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Real-Time Measurement of the Growth Rates of Individual Crystal Facets Using Imaging and Image Analysis
REAL-TIME MEASUREMENT OF THE GROWTH RATES OF INDIVIDUAL CRYSTAL FACETS USING IMAGING AND IMAGE ANALYSIS A Feasibility Study on Needle-shaped Crystals of L-Glutamic Acid X. Z. Wang , J. Calderon De Anda and K. J. Roberts Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, The University of Leeds, Leeds, UK.
Correspondence to: Professor X.Z. Wang, Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, The University of Leeds, Leeds LS2 9JT, UK. E-mail: x.z.wang@ leeds.ac.uk
DOI: 10.1205/cherd06203 0263–8762/07/ $30.00 þ 0.00 Chemical Engineering Research and Design Trans IChemE, Part A, July 2007 # 2007 Institution of Chemical Engineers
Abstract: Given that the fundamental process of crystal growth and its associated kinetic control is surface controlled, the use of a single scalar parameter, particle size, usually defined as a volume equivalent diameter, i.e., based on a spherical assumption of particle shape can be misleading for a number of practical crystallization systems, notably pharmaceutical products. Hence, measurement of the growth rate for each individual crystal surface in real-time and within processing reactors could open the way for the development of more effective process and concomitant product quality control. This paper presents the measurement of the growth rates of needle-shaped crystals in two dimensions using on-line imaging and image analysis techniques through a feasibility study of the batch crystallization of b form L-glutamic acid. The length and width of each needle-shaped crystal were measured every 60 s, ranging from 100 to nearly 180 mm in length and from 30 to 45 mm in width, and the values were used to estimate growth rates on both directions. The growth rate in length was found to be four to six times greater than for the width. The (101) plane was found to be the fastest growing surface of the morphology studied and an attempt has been made to estimate its growth-kinetics parameters from measurements of length, whilst it was harder to estimate kinetics from measurements of width for other crystal facets. Keywords: process imaging; image analysis; crystallization; shape control; crystal growth; glutamic acid.
INTRODUCTION
The modelling and control of industrial crystallization processes has often been subjected to some simplified assumptions notably associated with the characterization of the crystal growth rates from solution. For example, the growth rates of the individual crystal facets contained within a single polyhedral-shaped crystal have often been lumped into a single scalar variable, i.e., particle diameter, based on a spherical crystal shape assumption as used e.g., in population balance modelling and measurement (Alamdari and Tabkhi, 2004; Lim et al., 2002; Quintana-Hernandez et al., 2004). This is despite the fact that it is well known that the fundamental growth process and its associated kinetic control is surface dominated rather than being an intrinsic property that can be characterized via a simple scalar
There is a growing awareness of the importance of having well-defined active pharmaceutical ingredients (API) physical properties, such as particle size and shape in reducing product variability in downstream formulation. Hence, driven by regulatory pressing (e.g. FDA) (FDA and US Department of Health and Human Services, 2004), the use of process analytical technology (PAT) techniques for in-process control of product form is becoming increasingly important. In this paper recently developed in-process digital video microscopy (Calderon De Anda et al., 2005 a–c; Wang et al., 2005) is applied in a proof of concept study to assess its utility in the determination of crystallization kinetics in an in-reactor environment. 921
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variable (Clydesdale et al., 2005; Puel et al., 2003a, b; Zhang and Doherty, 2004). Hence, rigorous characterization of crystal growth rates and associated kinetic parameters need to be derived on a first principle basis directly from the growth mechanism associated with the propagation of individual crystal surfaces, i.e., as expressed in the time-evolving 3-D morphology of the growing crystals. This paper presents a feasibility study of real-time estimation of the growth rates of individual crystal faces using on-line imaging and image analysis. Real-time measurement of the growth rates of different faces is of great significance because it can potentially lead to precision control of the growth processes including manipulating the shape, size and other properties. In our previous studies, we have reported the use of on-line imaging and image analysis methods to monitor the onset of crystallization and polymorph transitions (Calderon De Anda et al., 2005b), developed an effective multi-scale image analysis technique for segmentation of the imaged crystals from complex backgrounds (Calderon De Anda et al., 2005c), and subsequently derived shape descriptors, classification techniques and novel process monitoring charts (Calderon De Anda et al., 2005a; Wang et al., 2005). The work presented here builds upon this previous work, focusing on estimating the face-specific growth rates of crystals within processing reactors. The experimental study involved the self-seeded cooling crystallization of L-glutamic acid in which the needleshaped b-form crystals were formed. The length and width of the needle-shaped crystals were measured during growth and the growth kinetics parameters for the different morphological directions assessed. The morphology of Lglutamic acid is well known (Kitamura, 1989), the b-form displaying a well formed needle-like morphology dominated by slow growing (021) and (010) faces together with fast growing (101) faces.
EXPERIMENTAL DETAILS The experiment was carried out using a 500 mL HEL Autolab batch reactor system (HEL) equipped with a condenser (to minimize solvent evaporation), a stainless steel pitched four-blade stirrer for agitation, pH probe and PT100 and turbidity probes for temperature and crystallization onset monitoring. The turbidity system (Gerson et al., 1991) comprised a phase matched pair of red light emitting diode source and silicon photodiode connected with fibre optic cable to a simple mirror probe reflectance device inserted into the vessel. Probe signals were logged onto the Autolab reactor computer system. Solution concentration was monitored using ATR-FTIR spectroscopy (Borissova et al., 2005; Dunuwila and Berglund, 1997; Fujiwara et al., 2002) (Bomem WorkIR with Dipper-210 ATR probe) was used with a concentration model built for glutamic acid. The calibration model was built using the PLSplusIQ software (www.thermogalactic.com). Second order derivative was used for baseline shift correction and the SEP [standard error of prediction, as defined by equation (1) below] was used as the criterion for the selection of the number of factors in the model. The model was developed using calibration data covering the temperature range of 40 –908C, and concentration range 3–62.5 (g-LGA/L-water). Details for the model can be
found from the reference (Borissova et al., 2005).
SEP ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP un u (Ci Cpi )2 ti¼1 n
(1)
where n is the number of samples in the training data set, Cpi is the matrix of predicted sample concentrations from the model, Ci is the matrix of known concentrations of the samples. L-glutamic acid was used for our experiments due to its similarity to practical active ingredients, particularly its organic nature, bonding and polymorphic ability. To carry out the experiment, a solution was prepared with 31.25 g of L-glutamic acid (Aldrich Chemicals, 99% purity) in 500 mL of fresh distilled water (798C saturation temperature). The solution was heated in the crystallizer vessel up to 908C and held at that temperature for approximately 1 h. A few particles were allowed to survive the dissolution which were observed by the imaging system and used as the seeds for cooling crystallization. The solution that contain dissolved crystal seeds was then cooled down to 358C at a cooling rate of 0.18C min21 and constant agitation of 300 rpm. For the estimation of growth rates in this proof of concept study, the temperature range used was from 69.2 to 66.78C and the relative supersaturation range was from 0.42 to 0.57. At higher solid concentrations, difficulties were encountered in image segmentation analysis because although the edge of individual crystals can still be identified, a methodology needs to be developed for extracting the individual crystals. The on-line imaging system described elsewhere (Calderon De Anda et al., 2005b) and developed by GlaxoSmithKline (GSK) (Wilkinson et al., 2000), comprised a Sony XC55 CCD camera fitted with Navitar Precise Eye/Mitutoyo optics is employed for image acquisition with a maximum frequency of up to thirty images per second with a pixel resolution of 640 480 and a field of view varying from 140 mm 105 mm to 16 mm 12 mm dependent on calibrated lenses employed. The camera, situated outside the reactor wall, has an imaging window attached to the external curved reactor wall to minimize convexity effects on the images. A xenon stroboscopic light source is used for illumination and the light is conducted using a fibre optic guide. Camera acquisition and strobe are synchronised to freeze the moving particles by using a camera interface box developed by the GSK researchers. The captured images are sent to a PC running Video Savantw software (IO Industries, Inc.) for acquisition, storage and management of the frames. For estimation of particle size, the camera was calibrated with a glass micrometer scale (Agar Scientific Ltd) using lenses for different magnifications. Figure 1 shows the experimental set-up with the on-line imaging system. On-line images of slurries with particles suspended in a solution pose much greater complexity for image analysis methods than images of particles obtained with off-line equipment. The major challenges lie in the fact that the slurries in a stirred reactor are in continuous motion, and that the variation of distances from the camera lens of particles captured in a snapshot makes some particles rather vague compared to others. In addition, the light effect and temporal changes of hydrodynamics within the reactor may lead to varied intensity
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Figure 1. The experimental set-up with a 0.5 L crystallizer vessel and the on-line imaging instrument.
in the image background. Such challenges have previously been noted (Patience, 2002) and hence in our work a multistep multiscale approach was developed, which in previous studies has proved to be very effective in extracting objects from the image background in images obtained by the GSK on-line microscopy system, as well as for the Lasentec PVM probe and off-line image acquisition systems (Calderon De Anda, 2005c).
can be explained as a consequence of the individual growth of some of the seeds, whilst the fines can mainly be attributed to the result of secondary nucleation by solute structures moved away from the surface of seeds as a result of several possible mechanisms. At this stage, the small volume fraction of crystals in the solution reduces the
RESULTS AND DISCUSSION Crystal Size and Operating Conditions The evolution of crystal length and width along the time axis is illustrated in Figures 2(a) and (b), respectively. Each point in the plots represents the average obtained from all the needle-shaped, single crystals detected in a time interval of 60 s, containing 300 images. The detection limit of size by the image analysis technique was from 30 mm due to the magnification used, producing trends going from approximately 100–180 mm in length and from 30 mm to 47 mm in width. As a consequence of the changes in supersaturation, some degree of size dispersion can be expected. At this point of the current study, we are uncertain to provide a reliable distribution or a standard deviation value to assume a normal distribution for the overall population of crystals in the reactor. Despite this, the changes in supersaturation for the analysed period were relatively small and the overall qualitative observations in the image data reflect good agreement with the quantitative trends in morphological measurements, length and width, obtained by image analysis. The mean values obtained were used here to investigate the crystal growth of the two morphological dimensions, length and width, in relation to experimental parameters, particularly supersaturation. Figure 3 shows the mean values of crystal length and width along with supersaturation and temperature. Two sample images obtained at point A are shown in Figure 4. At this point, effects of secondary nucleation were observed in the form of fluctuations of length and width measurements, as a result of the presence of seeds. Groups of crystals started to grow from single seeds though some others grew individually. Particularly interesting was the presence of considerably larger single particles together with fines. The presence of these long needles
Figure 2. Evolution of crystal length (a) and width (b) for the needleshaped glutamic acid crystals. Each point represents the mean values in the previous 60 s (i.e., 300 images). The time t ¼ 0 min corresponds to the onset temperature, i.e., 70.78C, as illustrated in Table 1.
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Figure 3. Evolution of b form glutamic acid crystal length and width along with supersaturation and temperature. Each point represents the previous 60 s (300 images). The time t ¼ 0 min corresponds to the onset temperature, i.e., 70.78C, as illustrated in Table 1.
possible attrition effects due to particle –particle or particle – reactor collisions, hence other mechanisms of secondary nucleation can be attributed such as the removal of solute
clusters from the seeds by fluid shearing forces. The potential nuclei released from the seed surface survive if they reach the critical size and as the supersaturation increases more nuclei succeed in achieving the survival condition and continue to growth. This effect made the crystallisation onset and the first instants of growth appear smooth and short termed rather than as an instantaneous event. Three sample images at points B, C and D, indicated in Figure 3, are shown in Figure 5 to illustrate the evolution of the growth of the crystals. On-line images obtained around the B reflected the eventual predominance of the amount of small crystals. This event reflects that the growth of more nuclei increased considerably at this stage and they continued to grow along with supersaturation. It is clear that from this point on, one of the morphological components measured presented a greater increase, corresponding to the crystal length, compared with the relatively small but yet increase in other component, the crystal width. It was observed that the on-line imaging and image analysis techniques could detect the existence of considerable number of needle-shaped crystals, distinguished from seeds, before depletion in turbidity measurements (Table 1). The turbidity measurements started to show depletion around the points C in Figure 3, where the crystals reach a mean value of 140 mm in length and 40 mm in width.
Estimation of Crystal Surface Growth Rates
Figure 4. In-process images obtained at point A in Figure 3, showing the effect of seeding as long needles and groups of crystals growing 200 mm). from single needles (
The estimation of the growth rates from the on-line images associated with the crystal length and width was investigated. The first few data points obtained by image analysis were not included in the estimation as they were obtained with insufficient amount of crystals to show a trend and during the period when the effect of seeds led to oscillations in crystal length. Hence, only the data plotted in Figure 6 were used. However, consequence of the simultaneous growth and nucleation events is that the mean values of length and width correspond to the average of the values measured for large
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Figure 6. The data points used to estimate the growth rates of length and width.
evaluating for length, RL ¼ DL/Dt, and for width, RW ¼ DW/ Dt, using a 10-point interval. The corresponding supersaturation is the supersaturation average for each interval. It should be noted that the values in Table 2 correspond to relative supersaturation, s ¼ Sav 2 1. Given that the experiment was carried out under cooling, during the analysed period the temperature changed from 69.28C at the first point to 66.78C at the last point of the plot in Figure 6. Thus, for each interval, the averaged values of temperature were also obtained. All these data are presented in Table 2. Please note the temperature range shown in Table 2, 68.38C 67.58C, is different from that represented by Figure 6 of 69.2 66.78C. This is because to calculate the growth rates in Table 2, we used a window of 10 points, in other words, the first point in Table 2, 68.38C was calculated by 10 points in the fist window including the point of 69.28C. The estimated growth rate values were compared to values reported in the literature. Kitamura and Ishizu (2000) investigated the growth rates of different faces of b-form crystals of glutamic acid using the single crystal method. Accordingly, for a supersaturation value, s, of 0.5 and at 258C the growth rate corresponding to the direction a of Figure 7 is around 1.3 1028 m s21. This reported value is lower compared with the values obtained here in the same direction a describing the crystal length, which are within the supersaturation range from 0.47 to 0.51 (Table 2). This difference is consistent with the difference in temperature. At higher temperatures Figure 5. In-process images, (a), (b) and (c) corresponding to points B, C and D in Figure 3, showing the evolution of the growth of the 200 mm). needle crystals (
growing crystals and newly created small crystals. Hence some degree of underestimation of the crystal growth may exist. Table 2 shows the growth rate values obtained when Table 1. Comparison of the temperature of crystallization obtained by two different techniques. In-process technique On-line imaging with image analysis Optical turbidity
Temperature Onset onset (8C) time-point (min) 70.7 68.3
0 24
Table 2. Estimated growth rate values for length and width of needleshaped crystals.
Temperature average, T (8C) 68.3 68.1 68.0 67.8 67.6 67.5 25
Relative supersaturation s ¼ S21 av
Crystal length growth rate RL 108 (m s21)
Crystal width growth rate RW 108 (m s21)
0.47 0.47 0.48 0.49 0.50 0.51 0.5
2.440 2.561 3.136 2.837 2.997 2.995 1.3
0.557 0.583 0.585 0.604 0.552 0.501 —
Value from literature (Kitamura and Ishizu, 2000).
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higher growth rates are expected. However, it needs to point out that the comparison on growth rates obtained from the current experiment and that from literature can only be based on looking at on the order of magnitude because in addition to the difference in temperatures, the hydrodynamic conditions between the two experiments are very different. Kitamura and Ishizu (2000) also reported that for the direction b perpendicular to the (010) face in Figure 7, the growth rates were too small to be measured. Here, the measurements of crystal width involved a combination of the faces in the b and c directions, i.e., (021) and (010) faces, as well as of the face (101) and the values of growth rates for crystal width are clearly lower than those for length as observed in Table 2. The estimated values of growth rate for the crystal width are from 4 to 6 times lower than those for crystal length. The behaviour of growth rate in length and width in relation to supersaturation is observed in Table 2. The data shows that for the growth rates in crystal length the tendency is to increase with increasing supersaturation, whereas the crystal width presents an unclear correlation between the growth rates and the supersaturation in the solution. The measurements of crystal length by image analysis correspond to the extreme points of the needle length. From a careful observation of the crystal morphology of the b-form crystals of L-glutamic acid, schematized in Figure 7, it is not difficult to realise that the relative growth rates along the normal to the (101) growth plane are the responsible for the growth in length. Nevertheless, the growth of the faces (010) and (021) is involved in the measurement of width, and the variation in the growth between these faces together with some degree in crystal size dispersion can explain the unclear trend in growth rate for the crystal width with respect to supersaturation. A further step would be to link the growth rates to the kinetics of different habit directions. The capability of the imaging technique to measure crystal growth rates could in principle be extended to the estimation of kinetic parameters and fundamental mechanisms associated to the different facets of the morphology studied. Although it is tempting, here we have not fitted the data to a kinetic model particularly given the non-isothermal conditions of the experiment. It is important to be aware that a kinetic model used to describe the growth in crystal length would in fact be
Figure 7. Schematic of the morphology of beta form crystals of glutamic acid. A view from the top (a) shows that the crystal width involves the faces f021g and f010g. The angle between the growth rate in the [101] direction, R(101), and the length axis, RL, is shown in (b).
describing the growth in the direction of a (100) face, perpendicular to the length direction, but that is in fact not expressed in the morphology of the crystals observed in this study. Instead, the growth on the crystal length is driven by the (101) family of faces, with the existence of an angle between the growth rate in length and the growth rate of the (101) face [Figure 7(b)]. Due to the rotation of the crystals in the threedimensional space in the solution, it is at this point difficult to provide a precise absolute estimation of the growth for the face (101), R(101). Nevertheless, since the faces (010) and (021) are normal to the measurements of length, the (101) plane is the only face associated with the growth in length and although the absolute size of the normal distance to this plane would be different from the values of length measured, all the values would be affected by the same angle and hence it is sensible to assume that the growth rates in length are already reasonably close to the growth rates normal to the faces (101), i.e., R(101). In contrast, for the crystal width it is harder to derive at this point sensible estimations of the growth corresponding to the faces (010) and (021) due to their combined affect on the measurements of crystal width. However, the results suggest that provided that the rotation of the crystal in the solution be quantified, the measurements obtained by the imaging technique can lead to the estimation of the kinetics parameters for growth of crystal facets with high accuracy.
FINAL REMARKS The estimation of crystal growth rates from real-time inprocess image data was investigated for needle-like b-form glutamic acid using on-line imaging and image analysis. The results show that the image analysis could detect the effect of the seeds by reflecting the presence of the first grown long-needle crystals. The dispersion of growth in length and width due to the uncontrolled crystallizing conditions could also be followed by image analysis at different time points by estimations of mean length and width along with the associated standard deviations, nevertheless tracking the overall growth trends of the crystals in the two different morphological directions. It was shown that for crystals of the b polymorph of glutamic acid the growth rate of the crystal length is from four to six times higher than that of crystal width. This data, combined with the Miller indexing of the morphology of the b-form crystals of glutamic acid led to the conclusion that the plane (101) is the fastest growing crystal face as it is this face that drives the growth in crystal length, whereas the growth measurement in crystal width corresponds to the combined growth of the faces (120) and (010). Growth rate measurements and supersaturation values obtained suggest that during the analysed period of crystallization the supersaturation showed some correlation with the growth rate in length whilst a clear relation was not observed with the growth rate in width. With a more rigorous analysis of the crystal rotation, the image analysis measurement obtained could lead to more accurate estimations of the growth (size) normal to the individual crystal faces, as well as the estimation of kinetic parameters and mechanisms associated with each face within the reactor environment. The current feasibility study using needle-shaped crystals has proved that it is possible to measure the crystal growth rate of each direction in real-time using imaging and image
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FEASIBILITY STUDY ON NEEDLE-SHAPED CRYSTALS analysis techniques. Future work needs to address other challenges. For example, crystal orientation, secondary nucleation or breakage may cause the growth rates to be under- or over-estimated if they are calculated directly from the changes of measured dimensional sizes. In addition, how to effectively make use of the measured information and select appropriate manipulating variables to achieve the control objective of shape remains a topic of future research.
REFERENCES Alamdari, A. and Tabkhi, F., 2004, Kinetics of hexamine crystallization in industrial scale, Chem Eng Process, 43(7): 803 –810. Borissova, A. Khan, S., Mahmud, T., Roberts, K.J., Andrews, J. and Dallin, P., 2005, On-line ATR-FTIR measurements of batch cooling crystallisation of L-glutamic acid, 7th World Congress of Chemical Engineering, Glasgow, P2-001. Calderon De Anda, J., Wang, X.Z., Lai, X. and Roberts, K.J., 2005a, Classifying organic crystals via in-process image analysis and the use of monitoring charts to follow polymorphic and morphological changes, J Process Control, 15(7): 785–797. Calderon De Anda, J., Wang, X.Z., Lai, X., Roberts, K.J., Jennings, K.H., Wilkinson, M.J., Watson, D. and Roberts, D., 2005b, Real-time product morphology monitoring in crystallization using imaging technique, AIChE J, 51(5): 1406–1414. Calderon De Anda, J., Wang, X.Z. and Roberts, K.J., 2005c, Multiscale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers, Chem Eng Sci, 60(4): 1053–1065. Clydesdale, G., Hammond, R.B., Ramachandran, V. and Roberts, K.J., 2005, Molecular modelling of the morphology of organic crystals in the presence of impurity species: Recent applications to naphthalene, phenanthrene, and caprolactam crystals, Mol Cryst Liq Cryst, 440: 235 –257. Dunuwila, D.D. and Berglund, K.A., 1997, ATR FTIR spectroscopy for in situ measurement of supersaturation, J Cryst Growth, 179 (1 –2): 185–193. FDA, US Department of Health and Human Services, 2004, Challenge and opportunity on the critical path to new medical products, www.fda.gov/oc/initiatives/criticalpath/. Fujiwara, M., Chow, P.S., Ma, D.L. and Braatz, R.D., 2002, Paracetamol crystallization using laser backscattering and ATR- FTIR spectroscopy: Metastability, agglomeration, and control, Cryst Growth Des, 2(5): 363– 370. Gerson, A.R., Roberts, K.J. and Sherwood, J.N., 1991, An instrument for the examination of nucleation from solution and its application to the study of precipitation from diesel fuels and solutions of normal-alkanes, Powder Technol, 65(1– 3): 243– 249. HEL, Hazard Evaluation Laboratories, http://www.helgroup.co.uk/. Kitamura, M., 1989, Polymorphism in the crystallization of L-glutamic acid, J Cryst Growth, 96(3): 541–546. Kitamura, M. and Ishizu, T., 2000, Growth kinetics and morphological change of polymorphs of L-glutamic acid, J Cryst Growth, 209(1): 138– 145. Lim, Y.I., Le Lann, J.-M., Meyer, X.M., Joulia, X., Lee, G. and Yoon, E.S., 2002, On the solution of population balance equations (PBE) with accurate front tracking methods in practical crystallization processes, Chem Eng Sci, 57(17): 3715–3732.
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Patience, D.B., 2002, Crystal engineering through particle size and shape, monitoring, modeling and control, PhD thesis, University of Wisconsin-Madison, USA. Puel, F., Fevotte, G. and Klein, J.P., 2003a, Simulation and analysis of industrial crystallization processes through multidimensional population balance equations. Part 1: a resolution algorithm based on the method of classes, Chem Eng Sci, 58(16): 3715– 3727. Puel, F., Fevotte, G. and Klein, J.P., 2003b, Simulation and analysis of industrial crystallization processes through multidimensional population balance equations. Part 2: a study of semi-batch crystallization, Chem Eng Sci, 58(16): 3729–3740. Quintana-Hernandez, P., Bolanos-Reynoso, E., Miranda-Castro, B. and Salcedo-Estrada, L., 2004, Mathematical modeling and kinetic parameter estimation in batch crystallization, AIChE J, 50(7): 1407– 1417. Wang, X.Z., Calderon De Anda, J., Roberts, K.J., Li, R.F., Thompson, G.B. and White, G., 2005, Advances in on-line monitoring and control of the morphological and polymorphic forms of organic crystals grown from solution (paper downloadable from http://www.kona.or. jp/html/about/index2005.html), KONA: Powders and Particle, (23): 69–85. Wilkinson, M.J., Jennings, K.H. and Hardy, M., 2000, Non-invasive video imaging for interrogating pharmaceutical crystallization processes, Microsc Microanal, 6(2): 996 –997. Zhang, Y.C. and Doherty M.F., 2004, Simultaneous prediction of crystal shape and size for solution crystallization, AIChE J, 50(9): 2101–2112.
ACKNOWLEDGEMENTS This work was carried out as part of Chemicals Behaving Badly project, a collaborative project funded by EPSRC together with support from an industrial consortium including ANSYS Europe Ltd, AstraZeneca, Bede Scientific Instruments Ltd, BNFL, Clairet Scientific Ltd, GlaxoSmithKline, HEL Ltd, Malvern Instruments, Pfizer and Syngenta. The academic partners are Leeds, Heriot-Watt and Newcastle Universities. We gratefully acknowledge all these sponsors and all members of this academic/industrial team and the industrial coordinator L.J. Ford. We would like to thank Dr Caiyun Ma for some useful discussions, and Dr Tariq Mahmud and Mr Shahid Khan for contributing the FTIR concentration model for L-glutamic acid developed in the work programme #1 of the CBB project. The authors would also like to thank Drs Gillian Thomson, Ruifa Li and Graeme White of Heriot-Watt University with whom we are working closely on the same work programme of the project. We would like to extend our thanks to GlaxoSmithKline for providing the imaging system, particularly to Kevin Jennings, Mike Wilkinson, Chris Price and David Lee at GlaxoSmithKline for providing support with the imaging system. The work has also benefited from the Vision project funded by Malvern Instruments Limited. The second author would like to acknowledge The Council of Science and Technology in Mexico (CONACYT) for providing the PhD scholarship for this research. The authors would like to thank Malvern Instruments Ltd for sponsoring the project of IntelliSense. The work presented has benefited from and is moving forward with the support of a new EPSRC grant (EP/C009541/1). The manuscript was received 26 October 2006 and accepted for publication after revision 24 February 2007.
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Correspondence to: Professor X.Z. Wang, Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, The University of Leeds, Leeds LS2 9JT, UK. E-mail: x.z.wang@ leeds.ac.uk
DOI: 10.1205/cherd06203 0263–8762/07/ $30.00 þ 0.00 Chemical Engineering Research and Design Trans IChemE, Part A, July 2007 # 2007 Institution of Chemical Engineers
Abstract: Given that the fundamental process of crystal growth and its associated kinetic control is surface controlled, the use of a single scalar parameter, particle size, usually defined as a volume equivalent diameter, i.e., based on a spherical assumption of particle shape can be misleading for a number of practical crystallization systems, notably pharmaceutical products. Hence, measurement of the growth rate for each individual crystal surface in real-time and within processing reactors could open the way for the development of more effective process and concomitant product quality control. This paper presents the measurement of the growth rates of needle-shaped crystals in two dimensions using on-line imaging and image analysis techniques through a feasibility study of the batch crystallization of b form L-glutamic acid. The length and width of each needle-shaped crystal were measured every 60 s, ranging from 100 to nearly 180 mm in length and from 30 to 45 mm in width, and the values were used to estimate growth rates on both directions. The growth rate in length was found to be four to six times greater than for the width. The (101) plane was found to be the fastest growing surface of the morphology studied and an attempt has been made to estimate its growth-kinetics parameters from measurements of length, whilst it was harder to estimate kinetics from measurements of width for other crystal facets. Keywords: process imaging; image analysis; crystallization; shape control; crystal growth; glutamic acid.
INTRODUCTION
The modelling and control of industrial crystallization processes has often been subjected to some simplified assumptions notably associated with the characterization of the crystal growth rates from solution. For example, the growth rates of the individual crystal facets contained within a single polyhedral-shaped crystal have often been lumped into a single scalar variable, i.e., particle diameter, based on a spherical crystal shape assumption as used e.g., in population balance modelling and measurement (Alamdari and Tabkhi, 2004; Lim et al., 2002; Quintana-Hernandez et al., 2004). This is despite the fact that it is well known that the fundamental growth process and its associated kinetic control is surface dominated rather than being an intrinsic property that can be characterized via a simple scalar
There is a growing awareness of the importance of having well-defined active pharmaceutical ingredients (API) physical properties, such as particle size and shape in reducing product variability in downstream formulation. Hence, driven by regulatory pressing (e.g. FDA) (FDA and US Department of Health and Human Services, 2004), the use of process analytical technology (PAT) techniques for in-process control of product form is becoming increasingly important. In this paper recently developed in-process digital video microscopy (Calderon De Anda et al., 2005 a–c; Wang et al., 2005) is applied in a proof of concept study to assess its utility in the determination of crystallization kinetics in an in-reactor environment. 921
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variable (Clydesdale et al., 2005; Puel et al., 2003a, b; Zhang and Doherty, 2004). Hence, rigorous characterization of crystal growth rates and associated kinetic parameters need to be derived on a first principle basis directly from the growth mechanism associated with the propagation of individual crystal surfaces, i.e., as expressed in the time-evolving 3-D morphology of the growing crystals. This paper presents a feasibility study of real-time estimation of the growth rates of individual crystal faces using on-line imaging and image analysis. Real-time measurement of the growth rates of different faces is of great significance because it can potentially lead to precision control of the growth processes including manipulating the shape, size and other properties. In our previous studies, we have reported the use of on-line imaging and image analysis methods to monitor the onset of crystallization and polymorph transitions (Calderon De Anda et al., 2005b), developed an effective multi-scale image analysis technique for segmentation of the imaged crystals from complex backgrounds (Calderon De Anda et al., 2005c), and subsequently derived shape descriptors, classification techniques and novel process monitoring charts (Calderon De Anda et al., 2005a; Wang et al., 2005). The work presented here builds upon this previous work, focusing on estimating the face-specific growth rates of crystals within processing reactors. The experimental study involved the self-seeded cooling crystallization of L-glutamic acid in which the needleshaped b-form crystals were formed. The length and width of the needle-shaped crystals were measured during growth and the growth kinetics parameters for the different morphological directions assessed. The morphology of Lglutamic acid is well known (Kitamura, 1989), the b-form displaying a well formed needle-like morphology dominated by slow growing (021) and (010) faces together with fast growing (101) faces.
EXPERIMENTAL DETAILS The experiment was carried out using a 500 mL HEL Autolab batch reactor system (HEL) equipped with a condenser (to minimize solvent evaporation), a stainless steel pitched four-blade stirrer for agitation, pH probe and PT100 and turbidity probes for temperature and crystallization onset monitoring. The turbidity system (Gerson et al., 1991) comprised a phase matched pair of red light emitting diode source and silicon photodiode connected with fibre optic cable to a simple mirror probe reflectance device inserted into the vessel. Probe signals were logged onto the Autolab reactor computer system. Solution concentration was monitored using ATR-FTIR spectroscopy (Borissova et al., 2005; Dunuwila and Berglund, 1997; Fujiwara et al., 2002) (Bomem WorkIR with Dipper-210 ATR probe) was used with a concentration model built for glutamic acid. The calibration model was built using the PLSplusIQ software (www.thermogalactic.com). Second order derivative was used for baseline shift correction and the SEP [standard error of prediction, as defined by equation (1) below] was used as the criterion for the selection of the number of factors in the model. The model was developed using calibration data covering the temperature range of 40 –908C, and concentration range 3–62.5 (g-LGA/L-water). Details for the model can be
found from the reference (Borissova et al., 2005).
SEP ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP un u (Ci Cpi )2 ti¼1 n
(1)
where n is the number of samples in the training data set, Cpi is the matrix of predicted sample concentrations from the model, Ci is the matrix of known concentrations of the samples. L-glutamic acid was used for our experiments due to its similarity to practical active ingredients, particularly its organic nature, bonding and polymorphic ability. To carry out the experiment, a solution was prepared with 31.25 g of L-glutamic acid (Aldrich Chemicals, 99% purity) in 500 mL of fresh distilled water (798C saturation temperature). The solution was heated in the crystallizer vessel up to 908C and held at that temperature for approximately 1 h. A few particles were allowed to survive the dissolution which were observed by the imaging system and used as the seeds for cooling crystallization. The solution that contain dissolved crystal seeds was then cooled down to 358C at a cooling rate of 0.18C min21 and constant agitation of 300 rpm. For the estimation of growth rates in this proof of concept study, the temperature range used was from 69.2 to 66.78C and the relative supersaturation range was from 0.42 to 0.57. At higher solid concentrations, difficulties were encountered in image segmentation analysis because although the edge of individual crystals can still be identified, a methodology needs to be developed for extracting the individual crystals. The on-line imaging system described elsewhere (Calderon De Anda et al., 2005b) and developed by GlaxoSmithKline (GSK) (Wilkinson et al., 2000), comprised a Sony XC55 CCD camera fitted with Navitar Precise Eye/Mitutoyo optics is employed for image acquisition with a maximum frequency of up to thirty images per second with a pixel resolution of 640 480 and a field of view varying from 140 mm 105 mm to 16 mm 12 mm dependent on calibrated lenses employed. The camera, situated outside the reactor wall, has an imaging window attached to the external curved reactor wall to minimize convexity effects on the images. A xenon stroboscopic light source is used for illumination and the light is conducted using a fibre optic guide. Camera acquisition and strobe are synchronised to freeze the moving particles by using a camera interface box developed by the GSK researchers. The captured images are sent to a PC running Video Savantw software (IO Industries, Inc.) for acquisition, storage and management of the frames. For estimation of particle size, the camera was calibrated with a glass micrometer scale (Agar Scientific Ltd) using lenses for different magnifications. Figure 1 shows the experimental set-up with the on-line imaging system. On-line images of slurries with particles suspended in a solution pose much greater complexity for image analysis methods than images of particles obtained with off-line equipment. The major challenges lie in the fact that the slurries in a stirred reactor are in continuous motion, and that the variation of distances from the camera lens of particles captured in a snapshot makes some particles rather vague compared to others. In addition, the light effect and temporal changes of hydrodynamics within the reactor may lead to varied intensity
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Figure 1. The experimental set-up with a 0.5 L crystallizer vessel and the on-line imaging instrument.
in the image background. Such challenges have previously been noted (Patience, 2002) and hence in our work a multistep multiscale approach was developed, which in previous studies has proved to be very effective in extracting objects from the image background in images obtained by the GSK on-line microscopy system, as well as for the Lasentec PVM probe and off-line image acquisition systems (Calderon De Anda, 2005c).
can be explained as a consequence of the individual growth of some of the seeds, whilst the fines can mainly be attributed to the result of secondary nucleation by solute structures moved away from the surface of seeds as a result of several possible mechanisms. At this stage, the small volume fraction of crystals in the solution reduces the
RESULTS AND DISCUSSION Crystal Size and Operating Conditions The evolution of crystal length and width along the time axis is illustrated in Figures 2(a) and (b), respectively. Each point in the plots represents the average obtained from all the needle-shaped, single crystals detected in a time interval of 60 s, containing 300 images. The detection limit of size by the image analysis technique was from 30 mm due to the magnification used, producing trends going from approximately 100–180 mm in length and from 30 mm to 47 mm in width. As a consequence of the changes in supersaturation, some degree of size dispersion can be expected. At this point of the current study, we are uncertain to provide a reliable distribution or a standard deviation value to assume a normal distribution for the overall population of crystals in the reactor. Despite this, the changes in supersaturation for the analysed period were relatively small and the overall qualitative observations in the image data reflect good agreement with the quantitative trends in morphological measurements, length and width, obtained by image analysis. The mean values obtained were used here to investigate the crystal growth of the two morphological dimensions, length and width, in relation to experimental parameters, particularly supersaturation. Figure 3 shows the mean values of crystal length and width along with supersaturation and temperature. Two sample images obtained at point A are shown in Figure 4. At this point, effects of secondary nucleation were observed in the form of fluctuations of length and width measurements, as a result of the presence of seeds. Groups of crystals started to grow from single seeds though some others grew individually. Particularly interesting was the presence of considerably larger single particles together with fines. The presence of these long needles
Figure 2. Evolution of crystal length (a) and width (b) for the needleshaped glutamic acid crystals. Each point represents the mean values in the previous 60 s (i.e., 300 images). The time t ¼ 0 min corresponds to the onset temperature, i.e., 70.78C, as illustrated in Table 1.
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Figure 3. Evolution of b form glutamic acid crystal length and width along with supersaturation and temperature. Each point represents the previous 60 s (300 images). The time t ¼ 0 min corresponds to the onset temperature, i.e., 70.78C, as illustrated in Table 1.
possible attrition effects due to particle –particle or particle – reactor collisions, hence other mechanisms of secondary nucleation can be attributed such as the removal of solute
clusters from the seeds by fluid shearing forces. The potential nuclei released from the seed surface survive if they reach the critical size and as the supersaturation increases more nuclei succeed in achieving the survival condition and continue to growth. This effect made the crystallisation onset and the first instants of growth appear smooth and short termed rather than as an instantaneous event. Three sample images at points B, C and D, indicated in Figure 3, are shown in Figure 5 to illustrate the evolution of the growth of the crystals. On-line images obtained around the B reflected the eventual predominance of the amount of small crystals. This event reflects that the growth of more nuclei increased considerably at this stage and they continued to grow along with supersaturation. It is clear that from this point on, one of the morphological components measured presented a greater increase, corresponding to the crystal length, compared with the relatively small but yet increase in other component, the crystal width. It was observed that the on-line imaging and image analysis techniques could detect the existence of considerable number of needle-shaped crystals, distinguished from seeds, before depletion in turbidity measurements (Table 1). The turbidity measurements started to show depletion around the points C in Figure 3, where the crystals reach a mean value of 140 mm in length and 40 mm in width.
Estimation of Crystal Surface Growth Rates
Figure 4. In-process images obtained at point A in Figure 3, showing the effect of seeding as long needles and groups of crystals growing 200 mm). from single needles (
The estimation of the growth rates from the on-line images associated with the crystal length and width was investigated. The first few data points obtained by image analysis were not included in the estimation as they were obtained with insufficient amount of crystals to show a trend and during the period when the effect of seeds led to oscillations in crystal length. Hence, only the data plotted in Figure 6 were used. However, consequence of the simultaneous growth and nucleation events is that the mean values of length and width correspond to the average of the values measured for large
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Figure 6. The data points used to estimate the growth rates of length and width.
evaluating for length, RL ¼ DL/Dt, and for width, RW ¼ DW/ Dt, using a 10-point interval. The corresponding supersaturation is the supersaturation average for each interval. It should be noted that the values in Table 2 correspond to relative supersaturation, s ¼ Sav 2 1. Given that the experiment was carried out under cooling, during the analysed period the temperature changed from 69.28C at the first point to 66.78C at the last point of the plot in Figure 6. Thus, for each interval, the averaged values of temperature were also obtained. All these data are presented in Table 2. Please note the temperature range shown in Table 2, 68.38C 67.58C, is different from that represented by Figure 6 of 69.2 66.78C. This is because to calculate the growth rates in Table 2, we used a window of 10 points, in other words, the first point in Table 2, 68.38C was calculated by 10 points in the fist window including the point of 69.28C. The estimated growth rate values were compared to values reported in the literature. Kitamura and Ishizu (2000) investigated the growth rates of different faces of b-form crystals of glutamic acid using the single crystal method. Accordingly, for a supersaturation value, s, of 0.5 and at 258C the growth rate corresponding to the direction a of Figure 7 is around 1.3 1028 m s21. This reported value is lower compared with the values obtained here in the same direction a describing the crystal length, which are within the supersaturation range from 0.47 to 0.51 (Table 2). This difference is consistent with the difference in temperature. At higher temperatures Figure 5. In-process images, (a), (b) and (c) corresponding to points B, C and D in Figure 3, showing the evolution of the growth of the 200 mm). needle crystals (
growing crystals and newly created small crystals. Hence some degree of underestimation of the crystal growth may exist. Table 2 shows the growth rate values obtained when Table 1. Comparison of the temperature of crystallization obtained by two different techniques. In-process technique On-line imaging with image analysis Optical turbidity
Temperature Onset onset (8C) time-point (min) 70.7 68.3
0 24
Table 2. Estimated growth rate values for length and width of needleshaped crystals.
Temperature average, T (8C) 68.3 68.1 68.0 67.8 67.6 67.5 25
Relative supersaturation s ¼ S21 av
Crystal length growth rate RL 108 (m s21)
Crystal width growth rate RW 108 (m s21)
0.47 0.47 0.48 0.49 0.50 0.51 0.5
2.440 2.561 3.136 2.837 2.997 2.995 1.3
0.557 0.583 0.585 0.604 0.552 0.501 —
Value from literature (Kitamura and Ishizu, 2000).
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higher growth rates are expected. However, it needs to point out that the comparison on growth rates obtained from the current experiment and that from literature can only be based on looking at on the order of magnitude because in addition to the difference in temperatures, the hydrodynamic conditions between the two experiments are very different. Kitamura and Ishizu (2000) also reported that for the direction b perpendicular to the (010) face in Figure 7, the growth rates were too small to be measured. Here, the measurements of crystal width involved a combination of the faces in the b and c directions, i.e., (021) and (010) faces, as well as of the face (101) and the values of growth rates for crystal width are clearly lower than those for length as observed in Table 2. The estimated values of growth rate for the crystal width are from 4 to 6 times lower than those for crystal length. The behaviour of growth rate in length and width in relation to supersaturation is observed in Table 2. The data shows that for the growth rates in crystal length the tendency is to increase with increasing supersaturation, whereas the crystal width presents an unclear correlation between the growth rates and the supersaturation in the solution. The measurements of crystal length by image analysis correspond to the extreme points of the needle length. From a careful observation of the crystal morphology of the b-form crystals of L-glutamic acid, schematized in Figure 7, it is not difficult to realise that the relative growth rates along the normal to the (101) growth plane are the responsible for the growth in length. Nevertheless, the growth of the faces (010) and (021) is involved in the measurement of width, and the variation in the growth between these faces together with some degree in crystal size dispersion can explain the unclear trend in growth rate for the crystal width with respect to supersaturation. A further step would be to link the growth rates to the kinetics of different habit directions. The capability of the imaging technique to measure crystal growth rates could in principle be extended to the estimation of kinetic parameters and fundamental mechanisms associated to the different facets of the morphology studied. Although it is tempting, here we have not fitted the data to a kinetic model particularly given the non-isothermal conditions of the experiment. It is important to be aware that a kinetic model used to describe the growth in crystal length would in fact be
Figure 7. Schematic of the morphology of beta form crystals of glutamic acid. A view from the top (a) shows that the crystal width involves the faces f021g and f010g. The angle between the growth rate in the [101] direction, R(101), and the length axis, RL, is shown in (b).
describing the growth in the direction of a (100) face, perpendicular to the length direction, but that is in fact not expressed in the morphology of the crystals observed in this study. Instead, the growth on the crystal length is driven by the (101) family of faces, with the existence of an angle between the growth rate in length and the growth rate of the (101) face [Figure 7(b)]. Due to the rotation of the crystals in the threedimensional space in the solution, it is at this point difficult to provide a precise absolute estimation of the growth for the face (101), R(101). Nevertheless, since the faces (010) and (021) are normal to the measurements of length, the (101) plane is the only face associated with the growth in length and although the absolute size of the normal distance to this plane would be different from the values of length measured, all the values would be affected by the same angle and hence it is sensible to assume that the growth rates in length are already reasonably close to the growth rates normal to the faces (101), i.e., R(101). In contrast, for the crystal width it is harder to derive at this point sensible estimations of the growth corresponding to the faces (010) and (021) due to their combined affect on the measurements of crystal width. However, the results suggest that provided that the rotation of the crystal in the solution be quantified, the measurements obtained by the imaging technique can lead to the estimation of the kinetics parameters for growth of crystal facets with high accuracy.
FINAL REMARKS The estimation of crystal growth rates from real-time inprocess image data was investigated for needle-like b-form glutamic acid using on-line imaging and image analysis. The results show that the image analysis could detect the effect of the seeds by reflecting the presence of the first grown long-needle crystals. The dispersion of growth in length and width due to the uncontrolled crystallizing conditions could also be followed by image analysis at different time points by estimations of mean length and width along with the associated standard deviations, nevertheless tracking the overall growth trends of the crystals in the two different morphological directions. It was shown that for crystals of the b polymorph of glutamic acid the growth rate of the crystal length is from four to six times higher than that of crystal width. This data, combined with the Miller indexing of the morphology of the b-form crystals of glutamic acid led to the conclusion that the plane (101) is the fastest growing crystal face as it is this face that drives the growth in crystal length, whereas the growth measurement in crystal width corresponds to the combined growth of the faces (120) and (010). Growth rate measurements and supersaturation values obtained suggest that during the analysed period of crystallization the supersaturation showed some correlation with the growth rate in length whilst a clear relation was not observed with the growth rate in width. With a more rigorous analysis of the crystal rotation, the image analysis measurement obtained could lead to more accurate estimations of the growth (size) normal to the individual crystal faces, as well as the estimation of kinetic parameters and mechanisms associated with each face within the reactor environment. The current feasibility study using needle-shaped crystals has proved that it is possible to measure the crystal growth rate of each direction in real-time using imaging and image
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REFERENCES Alamdari, A. and Tabkhi, F., 2004, Kinetics of hexamine crystallization in industrial scale, Chem Eng Process, 43(7): 803 –810. Borissova, A. Khan, S., Mahmud, T., Roberts, K.J., Andrews, J. and Dallin, P., 2005, On-line ATR-FTIR measurements of batch cooling crystallisation of L-glutamic acid, 7th World Congress of Chemical Engineering, Glasgow, P2-001. Calderon De Anda, J., Wang, X.Z., Lai, X. and Roberts, K.J., 2005a, Classifying organic crystals via in-process image analysis and the use of monitoring charts to follow polymorphic and morphological changes, J Process Control, 15(7): 785–797. Calderon De Anda, J., Wang, X.Z., Lai, X., Roberts, K.J., Jennings, K.H., Wilkinson, M.J., Watson, D. and Roberts, D., 2005b, Real-time product morphology monitoring in crystallization using imaging technique, AIChE J, 51(5): 1406–1414. Calderon De Anda, J., Wang, X.Z. and Roberts, K.J., 2005c, Multiscale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers, Chem Eng Sci, 60(4): 1053–1065. Clydesdale, G., Hammond, R.B., Ramachandran, V. and Roberts, K.J., 2005, Molecular modelling of the morphology of organic crystals in the presence of impurity species: Recent applications to naphthalene, phenanthrene, and caprolactam crystals, Mol Cryst Liq Cryst, 440: 235 –257. Dunuwila, D.D. and Berglund, K.A., 1997, ATR FTIR spectroscopy for in situ measurement of supersaturation, J Cryst Growth, 179 (1 –2): 185–193. FDA, US Department of Health and Human Services, 2004, Challenge and opportunity on the critical path to new medical products, www.fda.gov/oc/initiatives/criticalpath/. Fujiwara, M., Chow, P.S., Ma, D.L. and Braatz, R.D., 2002, Paracetamol crystallization using laser backscattering and ATR- FTIR spectroscopy: Metastability, agglomeration, and control, Cryst Growth Des, 2(5): 363– 370. Gerson, A.R., Roberts, K.J. and Sherwood, J.N., 1991, An instrument for the examination of nucleation from solution and its application to the study of precipitation from diesel fuels and solutions of normal-alkanes, Powder Technol, 65(1– 3): 243– 249. HEL, Hazard Evaluation Laboratories, http://www.helgroup.co.uk/. Kitamura, M., 1989, Polymorphism in the crystallization of L-glutamic acid, J Cryst Growth, 96(3): 541–546. Kitamura, M. and Ishizu, T., 2000, Growth kinetics and morphological change of polymorphs of L-glutamic acid, J Cryst Growth, 209(1): 138– 145. Lim, Y.I., Le Lann, J.-M., Meyer, X.M., Joulia, X., Lee, G. and Yoon, E.S., 2002, On the solution of population balance equations (PBE) with accurate front tracking methods in practical crystallization processes, Chem Eng Sci, 57(17): 3715–3732.
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Patience, D.B., 2002, Crystal engineering through particle size and shape, monitoring, modeling and control, PhD thesis, University of Wisconsin-Madison, USA. Puel, F., Fevotte, G. and Klein, J.P., 2003a, Simulation and analysis of industrial crystallization processes through multidimensional population balance equations. Part 1: a resolution algorithm based on the method of classes, Chem Eng Sci, 58(16): 3715– 3727. Puel, F., Fevotte, G. and Klein, J.P., 2003b, Simulation and analysis of industrial crystallization processes through multidimensional population balance equations. Part 2: a study of semi-batch crystallization, Chem Eng Sci, 58(16): 3729–3740. Quintana-Hernandez, P., Bolanos-Reynoso, E., Miranda-Castro, B. and Salcedo-Estrada, L., 2004, Mathematical modeling and kinetic parameter estimation in batch crystallization, AIChE J, 50(7): 1407– 1417. Wang, X.Z., Calderon De Anda, J., Roberts, K.J., Li, R.F., Thompson, G.B. and White, G., 2005, Advances in on-line monitoring and control of the morphological and polymorphic forms of organic crystals grown from solution (paper downloadable from http://www.kona.or. jp/html/about/index2005.html), KONA: Powders and Particle, (23): 69–85. Wilkinson, M.J., Jennings, K.H. and Hardy, M., 2000, Non-invasive video imaging for interrogating pharmaceutical crystallization processes, Microsc Microanal, 6(2): 996 –997. Zhang, Y.C. and Doherty M.F., 2004, Simultaneous prediction of crystal shape and size for solution crystallization, AIChE J, 50(9): 2101–2112.
ACKNOWLEDGEMENTS This work was carried out as part of Chemicals Behaving Badly project, a collaborative project funded by EPSRC together with support from an industrial consortium including ANSYS Europe Ltd, AstraZeneca, Bede Scientific Instruments Ltd, BNFL, Clairet Scientific Ltd, GlaxoSmithKline, HEL Ltd, Malvern Instruments, Pfizer and Syngenta. The academic partners are Leeds, Heriot-Watt and Newcastle Universities. We gratefully acknowledge all these sponsors and all members of this academic/industrial team and the industrial coordinator L.J. Ford. We would like to thank Dr Caiyun Ma for some useful discussions, and Dr Tariq Mahmud and Mr Shahid Khan for contributing the FTIR concentration model for L-glutamic acid developed in the work programme #1 of the CBB project. The authors would also like to thank Drs Gillian Thomson, Ruifa Li and Graeme White of Heriot-Watt University with whom we are working closely on the same work programme of the project. We would like to extend our thanks to GlaxoSmithKline for providing the imaging system, particularly to Kevin Jennings, Mike Wilkinson, Chris Price and David Lee at GlaxoSmithKline for providing support with the imaging system. The work has also benefited from the Vision project funded by Malvern Instruments Limited. The second author would like to acknowledge The Council of Science and Technology in Mexico (CONACYT) for providing the PhD scholarship for this research. The authors would like to thank Malvern Instruments Ltd for sponsoring the project of IntelliSense. The work presented has benefited from and is moving forward with the support of a new EPSRC grant (EP/C009541/1). The manuscript was received 26 October 2006 and accepted for publication after revision 24 February 2007.
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