution.
The square weighted distributions proved remarkably similar despite dissimilar
morphologies and a presumed difference in reflectivities. This was not expected to be
applicable to all materials and was supposed to be more a consequence of the
relatively low aspect ratio of the two materials.
2.2.4.4 Surrounding Medium
The medium in which particles are suspended may also influence the FBRM
measurement. This is typically down to differences in refractive index associated with
different solvents. Monnier et al., (1996) investigated the influence of solvent on the
size distribution of glass beads in water, THF, toluene and isoctane using the Par Tec
instrument. For the organic liquids the total counts increased at all concentrations as
refractive index decreases. This was not the case for water however where counts
were higher compared to those in isoctane despite having a higher refractive index.
This indicates that there is a parameter other than refractive index that influences the
total counts. The size distribution of the measured glass beads was very similar in all
solvents. Ruf et al., (2000) also showed the impact of refractive index by measuring
the chord length of a single 200μm ceramic bead in water, toluene and air. They
showed that when measuring the ceramic bead in solvent the actual focal point is
different to the nominal focal point. This is due to the difference in refractive index of
air, found inside the probe and the solvent, found outside the probe. It was suggested
that the focal point should be kept at or inside the window to minimise the number of
times the light passes through a medium of differing refractive index.
36
2.2.4.5 Temperature
In many particulate systems, specifically crystallization, the temperature of the system
will change. Monnier et al., (1996) investigated the effect of temperature on the
particle size distribution measured by the Par Tec 100 instrument by measuring the
evolution of mean size and total counts for glass beads in water as the suspension was
subjected to a series of heating and cooling ramps. Total counts decreased form 15000
s-1
to 12000 s-1
for an increase in temperature of 60 C. The counts returned to their
original value on cooling back to the original temperature. Mechanical effects on the
probe, i.e. expansion of the probe, were discounted due to the small variation in
temperature. The refractive index of the suspension changes with increasing
temperature and this may have an effect but the refractive index of the solvent
decreases with an increase in temperature and this should lead to an increase in counts
at elevated temperatures. No parameter was identified for this phenomenon. The mean
size was unaffected by any change in temperature.
37
2.2.5 Correlating FBRM measurements with other particle size analysers
In order to further validate the FBRM measurement a number of researchers have
sought to compare the chord length distribution measured by FBRM and the particle
size distribution measured by other size measurement techniques. Correlation between
the FBRM data and other particle size analysis techniques will depend on a number of
factors, including, technique investigated, particle system, and sampling mechanism.
FBRM data has been compared to a number of different techniques for various
systems and Heath et al., (2002) provides a comprehensive review of much of the
research carried out in this area.
Laser diffraction is an industry standard particle size analysis tool and is used
extensively in the pharmaceutical industry for product release. For this reason much
work has been devoted to comparing it to the FBRM measurement. Alfano et al.,
(2002) produced a correlation between the mean chord length measured by FBRM
and the median particle size measured by laser diffraction for microcrystalline
cellulose. However, it was noted that when assessing the validity of the correlation
two important issues come into play. Firstly, laser diffraction yields a true spherical
equivalent particle size distribution whereas FBRM provides a chord length
distribution that is related in a complex way to the true particle size and shape.
Secondly, to measure a particle size distribution using laser diffraction it is necessary
to pull a sample of the particulate system from the reaction vessel whereas the FBRM
probe measures the system in-situ. They noted that the FBRM probed the
microcrystalline cellulose slurry in the high-shear region of the impeller tip region,
whereas the laser diffraction measures sample aliquots taken from the low-shear bulk
38
region, transferred to a low-shear sample cell. Additionally, the laser diffraction
method required a 30-fold dilution of the sample. These two points clearly highlight
the difficulty associated with comparing these techniques. However, despite these
difficulties a number of researchers have achieved useful correlations between FBRM
and laser diffraction for spherical or near spherical particulate systems.
Heath et al., (2002) found a linear relationship between the d50 measured using laser
diffraction and the median chord length measured by the FBRM for aqueous
aluminium suspensions. For large particles the correlation was improved by applying
a length weighting to the FBRM data. A similar linear relationship between the
techniques was observed by Abbas et al., (2002), for aluminium oxide particles. In
this case, the volume weighted mean measured by FBRM and the volume based
diameter measured using laser diffraction were used and the study only concentrated
on particles less than 25μm. It was noted that a more critical evaluation of the any
correlation would only be possible if the size range studied was increased to 500μm or
more.
Another common particle size analysis technique that has been compared to the
FBRM measurement is sieving. McDonald et al., (2001) found a correlation between
the mean cube weight of the chord length distribution and the particle size measured
by sieving for plant cell suspension cultures. The correlation was dependent on the
system studied with wild Chinese cucumber cells producing a linear correlation and
rice cells producing a non-linear fit. Tadayyon & Rohani (1998) compared the size
distribution of ion exchange resin generated by the Par Tec 100 and sieving. For
spherical particles there was a good correlation between the volume weighted chord
39
length distribution and the unweighted particle size distribution generated by the
sieve. However the difference between the actual particle size distribution and the
chord length distribution increased as the particle shape deviated from a sphere and
the size distribution became non-normal..
Comparisons between the size distribution measured using the Par Tec 100 and
various measurement techniques, including laser diffraction, image analysis and
coulter counter have also been made. In general it has been observed that the Par Tec
device overestimated the size of the smallest particles and underestimated the size of
the largest particles. This was the case for a number of systems including fluvial
suspended sediments, Philips & Walling (1998), sand particles, Law et al., (1997),
and a variety of spherical particles (coulter microspheres, Bayer resin, orgasol and
micronized pharmaceutical compound), Monnier (1996). In the case of Philips &
Walling (1998), and Law et al., (1997), a calibration model was derived that
accounted for this deviation.
Sparks & Dobbs (1993) compared the spherical equivalent diameter and volume
diameter generated by the Par Tec device with a Microtrac laser forward scatter based
technique. For opaque lightly coloured material such as aluminium hydrate results
between the two methods correlated well. For large transparent particles the
correlation was poor.
40
Comparing size distributions using different techniques is difficult. First and foremost
the various techniques produce a measure of a different particle size property. Abbas
et al., (2002) noted that when comparing the size distribution of aluminium oxide
using laser diffraction Turbiscan and FBRM the Malvern returned a volume based
measure D[4,3], the Turbiscan measured a mean D[3,2] based on the volume fraction
and the FBRM reported a weighted mean based on a chord length distribution. Also,
as noted above, Alfano et al., (2002) saw the difficulties associated with correlating
between two techniques with different measurement principles as well as the inherent
sampling issues associated with off-line techniques. Despite these issues it is possible
to form useful correlations between the FBRM measurement and other size analysis
techniques. These correlations will vary greatly depending on the measurement
technique used and the system under investigation Heath et al., (2002).
.
It is clear that a large volume of research has been carried out on the comparison of
the FBRM method with other particle size analysis techniques. A number of empirical
correlations have been found that typically work well for spherical particles with
suitable optical properties. In addition to empirical methods theoretical studies have
been carried out to investigate whether the chord length distribution measured by
FBRM is a true representation of the particulate system in question.
2.2.6 Theoretical Modelling of FBRM Data
The use of theoretical methods to convert chord length data into particle size data has
gained much attention in recent years. Empirical methods have been used to compare
the chord length measurement with other particle size analysers and the degree of
41
success in these correlations depends greatly on the shape of the particle. A suitable
model that can convert the measured chord length of a variety of particle sizes and
shapes into a usable particle size distribution would prove useful in further validating
the FBRM measurement.
In a more practical sense a correlation between the FBRM measurement and another
particle size measurement device may prove useful industrially. The standard for
particle size measurement across many industries, most notably the pharmaceutical, is
laser diffraction. A calibration between the chord length data measured using FBRM
and the mean size measured by laser diffraction would prove extremely useful in
predicting whether a crystal slurry, for example, was within specification.
In addition to this practical application the conversion of chord length data into
particle size data is an extremely difficult problem and is mathematically complex.
The difficulty of the problem and the challenges associated with it have attracted
many researchers. Initial study in this area concentrated on modelling the FBRM
measurement of spherical particles. The projected area of a spherical particle is
always a circle no matter what the orientation of the particle. This makes conversion
of the chord length data into particle size data less challenging than for other
morphologies.
2.2.6.1 Modelling Spherical Particles
For spherical particulate systems, e.g. bubbles and droplets, a number of researchers
have had success converting chord length distributions into particle size distributions.
42
Liu et al., (1998) analysed the relationship between bubble sizes and chord lengths in
a heterogeneous bubbling system. They developed a forward transform to calculate a
chord length distribution from a known bubble size distribution and three backward
transforms to infer bubble sizes from a chord length distribution. The techniques were
tested and compared using Monte-Carlo simulations. Simmons et al., (1999)
presented two methods for estimating droplet size distributions from chord length
measurements. The first is a probability apportioning method (PAM), that assumes
particles are randomly cut and calculates the diameter probability distribution from
each chord size detected. The distribution is accumulated over all chord
measurements. The second is a finite element method (FEM) that solves
simultaneously the equations relating the chord data and the diameter distribution.
Both methods were tested on sets of chord data developed from ideal probability
functions, a distinct element simulation and actual experimental data. It was found
that the PAM method was fairly robust when the particle diameters were known, but
the FEM method was generally more applicable when there was a wide range of
unknown particle diameters. Both models predicted the chord length distribution of
droplets in a 63mm pipeline measured by FBRM with a high degree of accuracy. The
data returned by the FEM model proved slightly better. It was concluded that the
FEM model was better for engineering applications where actual particle sizes are
unknown. Langston et al., (2001) improved the PAM method proposed by Simmons
et al., (1999) to take into account particles of unknown size distribution by
incorporating Bayes theorem and an iterative procedure (PAM 2). The improvement
comes from the implementation of the chord length data in a collective manner rather
than in isolation. A range of particle size distributions were modelled accurately
where the size of the particles was known and unknown. Experimental studies were
43
carried out on droplets of oil in water and the model predicted the measured data
accurately.
Barrett & Glennon (1999), moved away from perfectly spherical systems and
successfully modelled FBRM chord length data of dilute aqueous alkaline frit
suspensions of known size distribution, using a method similar to that of Simmons et
al., (1999). They assumed that the suspension was dilute enough to ensure particle
interactions were negligible and a good agreement between experimental results and
the model was achieved. It was concluded that the good agreement was only expected
if the particles were spherical, or at least if the dominant projected area was circular in
profile.
The research outlined above indicates that there has been some success in modelling
the chord length distribution of spherical particles. When modelling spherical
particles a 2-dimensional approach is sufficient since the orientation of the particle
does not affect the projected area. However for non-spherical particles, i.e. ellipses,
the orientation of the particles plays a very important role since it affects the projected
area on which a chord length is measured.
Tadayyon and Rohani (1998) developed a model to predict the response of the Par
Tec instrument in measuring the CLD of a suspension of spherical and ellipsoidal
particles and to infer the actual PSD using the measured CLD output. The model
showed that the measured CLD was reasonably accurate for spherical particles
however the measurement progressively deteriorated as particles became more
44
ellipsoidal. For spherical particles the error in the mean sizes reported by both
methods was improved by employing a volume weighted mean on the Par Tec data.
A further aspect of this research used the Par Tec instrument to examine a suspension
of ion exchange resin of normal size distribution in water. The chord length data was
transformed into particle size data using a model and compared to the actual particle
size distribution measured by sieving. For a normal size distribution the model
predicted the particle size distribution well. However for a non-normal size
distribution the model performed poorly.
Tadayyon and Rohani (1999) showed this when the model they proposed was
unsuitable for ellipsoidal particles and became more and more unsuitable as the
ellipse became more elongated and less spherical. For real particulate systems,
especially crystal systems, the particle morphology is typically highly non-spherical.
In order to model the chord length distribution of a real particulate it is absolutely
necessary to analyse non-spherical particles.
2.2.6.2 Modelling Non-Spherical Particles
Modelling non-spherical particles is difficult due to the difference in the projected
area depending on the orientation of the particle. A number of researchers have
developed methods to account for this that are usually computationally intensive.
Langston et al., (2001) attempted to analyse non-spherical particles using a 2-
dimensional approach by developing the PAM 3 model, an improvement on the PAM
2 method, Langston et al., (2001). This model was used to predict particle size
distributions from chord length measurements in a 2-dimensional assessment of non-
45
circular shapes, which included circles, ellipses and “blocky” shapes. They supposed
that the model was applicable to 3-dimensional assessments but conceded that the
random orientation of the particles would have to be accounted for. Wynn (2003)
derived a relationship between the underlying particle size distribution and the
measured chord length distribution measured using FBRM. A number of assumptions
regarding the measurement technique were taken into account. The principle of the
method is similar to earlier “peeling” methods, but an improvement is made here in
that the method is shown to be stable in some cases, due to finer spacing of the size
intervals.
In order to account for random particle orientation a 3-dimensional approach is
needed. Ruf et al., (2000) proposed a 3-dimensional model to transform particle size
distributions into chord length distributions. The shapes of different particles were
defined mathematically and the 2-dimensional projection of each particle was
calculated for every possible orientation in 3-dimensional space. The chord length
distribution of each 2-dimensional projection was calculated and by summing these
individual distributions and applying suitable weightings the overall chord length
distribution was calculated. Theoretical chord length distributions were calculated for
a variety of particle morphologies including ellipsoids and cuboids. This model
represents an improvement on the 2-dimensional model proposed by Tadayyon &
Rohani (1999). It can account not only for particles of non-spherical morphology but
it can also account for particle populations of the same shape but different size.
Li & Wilkinson (2005) and Li et al. (2005) presented a theoretical and experimental
study of the conversion CLD to PSD and PSD to CLD for non-spherical particles.
Analytical solutions were used to calculate the PSD-CLD models for spherical and
46
ellipsoidal particles and numerical solutions were used to non-spherical particles. To
convert CLD to PSD a non-negative least squares (NNLS) method that is insensitive
to the measurement noise and the particle shape was employed. For the model to work
it is necessary to give it some information on the shape of the particles in question.
The effectiveness of the proposed methods was validated by extensive simulations. To
test the validity of these models on a real experimental system the particle size
distributions of ceramic beads, plasma aluminium and zinc dust were measured using
image analysis and compared to the restored particle distribution calculated from
experimental chord length data. Image analysis was used to define the shape of the
particles and values of sphericity, circularity and chunkiness were used in the model.
The results compared well for each of the test particles. The accuracy of the
translation depended heavily on particle shape, which determines the optimal aspect
ratio that is used in the model.
Worlitschek et al., (2005) developed a method to restore the PSD of a particulate
system from the measured CLD. The restoration constitutes a mathematically ill-
posed problem and its solution is based on a two-step procedure. Firstly, the
computation of a matrix that converts the PSD of a population of particles with given
shape into the corresponding CLD using a 3-dimensional geometric model and
secondly the solution of the resulting linear matrix equation for the PSD. The
restoration of the PSD of spherical particles represented the easiest case whereas for
non-spherical shapes such as octahedral and needles lead to highly ill-posed inversion
problems. However, experimental investigations were carried out on populations of
paracetamol crystals (octahedral shape) and the model predicted measured data
accurately.
47
It is clear that the goal of producing a particle size distribution from a measured chord
length distribution is becoming a reality. The restoration of the PSD of a spherical
population of particles from a measured CLD is a relatively simple problem and
recent work has shown that this is now possible to restore the PSD of populations of
ellipses and octahedra. Further work is needed to model highly non-spherical
morphologies such as needles.
2.2.7 FBRM for Crystallization Characterization
While the possibility of inferring actual particle size distributions from chord length
distributions may be possible in the future it should be noted that all of the current
models require some information on the shape of the particles. For a well-behaved
system of spherical particles, such as oil in water, this is possible, but for a
crystallization system this is extremely difficult and may be impossible. In a
pharmaceutical crystallization system, for example, the particle morphology is
typically needle-like and the aspect ratio is usually non-uniform. Attrition can lead to
numerous small crystals with a low aspect ratio and agglomeration can lead to large
disordered particles with no recognisable shape. For a polymorphic system the shape
of the crystals can change over the course of the crystallization. These factors make it
extremely difficult to decide on the shape of the crystals under investigation.
FBRM avoids this problem by assuming no shape. The chord length distribution is a
function of the size, shape and number of particles that enter the measurement zone.
In a crystallization system, the chord length distribution will change if the size, shape
48
or number of crystals entering the measurement zone changes. Consequently key
crystallization phenomenon such as nucleation, growth, attrition, agglomeration,
dissolution and polymorphic transition can be identified and monitored. To facilitate
identification and monitoring of these dynamic crystallization processes, PVM may
be implemented as c complementary technique. When one considers that this can be
done in situ in real time the power of FBRM for monitoring crystallization systems
become apparent. When changes to the chord length distribution can be related to
process parameters, such as cooling rate, anti-solvent addition rate, agitation or seed
loading, it is possible to design a crystallization that will produce a suitable particle
size distribution that behaves appropriately during downstream processing operations
such as filtration, drying and formulation.
The vast majority of the research carried out on crystallization using FBRM is
performed in the pharmaceutical industry. Due to the secretive nature of much of the
research it is rare for work to be published. In fact the number of published articles
documenting the use of FBRM for the characterization of crystallization is
surprisingly few. Some of the research undertaken industrially is presented at
conferences, the proceedings of which can be useful. Of particular use is the Mettler
Toledo Users Site www.mt.com/lasentec where papers presented at the annual Mettler
Toledo Real Time Analytics Users Forum (formerly the Lasentec User‟s Forum) are
posted. While one must be wary of studying in too much detail the research presented
by the vendor itself, it nonetheless provides a wealth of information on how FBRM
has been used to design, develop and optimise crystallization processes in many of the
major pharmaceutical companies.
49
2.2.7.1 Solubility Curve and Metastable Zone Width Determination
The starting point for the characterization of any crystallization system is the
solubility curve. Barrett and Glennon (2002) implemented the polythermal method,
Nyvlt (1968), to generate solubility data for aluminium potassium sulphate in water
and used FBRM to monitor the point of dissolution. They chose counts between 50μm
and 250μm to monitor the dissolution as large particles have a large surface area to
volume ratio, and hence dissolve at a slower rate, compared to smaller crystals. The
results obtained were in excellent agreement with other published data, Mullin et al.,
(1965). In addition to solubility data, metastable zone width information was
generated for the same system. FBRM was used to detect the onset of nucleation
when solutions of aluminium potassium sulphate were cooled at various rates. The
number of counts between 0 and 20 µm was used to detect the onset of nucleation.
Since the instrument was set to measure every 10 s this size range was chosen since
published data reported that aluminium potassium sulphate may grow up to 15 µm in
10 s. Fujiwara et al., (2002) performed similar work, on an aqueous paracetamol
system, but compared the three techniques for the measurement of the MSZW,
FBRM, ATR-FTIR and visual observation. FBRM proved to be the most effective
technique as it reliably identified the onset of nucleation earlier than the other
methods. Various weightings on the chord length distributions were examined to see
which provided the best data. A 1/length weighting was tested to concentrate on the
small particles but increased noise and drift of the background signal was observed. A
square weighting was also tested but resulted in a slightly delayed detection. The most
reliable response was from the unweighted chord length distribution and this
parameter was chosen to identify the onset of nucleation. Liotta and Sabesan (2004)
50
showed that this technique could be automated, reducing the time and manpower
associated with measuring solubility and MSZW information. To do this required a
link that enabled the crystallizer to respond in real time to the FBRM data. A solution
of high concentration was held at a temperature above the solubility point and then
cooled until crystals nucleated. The FBRM detected the onset of nucleation (point on
the metastable zone) and when the total counts exceeded a certain threshold the
cooling ramp was terminated and a heating ramp was initiated. This heating ramp
continues until the total counts fall to the baseline (solubility point). At this point the
solution is held at an elevated temperature and a solvent is added to dilute the system.
The process is then repeated to gather further solubility and metastable zone width
data. An automated technique such as this significantly reduces the time needed to
generate solubility and metastable zone width data.
2.2.7.2 Nucleation Kinetics
FBRM is very useful for tracking nucleation as it can focus on specific size ranges.
By focussing on the smallest chords and trending them over time it is possible to
characterize nucleation behaviour. This is also facilitated by the sensitivity FBRM
displays to small particles. Shi et al., (2003) used FBRM to monitor the evaporative
crystallization of burkeite. Using FBRM trends the crystallization process was divided
into three regimes each dependant on the dominant mechanism of nucleation
involved. Unusual nucleation behaviour was observed and the inhibitory effect of
specific impurities on the nucleation of burkeite was identified. Monnier et al., (1997)
used the Par Tec instrument to calculate nucleation rates for the batch crystallization
of adipic acid in water. Global nucleation rates were calculated using Par Tec and by
51
subtracting the primary nucleation rates from this it was possible to calculate
secondary nucleation parameters. This information was then used in a model that
predicted the final size distribution of the adipic acid crystals. The PSD calculated
using the model compared well image analysis.
Nucleation kinetics can also be inferred from MSZW information gathered using
FBRM. Barrett & Glennon (2002) used such information to calculate nucleation
kinetics for the aluminium potassium sulphate. The results agreed with literature data
2.2.7.2 Monitoring Crystal Size
Even though FBRM does not report the true particle size it is sensitive to the size,
shape and population of the crystal slurry. FBRM is useful for comparing these
parameters under different process conditions. Loan et al., (2002) showed this by
comparing ferrihydrite precipitation at two different pH values. They found that at
low pH smaller crystals were formed and that poor filtration characteristics for the
low pH process were as a result of the unsuitable particle size distribution. FBRM was
used for a similar purpose by Fujiwara et al., (2002) for the study of the aqueous
batch cooling crystallization of paracetamol. FBRM indicated an increase in the
number of crystals during a run where supersaturation was high due to nucleation. For
a second batch where supersaturation was kept at a constant low level there was no
increase in the number of crystals indicating the absence of nucleation.
FBRM has an advantage over off-line techniques die the fact that sampling is not
necessary. Loan et al. (2002) noted that the FBRM chord length measurement was a
superior indicator of the particle characteristics, compared with scanning electron
52
microscopy, since there was no sample removal, dilution or drying needed. A similar
advantage was noted by Abbas et al., (2002) in that FBRM was the only technique
suitable for reactive crystallization, since sampling was not needed. (Off-line
sampling of the nickel hydroxide particle resulted in the alteration of the
physiochemical structure).
Another advantage FBRM has over other techniques is that it can measure a lot of
data in real time. This allows dynamic crystallization processes such as growth,
breakage and agglomeration to be studied in real time. By focussing on size ranges of
interest it is possible to identify these crystallization mechanisms. For example,
breakage is identified by a decrease in the number of chords counted in large size
ranges, and an increase in the number of chords counted in small size ranges, as well
as a decrease in the mean chord length. Such analysis can be facilitated through the
use of PVM to identify particle morphology as well as differentiate between
mechanisms that are sometimes confused, such as growth and agglomeration. Abbas
et al., (2002) used FBRM to monitor the reaction crystallization of nickel hydroxide
and the cooling crystallization of ammonium sulphate. In each case FBRM monitored
the size of the crystals over the course of the batch. For the reactive crystallization an
initial increase in particle size was identified as agglomeration and the subsequent
reduction in particle size was identified as the breakage of these agglomerates due to
the action of the agitator. For the cooling crystallization the size of the particles
increased steadily over the course of the batch. Kougoulous et al., (2005b) used
FBRM to monitor the cooling crystallization of an organic fine chemical in a MSMPR
crystallizer. Upon heating a decrease in particle counts was observed indicating
dissolution of the organic fine chemical. Upon cooling counts increased indicating
53
nucleation. After a certain period of time the trends reached steady state and a re-
circulation loop was initiated. The FBRM trends remained steady indicating breakage
due to the action of the peristaltic pump was not an issue (Kougoulos 2005a). After a
certain period a secondary loop was opened and the MSMPR cooling crystallization
process commenced. Crystals were removed from the system and FBRM successfully
identified the size range of crystals removed. There was a significant decrease in
coarse crystals and a significant increase in fine crystals. This indicated that large
particles were being removed while the nucleation and growth of finer particles
became more evident. Complementary video images provided by process video
imaging (PVI) confirmed that this was the case. Steady state was identified after about
6-9 residence times by identifying the point at which the FBRM trends flat-lined. The
same researchers used FBRM to monitor particulate attrition and breakage of an
organic fine chemical in a turbulently agitated system Kougoulos et al., (2005c). A
dilute solution of the organic fine chemical in isopropyl alcohol and water was
agitated for 150 min at 300rpm. The impeller frequency was then increased to 400
rpm for 150 min and then to 500 rpm for a further 150 min. An FBRM probe placed
in the vessel monitored the chord length distribution over the course of the
experiment. The FBRM data indicated that that the number of fine particles (0-10 µm)
increased and the number of coarse particles (100-300 µm) decreased. The number of
intermediate particles (50-100 µm) initially increased and then decreased. FBRM was
successful in monitoring the death and birth of crystals by disruption for different size
classes and provided evidence of micro-attrition effects. The advantage of FBRM,
over other size measurement techniques for monitoring such a process was noted.
This was because FBRM can monitor, in situ, the crystal size distribution for different
particle size classes (fine, intermediate, coarse). Information on particle attrition and
54
disruption proved extremely useful for the prediction of particle behaviour upon scale
up.
The previous examples have shown some of the utility FBRM has for the design,
development and optimization of crystallization processes. In each case the chord
length distribution was sufficient to characterize the system and there was no need to
assume a shape or convert to a crystal size distribution. However, this can be achieved
using some of the modelling work outlined in the previous section. Worlitschek and
Mazzotti (2004) used FBRM, coupled with a model-based method to calculate
particle size distribution from the raw chord length data (Worlitschek et al., 2005), to
monitor particle size in the batch cooling crystallization of paracetamol in ethanol. An
optimal temperature trajectory that minimised the difference between the final PSD
(measured using FBRM) and a given optimal monomodal PSD was calculated using a
non-linear constrained minimization algorithm. The size distributions of the crystals
produced using optimal cooling profile and a simple linear cooling profile were
compared. The FBRM data combined with the CLD-PSD model showed that the
linear cooling profile produced a significant amount of small crystals while the
optimal cooling profile provided a PSD close to the desired monomodal distribution.
2.2.7.3 Temperature Cycling
Temperature cycling is a useful technique used to increase crystal size and reduce the
number of fines at the end of a batch. FBRM is useful here because the cycling
regime (number of cycles, cycle duration temperature change) may be optimised by
focussing on the removal of small chords and the increase in the mean chord length.
55
Doki et al., (2004) applied this technique from the start of a crystallization to ensure a
large final product crystal size and minimise the number of fine crystals. FBRM was
used to monitor the number of crystals formed in the aqueous batch cooling
crystallization of glycine. The goal of the research was to produce crystals of a
suitable particle size distribution and of the correct polymorphic form by alternating
the temperature profile and the termination temperature. Initial work showed that
there was a linear correlation between the number of crystals of glycine suspended in
a saturated solution and the number of counts measured by the FBRM. This
correlation was applicable for a number of size ranges. During the batch experiments
seed crystals were added at the saturation temperature and FBRM monitored the
number of crystals in solution as cooling progressed. When the number of crystals in
solution exceeded a threshold value (due to nucleation) cooling was terminated and a
heating step was initiated to dissolve the fine crystals. When the number of crystals
measured returned to the original value the cooling step was restarted. In this way
large particles of suitable size distribution were formed.
1.2.7.4 Polymorphic Transitions
In certain cases different polymorphs have different morphologies. Since FBRM is
sensitive to changes in shape for these certain instances it can be used to track
polymorphic transitions. O‟ Sullivan et al., (2003) used FBRM to monitor the
polymorphic transition of metastable δ-form mannitol to the stable β-form. A small
amount of δ-form mannitol was added to a saturated solution of β-form mannitol in
water. FBRM trends were used to track the appearance of the δ -form as it was
charged, the dissolution of the δ -form and the subsequent growth and nucleation of
56
the β –form. This δ-β transition was corroborated using off-line X-Ray Powder
Diffraction (XRPD) and differential scanning calorimetry (DSC) as well as in process
imagery provided by PVM.
2.2.7.5 Effect of Impurities
FBRM is useful for measuring the rate of crystallization. Impurities often effect the
crystallization and there impact can be assessed using FBRM. Scott & Black (2005)
used FBRM to monitor the effect of impurities on the crystallization of urea and an
in-house system. FBRM successfully identified a difference in the crystal growth rate
when impurities were absent and present. Crystallization in the presence of the
impurity was 7-times slower for the urea system and ~20-times slower for the in-
house system. The work identified the potential use of FBRM in the plant for the
monitoring of impurities. In commercial manufacture impurity levels often change
during process development and on scale-up. This can affect morphology and thus
downstream processing operations. Additionally if impurity levels increase but the
crystallization time remains the same the yield can be affected due to the retarded
growth rate. Alternatively, if the impurity level decreases and the crystallization time
remains the same, crystallization may be complete long before the end of the batch
resulting in a decrease in productivity. This can be avoided by in-process tests or by
the use of in-line technology.
57
2.3 PROCESS VIDEO MICROSCOPE (PVM)
2.3.1 Introduction
Imaging can provide useful information on the size and morphology of particles.
These variables are important in any particulate system. In a crystallization system the
size and shape of the crystals can influence downstream processing operations such as
filtration, drying and formulation. For example, fine particles have a large surface
area and can take a long time to dry. Large flat crystals may pack on top of each other
leading to long filtration times. In polymorphic systems imaging can be extremely
useful as it is often possible to distinguish between two polymorphic forms according
to their shape.
The traditional imaging method used to gather information on size, and more
specifically shape, has been off-line microscopy. Microscopy is a useful and relatively
cheap method to gather data but there are some problems associated with it. To gather
statistically robust data on the size of the particles hundreds of images must be taken
and processed. In addition obtaining a representative sample can be difficult and is
time consuming and labour intensive. Once a sample is isolated the slurry must be
dried and prepared for analysis. This can alter the particles and lead to data that is not
representative of what is actually going on in the process. For example brittle particles
can break upon isolation, generating a large umber of fine particles. This may lead
one to conclude that the process is producing the fine particles rather than the
sampling method.
58
For a crystallization system there are some additional problems. Sampling is
undesirable because once a sample is taken changes to the crystals can occur due to
further crystallization phenomena e.g. nucleation, growth, breakage agglomeration
dissolution or polymorphic transformation. In some situations it may not be possible
to isolate a sample at all due to process or safety constraints.
The problems associated with off line imaging can be avoided by imaging within the
process environment. The Particle Vision and Measurement (PVM) (Figure 1.6)
system is a probe-based, high-resolution video microscope that can perform such a
task. PVM uses six independent lasers of wavelength 905 µm to illuminate the
particulate system. The lasers‟ source is located at the back of the probe and the laser
light is sent to the probe tip via a fibre optic connection. At the tip there are six lenses
arranged hexagonally, each of which focus the light from one laser onto an area with a
fixed area of approximately 2 mm2. A micrometer positioned at the back of the probe
can focus this illuminated area into or out of the system under investigation. As
particles intercept the illuminated area light is reflected in all directions and some of it
is reflected back up the probe. The backscattered light passes through an objective
lens, focussing it onto a light sensitive charge coupled device (CCD) that produces the
image. A recording of this image is made using a CCD camera at the back of the
probe.
The PVM software allows 10 images per second to be recorded. Settings such as the
gain and the offset of the CCD can be changed using the software to optimise the
image. If the material under investigation backscatters too much light the laser
intensity can be reduced. A useful feature of the software is that the laser intensity,
59
gain and offset can be altered automatically at specified time intervals to optimise the
images obtained in a dynamic process, such as crystallization. This means the probe
can be left unattended while images of sufficient quality are recorded.
A typical PVM unit consists of a probe, a field unit (which contains the electronics)
and a computer for image storage and review. An FBRM 700L was used for this
research and is 25 mm in diameter, has a wetted length of 300 mm and is made from
stainless steel. The window of the probe is made from sapphire and a kalrez o-ring is
used to seal the window to the probe tip. A more detailed description of the mode of
operation of PVM is available elsewhere (Barrett (2002)).
PVM is an extremely useful tool in for the study of particulate systems as it can
provide qualitative information on the size and shape of the particles in situ and in
real time (Figure 1.7). Furthermore analysis of a sufficient number of PVM images
can provide qualitative information on the size of the particles being studied.
Additionally when PVM is used in conjunction with the quantitive information that
the FBRM can provide, it is possible to achieve an excellent understanding of the
particulate system under investigation.
60
Figure 2.6: PVM internals (Mettler Toledo User‟s Site)
Figure 2.7: PVM images
61
2.3.2 Characterization of Particulate Systems Using PVM
O‟ Rourke & MacLoughlin (2005) PVM to analyse the evolving droplet size
distributions in lean silicone oil-water dispersions in an agitated system of standard
geometry. A satisfactory level of agreement between results obtained using PVM and
the more traditional sampling method was achieved. PVM held an advantage over the
sampling method in that it was less labour intensive and was suitable when there was
a large density difference between the two phases. However a minimum of three
minutes was required to acquire the large number of images needed for a
representative sample. This made PVM unsuitable form monitoring very rapid
changes in the droplet size distribution.
Barrett and Glennon (2002) used PVM to monitor the crystallization and dissolution
of potassium aluminium sulphate. PVM was used to validate the rapid increase in the
number of coarse crystals identified by the FBRM as the solution was cooled. PVM
images highlighted the presence of fine crystals, but also the presence of large single
crystals and agglomerates thus validating the FBRM observation. PVM images taken
during the heating period showed the complex nature of the dissolution process. A
clear transition between the distinctive octahedral shape of the crystals to a more
rounded morphology was observed.
McDonald et al., (2001) used PVM to study rice, tobacco and wild Chinese cucumber
cell suspensions. The PVM clearly identifies the different morphologies of the
different suspensions without the need to sample.
62
2.4 ATTENUATED TOTAL REFLECTANCE FOURIER TRANSFORM
INFRARED SPECTROSCOPY (ATR-FTIR)
2.4.1 Introduction
Supersaturation is a non-equilibrium state where there is more solute dissolved in
solution than in the equilibrium state at a given temperature. A solute will remain in
solution until there is a sufficient level of supersaturation to induce crystal formation.
Supersaturation is the driving force for subsequent crystal nucleation, growth and
agglomeration. As such it impacts on the final crystal size distribution. By measuring
supersaturation the underlying mechanism that governs the final crystal size
distribution can be controlled. In doing so the opportunity to produce crystals of a
suitable size, number and morphology is afforded. A method for the direct
measurement of supersaturation has been proposed by Löfflemann and Mersmann,
(2002), however the most common technique for measuring supersaturation is to
measure the solution concentration and infer the prevailing supersaturation from
solubility data. In this case it is more accurate to say that supersaturation is being
monitored, rather than measured.
Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy
has emerged as a useful technique for the measurement of solution concentration in
crystallizations. It has an advantage over other concentration measurement
techniques, as sampling is not required. This eliminates the problems associated with
temperature-controlled external sampling loops and removes the need for phase
separation devices. Additionally, multiple solute and multiple solvent crystallizations
63
can be characterised. This can be useful for anti-solvent and reactive crystallization as
well as crystallizations where impurities are present. With accurate solubility data the
measured solution concentration can be used to infer the prevailing level of
supersaturation.
Briefly, ATR-FTIR spectroscopy measures solution concentration by irradiating the
solution with infrared light to produce an infrared spectrum. This spectrum is
characteristic of the vibrational structure of the substance in immediate contact with
the ATR probe and can be described as a unique “fingerprint” of the liquid phase. An
ATR crystal is chosen so the depth of penetration of the infrared energy is smaller
than the liquid phase barrier between the probe and the solid crystal particles. Hence
when the probe is inserted into a crystal slurry the probe should be in immediate
contact with the liquid phase and interference from the solid phase should be
negligible. Despite this assumption some researchers have noted interference of the
spectrum by the solid phase in crystallization systems (O‟ Sullivan, 2005). The liquid
phase concentration is a function of the infrared spectrum generated and a calibration
model is used to inter-relate the data. . A more detailed description of spectroscopy in
general and the mode of operation of the ATR-FTIR probe is available elsewhere,
(Dunuwilla et al., 1994; Togkalidou et al., 2001; Lewiner et al., 2001a; O‟ Sullivan,
2005).
2.4.2 Initial Work Using Various Calibration Models
Initial studies using ATR-FTIR to measure solution concentration provide a useful
insight into the evolution of the technique, the problems associated with using it and
64
perhaps most usefully the various calibration models that relate the infrared spectrum
to the solution concentration.
Dunuwilla et al., (1994) were one of the first researchers to examine the use of ATR-
FTIR for the measurement of solution concentration. The solubility of citric acid in
aqueous solution was measured by taking a sample from a crystallization vessel and
placing it in a micro circle open boat cell equipped with an ATR rod. Relatively good
agreement between the measured data and literature values was achieved. However at
high temperatures the experimental solubility value was overestimated. Subsequent
research by Grön & Roberts (1999) repeated this work in situ and showed results with
increased accuracy at all temperatures. This indicates that the sampling method used
by Dunuwilla et al., (1994) may have been poor.
Dunuwilla & Berglund (1997) were the first researchers to use the ATR-FTIR
technique to monitor supersaturation in situ. The crystallization of maleic acid from
aqueous solutions was examined using a parabolic, intermediate parabolic and linear
cooling profile. The supersaturation was monitored for each of the cooling profiles
and the final crystal size distribution was measured at the end of each batch by
sieving. The ATR-FTIR successfully monitored the supersaturation in each case.
Supersaturation for the parabolic cooling profile remained low and constant
throughout the batch and large crystals were formed. For the linear cooling profile
supersaturation was higher over the course of the crystallization resulting in an
average crystal size that was half that produced using the parabolic cooling profile.
65
In this work supersaturation, solubility and the metastable zone width were
represented in terms of the ratio of the transmittance of two maleic acid peaks,
however a calibration between the transmittance ratio, concentration and temperature
was supplied and the solubility data generated was within 3% of literature values.
Lewiner et al., (2001a) used ATR-FTIR to study the crystallization of three fine
chemical products. For this study a different calibration model was used that
calibrated the height of one relevant solute peak with the concentration of the solute
and the temperature. The crystallization of Bifenox (a weed killer) from methanol
solutions by cooling was examined. Combining supersaturation data with information
on the size of the product crystals (measured by SEM and laser diffraction) it was
concluded that the final product size was mainly controlled by agglomeration and
secondary nucleation. The second system studied was the cooling crystallization of
another weed killer, isoproturon (IPU), from ethanol solutions. The supersaturation
measurements combined with information on the final crystal size distribution
gathered using image analysis indicated a different crystallization behaviour. In this
case there were large variations in the metastable zone width and low levels of
supersaturation due to very fast crystal growth. Finally the cooling crystallization of
an active pharmaceutical ingredient, compound F was studied. Compound F exhibited
four polymorphic forms and the ATR-FTIR successfully monitored the transition
from one form to the other over the course of a cooling crystallization.
Togkalidou et al., (2001) combined ATR-FRIR spectroscopy with robust
chemometrics to produce a very accurate calibration model for the estimation of the
solubility of potassium dihydrogen phosphate (KDP) in water. A number of
66
chemometric models were applied and the one that gave the most accurate predictions
was selected. In this case the most accurate model arose from choosing a whole
region of the spectrum rather than a single peak or a number of peaks. This improved
accuracy may have been due to a significant shift in one of the peaks meaning more
accurate predictions were made when all the data in the region was used or, it may
have been due to an increase in the apparent signal-to-noise ratio resulting from the
random noise in the absorbance being averaged over many more frequencies. The
solubility of KDP in water was measured using a number of models and the results
were compared. The solubility data gathered using a single peak chemometric model
proved inaccurate, while the data produced using the whole spectral region proved
this most accurate. This indicates that multiple absorbances and chemometrics
produce the most accurate calibration models. The most accurate model produced
concentration estimates with and accuracy of ± 0.12 wt%.
The research outlined above outline three different methods for the calibration of the
infrared spectrum with concentration. Lewiner et al., (2001b) commented on the
accuracy of the data presented by Togkalidou et al., (2001), describing it as
“remarkable”. However they noted that in industry there is a reluctance to use, so
called chemometric methods since a calibration using well-defined absorption bands
has a clearer chemical and physical meaning. However many of the ATR-FTIR
probes available for commercial use include useful chemometric packages that can be
used simply and effectively to generate an accurate calibration model. For the purpose
of this study the chemometric software package QuantIR, supplied with the Mettler
Toledo ReactIR 4000 ATR-FTIR probe will be used to generate calibration models.
67
2.4.3 The Use of ATR-FTIR to Characterize Crystallization Processes
2.4.3.1 Seeding
Lewiner et al., (2001b) continued previous studies of the cooling crystallization of
isoproturon (IPU) from ethanol solutions using a calibration model based on
absorption at a single peak. Unseeded crystallization resulted in large variations in the
metastable zone width, to the extent that no identifiable metastable zone width could
be identified at a constant cooling rate. This variability resulted in a highly variable
final crystal size. Supersaturation measurements indicated that the point of nucleation
was followed by a dramatic decrease in the supersaturation regardless of the cooling
rate used, indicating that supersaturation was minimal during the growth period. By
introducing a seeded crystallization the variability in the metastable zone width was
reduced and the final mean crystal size was increased and the CSD narrowed. A
suitable seeding temperature was identified by monitoring the supersaturation for runs
where the seed was introduced at different temperatures. When the seed was
introduced close to the solubility curve supersaturation continued to increase until
there was a burst of secondary nucleation. When the seed was introduced close to the
metastable zone width a rapid decrease in the supersaturation was observed similar to
the unseeded case indicating the introduction of the seeds was not effective. An
intermediate seeding temperature proved the most effective as the final crystal size
increased and the coefficient of variation decreased. With a suitable seeding
temperature identified it was possible to assess the effect of the seed mass and the
cooling rate after seeding. Supersaturation was measured for different cooling rates
after seeding. Higher cooling rates led to increased supersaturation and hence a higher
nucleation rate that was confirmed by a decreased final product size. With a suitable
68
cooling rate identified the effect of the seed mass was assessed. An increase in the
mass of seeds added resulted in a decrease in the prevailing level of supersaturation
due to the increased surface area available for growth.
2.4.3.2 Oiling Out
Groen & Roberts, (2001) combined ATR-FTIR supersaturation measurements with
optical turbidity measurements to identify liquid-liquid phase separation (also known
as oiling out) in the cooling crystallization of citric acid. Batch cooling experiments
were conducted and a reduction in optical transmittance was observed a significant
length of time prior to the onset of nucleation measured by a reduction in the
measured supersaturation. This change in the optical properties of the system without
crystallization indicated the formation of non-crystalline phase. Oiling out was
suggested as a possible explanation for the phenomenon as the supersaturation level
was extremely high and previous researchers had noted similar results for the citric
acid water system. The actual reduction in the solute concentration due to the phase
separation was estimated to be about 1 % (w/w), enough to facilitate nucleation. This
value was similar to the peak-to-peak noise of the ATR-FTIR signal. Hence no
change in the supersaturation was observed.
69
CHAPTER 3: SOLUBILITY MEASUREMENT FOR AN ANTI-
SOLVENT SYSTEM USING GRAVIMETRIC ANALSYSIS, ATR-
FTIR AND FBRM
3.1 ABSTRACT
The solubility of benzoic acid in ethanol-water mixtures was measured using
gravimetric analysis (solid analysis and liquid analysis), Focused Beam Reflectance
Measurement (FBRM) and Attenuated Total Reflectance – Fourier Transform Infra
Red Spectroscopy (ATR-FTIR). A suitable method that describes the solubility curve
for an anti-solvent addition system is presented. By expressing the solubility on an
anti-solvent free basis, the dilution effect may be eliminated and expression of the
metastable zone width (MSZW) and supersaturation is simplified. The FBRM, ATR-
FTIR and solid analysis methods produced similar solubility data validating the
techniques and highlighting the accuracy of the data. The liquid analysis
underestimated the solubility. This may have been due to the esterification of the
benzoic acid whilst drying. A correlation between the measured data and the
predictions of the UNIQUAC liquid activity coefficient model is also presented.
3.2 INTRODUCTION
Key to the successful design and optimisation of a batch crystallization process is
knowledge of the solubility over the range of operating conditions likely to be
encountered during the batch. In processes involving the crystallization of a solute
through the addition of an anti-solvent to a saturated, or near saturated, solution the
70
solute solubility should be known over a wide range of solvent mixture
concentrations. Gravimetric analysis is the traditional standard method for the
determination of solubility (Mullin, 1993) and its use has been widely reported for a
range of anti-solvent systems (e.g. Hoijati & Rohani, 2006a; Granberg and Rasmuson,
2000). Perhaps its principle drawback is the time-consuming nature of the method.
The use of in ATR-FTIR has become an increasingly popular technique for
investigation of solubility characteristics in crystallization systems including anti-
solvent processes (Hoijati & Rohani, 2006a). ATR-FTIR requires a substantial effort
to initially calibrate the system, but having developed the calibration data set,
subsequent application is relatively quick. Investigations based on the Focussed Beam
Reflectance Measurement (FBRM) probe have generally involved cooling
crystallization processes through the application of an automated polythermal method
for solubility determination (Barrett and Glennon, 2002). FBRM-based methods have
the advantage of requiring no calibration and of rapid measurement duration.
However, measurement of solubility in anti-solvent systems is not immediately
amenable to automation.
A priori prediction of solubility has been less successfully demonstrated to date,
although the use of liquid activity coefficient models has facilitated good correlation
of available experimental data (Gracin et al, 2002; Hoijati & Rohani, 2006b).
In this paper, a comparison of these three techniques for the measurement of solubility
in anti-solvent systems is presented, using benzoic acid-ethanol-water as the model
system. The correlation between the measured data and the predictions of the
UNIQUAC liquid activity coefficient model is also presented.
71
3.3 EXPRESSION OF SOLUBILITY
In an anti-solvent system, supersaturation is generated by a combination of reduced
solubility due to the action of the anti-solvent, and dilution. Typically, solubility is
expressed in terms of the mass of solute per total mass (or volume) of solvent and
anti-solvent. The combination of these factors makes expression of key crystallization
variables, specifically supersaturation and the metastable zone width, difficult. To
overcome this problem researchers have generally chosen to depict the path of anti-
solvent addition on the solubility curve as a diagonal line that accounts for the dilution
and reduced solubility (Granberg et al., 2001; Pina et al., 2001; Takiyama et al., 1998;
Kaneko et al., 2002). Sometimes dubbed a “mixing line” this diagonal line represents
the apparent concentration of the solution once the solution and anti-solvent have
been mixed, in the absence of crystallization. In this case expression of the
supersaturation and metastable zone width becomes potentially complex.
Figure 3.1 is a traditional solubility curve with the solubility expressed in terms of the
total mass of solvent and anti-solvent. A diagonal line on the solubility curve
represents the change in concentration as anti-solvent is added. Expression of the
supersaturation generation rate is, for such systems, cumbersome (Pina et al., 2001).
72
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 20 40 60 80
anti-solvent %
g s
olu
te /
(g
so
lven
t + a
nti
-so
lven
t)
Solubility Data
Solute Concentration
Figure 3.1: Change in concentration, as anti-solvent is added.
Solubility is generally given as a function of anti-solvent concentration, i.e. cs = cs(m).
Therefore,
s sdc dc dm
dt dm dt Eq. 3.1
When concentrations are reported in terms of g/g solution, and a constant anti-solvent
addition rate is used:
a Rt
m ts w a Rt
Eq. 3.2
2
R s wdm
dt s w a Rt Eq. 3.3
s
c ts w a Rt
Eq. 3.4
2
dc sR
dt s w a Rt Eq. 3.5
73
Therefore, the supersaturation generation rate is given by
s
s
2
dcsR R s w
d c dc dc dm
dt dt dt s w a Rt Eq. 3.6
When concentrations are reported in terms of g/g solvent (i.e. on an anti-solvent-free
basis):
a Rt
m tw
Eq. 3.7
dm R
dt w Eq. 3.8
s
c tw
Eq. 3.9
dc
0dt
Eq. 3.10
Therefore, the supersaturation generation rate is given by
sd c dc R
dt dm w Eq. 3.11
By defining all concentrations on an anti-solvent free basis, the solute concentration
follows a horizontal path as anti-solvent is added and is analogous to cooling on a
temperature-concentration curve (3.2). In this way the metastable zone width is
clearly defined as the difference in anti-solvent concentration at the point of
nucleation and at the point of saturation. The supersaturation is the difference (or
ratio) between the actual concentration and the equilibrium concentration.
74
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 1 2 3 4 5
g anti-solvent / g solvent
g s
olu
te /
g s
olv
en
t
Solubility Data
Solute Concentration
Figure 3.2: Change in concentration (anti-solvent free basis), as anti-solvent is added.
3.4 EXPERIMENTAL WORK AND ANALYSIS
3.4.1 Gravimetric Analysis
An ethanol-water mixture of known composition is charged to a 500ml glass jacketed
vessel and the temperature allowed to equilibrate at 25 C. A known excess of benzoic
acid is added making the total solution mass about 150 g. The solution is then stirred
at 300rpm for 2 hours. Ideally the solution should be left for 24 hours, however
experiments conducted using the ATR-FTIR indicate that equilibrium is reached after
as little as 10 minutes. At this point the slurry is filtered and the filtrate and filter cake
placed in separate conical flasks and covered with filter paper. The filter paper allows
MSZW (g/g)
ΔC (g/g)
75
the evaporation of the solvent but prevents the apparent sublimation of the solute, a
phenomenon observed during initial runs. The conical flasks is placed in an oven at
80 C and left for a number of hours. The mass of the filtrate and filter cake are
measured at regular intervals to identify when drying is complete. When three
consecutive measurements yield three similar masses the sample is deemed to be dry.
This typically occurred after 24 hrs for the filter cake and 60 hours for the filtrate
(Figure 3.3).
Two methods were used to determine the solubility. Firstly, the mass of solute
remaining once all of the solvent from the filtrate had evaporated provided „liquid
analysis‟ solubility. Secondly, the mass of undissolved solute remaining in the filter
cake once it had been dried was subtracted from the initial charge of solute to the
vessel to provide „solid analysis‟ solubility (3.4).
It is clear that there is a systematic difference between the solubility values generated
from the solid analysis and the liquid analysis. The liquid analysis method appears to
underestimate the solubility over all the water concentrations. There are a number of
possible reasons for this. It was difficult to transfer all of the slurry from the
crystallizer into the filter. Every effort was made to ensure all the saturated solution
and solute was transferred but inevitably some was left in the crystallizer. It is also
possible that there may have been an esterification reaction between the benzoic acid
and the alcohol at the elevated temperatures in the oven. It is possible that an ester
formed and evaporated with the rest of the solvent mixture. By drying samples at a
low temperature in a vacuum oven it may be possible to avoid this problem.
76
0
40
80
120
160
0 20 40 60 80 100 120 140 160
Time (hrs)
Fil
trate
Mass (
g)
0
2
4
6
8
10
12
14
16
Filte
r C
ak
e M
as
s (
g)
Filtrate
Filter Cake
3.3: Typical drying curve for gravimetric analysis run
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
(g water/ g ethanol)
(g b
en
zoic
acid
/ g e
thano
l)
Solid Analysis
Liquid Analysis
Figure 3.4: Solubility of benzoic acid in ethanol water at 25 C – Gravimetric Analysis
77
3.4.2 Polythermal Method Using FBRM
An ethanol-water mixture of known composition, and a mass of about 1200g, is added
to a 2-litre stainless steel jacketed vessel fitted with a Julabo chiller, to provide
temperature control, and a condenser to prevent evaporation. A known mass of
benzoic acid is charged to the vessel and the solution heated slowly. The slurry is
monitored using FBRM and the dissolution temperature noted. At this point the
solution is cooled to below the saturation temperature and another known mass of
benzoic acid is charged. The slurry is reheated and the point of dissolution noted
giving a new saturation temperature. In this way the solubility of benzoic acid in a
given mixture of ethanol and water is calculated for a range of temperatures close to
25 C and the actual solubility at 25 C can be interpolated. Once a sufficient number
of data points are gathered to allow interpolation more ethanol is added and the
process repeated so the solubility for another concentration can be calculated. In this
way the solubility for a full range of ethanol-water compositions at 25 C can be
gathered.
Figure 3.5 is a plot of a typical run for the dissolution of the benzoic acid. Counts
between 100 and 1000 microns are used to identify the point of dissolution. This is
because large particles are the last to dissolve due to their high surface area to volume
ratio. The point at with the counts dropped to zero was clear for all the runs making
identification of the saturation temperature simple.
It is important to assess the effect of heating rate on the dissolution temperature
measured by FBRM. If the heating rate is too high, dissolution may be observed at a
78
temperature above that of the actual saturation temperature. A number of runs were
carried out to assess the saturation temperature measured at different heating rates
(Figure 3.6). A heating rate above 0.3 C/min is too fast and the saturation temperature
is overestimated. A heating rate of 0.2 C/min was chosen for all the runs since this
rate garnered the same saturation temperature as for a heating rate of 0.1 /min but
allows a shorter batch time.
Figure 3.7 shows the solubility of benzoic acid in four ethanol-water mixtures close to
25 C. As expected the solubility increases with increasing temperature and decreasing
water concentration. By interpolating these curves the solubility at 25 C in various
ethanol-water mixtures can be evaluated (Figure 3.8).
79
15
20
25
30
180 205 230 255 280
Time (min)
Tem
pera
ture
(°C
)
0
50
100
150
200
250
300
#/s
(100-1
000 m
icro
ns)
Temperature
#/s (100-1000 microns)
Figure 3.5: Nucleation and dissolution of benzoic acid monitored using FBRM
23.2
23.3
23.4
23.5
23.6
0 0.1 0.2 0.3 0.4 0.5
Heating Rate (°C/min)
Sat
ura
tio
n T
em
pera
ture
(°C
)
Figure 3.6: Heating rate vs. saturation temperature
80
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
15 20 25 30
Temperature (°C)
g b
enzo
ic a
cid/g
eth
an
ol
0.90 (g w ater/g ethanol)
1.37 (g w ater/g ethanol)
2.08 (g w ater/g ethanol)
3.37 (g w ater/g ethanol)
Figure 3.7: Temperature vs. Solubility in various water concentrations
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Water Concentration (g water/ g ethanol)
So
lub
ilit
y (
g b
en
zo
ic a
cid
/ g
eth
an
ol)
Figure 3.8: Solubility of benzoic acid in ethanol water mixtures measured using FBRM
81
3.4.3 Solubility Measurement Using ATR-FTIR Technique
3.4.3.1 Calibration of Probe
ATR-FTIR spectroscopy can be used to measure the concentration of a dissolved
solute. The IR absorbance measured by the probe (Mettler Toledo, React IR 4000) is
a function of concentration of the dissolved solute and a calibration is needed to
convert the absorbance data into concentration. Operation at a constant temperature of
25 C eliminated any temperature effect on the absorbance.
The method of partial least squares (PLS) has been commonly used to perform such
calibrations. Although the use of PLS does not produce a physical understanding of
the system, it is well suited for the primary goal of prediction and estimation (Liotta &
Sabesan 2004). A PLS model developed by Mettler-Toledo (Quant- IR) was used to
perform the calibration. A region to two-point baseline model was used as this
produced the lowest error. The spectral region used was between wave numbers 1003
and 1814 cm-1
(Figure 3.9).
40 standards were used to calibrate the ATR-FTIR probe and 12 standards were used
to validate the model (Figure 3.10). Spectra for all the standards were taken in a
500ml glass jacketed vessel at a temperature of 25 C. A concerted effort was made to
get standards inside the metastable zone. However the system exhibits a very narrow
metastable zone width and often nucleation would occur before a measurement could
be taken. This was especially the case at low water concentration where the solubility
curve is steep and high supersaturation is generated for a small increase in water
82
concentration. It was relatively easy to gather standards inside the metastable zone
width at higher water concentrations where the solubility curve is less steep.
0
0.2
0.4
0.6
0.8
1
1.2
100011001200130014001500160017001800
Wavenumber (cm -1)
Abso
rbance
Figure 3.9 Spectral region chosen for quant
Figure 3.10: Standards used to calibrate and validate the ATR-FTIR probe. Weak validation points
arrowed.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
(g water/ g ethanol)
(g b
en
zoic
acid
/ g
eth
an
ol)
Calibration Points
Validation Points
83
Figure 3.11 shows the comparison between the known and predicted water
concentration calculated using the PLS model. Despite the excellent correlation
coefficient, there is a slight offset of about 4% between the known and predicted
concentration. Closer investigation of the validation set indicates that this offset is due
to an unusually high error for two of the low water concentration experiments
(indicated in Figure 3.10). For these validation points the error is 5.2% and 5.0%,
while for the other 10 validation points the error is always less than 3%. If these
values are removed from the validation set the offset is reduced to below 0.1%. It is
possible that the calibration model is less accurate at these low water concentration
values. Inspection of Figure 3.10 indicates that the validation solutions that produce
the largest error are relatively isolated compared to the calibration set. The
relationship between known and predicted benzoic acid concentration is excellent
with no offset (Figure 3.12). This indicates that if there is a weakness in the
calibration model at low water concentrations, the prediction of the benzoic
acid concentration is not affected. The average error was 1.8% of the measured water
concentration and 1.4% of the measured benzoic acid concentration.
84
y = 0.9642x + 0.0422
R2 = 0.9979
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Water Concentration Known (g/g)
Wate
r C
on
centr
atio
n C
alc
ula
ted
(g/g
)
Figure 3.11: Predicted vs. Known water concentration
y = 1.0003x - 0.001
R2 = 0.9993
0.0
0.1
0.2
0.3
0.4
0.5
0.0 0.1 0.2 0.3 0.4 0.5
Benzoic Acid Concentration Known (g/g)
Ben
zo
ic A
cid
Co
ncen
trati
on C
alc
ula
ted (
g/g
)
Figure 3.12: Predicted vs. Known benzoic acid concentration
85
In order to further validate the model a number of experiments were performed under
different conditions and the predicted water concentration and benzoic acid
concentration was compared to the measured values. Table 3.2 compares known and
calculated water and benzoic acid concentrations across the three separate validation
standards. In each case the predicted concentration is within an acceptable margin of
error. Liotta & Sabesan (2004) noted the level of agitation can affect the measurement
of spectra. To assess the effect of agitation intensity, a standard solution was
measured at various levels of agitation. Table 3.2 shows the effect an increase in the
agitation rate has on the water and benzoic acid concentration calculated by the
calibration model. In each case the predicted concentration was acceptable. Since the
crystallization under investigation is an anti-solvent addition and the total volume in
the vessel will increase over time, the effect of total volume on the calibration model
was assessed. Standards of the same composition but of different total volumes were
made up and measured. Table 3.3 shows the difference between the known and
calculated values. Once again the concentrations were within acceptable limits.
86
Table 3.1: Error across three standards for (a) water concentration and (b) benzoic acid concentration
(a) (b)
Water Concentration (g/g) Benzoic Acid Concentration (g/g)
Standard # Known Calculated Error % Standard # Known Calculated Error %
1 1.292 1.255 -2.90 1 0.188 0.193 +2.55
2 1.292 1.292 0.00 2 0.188 0.189 +0.34
3 1.292 1.263 -2.2 3 0.188 0.186 -0.96
Table 3.2: Effect of agitation on quant: (a) water concentration and (b) benzoic acid concentration
(a) (b)
Water Concentration (g/g) Benzoic Acid Concentration (g/g)
rpm Known Calculated Error % rpm Known Calculated Error %
200 1.292 1.292 0.00 200 0.188 0.189 +0.34
300 1.292 1.277 -1.20 300 0.188 0.187 -0.55
400 1.292 1.271 -1.46 400 0.188 0.187 -0.44
Table 3.3: Effect on volume on quant: (a) water concentration and (b) benzoic acid concentration
(a) (b)
Water Concentration (g/g) Benzoic Acid Concentration (g/g)
Volume (ml) Known Calculated Error % Volume (ml) Known Calculated Error %
110 1.292 1.277 -1.20 110 0.188 0.186 -0.55
220 1.292 1.259 -2.55 220 0.188 0.188 -1.72
330 1.292 1.287 -0.40 330 0.188 0.187 -0.80
Finally, the effect of a hold period on the solution was investigated. The purpose of
this was two-fold. Firstly it gives an indication of the error associated between two
different measurements. Secondly it indicates if evaporation plays a role. A standard
solution was made up and spectra were taken every three minutes for one hour. 3.13
shows the plot of water and benzoic acid concentration against time.
87
0.0
0.5
1.0
1.5
2.0
2.5
0 10 20 30 40 50 60
Time (min)
wate
r co
ncen
trati
on (
g/g
)
0.0
0.1
0.2
0.3
0.4
0.5
benzo
ic a
cid
con
centr
ati
on
(g/g
)
w ater concentration - calculated
w ater concentration - know n
benzoic acid concentration - calculated
benzoic acid concentration - know n
Figure 3.13: Effect of a hold period on the quant model
The validation experiments combined with the low average error across 12 validation
samples of different concentration indicate that the calibration model is suitable for
the range of concentrations and operating conditions encountered for this study.
3.4.3.2 ATR-FTIR Solubility Measurement
The method applied in this case was similar to that outlined for the gravimetric
analysis but instead of weighing the mass of undissolved solute at the end of the hold
period, a direct measure of the concentration was taken using the ATR-FTIR.
Benzoic acid was ground in a pestle and mortar for a number of minutes to ensure as
small a particle size as possible. An excess of the milled benzoic acid was added to an
ethanol-water mixture, of known composition and mass roughly equal to 150g, that
had been stirring for 30 minutes in a 500ml glass lined, temperature controlled vessel.
88
The temperature was allowed to equilibrate at 25 C and the slurry was then stirred at
300rpm for 30mins. Spectra were taken every 30s and the calibration model was
applied so the benzoic acid concentration and water concentration could be calculated.
At the end of the 30-minute hold period a known mass of ethanol was added to the
slurry to alter the solubility and the procedure was repeated. This allowed a number of
solubility measurements to be gathered over time. Figure 3.14 shows the results from
one of the solubility determination experiments using ATR-FTIR. Six ethanol
additions were made, allowing seven points on the solubility curve to be measured. A
hold period of 30 mins was deemed sufficient since the concentration of benzoic acid
in solution increased to an equilibrium level, and remained constant at that level, after
as little as 10 mins (Figure 3.14). In viscous solutions and at low temperatures it can
take a number of hours or even days for a system to reach equilibrium. However in
this case it appears that 30mins is sufficient to allow the system to equilibrate. Unlike
increasing temperature, which takes time to increase solubility, the addition of the
solvent (in this case ethanol) is instantaneous which leads to a rapid increase in
solubility. Additionally, the benzoic acid had been milled to a small size prior to
addition. It is possible that this improved its dissolution properties. The water
concentration and benzoic acid concentration at the end of each 30-minute hold period
were used to generate the solubility data. In all 18 points on the solubility curve were
calculated (Figure 3.15).
89
0
0.5
1
1.5
2
2.5
3
0 2000 4000 6000 8000 10000 12000
Time (s)
Wate
r C
on
centr
atio
n (
g/g
)
0
0.1
0.2
0.3
0.4
0.5
0.6
Ben
zo
ic A
cid
Co
ncen
trati
on (
g/g
)
Water Concentration
Benzoic Acid Concentration
Figure 3.14: Time vs. water concentration and benzoic acid concentration
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Water Concentration (g water/ g ethanol)
So
lub
ilit
y (
g b
en
zoic
acid
/ g
eth
an
ol)
Figure 3.15: Solubility curve of benzoic acid in ethanol-water using ATR-FTIR
90
3.4.4 Overall Solubility Measurement
The solubility data gathered using the four different methods compares well (Figure
3.16). For the FBRM, ATR-FTIR and solid analysis techniques the solubilities
measured similar data for the solubility. Interestingly, the solubility increases for a
small increase in water concentration at high ethanol concentration. This is not
unusual and solute systems have been shown to exhibit increased solubility in mixed
solvents even if one of them is an anti-solvent (Granberg & Rasmuson, 2000; Hojjati
& Rohani, 2006a).
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00
Water Concentration (g water / g ethanol)
g b
en
zo
ic a
cid
/ g
eth
an
ol
ATR-FTIR Solubility
FBRM Solubiliy
Solid Analysis
Liquid Analysis
Figure 3.16. Solubility of benzoic acid in ethanol-water using four different methods
To establish the thermodynamic consistency of the measured data, the recorded
solubility can be compared with the theoretical predictions of the UNIQUAC model
for liquid activity coefficients (Hoijati & Rohani, 2006b).
At equilibrium,
91
S L
i i i i if T,P x T,P,x f T,P Eq. 3.12
where
L fus
i m
S
i m
f T, P h T , P 1 1ln
f T, P R T T Eq. 3.13
The UNIQUAC model for prediction of the liquid activity coefficients is given by
c r
i i iln ln ln Eq. 3.14
where
c i i ii i i j j
ji i i
zln ln q ln l x l
x 2 x Eq. 3.15
and
j ijr
i i j ji
j j k kj
k
ln q 1 ln Eq. 3.16
with
i ii
j j
j
x q
x q Eq. 3.17
i ii
j j
j
x r
x r Eq. 3.18
and
i i i i
zl r q r 1
2 Eq. 3.19
The biggest challenge in the use of the UNIQUAC model is the determination of the
binary interaction parameters, ij. To establish the consistency of the measured data
reported here with the model, the binary interaction parameters are estimated by non-
linear regression of the measured data. The sum of squares of the difference between
the experimental and model data was minimized by changing the binary interaction
parameters using SOLVER in Microsoft Excel. The goodness of fit can then be used
92
as an indication of the consistency. A comparison of the model predictions with the
experimental data is shown in Figure 3.17, using the pure component data given in
Table 3.4 and the regressed binary interaction parameters reported in Table 3.5. As
shown, the UNIQUAC model follows the observed trends, with the measured increase
in the solubility of benzoic acid in ethanol for low water concentrations well
described by the model.
Table 3.4.: Pure component data for UNIQUAC model (Yaws, 2003, Reid et al., 1987)
hfus
(J/mol) Tm (K)
ri
qi
benzoic acid 16,230 395 4.323 3.344
ethanol - - 2.1055 1.972
water - - 0.920 1.400
Table 3.5. Estimated binary interaction parameters for benzoic acid (1) - ethanol (2) - water system.
12 13 21 23 31 32
0.359 8.774 0.000 9.214 0.000 2.465
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 0.5 1 1.5 2 2.5 3 3.5 4
water conc (g/g ethanol)
so
lute
co
nc (
g/g
eth
an
ol)
Figure 3.17. Comparison of measured FBRM solubility data with predictions of fitted UNIQUAC
model.
93
3.4 DISCUSSION
Three methods for the determination of the solubility of an anti-solvent system have
been presented. Gravimetric analysis provides a cheap and simple method for
estimating the solubility. However this method can be time consuming with the
recommended hold time being up to 24 hours. While the solid analysis provided
accurate data the liquid analysis underestimated the solubility either through the
saturated solution remaining in the crystallizer or an esterification reaction occurring
whilst drying. FBRM provided accurate data and lends itself to automation. While a
turbidity probe would measure the solubility in a similar way the FBRM will provide
further information after the nucleation event and is more sensitive. ATR-FTIR
provided much information about the dissolution process. It was clear that a 30-
minute hold time was sufficient. To gather quantitive data it is necessary to build a
calibration model. While this is time consuming, once it is complete, it is possible to
monitor supersaturation during crystallization by measuring the liquid phase
concentration in situ.
A suitable method to express solubility for an anti-solvent system has been presented
that allows for clear depiction of the metastable zone width and supersaturation in
terms of simple units. The solubility of benzoic acid at 25 C has been measured for a
full range of ethanol-water mixtures. The ATR-FTIR, FBRM and solid analysis
methods all produced similar data while the liquid analysis systematically
underestimated the solubility. The trends observed are in agreement with theoretical
94
considerations and are similar to trends reported for other ternary anti-solvent
systems.
95
CHAPTER 4: THE EFFECT OF MIXING ON THE
METASTABLE ZONE WIDTH IN ANTI-SOLVENT
CRYSTALLIZATION
4.1 ABSTRACT
The effects of anti-solvent addition rate and location, and agitation speed on the
metastable zone width (MSZW) of an anti-solvent system were investigated using
Focused Beam Reflectance Measurement (FBRM) and Attenuated Total Reflectance
Fourier Transform Infra-Red Spectroscopy (ATR-FTIR). Benzoic acid in ethanol-
water mixtures, with water acting as the anti-solvent, was chosen as the model system
and was studied at a 500 mL scale.
FBRM proved to be the more sensitive method for the detection of nucleation. In
general, the MSZW widened with increasing addition rate, with the effect most
pronounced when the anti-solvent was added close to the impeller. At this location, an
increase in agitation intensity resulted in a narrower MSZW for all addition rates. For
an addition location close to the vessel wall, the MSZW was narrower and the impact
of addition rate and agitation were less pronounced. Substantial variation in the
MSZW was also observed, with nucleation occasionally occurring at bulk
concentrations less than the saturation level. It is proposed that the MSZW is
influenced by the differing degrees of anti-solvent incorporation at each addition
location. Close to the impeller anti-solvent is rapidly incorporated leading to
consistent results, but, close to the vessel wall, incorporation is hindered by
96
unfavourable mixing conditions leading to premature nucleation and more
variability*.
Using the MSZW information nucleation kinetics at two different agitation intensities
were estimated. Using this data, an agitation dependent expression for the nucleation
rate was generated.
4.2 INTRODUCTION
The addition of an anti-solvent to a concentrated solution to induce crystallization of
the solute through a reduction in its solubility in the combined solvent system is a
power isolation and purification technique particularly where the temperature
coefficient of solubility is low or where the solute is unstable at elevated
temperatures. Its use in the pharmaceutical industry is, therefore common. Particular
problems associated with anti-solvent crystallization include wide batch-to-batch
variability, fine and irregularly shaped product crystals and solvate/hydrate formation.
The effects of various process parameters, including type of anti-solvent (Takiyama et
al., 1998; Oosterhof et al., 1999), feed concentration (Holmbäck & Rasmuson, 1999;
Barata & Serrano 1998(b)) solution concentration (Kaneko et al., 2002; Kitamura &
Sugimoto, 2003), anti-solvent addition rate (Beckmann, 1999; Holmbäck &
Rasmuson, 1999) and agitation intensity (Takiyama et al. 1998; Yu et al., 2005), on
the size, number, shape, degree of agglomeration and polymorphic form of product
crystals have been reported. However, less attention has been paid to the metastable
zone width and its relationship with the process conditions (Guo et al., 2005; Pina et
al., 2001).
* This proposal has been confirmed by the application of computational fluid dynamics (CFD) to characterise mixing conditions
at both locations. A full treatment of the application of CFD to this system can be found in Appendix A.
97
The metastable zone width is an extremely important parameter in the design and
optimisation of crystallization processes. A solute will remain in solution until a
sufficiently high level of supersaturation is generated to induce spontaneous
nucleation. Typically, it is desirable to operate away from this metastable limit so as
to ensure reliable process performance. Metastable zone information can be used to
optimise crystallization processes (Ulrich & Strege, 2002) and calculate nucleation
kinetics (Nyvlt, 1968). The MSZW may be affected by various process parameters,
such as supersaturation generation rate (Barrett & Glennon, 2002), agitation speed
(O‟Sullivan, 2005) and the presence of impurities (Myerson & Jang, 1995; Sayan &
Ulrich, 2001).
Recent research aimed at modeling the impact of mixing on crystallization has
focused on reactive crystallization (Tavare, 1995; Phillips et al., 1999; David, 2001;
Torbacke & Rasmusson, 2001 & d2004). Little work has been conducted studying the
impact of mixing on the MSZW for anti-solvent systems. Mixing is critical for all
crystallization systems but in the case of anti-solvent addition adequate mixing is
needed to incorporate the anti-solvent into the bulk solution and maintain a constant
level of supersaturation throughout the crystallizer, over a potentially wide volume
range. This must be achieved while considering many other mixing sensitive
parameters such as solids suspension, attrition, impurity profile, agglomerate
formation/break-up, of entrainment of gas/vapor from the headspace and rate of heat
transfer (Paul, 2005). In this work, the effect of addition rate, agitation speed and feed
point location on MSZW are investigated at a 500 mL scale. FBRM and ATR-FTIR
are implemented to detect nucleation and the results are compared. Appendix A
98
highlights the use of Computational Fluid Dynamics (CFD) software is used to model
the mixing behaviour of the systems under investigation and to provide an explanation
of the observed results.
With accurate MSZW information it is possible to estimate nucleation kinetics. This
is achieved my modifying classical nucleation theory (Nyvlt, 1968) for an anti-solvent
system. Much of the literature focuses on static induction time experiments to
estimate nucleation kinetics for an anti-solvent system (Barata & Serrano, 1996(b)).
However useful nucleation kinetics can be gathered using dynamic experiments and
this is the method will be used here.
4.3. EXPERIMENTAL METHODS
The solute in this work, benzoic acid, is soluble in ethanol (58.36 g/100g @ 25 C)
and essentially insoluble in water (0.25g/100g @ 25 C) (Perry). All metastable zone
width measurements were carried out at 25 C.
An initially undersaturated solution, containing 75g water, 75g ethanol and 21g
benzoic acid, was held at 25 C in a 500 mL glass jacketed vessel with a Julabo chiller
fitted for temperature control. A motor driven, pitched blade impeller provided
agitation. FBRM (S400 A; Mettler Toledo), ATR-FTIR (ReactIR 4000; Mettler
Toledo) and temperature probes were placed in the vessel to monitor the
crystallization and as a consequence provided baffling (Figure 4.1). Water at 25 C
was fed to the vessel at a variety of rates (0.05 gs-1
, 0.14 gs-1
, 0.24 gs-1
, 0.34 gs-1
and
99
0.48 gs-1
). Two addition locations (Figure 4.1) and two agitation intensities (325 rpm
and 475 rpm) were investigated. All experiments were performed in triplicate and the
reported values are the mean of the three experiments. The error bars reported for the
standard deviation of the mean. FBRM and ATR-FTIR were used to detect nucleation
and the MSZW is reported in terms of concentration (g anti-solvent / g solvent) as
outlined in Chapter 2. Solubility data for the system can also be found in Chapter 2.
Figure 4.1: Crystallizer configuration
100
4.4 RESULTS AND DISCUSSION
4.4.1 Comparison of FBRM and ATR-FTIR for the detection of nucleation
FBRM and ATR-FTIR detect the formation of crystals in different ways. FBRM
detects the point at which crystals are present in the solution, whereas ATR-FTIR
detects the point at which the solution concentration decreases, indicating solute is
being forced out of solution and crystals are being formed. To measure the actual
solution concentration a calibration model is needed, but for the purpose of
identifying the point of nucleation such a model is not necessary, since it is sufficient
to examine peaks in the spectrum associated with benzoic acid.
Figure 4.2: ATR-FTIR waterfall plot for standard MSZW experiment
Figure 4.2 shows a waterfall plot for a typical crystallization. The peak at 1275 cm-1
represents the C-O bond present in the benzoic acid molecule. The peak at 1480 cm-1
Benzoic Acid Peak
101
is representative of a C-C bond indicating an aromatic group. After a 10-minute hold
period to equilibrate the temperature, water is added, in this case, at 0.045 g/s (and
agitation speed of 475 rpm). Examining the peak at 1275 cm-1
the concentration of
benzoic acid in solution decreases at a steady rate due to dilution. After about 5
minutes there is a sharp decrease in this peak, indicating that the concentration of
benzoic acid in solution is decreasing at a faster rate, due to the formation of benzoic
acid crystals. In this way the onset of nucleation can be identified using ATR-FTIR
with no need for a calibration model.
The height of a specific peak can be trended against time so the onset of nucleation
can be identified easily. Figure 4.3 (a) shows the comparison between FBRM counts
(10-100 μm), and the height of the benzoic acid peak at 1275 cm-1
. It was noted that
the counts per second data in the region 0-10 μm exhibited more noise than the counts
data in the 10-100 μm region. Therefore, the count rate in the latter region was used
for nucleation detection. The chosen range also ensured that agglomerates of newly
formed crystals were also detected. In general, however, it is advisable to choose the
most sensitive detection range by inspection. The FBRM indicates clearly the point at
which the first crystals are observed while the decrease in intensity of the benzoic acid
peak indicates the point at which solute comes out of solution. Figure 4.3(b) focuses
on the region where nucleation occurs and shows that FBRM detects the onset of
nucleation before the ATR-FTIR. For all addition rates the FBRM detected the point
of nucleation before the ATR-FTIR. (Figure 3.4) with the difference most pronounced
at higher addition rates. FBRM has an advantage over ATR-FTIR in detecting the
point of nucleation due to its much shorter sampling time of 2 seconds rather than 30
seconds. It is possible to scan faster with the ATR-FTIR probe, but this increases the
102
likelihood of errors in the measurement. For this reason FBRM was chosen to indicate
the point of nucleation.
0
500
1000
1500
2000
2500
3000
3500
0 5 10 15 20 25
TIme (min)
#/s
10-1
00 m
icro
ns
0
0.02
0.04
0.06
0.08
0.1
0.12
Rela
tive A
bs
FBRM
ATR-FTIR
Figure 4.3 (a): Time vs. FBRM counts and peak height
5
10
15
20
12 13 14 15 16 17 18
TIme (min)
#/s
10-1
00 m
icro
ns
0.02
0.04
0.06
0.08
0.1R
ela
tiv
e A
bs
FBRM
ATR-FTIR
Figure 4.3 (b): Time vs. FBRM counts and peak height: Point of nucleation
FBRM Nucleation
ATR-FTIR Nucleation
103
0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3
Addition Rate (g/s)
MS
ZW
(g
/g)
ATR-FTIR
FBRM
Figure 4.4: Comparison of MSZW measured by FBRM and ATR-FTIR
104
4.4.2 Impact of Process Parameters on the MSZW
Figure 4.5 shows the impact of addition rate and agitation intensity on the MSZW
when anti-solvent is added close to the impeller (addition point 1 in Figure 3.1). At a
given agitation intensity the MSZW becomes wider as addition rate increases. The
increase in MSZW with increasing supersaturation generation rate is typical of most
crystallization systems.
For each addition rate studied an increase in the agitation intensity resulted in a
natrower MSZW. Agitation can have two effects on the metastable zone width.
Increased agitation can narrow the MSZW by increasing the probability of solute
molecules contacting to form the critical sized nuclei necessary for nucleation.
However this increase in agitation can also break up these clusters widening the
MSZW. For an anti-solvent system there is the added factor of mixing between the
solvent and anti-solvent.
105
0.0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3 0.4 0.5
Addition Rate (g water/s )
MS
ZW
(g
wate
r/g
eth
an
ol)
475 rpm - impeller addition location
325 rpm impeller addition location
Figure 4.5: Addition Rate vs. MSZW – addition location close to the impeller
Similar experiments were performed with an addition point close to the wall of the
vessel (addition point 2 in Figure 4.1). In this case the addition rate had substantially
less effect on the MSZW (Figure 4.7). In general the MSZW is significantly narrower
when the anti-solvent is added close to the wall. This can be explained by considering
the unfavourable mixing conditions at the wall. Addition close to the impeller ensures
rapid incorporation of the anti-solvent into the bulk solution, whereas addition close
to the wall leads to areas of locally high supersaturation close to the addition location.
There was also a far greater variability in the MSZW, especially at 325 rpm. At high
addition rates it becomes harder to incorporate the anti-solvent into the bulk solution.
This leads to an area of local supersaturation close to the feed point. This can result in
premature nucleation often at bulk concentrations below the saturation level.
Increasing the agitation facilitates dissipation of the local supersaturation. This
106
explains why increasing the agitation intensity results in a wider MSZW as well as
reduced variability.
-0.1
0.0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3 0.4 0.5
Addition Rate (g water/s )
MS
ZW
(g
wate
r/g
eth
an
ol)
475 rpm - wall addition location
325 rpm - wall addition location
Figure 4.7: Addition Rate vs. MSZW – addition location close to the vessel wall
4.4.3 Nucleation Kinetics
With sufficient metastable zone width data it is possible to calculate nucleation
kinetics for the anti-solvent system. It is important to identify an operating regime
where kinetics may be reliably calculated. In the situation where anti-solvent was
added at the wall location, and at 325 rpm, it is probable that true nucleation kinetics
cannot be estimated. This is because the observed nucleation occurs due to excessive
local supersaturation near the addition point rather than true supersaturation in the
bulk of the liquid. When the addition location is close to the impeller, the
nucleationdoes not appear to be so dominated by hydrodynamic conditions. In this
case useful kinetic information may be estimated.
107
Classical nucleation theory (Nyvlt, 1968) can be applied to the MSZW data as used
with modifications for anti-solvent addition rather than cooling.
no Ck
dt
dNJ 4.1
During anti-solvent addition, the rate of supersaturation generation can be expressed
as a function of the specific anti-solvent addition rate,
dA
dcR
dt
cd s. 4.2
At the point of nucleation, the supersaturation is related to the metastable zone width
by
dA
dcAC s.max
max 4.3
where the MSZW is given by
nucs AAAmax 4.4
Using the solubility data reported in Chapter 3 dcs/dA is equal to 0.2231 g solute g-1
solvent g-1
anti-solvent for the benzoic acid-ethanol-water system. At the point of
108
nucleation, it may be assumed that the rate of supersaturation generation equals the
rate of formation of new crystals which is given by
nn
nnuco CkCrk
dt
dM 3 4.5
Combining equations 4.2, 4.3 and 4.4 yields,
maxlnlnln1ln AnkdA
dcnR n
s 3.7
Using equation 4.7 a plot of ln (R) verses ln (ΔAmax), will yield a line of slope n.
(Figure 4.8). Fitting a simple trend line to the data allows n and kn to be calculated.
Figure 4.8: ln (MSZW) vs. ln (addition rate) with trend-lines
109
Figure 4.8 clearly indicates that agitation has an impact on the nucleation kinetics. In
order to study the effect of agitation on the kinetics it is proposed that the nucleation
rate expression can be modified to include an expression for the agitation intensity
(β),
dA
dcrA
dA
dckCNkJ s
n
sn
n
n max 4.8
Taking logarithms again,
)ln(lnlnln1ln max NAnkdA
dcnR n
s 4.9
In this case a plot of ln(R) vs. ln( maxA ) will once again yield a straight line. In order
to solve for n, kn and β a non-linear regression was applied to the data using the
known values of dA
dc*
, N and maxA . The regression analysis yielded the following
expression for the nucleation rate (Equation 4.10).
5.21.178.2 CNJ 4.10
From equation 4.9, the metastable zone width can be obtained thus
110
111
'max
ns
n
ndA
dc
Nk
RA 4.11
Good agreement is obtained between the predictions of equation 4.11 and the
experimental data (see Figure 4.9), supporting the contention that impeller speed
plays a critical role in determining the level of nucleation in the vessel, and thus
providing a correlation to facilitate quantification of the role of impeller speed in the
determination of nucleation rates. This is consistent with the observations made based
on the measured trends in metastable zone width.
Figure 4.9: Metastable zone widths predicted form Equation 4.11
111
4.5 DISCUSSION
The effect of addition rate and location, along with agitation intensity, on the
metastable zone width has been investigated for an anti-solvent crystallization. An
addition location close to the impeller results in a repeatable crystallization and a
positive correlation between the addition rate and the MSZW. An addition location
close to the wall of the vessel results in a significantly narrower MSZW, and more
random nucleation behaviour.
The effect of agitation intensity depends on the addition location. When anti-solvent
is added close to the impeller, an increase in agitation intensity results in a narrower
MSZW, possibly due to the increased probability of contact between solute
molecules. When anti-solvent is added close to the wall, an increase in agitation
results in a wider MSZW and a significant improvement in the batch-to-batch
repeatability. These results can be explained in terms of mixing conditions at each of
the addition locations. Close to the impeller, mixing conditions allow for the rapid
incorporation of the anti-solvent and a homogenous mixture of solution and anti-
solvent. However, close to the wall, mixing conditions are less suitable and areas of
supersaturation build up leading to narrower MSZWs and a reduction in the batch-to-
batch repeatability. In this situation, when the agitation is increased, the local areas of
supersaturation can be dissipated, to some degree, and the MSZW is wider and the
batch-to-batch repeatability improves.
By modifying classical nucleation theory for an anti-solvent system, it was possible to
estimate the nucleation order and the nucleation constant at both agitation intensities,
112
and for the addition location close to the impeller. Only this location was studied as
addition close to the wall resulted in unrepeatable experiments and nucleation was
judged to be a result of local areas of supersaturation close to the addition location
rather than as a result of supersaturation in the bulk solution. Agitation clearly
impacted on the nucleation kinetics and to account for this the nucleation rate
expression was modified, to account for agitation, and solved using a non-linear
regression. The modified equation fitted the experimental data adequately.
113
CHAPTER 5: THE USE OF FBRM AND ATR-FTIR TO
MONITOR ANTI-SOLVENT CRYSTALLIZATION AND
ESTIMATE GROWTH RATE KINETICS
5.1 ABSTRACT
The unseeded, anti-solvent crystallization of benzoic acid, from ethanol-water
mixtures using water as an anti-solvent, is presented. Focussed Beam Reflectance
Measurement (FBRM) and Attenuated Total Reflectance Fourier Transform Infrared
spectroscopy (ATR-FTIR) were used to monitor the solid and liquid phase
respectively. FBRM was used to track crystal growth and ATR-FTIR was used to
measure the water concentration and the benzoic acid concentration. In combination
with previously gathered solubility data (Chapter 3) the measured concentration data
was used to monitor the supersaturation. Using previously gathered MSZW
information
(Chapter 4), optimal mixing conditions were chosen to ensure a
repeatable crystallization with a low nucleation rate. Growth rate kinetics were
estimated by combining suitable statistics, from the FBRM chord length distribution,
with the supersaturation data.
5.2 INTRODUCTION
In recent years the use of in situ tools for the study of crystallization systems has
gained much attention. This has been driven by the need to rapidly gather
representative, real time data, without sampling. Sampling can be time and labour
intensive, unrepresentative, and may require the sample crystals to be prepared in
114
some way, typically through sonication or dilution. Also, the FDA‟s Process
Analytical Technology (PAT) initiative (FDA website) encourages the use of in situ
tools to monitor processes such as crystallization.
FBRM and ATR-FTIR have emerged as excellent tools for the study of crystallization
with much work in the literature devoted to their application. FBRM is a probe-based
instrument that measures a chord length distribution of the crystal slurry, which is a
function of the dimension, population and shape of the crystals in the solution. It is
extremely sensitive to fine crystals and can measures tens of thousands of crystals per
second allowing a very robust real-time measurement to be gathered. FBRM has,
amongst other things, been used to measure solubility and the MSZW (Barrett &
Glennon 2002), study crystal size (Kougoulos et al., 2005), and assess the effect of
impurities on batch time (Scott and Black, 2005).
ATR-FTIR is another probe-based tool that allows the liquid phase concentration to
be measured once a calibration model has been created. Importantly, for the anti-
solvent system the ATR-FTIR probe offers the opportunity to measure both the solute
and anti-solvent concentration. In combination with accurate solubility data it is then
possible to monitor supersaturation. ATR-FTIR has been used to optimise
crystallization processes (Lewiner et al., 2001) and monitor and control
supersaturation (Liotta & Sabesan 2004).
The mode of operation of both of these instruments is outlined in detail elsewhere
(Sparks & Dobbs 1994; Dunwilla & Berglund 1997) and a detailed review of each of
these instruments can be found in Chapter 2.
115
By combining supersaturation data, gathered using ATR-FTIR, with growth rate data,
gathered using FBRM, it is possible to estimate growth rate kinetics according to
Equation 5.1 (Mullin 1993).
G = kgΔCg 5.1
A plot of supersaturation against growth rate allows the growth coefficient and the
growth order to be estimated. This information is useful for the estimation of batch
time and calculation of suitable anti-solvent addition rates. By assessing the growth
rate and supersaturation under different mixing conditions in the laboratory the impact
of scale on the growth kinetics can be identified.
5.3 MATERIALS AND METHODS
A solution consisting of 75 g of water 75 g of ethanol and 21 g of benzoic acid was
made up and placed in the 500 mL glass reactor. FBRM, ATR-FTIR and temperature
probes provided baffling and a pitched blade impeller provided agitation. The
temperature was allowed to equilibrate at 25 C and the solution was held for 20
minutes to ensure all the benzoic acid had dissolved. Previous work identified that a
slow addition rate close to the impeller, with high agitation produced the most robust
crystallization in terms of consistent metastable zone width and low nucleation rate
(Chapter 4). With this in mind, water was added close to the impeller at a rate of
0.065 g/s for 45 mins, and the agitation was set at 475 rpm. The contents of the vessel
were held for a further 15mins after the end of the addition period to ensure the
supersaturation was consumed and the crystallization complete. Three similar
116
experiments were performed (Run 1, Run 2, Run 3). In the case of Run 3 the addition
period was 40 mins and the hold period was 5 mins.
5.4 RESULTS AND DISCUSSION
5.4.1 FBRM Results
A comparison of the three runs was made in order to assess the repeatability of the
crystallization under the chosen process parameters. One useful aspect of the FBRM
measurement technique is its ability not only to measure the degree of change of the
size, number and shape of the crystals, but to also measure their rate of change. Figure
5.1 shows three FBRM statistics plotted against time, for each run.
Figure 5.1 focuses on #/s (1-10 microns) and is useful for tracking nucleation, as it
highlights the shortest chord lengths. It is clear that the nucleation rate for each run is
similar and that there is no secondary nucleation event apparent, a phenomenon
typically characterised by a sudden increase in fine chords after the primary
nucleation event.
Figure 5.2 focuses on the larger crystals and tracks #/s (100-1000 microns) and
highlights the longer chord lengths, making it useful for tracking growth. The initial
increase in this statistic occurs after the increase in #/s (1-10 microns) indicating that
this statistic is less sensitive to the primary nucleation event, and that it takes some
time for the crystals to grow into larger size ranges.
117
The initial rate of increase in #/s (100-1000 microns) is rapid, when supersaturation is
high, and gradually plateaus as supersaturation is consumed. A similar result is
observed for the mean square weighted chord length (MSQW) (Figure 5.3). A square
weighting emphasises the long chords and de-emphasises the fine chords. This makes
the MSQW a very useful statistic for tracking crystal growth. Prior to the nucleation
event there is significant noise in the MSQW measurement. Even though count data
are low the MSQW is influenced by small changes in the chord length distribution.
0
200
400
600
800
1000
1200
1400
0 600 1200 1800 2400 3000 3600
Time (s)
#/s
(1-1
0 m
icro
ns)
run 1
run 2
run 3
Figure 5.1: FBRM data for three similar experiments: (a) #/s 1-10 microns
a
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
#/s (100-1000 m
icro
ns)
Run 1
Run 2
Run 3
a
118
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
Time (s)
#/s
(100-1
000 m
icro
ns)
run 1
run 2
run 3
Figure 5.2: FBRM data for three similar experiments: #/s 100-1000 microns
0
50
100
150
200
250
300
350
400
0 600 1200 1800 2400 3000 3600
Time (s)
Sq
uare
Weig
hte
d M
ean
Ch
ord
Len
gth
(m
icro
ns)
run 1
run 2
run 3
Figure 5.3: FBRM data for three similar experiments: square weighted mean chord length (microns)
b
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
#/s (100-1000 m
icro
ns)
Run 1
Run 2
Run 3
c
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
#/s (100-1000 m
icro
ns)
Run 1
Run 2
Run 3
a
119
The chord length distribution itself can also be used to assess repeatability and is very
useful for identifying crystallization mechanisms. Figure 5.4 shows the (a)
unweighted and (b) square weighted chord length distribution for each run at three
different points in time. Immediately it is clear that the chord length distribution is
similar for each run at each time point.
Figure 5.4 also shows that the crystallization is growth dominated. After 30 minutes
there is little change in the unweighted distribution, but a large increase in the square
weighted distribution. This indicates that after the primary nucleation event, growth
dominates the crystallization. Slight differences in the trends and chord length
distributions between the first two batches and the third batch may be due to the small
difference in operating conditions.
It is sometimes difficult, based on FBRM data alone, to discern between crystal
growth and agglomeration. Typically FBRM measures agglomeration as a reduction
in fine chords and a corresponding increase in large chords as small crystals come
together to form large agglomerates. However, if growth and agglomeration are
occurring simultaneously this may not be the case and fine counts may actually
increase as the likelihood of measuring a short chord increases as the crystals get
larger. PVM images taken during a similar crystallization* show that the benzoic acid
forms large well formed elongated platelets. It also appears that the crystals come
together to form loosely bound agglomerates or flocs (Figure 5.5) PVM is
advantageous for this analysis as taking samples and analyzing the crystals under a
* It was not possible to insert the FBRM, ATR-FTIR and PVM probes into the crystallizer at the same
time. PVM images were taken from an experiment performed under the same conditions but without
the FBRM and ATR-FTIR probes present. It is assumed that the different mixing conditions
encountered do not affect the crystallization appreciably.
120
microscope can lead to misleading information as a result of agglomeration during
filtration or drying, or simply because crystals lie on top of each other and resemble
agglomerates under the microscope.
121
0
20
40
60
80
100
120
140
1 10 100 1000 10000
Chord Length (microns)
#/s
run1 t = 20 mins
run2 t = 20mins
run3 t = 20 mins
run1 t = 30 mins
run2 t = 30 mins
run3 t = 30 mins
run1 t = 50 mins
run2 t = 50 mins
run 3 t = 50 mins
0
2
4
6
8
10
12
14
1 10 100 1000
Chord Length (microns)
#/s
run1 t = 20 mins
run2 t = 20mins
run3 t = 20 mins
run1 t = 30 mins
run2 t = 30 mins
run3 t = 30 mins
run1 t = 50 mins
run2 t = 50 mins
run 3 t = 50 mins
Figure 5.4: Chord length distributions at three time stamps for each run (a) unweighted; (b) square
weighted
122
Figure 5.5: PVM images taken after 30 minutes of crystallization
123
5.4.2 ATR-FTIR Results
Figure 5.6(a) shows water concentration and benzoic acid concentration, for the three
runs, gathered using ATR-FTIR. The linear increase in the mass ratio of water is
evident, as is the end of the addition period at 2700s for runs 1 and 2 and 2400s for
run 3. Prior to nucleation the benzoic acid concentration remains essentially constant,
with a slight decrease apparent. This subtle change is most likely due to a weakness in
the calibration model in the region close to the starting point of the crystallization. It
may also be a result of variations in temperature due to a heat of mixing. The point at
which the concentration decreases dramatically represents a point on the MSZW and
coincides with the increase in #/s (1-10 microns) (Figure 5.1). In general FBRM is
more sensitive to the nucleation event than ATR-FTIR (Chapter 4).
Supersaturation is calculated by combining accurate solubility data (Chapter 3) with
the measured concentrations (Figure 5.46(a)) and is shown in Figure 5.4 (b). The
point of nucleation is observed when the supersaturation drops and it is clear that all
of the supersaturation is consumed soon after the end of the addition period (2700s).
This indicates that a short hold period is sufficient to ensure the crystallization is
complete and the yield is maximised.
124
0.5
1.0
1.5
2.0
2.5
0 600 1200 1800 2400 3000 3600
Time(s)
Wate
r C
on
cen
trati
on
(g
/g)
0.1
0.2
0.3
0.4
Ben
zo
ic A
cid
Co
ncen
trati
on
(g
/g)
Water Run 1 Water Run 2 Water Run 3
Benzoic Run 1 Benzoic Run 2 Benzoic Run 3
-0.12
-0.09
-0.06
-0.03
0.00
0.03
0.06
0 600 1200 1800 2400 3000 3600
Time (s)
Su
pers
atu
rati
on
(g
/g)
run 1
run 2
run 3
Figure 5.6: ATR-FTIR data for three similar runs: (a) Water concentration and benzoic acid
concentration; (b) Supersaturation
a
b
125
5.4.3 Growth Rate Kinetics Estimation
Section 5.4.1 shows that nucleation dominates only in the early stages of the
crystallization and that growth with some agglomeration is dominant for the
remainder. Growth rate kinetics are estimated once the primary nucleation event is
complete. This exact point is open to interpretation, however, close examination of
Figure 5.1 shows that the initial rapid increase in #/s (1-10 microns) is probably over
after about 1300 s. With the region of interest identified, it is necessary to choose a
statistic that will suitably track the growth. As outlined in section 4.4.1 #/s (100-1000
microns) and the MSQW are track the growth phase of the crystallization in a similar
fashion. For the purpose of this study the MSQW will be used to estimate the growth
rate kinetics as it is measured in units of dimension rather than population-per-second
facilitating interpretation.
The measurement of accurate growth rate kinetics in this case is difficult due to the
initial uncontrolled nucleation event and the subsequent loose agglomeration outlined
in the previous section. While pure crystal growth is not apparent in this system the
method described will serve to highlight the possibility of rapidly measuring growth
rates in-process and in real-time. For a seeded crystallization with no agglomeration
the accuracy of this method for estimating the growth kinetics maybe improved.
The growth rate is measured by applying the first derivative to the MSQW in the
FBRM software according to Eq. 5.2 and is plotted in Figure 5.7.
1
1'
kk
kkk
tt
vvv Eq. 5.2
126
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0 500 1000 1500 2000 2500 3000 3500 4000
Time (s)
MS
QW
(m
icro
ns)
run 1
run 2
run 3
Figure 5.7: Growth rate data gathered using FBRM data after 1300s: square weighted mean chord
length
The FBRM and ATR-FTIR data were combined, according to Equation 5.1, to
measure growth rate kinetics. By plotting supersaturation (Figure 5.4(b)) against
growth rate (Figure 5.7) the kinetic parameters g and kg can be estimated. Figure 5.8
shows that, as expected, high growth rates occur at high supersaturation. A non-linear
least squares fit was applied to the data and is also shown in Figure 5.8. In this case
the growth order is close to unity.
127
0
0.05
0.1
0.15
0.2
0.25
0.3
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035
Supersaturation (g./g)
Gro
wth
Rate
(m
icro
ns
/s)
Model
Run 1
Run 2
Run 3
Figure 5.7: Growth rate kinetics using three experiments
To eliminate scatter from the data a second approach was taken where the growth rate
and supersaturation data was averaged over the three experiments (Figure 5.7) and
then combined. Figure 5.9 shows a plot of the averaged supersaturation against the
averaged growth rate. Clearly this approach results in less scatter to the data. Since the
experiments were repeatable this approach was deemed suitable for calculating the
average growth rate over a number of crystallizations. The calculated value of 1.1 for
the growth order is in the acceptable range for organic crystallization processes.
G = 3.61ΔC0.91
128
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0 600 1200 1800 2400 3000 3600
Time (s)
Su
pe
rsa
tura
tio
n (
g/g
)
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0 600 1200 1800 2400 3000 3600
Time (s)
Averg
aed
Gro
wth
Rate
- S
QW
M (
mic
ron
s/s
)
Figure 5.8: (a) Supersaturation average; (b) FBRM growth rate average
a
b
129
0
0.05
0.1
0.15
0.2
0.25
0 0.005 0.01 0.015 0.02 0.025 0.03
Supersaturation (g/g)
Gro
wth
Rate
(M
SQ
W -
mic
ron
s/s
)
Model
Experimental
Figure 5.9: Growth rate kinetics estimated using averaged values
G = 63.24ΔC1.27
G = 7.8ΔC1.1
130
5.5 CONCLUSIONS
FBRM and ATR-FTIR have been successfully used to monitor the anti-solvent
crystallization of benzoic acid from ethanol water mixtures using water as the anti-
solvent. FBRM showed that the nucleation rate for each experiment was similar and
that under the chosen process conditions the crystallization was robust and repeatable.
ATR-FTIR successfully tracked the water concentration and the benzoic acid
concentration, and in conjunction with previously gathered solubility data the
supersaturation was monitored.
Growth rate data were calculated by combining the supersaturation data and growth
rate data, calculated using the square weighted mean from the FBRM data. A growth
rate order of 1.1 was calculated, a suitable number for an organic crystallization
system.
The accuracy of the measured kinetics is questionable due to the uncontrolled
nucleation events prior to growth and the agglomeration during the growth phase;
however this technique highlights the ease with which growth rate kinetics may be
estimated in-process. This also opens the opportunity to measure the growth rate
kinetics in real time by combining the FBRM and ATR-FTIR data as the
crystallization is being performed. By choosing a system where agglomeration is
minimal and nucleation can be minimised, and by using seed, the accuracy of the
growth rate kinetics may be improved.
131
CHAPTER 6: SCALE-UP OF ANTI-SOLVENT
CRYSTALLIZATION
6.1 ABSTRACT
The pilot-scale crystallization of benzoic acid, from ethanol-water mixtures, using
water as the anti-solvent is presented. With extensive small-scale characterisation
performed it was possible to increase the scale of the crystallization from the
laboratory scale (500 mL) to the pilot plant scale (70 L) with a view to assessing
crystallization behaviour on scale-up. Prior to the experimental work the pilot scale
vessel was modelled using computational fluid dynamics (CFD) in order to assess the
mixing regime.
A scale-up strategy based on the laboratory scale experiments was implemented and
three batches were run at the pilot-scale under various operating conditions to assess
which parameters most closely met the requirements of producing crystals of a similar
size. CFD was used to choose a suitable anti-solvent addition point. Each run was
monitored in-line using Focussed Beam Reflectance Measurement (FBRM) and
Particle Vision and Measurement (PVM).
Similar chord length distributions were achieved at the laboratory-scale and the pilot-
scale indicating that the operating conditions chosen were suitable in terms of
conserving the particle size on scale-up.
132
6.2 INTRODUCTION
While extensive characterisation work is possible at the laboratory scale there is
almost always difficulty associated with reproducing similar results at a large-scale.
This is most often due to differences in mixing conditions between scales. An
intermediate, pilot-plant scale is often used to try and predict how a crystallization
may behave upon scale-up without the expense, time and labour associated with
scaling directly to the plant. With adequate characterisation at the small scale a small
number of experiments can be performed at the pilot scale giving valuable
information regarding potential plant scale issues. In the pharmaceutical industry this
is often performed in tandem with the production of material for use in clinical trials.
In order to assess the impact of scale on the crystallization, key parameters must be
measured. In situ tools provide the opportunity to do this without the need for
sampling and can continuously monitor in real time the progression of the
crystallization. Critically they can be implemented at a variety of scales allowing a
direct comparison to be made between experiments conducted in the laboratory, pilot
plant and in production.
In this study, data gathered at the small scale along with CFD models were used to
choose suitable process conditions at the pilot scale to produce a robust crystallization
with a particle size similar to that produced at the laboratory scale. The laboratory
studies indicated that agitation intensity, addition location and addition rate were the
critical parameters that needed to be controlled. At this scale a robust and repeatable
crystallization was achieved when the agitation intensity was high, the addition rate
133
was low and the addition location was optimized to ensure rapid incorporation of the
anti-solvent into the bulk solution. With this in mind a scale-up protocol was devised.
The addition rate was chosen to ensure a similar batch time at the lab and pilot scales.
The agitation intensity was chosen as the maximum that would not result in
entrainment of bubbles into the system. The addition location was chosen based on
CFD results for the large scale vessel .these indicated that the optimal addition
location may change during the batch so experiments were designed to take advantage
of that.
6.3 MATERIALS AND METHODS
For the laboratory-scale experiment 75g of ethanol, 75g of water and 21g of benzoic
acid were added to a 500 mL glass vessel, fitted with a pitched blade impeller set at
475 rpm, and held at 25°C. Water was added at 0.065 g/s above the liquid surface
close the impeller for 45 mins. This was followed by a 15 minute hold period. These
experimental conditions resulted in a repeatable crystallization based on the FBRM
trends (Chapter 5).
The same solution concentration was employed at the pilot-scale where 12.5 kg of
ethanol, 12.5 kg of water and 3.5 kg of benzoic acid were added to a 70 L glass stirred
tank reactor fitted with a Rushton turbine and four baffles. Figure 6.1 shows the vessel
dimensions and the position of the probes and internals. The contents were stirred at
130 rpm for 30 mins to ensure complete dissolution of the benzoic acid. This
agitation intensity was the highest possible without entraining air into the system.
Water was then added at 10 gs-1
for 45 mins in order to achieve the same batch time
134
as the experiments conducted at the laboratory-scale. At the end of the addition period
the solution was held for a further 15 minutes to ensure complete crystallization.
Three pilot-scale batches were run under different mixing conditions and addition
locations. For batch A the agitation was set at 130 rpm, the highest agitation intensity
that did not result in a vortex, and the anti-solvent was added at point 2 (Figure 6.1).
This addition location was chosen based on CFD simulations showing the highest
downward velocity at this point (Appendix A). For batch B the same addition location
was chosen (point 2) but in this case the agitation was increased from 157 rpm to 250
rpm so as to maintain the same power per unit volume for the duration of the batch
(Figure 6.2). The starting agitation of 157rpm was calculated based on a constant
power per unit scale up from the laboratory vessel. At the start of the batch vortexing
and air entrainment were noticeable but dissipated as the volume of the batch
increased. For batch C the same agitation profile as that used for batch B was
implemented but the addition point was changed five minutes into the experiment
from point 1 to point 2. This decision was based on CFD simulations indicating the
optimal addition location changed as the volume in the vessel increased (Appendix
A). Table 6.1 summarizes the operating conditions used for the three pilot-scale
batches.
135
Figure 6.1: Pilot-scale vessel and internals; (1) Addition Location 1; (2) Addition
Location 2; (3) PVM probe; (4) FBRM probe; (5) Overflow
0
100
200
300
400
500
600
0 10 20 30 40 50 60
Time (mins)
P/V
(W
/m3)
0
50
100
150
200
250
300
350
400
Ag
ita
tio
n In
ten
sit
y (
rpm
)
P/V
Rpm
Figure 6.2: Time vs. agitation intensity and power-per-unit-volume for batches B and C
136
Table 6.1: Summary of Pilot-Scale Batches
6.3 RESULTS AND DISCUSSION
6.3.1 FBRM Results
Figure 6.3 shows the chord length distributions measured at the end of batches A, B
and C. Clearly batches B and C, with constant power-per-unit-volume, are very
similar at the fine (unweighted) and coarse (square weighted) ends of the distribution.
The change in addition location implemented in Batch C appears to have had no
effect.
For Batch A, a slight increase in fine and coarse particles compared to the other two
batches is observed. In this case it is difficult to ascertain whether this is a function of
the crystallization mechanism or a function of the presentation of the crystals to the
FBRM window. At 130 rpm the mixing was visibly inadequate to fully suspend the
crystal slurry and there were a number of dead zones at the top, bottom and sides of
the reactor. Evidence of this segregation is shown in Figure 6.4 where samples taken
from the top and bottom of the pilot-scale vessel are compared off-line using FBRM.
The chord length distributions show significantly more fine material for the sample
Addition Rate Agitation Intensity Addition Location
Batch A 10gs-1
130 rpm
2
Batch B
10gs-1
(157 rpm – 200rpm) 2
Batch C 10gs-1
(157 rpm – 200rpm) Points 2 & 1
137
taken from the top and significantly more coarse material for the sample taken at the
bottom. Smaller crystals floated to the top of the vessel and were not re-incorporated
into the bulk of the slurry while larger crystals sank to the bottom and were not re-
suspended. Thus, for batch A, the chord length distribution measured by the FBRM in
a turbulent zone close to the impeller may not be representative of the crystallization
as a whole.
0
20
40
60
80
100
120
1 10 100 1000
Chord Length (microns)
#/s
Batch A
Batch B
Batch C
138
0
2
4
6
8
10
12
14
16
18
1 10 100 1000
Chord Length (microns)
#/s
Batch A
Batch B
Batch C
Figure 6.3: Chord Length Distributions for Batches A, B and C; (a) unweighted, (b) square weighted
0
500
1000
1500
2000
2500
3000
1 10 100 1000
Chord Length (microns)
#/s
Bottom Sample
Top Sample
139
0
1
2
3
4
5
6
1 10 100 1000
Chord Length (microns)
#/s
Bottom Sample
Top Sample
Figure 6.4: Offline FBRM measurements taken from samples from the top and bottom of the pilot plant
vessel at the end of Batch A
The large degree of segregation observed during batch A indicates that agitating at
130 rpm for the duration of the batch is not adequate to ensure a robust and repeatable
crystallization. Batches B and C during which the agitation was increased to maintain
a constant power per unit volume resulted in much better mixing and no segregation.
Additionally these batches produced an almost identical chord length distribution
indicating repeatability, assuming the impact of changing the addition location (batch
C) was negligible.
In order to assess the effectiveness of the scale-up in terms of the crystal size
predicted, chord length distributions at the end of batch B are compared with the
140
standard laboratory experiment. In order to effectively compare the chord length
distributions the FBRM data are normalized to allow for the differences in scale.
Figure 6.5 indicates that in general the chord length distribution measured at each
scale is very similar. There are small differences at the fine end of the distribution
where there is slightly more fine material produced at the pilot scale. At the coarse
end of the distribution the distributions are essentially identical with the lab laboratory
scale producing slightly larger material. There is an increase in the number of fine
crystals for the pilot-scale crystallization compared to the laboratory-scale
experiment. Additionally the squared weighted chord length distribution indicates the
size of the largest crystals is essentially identical with the laboratory-scale
crystallization producing slightly larger crystals. Statistics from each distribution are
compared in Table 6.2 and indicate the similarity between the crystallization
conducted at both scales.
141
0
0.5
1
1.5
2
2.5
3
1 10 100 1000
Chord Length (microns)
#/s
Standard Laboratory
Batch B
0
1
2
3
4
5
1 10 100 1000
Chord Length (microns)
#/s
Standard Laboratory
Batch B
Figure 6.5: Comparison of standard laboratory experiment with batch B (a) unweighted and (b) square
weighted
142
Table 6.3: Summary of Chord Length Distribution Statistics
The data shown in Table 6.3 is complemented by the PVM images taken for each
crystallization, show in Figure 6.6. The images indicate the similarity in crystal size
for each batch. They also help validate the FBRM measurement and indicate that the
mean square weight is probably a good indicator of the length of the crystals while the
unweighted mean is probably a good indicator of the width of the crystals.
Clearly the scale-up has been effective in terms of producing crystals of a similar size
at both scales at the end of the batch. However the in-situ tools offer the opportunity
monitor the crystallization in its entirety. Figure 6.7 compares FBRM trends in two
size ranges for the laboratory scale experiment and Batch B.
Comparison of the trends show that even though the chord length distributions are
similar at the end of the batch the mechanism through which this is achieved is
different depending on the scale. By studying #/s (1-10 microns) (Figure 6.7) it is
clear that nucleation occurs earlier at the pilot scale. It is difficult to see if the rate of
nucleation was similar at both scales as some probe coating was observed at the pilot
scale just after nucleation. The probe was manually cleaned soon after and the counts
returned to normal. The rate of increase of the large counts (100-1000 microns) is
Median
(μm)
Mean
(μm)
MSQW
(μm)
% counts
(1-5 μm)
% counts
(5-100 microns)
% counts
(100-1000 microns)
Standard
Laboratory
20.53 43.13 222.52 11.9 76.2 11..9
Batch B 19.54 41.12 208.9 15.5 73.4 11.1
143
very similar for each batch. Undoubtedly agglomeration is playing some part in the
crystallization as shown in the PVM images (Figure 6.7 (b)). The fact that the FBRM
trends are similar at both scales indicates suitable process parameters have been
chosen and the change in mixing conditions between the scales does not impact the
kinetics of the crystallization.
Figure 6.6: PVM images of (a) Pilot Scale (b) Laboratory Scale.
a
b
144
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000 3500 4000
Time (s)
#/s
(1-1
0)
Pilot-Scale
Lab-Scale
Figure 6.7: Comparison of #/s (1-10 microns) between batch B and lab scale
0
100
200
300
400
500
600
0 500 1000 1500 2000 2500 3000 3500 4000
Time (s)
#/s
(100-1
000)
Pilot-Scale
Lab-Scale
Figure 6.8: Comparison of #/s (100-1000 microns) between batch B and lab scale
145
6.4 DISCUSSION
Crystallization scale-up requires knowledge of every impact the change in scale has
on the crystallization. There are liquid-liquid interactions to be taken into account i.e.
how the antisolvent is incorporated into the bulk solution. This is influenced by scale,
geometry, type of impeller, agitation intensity, addition location, addition rate, feed
pipe diameter and solution and antisolvent concentration (i.e. liquid viscosities and
densities). All of these factors can influence the micro-, meso- and macromixing
properties of the system and hence the supersaturation profiles within the system. In
turn the supersaturation profile impacts the nucleation, dissolution, growth and
agglomeration rates. These in turn impact on the yield, purity and size of the crystal
product. With this in mind it becomes clear that to try and scale an antisolvent
crystallization it is impossible to do so based on a single traditional scale up parameter
such as blend time or power per unit volume as no single parameter can take into
account all of the factors.
Equally important is the fact that a change in scale impacts on the physical aspects of
the crystallization. Thus, segregation is more likely to occur at larger scales where the
mixing is not sufficient to fully suspend the crystal slurry (as was the case in Batch A
of this study). Similarly, attrition and secondary nucleation rates will be affected by a
change in scale. In the lab where the surface area to volume ratio is high, attrition and
secondary nucleation rates are more sometime significantly elevated compared to
larger scales where this ratio is low.
146
With these points in mind care should taken when scaling crystallization processes,
specifically antisolvent crystallization, not to make assumptions based on a single
mixing parameter. In many cases by combining a sound understanding of the
crystallization system with a degree of common sense, effective scale-up can be
achieved, as observed in this study. For example, by choosing a pitched blade
impeller better crystal suspension may be achieved but this may be at the cost of
entraining air into the system for a prolonged period. By studying the impact of
mixing conditions on attrition levels in the laboratory the choice of impeller may
become easier.
In order to scale-up effectively, intimate knowledge of every impact the change in
scale will have on the crystallization is necessary. Current research in this area
focuses on the use of CFD coupled with the population balance equations. This
approach has the ability to take into account almost every parameter that can affect
the liquid-liquid interactions. However it remains some way off in terms of predicting
crystallization mechanisms such as growth rates, nucleation rates and agglomeration
rates that are influenced in competing directions by the mixing effects. Despite this
the approach appears to be the only one available that can fully take into account all
of the variables that impact crystallization scale-up.
Before this becomes a reality there are still some common sense approaches that can
be taken to enhance the likelihood of scale-up success. For antisolvent crystallization
homogeneity is vital. By eliminating areas of local supersaturation problems such as
elevated nucleation rates, incorrect polymorph nucleation and phase separation can be
avoided. This can be achieved be adding slowly, at high agitation in a good addition
147
location. However, care must be taken to ensure that these parameters are suitable.
For example the addition rate must not be so slow that the batch-time is compromised
and throughput reduced. The agitation rate must not be so high as to introduce
significant attrition and/or secondary nucleation. If this is a problem there are other
techniques that can be used to improve dispersion such as adding anti-solvent through
a narrow pipe in the form of a jet facilitating dispersion. Finally the choice of addition
point can be aided by employing CFD to model how the anti-solvent will be
dispersed.
6.5 CONCLUSION
The pilot-scale crystallization of benzoic acid from ethanol-water mixtures using
water as an anti-solvent has been studied. Adequate scale-up in terms of particle size
was achieved. CFD was used to assess the ideal addition location and it was observed
that this point changed with the increase in the liquid volume. Changing the addition
location during a batch however had no effect on the final crystal product. Effective
scale-up in terms of particle size was achieved by using extensive data gathered at the
laboratory scale to design a scale-up strategy. Results at the lab scale showed that a
slow addition rate, intense agitation and a precise addition location (based on CFD)
allowed for a robust and repeatable crystallization. These conditions were mimicked
at the pilot scale and results showed that the final product particle size, in terms of
chord length distribution and in process images, was almost identical. This was
achieved for a vessel that was geometrically dissimilar to laboratory scale vessel
where the initial characterization studies were conducted. This study shows that scale-
up is possible without relying on traditional scale-up parameters such as power per
148
unit volume. And it is proposed that for anti-solvent crystallization using such
parameters may not be the most useful approach for crystallization given the
numerous mixing interactions that impact on crystallization. Current research aimed
at modelling the complete crystallization process using computational fluid dynamics
coupled with the population balance will in the future be useful but is still limited.
Currently, common sense allied with sound knowledge of the crystallization
mechanisms for a given system are the most useful tools for effective crystallization
scale-up.
149
7. THESIS CONCLUSIONS
The anti-solvent crystallization of benzoic acid from ethanol-water solutions using
water as the anti-solvent at multiple scales has been presented. A thorough review of
the literature indicated that anti-solvent crystallization has been neglected, in favour
of the study of cooling crystallization, and, in the cases where anti-solvent
crystallization is studied the model system used is often inorganic. While some
crystallization process parameters have been studied in detail (addition rate,
concentration and agitation intensity) many have been neglected, including, anti-
solvent addition location and supersaturation.
The starting point for crystallization characterization is the solubility curve and
metastable zone width. For this study, the solubility was measured using three
separate techniques – gravimetric analysis, FBRM, and ATR-FTIR – and the results
were in good agreement. The gravimetric method proved a simple and cost effective
technique, however, it can be time consuming and there was some disagreement
between the results of the “liquid” and “solid” analysis. The ATR-FTIR technique
required the development of a calibration model before the solubility could be
measured. However, once the model was developed and validated the solubility was
measured rapidly. The ATR-FTIR method can also be used in other ways to study
crystallization experiments – most notably to monitor supersaturation (Chapter 5) –
and in this respect can offset the significant investment required for implementation.
The FBRM technique can be used to rapidly measure solubility by quickly identifying
the point of crystal dissolution. It does not require any calibration and its application
as the standard in process tool for crystallization characterization makes it an obvious
150
choice for measuring solubility. In order to validate the experimental results garnered
using the three techniques the UNIQAC method was used to model the system. The
model results correlated well with the experimental values even in the region at low
anti-solvent concentration where solubility increased with anti-solvent concentration.
This solubility measurement work highlighted the varied methods available to
investigators wishing to gather accurate solubility information and also provided the
platform necessary for further characterization of this system including the metastable
zone width and nucleation kinetics under varied process conditions.
The metastable zone width was measured at the 500 mL scale, under various process
conditions including addition rate and location, and, agitation intensity. As expected
increasing the addition rate resulted in a wider metastable zone width, however, the
influence of addition location proved to be extremely important with variable and
sometimes negative metastable zone widths occurring when anti-solvent was added
close the wall of the vessel. This problem was exacerbated at low agitation intensities.
The inadequate incorporation of the antisolvent into the bulk solution was identified
as a possible cause of this variability and a study of the vessel using computational
fluid dynamics confirmed this to be the case. Nucleation kinetics were estimated for
the well mixed case where anti-solvent was added close to the impeller and the
influence of agitation intensity on the kinetics was quantified. A unique expression
relating the nucleation rate to the agitation intensity was developed by modifying
classic nucleation theory for the anti-solvent case. This study highlighted the sensitive
and sometimes counterintuitive nature of crystallization processes. By moving the
addition location a very short distance a significant difference in the repeatability and
robustness of the metastable zone width was observed. The sensitivity of this system
151
to such small changes highlights some of the difficulties associated with scale-up and
achieving a consistent process and product performance.
In order to facilitate efficient scale-up a series of crystallizations experiments were
undertaken at the 500 mL scale to identify suitable operating parameters that would
ensure a repeatable and robust crystallization. The in situ tools offered the opportunity
to monitor each crystallization in real time for the full duration of the experiment and
allowed the comparison of each crystallization over the entirety of each run. High
agitation intensity and a slow addition rate resulted in a very repeatable crystallization
in terms of supersaturation, monitored using ATR-FTIR, and particle size, monitored
using FBRM. The in situ data also allowed the elucidation of growth rate kinetics for
the system. By combining the ATR-FTIR supersaturation information with growth
rate data, generated using the FBRM, a kinetic expression for growth was rapidly
estimated. This method highlights the opportunity to monitor growth rate kinetics in
situ and in real time for any crystallization process. The ability to do this may prove
useful for the control of crystallization processes in the lab as well as at production
scales. The ability to modify the growth rate in real time by tuning process parameters
would be a challenging but potentially valuable alternative to current production scale
crystallization practices.
Current crystallization practice requires the efficient and effective scale-up of the
crystallization from the lab to production, typically with the aid of an intermediate
pilot scale. In order to study the sale-up behaviour of this system three 70 L pilot scale
batches were conducted in a geometrically dissimilar vessel under different process
conditions. The process conditions implemented were chosen by combining the
152
detailed information gathered over the course of this study - accurate solubility
measurement, the impact of agitation on nucleation kinetics, and the choice of suitable
process parameters to ensure repeatability. Computational fluid dynamics was also
used to model mixing patterns in the vessel allowing suitable addition locations to be
chosen. A successful scale-up was achieved based on repeatability, short batch time,
and a similar yield and particle size as the laboratory scale process. The FBRM
distributions and PVM images clearly showed the similarity in particle size and
morphology at both scales. However, significant segregation and premature
nucleation in the pilot scale vessel were observed indicating further work would be
needed before scaling up to the full production scale.
153
8. NOMENCLATURE
A anti-solvent concentration (g antisolvent/ g solvent)
c concentration (g/g)
cs saturation concentration
f fugacity
g growth rate order
G growth rate
i component in UNIQUAC method
j component in UNIQUAC method
J nucleation rate
k component in UNIQUAC method
kn nucleation rate constant
kg growth rate constant
n nucleation order
N agitation intensity (rpm)
m mass ratio of anti-solvent to solvent (-)
P pressure
R anti-solvent addition rate (gs-1
)
s mass of solute (g)
T temperature
Tm melting point
t time (s)
w mass of solvent (g)
154
Symbols
α shape factor
β agitation parameter for nucleation kinetics
ΔC supersaturation (g/g)
ΔAmax metastable zone width
ΔAmax latent heat of fusion
Γ liquid activity coefficient
ρ density
τ binary interaction parameter
155
9: REFERENCES
Abbas, A., D. Nobbs, and J. A. Romagnoli, "Investigation of on-line optical particle
characterization in reaction, and cooling crystallization systems. Current state of the
art," Measurement Science & Technology 13 (3): 349-356 (2002)
Akiti, O. and P. M. Armenante, "Experimentally-validated micromixing-based CFD
model for fed-batch stirred-tank reactors," Aiche Journal 50 (3): 566-577 (2004)
Alfano, J. C. et al., "Polyelectrolyte-induced aggregation of microcrystalline
cellulose: Reversibility and shear effects," Journal of Colloid and Interface Science
223 (2): 244-254 (2000)
Barata, P. A. and M. L. Serrano, "Salting-out precipitation of potassium dihydrogen
phosphate (KDP) .2. Influence of agitation intensity," Journal of Crystal Growth 163
(4): 426-433 (1996)
Barata P.A., Serrano M.L., “Salting-out precipitation of potassium dihydrogen
phosphate (KDP). III. Growth Process”, Journal of Crystal Growth, 194, 1, 101-108
(1998)
Barata P.A., Serrano M.L., “Salting-out precipitation of potassium dihydrogen
phosphate (KDP). IV. Characterization of Final Product”, Journal of Crystal Growth,
194, 1, 109-118 (1998)
Barrett, P. and B. Glennon, "In-line FBRM monitoring of particle size in dilute
agitated suspensions," Particle & Particle Systems Characterization 16 (5): 207-211
(1999)
156
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)
Barrett, P. et al., "A review of the use of process analytical technology for the
understanding and optimization of production batch crystallization processes,"
Organic Process Research & Development 9 (3): 348-355 (2005)
Beckmann, W., "Nucleation phenomena during the crystallisation and precipitation of
Abecarnil," Journal of Crystal Growth 199: 1307-1314 (1999)
Blanco, A. et al., "Focused beam reflectant measurement as a tool to measure
flocculation," Tappi Journal 1 (10): 14-20 (2002)
Bohlin, M. and A. C. Rasmuson, "Importance of macromixing in batch cooling
crystallization," Aiche Journal 42 (3): 691-699 (1996)
Borissova, A. et al., "Examination of the semi-batch crystallization of benzophenone
from saturated methanol solution via aqueous antisolvent drowning-out as monitored
in-process using ATR FTIR spectroscopy," Crystal Growth & Design 4 (5): 1053-
1060 (2004)
Charmolue, H. and R. W. Rousseau, "L-Serine Obtained by Methanol Addition in
Batch Crystallization," Aiche Journal 37 (8): 1121-1128 (1991)
Choi, YC., Morgenroth, E., “Monitoring biofilm detachment under dynamic changes
in shear stress using laser-based particle size analysis and mass fractionation”, Water
Sci Technol., ;47, 5, 69-76 (2003)
157
Clarke, M. A. and P. R. Bishnoi, "Determination of the intrinsic rate constant and
activation energy of CO2 gas hydrate decomposition using in-situ particle size
analysis," Chemical Engineering Science 59 (14): 2983-2993 (2004)
David, R., "General rules for prediction of the intensity of micromixing effects on
precipitations," Powder Technology 121 (1): 2-8 (2001)
De Clercq, B., P. A. Lant, and P. A. Vanrolleghem, "Focused beam reflectance
technique for in situ particle sizing in wastewater treatment settling tanks," Journal of
Chemical Technology and Biotechnology 79 (6): 610-618 (2004)
Derdour, L. et al., "Real-time evaluation of the concentration of impurities during
organic solution crystallization," Powder Technology 129 (1-3): 1-7 (2003)
Doki, N. et al., "Process control of seeded batch cooling crystallization of the
metastable alpha-form glycine using an in-situ ATR-FTIR spectrometer and an in-situ
FBRM particle counter," Crystal Growth & Design 4 (5): 949-953 (2004)
Dowding, P. J., J. W. Goodwin, and B. Vincent, "Factors governing emulsion droplet
and solid particle size measurements performed using the focused beam reflectance
technique," Colloids and Surfaces A-Physicochemical and Engineering Aspects 192
(1-3): 5-13 (2001)
Dunuwila, Dilum D., I. I. Carroll, and Kris A. Berglund, "An investigation of the
applicability of attenuated total reflection infrared spectroscopy for measurement of
solubility and supersaturation of aqueous citric acid solutions," Journal of Crystal
Growth 137 (3-4): 561-568 (1994)
158
Dunuwila, Dilum D. and Kris A. Berglund, "ATR FTIR spectroscopy for in situ
measurement of supersaturation," Journal of Crystal Growth 179 (1-2): 185-193
(1997)
Feng, L. L. and K. A. Berglund, "ATR-FTIR for determining optimal cooling curves
for batch crystallization of succinic acid," Crystal Growth & Design 2 (5): 449-452
(2002)
Franke, D. and W. Gosele, "Modeling precipitation with a hydrodynamic approach,"
Chemie Ingenieur Technik 71 (11): 1245-1252 (1999)
Fujiwara, M. et al., "Paracetamol crystallization using laser backscattering and ATR-
FTIR spectroscopy: Metastability, agglomeration, and control," Crystal Growth &
Design 2 (5): 363-370 (2002)
Gee, E.A., Cunnigham, William K., Heindl, R., “Pilot Plants: Production of Iron-Free
Alum”, Ind. Eng. Chem., 39, 9, 1178 - 1188 (1947)
Gong, J. B. et al., "Simulation and scale-up of barium sulphate precipitation process
using CFD modeling," Chinese Journal of Chemical Engineering 13 (2): 167-172
(2005)
Gong, J. B. et al., "Simulation and scale-up of barium sulphate precipitation process
using CFD modeling," Chinese Journal of Chemical Engineering 13 (2): 167-172
(2005)
Granberg, Roger A. et al., "Primary nucleation of paracetamol in acetone-water
mixtures," Chemical Engineering Science 56 (7): 2305-2313 (2001)
159
Granberg R. A., Rasmuson, A.C., “Solubility of Paracetamol in Pure Solvents”, J.
Chem. Eng. Data, 44 (6), 1391 -1395 (1999)
Groen, H., Roberts, K.J., “Application of ATR FTIR spectroscopy for on-line
determination of solute concentration”, Proceedings of the 14th International
Symposium on Industrial Crystallization (1999)
Groen, H. and K. J. Roberts, "Nucleation, growth, and pseudo-polymorphic behavior
of citric acid as monitored in situ by attenuated total reflection Fourier transform
infrared spectroscopy," Journal of Physical Chemistry B 105 (43): 10723-10730
(2001)
Heath, A. R. et al., "Estimating average particle size by focused beam reflectance
measurement (FBRM)," Particle & Particle Systems Characterization 19 (2): 84-95
(2002)
Heffels, C. et al., "Control of particulate processes by optical measurement
techniques," Particle & Particle Systems Characterization 15 (5): 211-218 (1998)
Holmback, X. and A. C. Rasmuson, "Size and morphology of benzoic acid crystals
produced by drowning-out crystallisation," Journal of Crystal Growth 199: 780-788
(1999).
Hukkanen, E. J. and R. D. Braatz, "Measurement of particle size distribution in
suspension polymerization using in situ laser backscattering," Sensors and Actuators
B-Chemical 96 (1-2): 451-459 (2003)
160
Jeffers, P. et al., "Focussed beam reflectance measurement (FBRM) monitoring of
particle size and morphology in suspension cultures of Morinda citrifolia and
Centaurea calcitrapa," Biotechnology Letters 25 (23): 2023-2028 (2003)
Kaneko, S. et al., "Effect of supersaturation on crystal size and number of crystals
produced in antisolvent crystallization," Journal of Chemical Engineering of Japan 35
(11): 1219-1223 (2002)
Kitamura, M. and M. Sugimoto, "Anti-solvent crystallization and transformation of
thiazole-derivative polymorphs--I: effect of addition rate and initial concentrations,"
Journal of Crystal Growth 257 (1-2): 177-184 (2003)
Kougoulos, E., A. G. Jones, and M. W. Wood-Kaczmar, "Modelling particle
disruption of an organic fine chemical compound using Lasentec focussed beam
reflectance monitoring (FBRM) in agitated suspensions," Powder Technology 155
(2): 153-158 (2005)
Kougoulos, E., A. G. Jones, and M. W. Wood-Kaczmar, "Estimation of crystallization
kinetics for an organic fine chemical using a modified continuous cooling mixed
suspension mixed product removal (MSMPR) crystallizer," Journal of Crystal
Growth 273 (3-4): 520-528 (2005)
Kougoulos, E. et al., "Use of focused beam reflectance measurement (FBRM) and
process video imaging (PVI) in a modified mixed suspension mixed product removal
(MSMPR) cooling crystallizer," Journal of Crystal Growth 273 (3-4): 529-534 (2005)
Kougoulos, E., A. G. Jones, and M. Wood-Kaczmar, "CFD modelling of mixing and
heat transfer in batch cooling crystallizers - Aiding the development of a hybrid
161
predictive compartmental model," Chemical Engineering Research & Design 83
(A1): 30-39 (2005)
Kougoulos, E., A. G. Jones, and M. W. Wood-Kaczmar, "A hybrid CFD
compartmentalization modeling framework for the scaleup of batch cooling
crystallization processes," Chemical Engineering Communications 193 (8): 1008-
1023 (2006)
Langston, P. A. et al., "Particle and droplet size analysis from chord measurements
using Bayes' theorem," Powder Technology 116 (1): 33-42 (2001)
Law, D. J., A. J. Bale, and S. E. Jones, "Adaptation of focused beam reflectance
measurement to in-situ particle sizing in estuaries and coastal waters," Marine
Geology 140 (1-2): 47-59 (1997)
Lewiner, F. et al., "Improving batch cooling seeded crystallization of an organic
weed-killer using on-line ATR FTIR measurement of supersaturation," Journal of
Crystal Growth 226 (2-3): 348-362 (2001)
Lewiner, F. et al., "On-line ATR FTIR measurement of supersaturation during
solution crystallization processes. Calibration and applications on three solute/solvent
systems," Chemical Engineering Science 56 (6): 2069-2084 (2001)
Lewiner, F. et al., "An Online Strategy To Increase the Average Crystal Size during
Organic Batch Cooling Crystallization," Industrial & Engineering Chemistry
Research 41 (5): 1321-1328 (2002)
162
Li, M., D. Wilkinson, and K. Patchigolla, "Comparison of particle size distributions
measured using different techniques," Particulate Science and Technology 23 (3):
265-284 (2005)
168. Li, M. Z., D. Wilkinson, and K. Patchigolla, "Determination of non-spherical
particle size distribution from chord length measurements. Part 2: Experimental
validation," Chemical Engineering Science 60 (18): 4992-5003 (2005)
Li, M. Z. and D. Wilkinson, "Determination of non-spherical particle size distribution
from chord length measurements. Part 1: Theoretical analysis," Chemical Engineering
Science 60 (12): 3251-3265 (2005)
Liotta, V. and V. Sabesan, "Monitoring and feedback control of supersaturation using
ATR-FTIR to produce an active pharmaceutical ingredient of a desired crystal size,"
Organic Process Research & Development 8 (3): 488-494 (2004)
Liu, Weidong, Nigel N. Clark, and Ali Ihsan Karamavruc, "Relationship between
bubble size distributions and chord-length distribution in heterogeneously bubbling
systems," Chemical Engineering Science 53 (6): 1267-1276 (1998)
Loan, M. et al., "Iron oxy-hydroxide crystallization in a hyd ro metallurgical residue,"
Journal of Crystal Growth 235 (1-4): 482-488 (2002)
Löffelmann, M., Mersmann, A., “A New Method for Supersaturation Measurement”,
International Journal of Modern Physics B, 16, 01-02, 391-398 (2002)
Madras, G. and B. J. Mccoy, "Mixing effects on particle precipitation," Industrial &
Engineering Chemistry Research 44 (14): 5267-5274 (2005)
163
Marcant, B. and R. David, "Experimental-Evidence for and Prediction of
Micromixing Effects in Precipitation," Aiche Journal 37 (11): 1698-1710 (1991)
Marchisio, D. L. et al., "Effect of fluid dynamics on particle size distribution in
particulate processes," Chemical Engineering & Technology 29 (2): 191-199 (2006)
McDonald, K. A., A. P. Jackman, and S. Hurst, "Characterization of plant suspension
cultures using the focused beam reflectance technique," Biotechnology Letters 23 (4):
317-324 (2001)
Monnier, O. et al., "Model identification of batch cooling crystallizations through
calorimetry and image analysis," Chemical Engineering Science 52 (7): 1125-1139
(1997)
Mori, Y. et al., "CFD study of local crystal growth rate in a continuous suspension
crystallizer," Journal of Chemical Engineering of Japan 35 (11): 1178-1187 (2002)
Mullin, J.W., Garside, J., Unahabhokha, R., “Diffusivities of Ammonium and
Potassium Alums in Aqueous Solutions”, Journal of Applied Chemistry of the USSR,
(1965)
Mullin, J., Teodossiev, N., Söhnel, O., “Potassium sulphate precipitation from
aqueous solution by salting-out with acetone”, Chemical Engineering and Processing,
26, 2, 93-99 (1989)
Mydlarz, J. and A. G. Jones, "Crystallization and Agglomeration Kinetics During the
Batch Drowning-Out Precipitation of Potash Alum with Aqueous Acetone," Powder
Technology 65 (1-3): 187-194 (1991)
164
Nyvlt. J., “Kinetics of nucleation in solutions”, Journal of Crystal Growth, 3, 377-
383, (1968)
O‟Grady D., et al, 'The Effect of Mixing on the Metastable Zone Width and Nucleation
Kinetics In the Anti-solvent Crystallization of Benzoic Acid, Chemical Engineering Research
and Design, Transactions IChemE part A, 85 (7) 945-953, 2007
O‟Grady D. & Glennon B.,, „Solubility Measurement for an Anti-solvent Crystallization
System Using Gravimetric Analysis, ATR-FTIR and FBRM', Crystal Growth and Design, in
press
O'Rourke, A. M. and P. F. MacLoughlin, "A comparison of measurement techniques
used in the analysis of evolving liquid-liquid dispersions," Chemical Engineering and
Processing 44 (8): 885-894 (2005)
O'Sullivan, B. et al., "In situ monitoring of polymorphic transitions," Organic Process
Research & Development 7 (6): 977-982 (2003)
O'Sullivan, B. and B. Glennon, "Application of in situ FBRM and ATR-FTIR to the
monitoring of the polymorphic transformation of D-mannitol," Organic Process
Research & Development 9 (6): 884-889 (2005)
Oosterhof, H., G. J. Witkamp, and G. M. van Rosmalen, "Some antisolvents for
crystallisation of sodium carbonate," Fluid Phase Equilibria 155 (2): 219-227 (1999)
Paul, E. L., H. H. Tung, and M. Midler, "Organic crystallization processes," Powder
Technology 150 (2): 133-143 (2005)
165
Pearson, A. P., B. Glennon, and P. M. Kieran, "Comparison of morphological
characteristics of Streptomyces natalensis by image analysis and focused beam
reflectance measurement," Biotechnology Progress 19 (4): 1342-1347 (2003)
Pearson, A., Glennon, B., Kieran, P., “Monitoring of cell growth using the focused
beam reflectance method”, Journal of Chemical Technology & Biotechnology, 79, 10,
1142 -1147 (2004)
Pettersson, M. and A. C. Rasmuson, "Hydrodynamics of suspensions agitated by
pitched-blade turbine," Aiche Journal 44 (3): 513-527 (1998)
Pina, C. M., L. FernandezDiaz, and M. Prieto, "Crystallization of beta''-LiNH4SO4
and (NH4)(2)SO4 in gels: Growth morphology and epitaxy phenomena," Journal of
Crystal Growth 177 (1-2): 102-110 (1997)
Pina, C. M. et al., "Metastability in drowning-out crystallisation: precipitation of
highly soluble sulphates," Journal of Crystal Growth 222 (1-2): 317-327 (2001)
Pohorecki, R. and J. Baldyga, "New Model of Micromixing in Chemical Reactors .1.
General Development and Application to a Tubular Reactor," Industrial &
Engineering Chemistry Fundamentals 22 (4): 392-397 (1983)
Rohani, S. and J. Baldyga, "Micromixing Described in Terms of Inertial Convective
Disintegration of Large Eddies and Viscous Convective Interactions Among Small
Eddies .2. Semibatch and Continuous Stirred Tank Reactors," Chemical Engineering
Science 42 (11): 2611-2619 (1987)
Rohani, S. and J. Baldyga, "Mathematical-Modeling of Micromixing in Liquid
Liquid-Systems," Hungarian Journal of Industrial Chemistry 16 (1): 85-97 (1988)
166
Ruf, A., J. Worlitschek, and M. Mazzotti, "Modeling and experimental analysis of
PSD measurements through FBRM," Particle & Particle Systems Characterization 17
(4): 167-179 (2000)
Scott, C. and S. Black, "In-line analysis of impurity effects in crystallization,"
Organic Process Research & Development 9 (6): 890-893 (2005)
Sha, Z. et al., "Application of CFD simulation to suspension crystallization-actors
affecting size-dependent classification," Powder Technology 121 (1): 20-25 (2001)
Shi, B., W. J. Frederick, and R. W. Rousseau, "Effects of calcium and other ionic
impurities on the primary nucleation of burkeite," Industrial & Engineering
Chemistry Research 42 (12): 2861-2869 (2003)
Simmons, M. J. H., P. A. Langston, and A. S. Burbidge, "Particle and droplet size
analysis from chord distributions," Powder Technology 102 (1): 75-83 (1999)
Sistare, F., St. Pierre Berry, L., Mojica, C., “Process Analytical Technology: An
Investment in Process Knowledge”, Org. Process Res. Dev., 9, 3, 332 -336 (2005)
Sparks, R.G., Dobbs, C.L., “The use of laser backscatter instrumentation for the on-
line measurement of the particle size distribution of emulsions” 10, 5, 279-289 (1993)
Tadayyon, A., Rohani, S., “Determination of Particle Size Distribution by Par-Tec®
100: Modeling and Experimental Results” Particle and Particle Systems
Characterization, 15, 3, 127-135 (1998)
Tadayyon, A. and S. Rohani, "Control of fines suspension density in the fines loop of
a continuous KCI crystallizer using transmittance measurement and an FBRM (R)
probe," Canadian Journal of Chemical Engineering 78 (4): 663-673 (2000)
167
Takiyama, H., T. Otsuhata, and M. Matsuoka, "Morphology of NaCl crystals in
drowning-out precipitation operation," Chemical Engineering Research & Design 76
(A7): 809-814 (1998)
Tavare, N. S., "Mixing, Reaction, and Precipitation - Interaction by Exchange with
Mean Micromixing Models," Aiche Journal 41 (12): 2537-2548 (1995)
Togkalidou, T. et al., "Solute concentration prediction using chemometrics and ATR-
FTIR spectroscopy," Journal of Crystal Growth 231 (4): 534-543 (2001)
Togkalidou, T. et al., "Solution concentration prediction for pharmaceutical
crystallization processes using robust chemometrics and ATR FTIR spectroscopy,"
Organic Process Research & Development 6 (3): 317-322 (2002)
Torbacke, M. and A. C. Rasmuson, "Influence of different scales of mixing in
reaction crystallization," Chemical Engineering Science 56 (7): 2459-2473 (2001)
Torbacke, M. and A. C. Rasmuson, "Mesomixing in semi-batch reaction
crystallization and influence of reactor size," Aiche Journal 50 (12): 3107-3119
(2004)
Vittorf, N.M. “Separation of aluminium-sulphate from iron by means of alcohol”,
Institut prikladnoi mineralogii i petrografii, Moscow. Transactions (1924)
Wei, H et al., "Computational fluid dynamics modeling of the precipitation process in
a semibatch crystallizer," Industrial & Engineering Chemistry Research 40 (23):
5255-5261 (2001)
168
Woo, X. Y. et al., "Simulation of mixing effects in antisolvent crystallization using a
coupled CFD-PDF-PBE approach," Crystal Growth & Design 6 (6): 1291-1303
(2006)
Worlitschek, J. and M. Mazzotti, "Choice of the focal point position using lasentec
FBRM," Particle & Particle Systems Characterization 20 (1): 12-17 (2003)
Worlitschek, J. and M. Mazzotti, "Model-based optimization of particle size
distribution in batch-cooling crystallization of paracetamol," Crystal Growth &
Design 4 (5): 891-903 (2004)
Worlitschek, J., Hocker, T., Mazzotti, M., “Restoration of PSD from Chord Length
Distribution Data using the Method of Projections onto Convex Sets”, Particle &
Particle Systems Characterization, 22, 2, 81-98 (2005)
Wynn, E. J. W., "Relationship between particle-size and chord-length distributions in
focused beam reflectance measurement: stability of direct inversion and weighting,"
Powder Technology 133 (1-3): 125-133 (2003)
Yu, Z. Q., R. B. H. Tan, and P. S. Chow, "Effects of operating conditions on
agglomeration and habit of paracetamol crystals in anti-solvent crystallization,"
Journal of Crystal Growth 279 (3-4): 477-488 (2005)
Zauner, R. and A. G. Jones, "Scale-up of continuous and semibatch precipitation
processes," Industrial & Engineering Chemistry Research 39 (7): 2392-2403 (2000)
Zauner, R. and A. G. Jones, "Scale-up of Continuous and Semibatch Precipitation
Processes," Industrial & Engineering Chemistry Research 39 (7): 2392-2403 (2000)
169
Zauner, R. and A. G. Jones, "Mixing effects on product particle characteristics from
semi-batch crystal precipitation," Chemical Engineering Research & Design 78 (A6):
894-902 (2000).
Zauner, R. and A. G. Jones, "On the influence of mixing on crystal precipitation
processes - application of the segregated feed model," Chemical Engineering Science
57 (5): 821-831 (2002)
Zhu, Y. et al., "Study of the solubility and the metastable zone of 1,3-
dihydroxyacetone for the drowning-out process," Journal of Crystal Growth 257 (3-
4): 370-377 (2003).
Zijlema, T. G. et al., "The production of pure NaCl by the antisolvent crystallisation
of NaCl center dot 2H(2)O and a consecutive recrystallisation step," Journal of
Crystal Growth 199: 789-795 (1999)
Zijlema, T. G. et al., "Antisolvent crystallization as an alternative to evaporative
crystallization for the production of sodium chloride," Industrial & Engineering
Chemistry Research 39 (5): 1330-1337 (2000)
170
171
APPENDIX A
APPLICATION OF COMPUTATION FLUID DYNAMICS TO EXPLAIN
VARIATION IN THE MSZW FOR ANT-SOLVENT ADDITION
CRYSTALLIZATION
Des O‟ Grady, Mark Barrett, Eoin Casey, Brian Glennon
Chapter 3 focuses on the study of the impact of process parameters on the MSZW of
an anti-solvent system and chapter 5 looks at the scale up of the crystallization to the
pilot-plant. This technical note will focus on the application of velocity profiles within
crystallization vessels at both scales, predicted using Computational Fluid Dynamics
(CFD), to model crystallization behaviour. This work was carried out by Mark Barrett
and Eoin Casey in conjunction with Des O‟Grady and Brian Glennon. A more in-
depth analysis of the use of CFD to model mixing for this crystallization system can
be found elsewhere (O‟Grady et al., 2007)
1. Abstract
Computational Fluid Dynamics is used to model mixing conditions in a 500 mL
vessel and a 70L vessel used to crystallize benzoic acid from ethanol water mixtures
using water as the anti-solvent. At the 500mL scale, anti-solvent addition close to the
impeller results in consistent MSZWs at all addition rates. However, when the
addition location is close to the vessel wall the MSZW is variable and premature
nucleation is common. CFD modelling indicated high downward velocities close to
172
the impeller but upward velocities close to the wall. This suggests that anti-solvent
added close to the impeller is more rapidly incorporated into the vessel, limiting the
potential for locally high supersaturation. However, close to the wall the anti-solvent
is less rapidly incorporated and local regions of high supersaturation may be expected.
This provides an explanation for the variations in MSZW behaviour observed. Similar
velocity profiles were calculated for the 70 L pilot plant vessel in order to predict
optimal addition location prior to crystallization. The profiles indicate that the optimal
addition location changes as the volume in the reactor increases.
2. Introduction
Computational Fluid Dynamics have been used to model semi-batch reactive
crystallizations and has been used to predict the effect of mixing on the
supersaturation distribution and the resulting size of the crystals (Wei et al., 2001,
Baldyga & Orciuch, 2001). It has also been used to study the effect of mixing on
product yield (Akiti & Armenante, 2004). CFD has also been used in conjunction
with crystallization kinetics and solubility data to simultaneously solve the mass and
population balances for a reactive crystallization, allowing the impact of feed rate,
agitation intensity, feed point and feed tube diameter on nucleation rate and crystal
size to be illustrated (Zauner and Jones, 2001). While the focus of CFD research has
been on reactive crystallization there is clearly scope for the use of the technique to
model mixing in anti-solvent crystallization processes.
In this study, CFD is used to generate theoretical velocity profiles for the vessel prior
to nucleation, in an effort to understand the observed trends in metastable zone width.
173
Velocity in the z-direction was chosen as a suitable parameter for assessing the
mixing regime, as movement of the liquid in this plane is vital for the incorporation of
anti-solvent into the bulk solution.
3. Computation Fluid Dynamics Model
To investigate the velocity profiles prior to nucleation, the system is treated as a
single phase and the possible solid-liquid interactions after nucleation are not
considered. Initially, the 500 ml vessel and its complex baffling system (the FBRM,
REACT-IR and temperature probes) are constructed within GAMBIT 2.1.6. GAMBIT
is a software package designed to aid in the construction and meshing of systems for
computational fluid dynamics (CFD). The precise geometry and dimensions of the
system (pitch blade impeller, probes etc.) are created in the graphical user interface
allowing for the meshing of the vessel and its internals, along with the assigning of
zone types and system specifics (i.e. fluid viscosity, internal reactor temperature and
agitation speed).
A large number of individual volumes created in the construction of the geometry are
then meshed. The system contains 823 individual volumes and 475,000 cells. An
unstructured hexahedral meshing scheme is applied, as less numerical diffusion errors
are evident then in a tetrahedral-based mesh. The mesh created in GAMBIT is then
exported to FLUENT 6.1.22 for solution of the momentum and continuity equations
for the turbulent flow within the crystallizer. The „Multiple Reference Frame‟ (MRF)
approach is applied to the system and the flow equations solved using the SIMPLE
algorithm. The Reynolds number for the agitation speeds assessed lie between
12,500 (325 rpm) and 18,500 (475rpm). The standard k-epsilon turbulence model
174
was chosen to model the flows. In addition, a no-slip boundary condition was
imposed on all walls and the free liquid surface is modelled with no vortex, a zero-
flux and zero-stress conditions. To validate the model the tip speed of the impeller is
calculated and compared to the tip speed calculated using the model. The model
underestimated the tip speed by about 3% at 325 and 475 rpm indicating the model is
suitable.
4. Results
4.1 500mL Scale
The difference in the behaviour of the MSZW can be explained in terms of mixing at
different regions of the vessel. Figures 1 and 2 depict velocity in the z-direction
through a cross section of the vessel at 475 rpm and 325 rpm respectively. Velocity in
this direction was chosen, as it will impact on the incorporation of the anti-solvent
into the bulk of the vessel. It is clear from Figure 1 that close to the impeller the
velocity is in the downward direction, but close to the wall the velocity is in the
upward direction. This means that when anti-solvent is added close to the impeller it
is incorporated easily into the bulk solution. However, when it is added close to the
wall this incorporation is more difficult. This may lead to an area of local
supersaturation close to the feed point and premature, variable nucleation as observed
experimentally. Furthermore, analysis of the same velocity profiles at lower agitation
levels indicate that velocity in the downward direction is less intense and velocity in
the upward direction is more pronounced. This explains the greater variability and
175
narrower MSZWs when anti-solvent is added close to the wall at lower agitation
levels.
176
Figure 1: Velocity profiles in the z-direction at 475 rpm through a central cross section of the vessel
Figure 2: Velocity Profiles in the z-direction at 325 rpm through a central cross section of the vessel
177
4.2 70 L Scale
At the 70 L scale CFD models indicate that the optimum feed location changes over
the course of the crystallization. Initially, when the liquid level is close to the plane of
the impeller the most intense downward velocities, and hence optimal addition
location, are found close vessel wall (Figure 3). However, as the volume in the vessel
increases the optimal location moves closer to the impeller shaft (Figure 4). These
results assisted in the choice of addition location in Chapter 5. Two batches were
conducted with the feed point close to the wall for the entire run and for the final
batch the feed point was changed after some time to take advantage of the change in
the optimal feed location.
178
Figure 3: Velcoity profiles in the z-direction at (a) 32 L and (b) 64 L showing change
in optimum feed location
179
5. Discussion
The experimental results outlined in Chapter 3 can be readily accounted for by
considering the different mixing regimes depending on the process conditions. CFD is
a very useful tool for modelling mixing conditions within a crystallizer and in this
case the CFD model explains the experimental results. Close to the impeller, mixing
conditions favour anti-solvent incorporation leading to consistent MSZW results,
whereas close to the wall anti-solvent incorporation is hindered leading to variability
in the nucleation mechanism and non-repeatable MSZW measurements. This effect is
exacerbated at lower agitation intensities where mixing conditions become even less
favourable.
6. References
Akiti O., & Armenante P., “Experimentally-validated micromixing-based CFD model
for fed-batch stirred-tank reactors”, AIChE, 50 (3) 566-577 (2004)
Bakldyga J., & Orciuch W., „Barium Sulphate precipitation in a pipe – and
experimental study and modelling“, Chemical Engineering Science, 58 (7) 2435-2444
(2001)
Wei H., et al., "Computational Fluid Dynamics Modelling of the Precipitation Process
in a Semibatch Crystallizer," Industrial & Engineering Chemistry Research 40 (23):
5255-5261 (2000)