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Jörg Worlitschek Terry Redman Herbert Briggeler Olivier Ubrich Paul Barrett Brian O’Sullivan Claude Didierjean

Application Note

Relative Supersaturation Control Batch Cooling Crystallization 1 Abstract

Running crystallizations in a robust manner is key processes. In practice, however, crystallization procesthe process due to short term time limits. In this papedesign and run relative supersaturation control for a cdure is applied to run a supersaturation controlled crylizer with real-time analysis of both, solid and liquid pline, that understanding and designing a robust crystarapidly when using an automated approach based on rate temperature and process control. 2 Introduction

The concept of designing and controlling batch crystaedge of the metastable zone is widely discussed. In mzation processes is obtained by keeping the concentlevel above solubility, i.e. keeping supersaturation lotypically leads to crystallizations dominated by crystaltion within the ongoing crystallization [Barrett, 2002], [DReasons to keep the crystallization within a robust patal growth to a desired particle size, obtaining narrodownstream processes, or reducing the risk of creati[Matthews, 1997], [Worlitschek 2003]. In this paper, wmechanism to control the trajectory of a batch crystalliration stays at a constant level within the metastable zThough widely discussed [Groen, 2003], [Lewiner, 200applied in the daily practice of crystallization design [Lmizing a crystallization step often do not allow for a deto problems in the later stages of scale-up and downst

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Date August 2005

Document AN-SA-002/2005 V1.0

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Using the METTLER TOLEDO crystallizer, a two step procedure is applied to run a supersatu-ration controlled crystallization. In a first experiment, the Automated Lab Reactor LabMax runs an automated metastable zone width experiment based on the data of the real-time particle system analysis Lasentec FBRM. In parallel, the mid-infrared spectrometer ReactIR provides online information on the concentration in the liquid phase using an attenuated total reflec-tance (ATR) probe. Based on that data, a crystallization with approximately constant super-saturation profile is designed. Within a second experiment, the lab reactor uses the informa-tion of the mid-infrared spectra to control the temperature in such a way, that the supersatura-tion stays constant throughout the run. The Lasentec FBRM probe monitors the solid phase to guarantee the desired evolution of the particle population. The aim of this paper is to empha-size, that understanding and designing a robust crystallization process can be achieved rather rapidly when using an automated approach based on feedback control, accurate process and temperature control, and real-time analytics.

Fig. 1: Basic sequence of experiments 1 (left) and 2 (right): In experiment 1, solubility and metastable

zone width (MSZW) are determined by heating up the system slowly, followed by cooling-heating-dilution cycles. Experiment 2 consists of the steps a) cooling to achieve a supersatu-rated solution, b) seeding, c) controlled cooling based on IR measurement, and d) equilibration at constant temperature. The Automated Lab Reactor LabMax runs the experiments based on the real-time data of Lasentec FBRM and ReactIR.

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3 Apparatus and Materials

Fig. 2: Crystallization setup including an automated LabMax reactor with a 4-blade propeller stirrer

(pumping downward), a weight-controlled dosage system, and temperature measurement, the Lasentec FBRM D 600 L probe to provide real-time data on particle count and dimension

(left hand side), the mid-infrared probe including the ReactIR 4000 spectrometer and the Attenuated Total Re-

flectance (ATR) probe conduit (front). 3.1 Real-time Analysis of the Liquid Phase: Mid-infrared Spectrometer

ReactIR The mid-infrared spectrometer ReactIR 4000 is used to monitor the liquid phase throughout the crystallization. The spectrometer is connected to an attenuated total reflectance (ATR) 16-mm DiComp probe with a diamond crystal at the probe tip. The combination allows for the real-time measurement of mid-infrared spectra showing high sensitivity (down to concentra-tions of 0.1 w%) and high linearity of the absorbance throughout a wide concentration range. The detector is cooled with liquid nitrogen and dry air at ambient temperature is used to purge the system. In this study, the spectra are collected with a measurement interval of 2 minutes, where 128 scans build up one spectra. The spectra are collected accounting for a background measurement of air. The ATR probe tip is located approximately 1 cm above the stirrer plane in a highly turbulent region to avoid a coating of the probe.

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3.2 Real-time Analysis of the Solid Phase: Lasentec FBRM The measurement principle of the focused beam reflectance measurement is illustrated in figure 3. A focused laser beam rotates at high speed and propagates into the particle suspen-sion to be monitored. When the focused laser beam crosses a particle in front of the probe, a signal is backscattered into the probe. The length of the scanned chord is determined in the electronics of the system and transferred into a chord length distribution histogram. Thus, the Chord Length Distribution (CLD) provides online particle count and particle dimension infor-mation. Typically around 10 000 particles are measured and counted each second providing a new CLD to track the dynamics of the ongoing particulate process.

Fig. 3: Focused beam reflectance measurement (FBRM): FBRM probe tip (left) and chord measure-

ment (right): the laser beam direction is perpendicular to the paper. In this study, the FBRM D600 L system was set to collect CLDs with a laser speed of 2 m/s, applying the standard ‘F-electronic’ mode and using a measurement duration of 15 seconds. The system was purged with dry air, thus allowing one to run in a temperature range -20 to 150 °C. The probe tip was located at stirrer blade level with a distance of 5 mm to the reactor wall. The probe is directed toward the liquid stream, i.e. with an angle of approx. 5° with re-spect to vertical in a plane parallel to the reactor wall.

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3.3 The Automated Lab Reactor LabMax The Automated Lab Reactor (ALR) provides precise and repeatable control of critical reaction variables (temperature, stirrer speed, etc.) and automation of routine experimental procedures (dosing, pH control, etc.), allowing the rapid optimization of critical reaction variables (catalyst, solvent, pressure, dosing rate, etc.). Automated reactor and real-time analytics are highly con-nected and interact with respect to display, process control, and data management (see fig-ures 4 and 5).

Fig. 4: Left: Setting up the crystallizer; the process diagram of the WinRC for ALR 7.5 software includ-

ing the LabMax instrument (top), and the reactor (middle), a weight-based dosing device (top left), the ReactIR spectrometer (bottom left) and the Lasentec FBRM (bottom right).

Right: Example of the LabMax process sequence; the highlighted step includes the control of the temperature of the reactor contents Tr by a process variable Z that introduces a ReactIR spectrum information. Details are given in figure 5.

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Fig. 5: Implementing a control of the temperature of the reactor contents Tr by means of a control vari-

able. Here, the external control variable ‘Z01’ is defined as a function of the ReactIR peak trend information as given in the dialog box on the bottom left hand side.

3.4 Materials Paracetamol (4-Acetamidophenol), 98% and Ethanol pro analysis were obtained from Fluka AG, Buchs, Switzerland.

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4 Results

The design of the crystallization process is applied for a crystallization of paracetamol in etha-nol. In both experiments, the initial composition in the reactor is 100 g of paracetamol and 300 g of ethanol. Both experiments are run at a stirrer speed of 500 rpm. 4.1 Experiment 1: Determination of Solubility and the Metastable Zone Width Figure 1 (left) shows the sequence of the first experiment to determine solubility and metasta-ble zone width: The experiment is started at low temperature (i.e. -10 °C), with solid phase in excess. Slow heating of 0.5 K/min allows the system to stay in equilibrium while heating to complete dissolution.

Fig. 6: Process graph of experiment 1 to determine solubility and metastable zone width: the graph

shows the volume Vr and temperature Tr of the reactor contents as well as the number of chords counted by FBRM in the fine chord size range 1−5 µm and the chords counted in a coarse chord size range 30−86 µm. When the FBRM detects complete dissolution (here set to: coarse count/sec below 50), the temperature stays constant for 15 min and is decreased at the same rate until nucleation is detected (FBRM fine count above 50). The increasing volume of the reactor contents highlights the following automated dilution step. The sequence is repeated at 4 additional dilution levels.

At 0 °C the temperature is held constant to ensure that the real-time analytics responses stay constant immediately as well, i.e. to ensure that the system is close to equilibrium during the heat up. When Lasentec FBRM detects complete dissolution, the system cools down to the point of first detection of nucleation, followed by a heating step to the point where complete dis-solution takes place. The detection of dissolution is found to be most robust using the detection of the final disappearance of coarse particles, while nucleation is detected by the first fine parti-cles appearing in the system. Solvent is added automatically (here, 50 mL ethanol at each step) and a second series of cooling and heating is performed. The sequence is repeated for several heating-cooling-dilution steps. Thus, a typical dynamic experiment to determine the metastable zone width is performed in a fully automated way. In a former study, the comparison of such a fully automated experiment with a classical MSZW dynamic experiment lead to 75% time re-duction when running the experiment in a fully automated manner.

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Figure 7 shows the resulting estimated solubility and MSZW data. The experimental data are fit using the Schroeder van Laar solid-liquid equilibrium equation combined with an NRTL model of the activity coefficient [Prausnitz, 2000]. The determined solubility is slightly lower than the given literature data. This can be explained by the dynamic character of the determi-nation, i.e., that complete equilibration is slightly delayed during the heating. A metastable zone width of approximately 15 °C was detected for the given process conditions of a cooling rate of 0.5 °C/min and a stirrer speed of 500 rpm. It is worth noting, that such dynamic meas-urement provides only a rough estimation of the thermodynamic equilibrium. It constitutes, however, valuable data for process design purposes.

Fig. 7: Resulting metastable zone based on FBRM fine and coarse count detection limits: The trian-

gles and cubes show the values of estimated solubility and MSZW limit, respectively. The val-ues indicated by crosses are equilibrium literature data determined at constant temperature and a liquid phase analysis [Worlitschek, 2002].

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4.2 Experiment 2: Crystallization Based On Relative Supersaturation Control Within a second step, a crystallization is designed and run, where the supersaturation is kept at an approximately constant level within the metastable zone width as shown on the right hand side of figure 1. Experiment 1 lead on the one hand to an estimation of solubility and metastable zone width as given in figures 6 and 7. On the other hand, experiment 1 resulted in a set of infrared spectra collected throughout the run as given in figure 8. The data allows one to choose a region of the spectra that is sensitive to the concentration changes. Here, the peak area between 1526.05 cm-1 and 1497.11 cm-1 above a baseline having the same limits was selected (see figure 8, right). The peak area over a baseline constitutes a rather robust means to describe concentration changes in the case of significant peaks of the compound of interest as in this investigated system. It is worth noting, that the infrared data includes as well the spectra at defined concentration levels during the cooling steps, i.e. when no solid phase is present. This data can be used for a calibration of the concentration with respect to the IR spectra data. Depending on the complexity of the spectra, either rather simple functions or chemometric models such as PLS can be used to perform such a calibration [Fevotte, 2002], [Togkalidou, 2002]. In this paper, we omit such a calibration to highlight that a rather straight-forward approach can be used to successfully run robust crystallization processes.

abs or banc e

process time wavenumber /cm-1 wavenumber /cm-1

Fig. 8: Analysis of mid-infrared spectra data from experiment 1; Left: spectra in the wavenumber region between 1200 and 1800 cm-1 throughout the measure-

ment. Right: The peak between 1526.05 cm-1 and 1497.11 cm-1 over the baseline having the same

limits was chosen for further analysis of the paracetamol concentration in the liquid phase and its control within experiment 2.

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Figure 9 illustrates the design of the relative supersaturation control for experiment 2 based on the plotted data of experiment 1: The chosen peak area increases during the heat up from -10 to 50 °C due to increased concentration in the liquid phase. At the first cooling step, the peak area slightly increases due to temperature effects on the spectra. From the nucleation point on, the concentration and thus the peak area decreases due to nucleation, the following dis-solution step, and the crystallization throughout the waiting step. Heating the system again leads to an increase of the peak area. The resulting controlled crystallization run is given in figure 10.

Fig. 9: Designing the relative supersaturation control: The peak area of the chosen infrared spectra

peak measured throughout experiment 1 is plotted versus Tr: The plot illustrates, that the me-tastable zone is clear as well in the peak area/temperature plane. For the design of the crystal-lization control, the process trajectory shown as a dotted line was chosen. The process trajec-tory describes Tr as a function of the peak area, i.e. the inverse function of the shown dotted red line. The line refers to a supersaturation of 8 °C, i.e. a supersaturation in the middle of the MSZW as shown in figure 7.

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Fig. 10: Process graph of expermiment 2, i.e. running a crystallization of paracetamol in ethanol by

relative supersaturation control: The graph includes the number of FBRM chords counted per second in the chord size ranges 1 – 5 µm and 30 – 86 µm, respectively, the temperature of the reactor contents Tr, and the peak area of chosen infrared spectra. The temperature of the re-actor contents is decreased from 55 °C to 42 °C, i.e. to the supersaturated solution. Seeds are added and the controlled cooling based on a function (reactor contents temperature being a function of infrared spectra data), as described in figure 9, starts. When reaching 0 °C, the temperature is kept constant to allow for an equilibration of the system. The FBRM measure-ment highlights the desired crystallization behavior. While the coarse particles increase signifi-cantly throughout the crystallization, the fine count remains rather low, thus indicating a robust crystallization. In this particular experiment, the system is heated at the very end to proof the consistency of the IR measurement.

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5 Conclusions

A crystallization of paracetamol in ethanol was designed to follow a defined path within the MSZW. A first automated experiment was used to obtain solubility and MSZW data. The LabMax did allow for full automation of this experiment based on FBRM measurements. A former study had shown that such automation reduced the experimental time to run a dynamic metastable zone width experiment by 75%. The resulting infrared spectra of this first experi-ment were used to design an online control based on the measurement of the liquid phase via ReactIR. The advanced control features of the LabMax were used to directly integrate the temperature control based on the ReactIR online measurement. Figure 9 summarizes the process profile of the second experiment following a defined supersaturation profile. The re-sulting temperature vs. time profile obtained during the relative supersaturation control as given in figure 10 shows at the beginning the typical behavior of a slower start followed by a higher temperature gradient. Towards the end of the process, however, the temperature profile flattens out, i.e. is different to a normally assumed temperature profile characteristic of optimal cooling. Agglomeration might be one of the reasons why the temperature is controlled in such a way. More investigation would be necessary to describe that behavior. The profile highlights the necessity of online monitoring and online control, where the temperature profile is capable to react to the needs of the process. The results highlight that a sound process design is achievable in a realistic time frame when automation and real-time analytics are combined. The combination of LabMax, ReactIR, and FBRM perfectly allows for such automation.

Fig. 11: Chosen infrared peak area versus temperature of the reactor contents Tr throughout experi-

ment 2. The graph underlines solubility and supersaturation within the peak area / Tr plane.

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6 Literature

Barrett, P., Glennon, B., O’ Sullivan, B., Solubility Curve and Metastable Zone Width Determination Using Lasentec FBRM and PVM, Lasentec Users Forum, Charleston, USA, February 24th –27th, 2002. Dunuwila, D. D., Berglund, K. A., ATR FTIR spectroscopy for in situ measurement of supersaturation, Journal of Crystal Growth 179, (1997), 185-193. Fevotte, G., New perspectives for the online monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy, International Journal of Pharmaceutics, 241, (2002), 263–278. Groen, H., Mougin, P., Alistair T., White, G., and Wilkinson, D., Dynamic In-Process Examination of Par-ticle Size and Crystallographic Form under Defined Conditions of Reactant Supersaturation as Associ-ated with the Batch Crystallization of Monosodium Glutamate from Aqueous Solution, Ind. Eng. Chem. Res., 42, (2003), 4888-4898. Lewiner, F., Klein, J.P., Puel, F., Fevotte, G., Online ATR FTIR measurement of supersaturation during solution crystallization processes. Calibration and applications on three solute/solvent systems, Chemi-cal Engineering Science, 56, (2001), 2069-2084. Liotta, V., and Sabesan, V., 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, (2004), 488-494. Matthews, H., Model identification and control of batch crystallization for an industrial-chemical system (slurry filtration), Ph.D. Thesis, University of Wisconsin-Madison, 1997. Prausnitz, J.M., Lichtenthaler, R.N., Azevedo, E.G., Molecular thermodynamcs of fluid-phase equilibria, Prentice Hall PTR, Upper Saddle River, NJ, 1999 Togkalidou, T., Tung, H.H., Sun, Y., Andrews, A., Braatz, R.D., Solution Concentration Prediction for Pharmaceutical Crystallization Processes Using Robust Chemometrics and ATR FTIR Spectroscopy, Organic Process Research & Development, (2002), 6, 317-322. Worlitschek, J., Mazzotti, M., Monitoring, Modeling, and Optimization of Batch Cooling Crystallization, Cryst. Growth and Des., (2003), 4, 279.