LabMax ReactIR FBRM. Relative Supersaturation Control in Batch Cooling Crystallization. Application Note. 1 Abstract.

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1 Jörg Worlitschek Terry Redman Herbert Briggeler Olivier Ubrich Paul Barrett Brian O Sullivan Claude Didierjean Mettler Toledo GmbH, AutoChem Address Sonnenbergstrasse 74 PO Box, CH-8603 Schwerzenbach Phone Fax Internet Date August 2005 Document AN-SA-002/2005 V1.0 support.rxe@mt.com Application Note Relative Supersaturation Control in Batch Cooling Crystallization LabMax ReactIR FBRM 1 Abstract Running crystallizations in a robust manner is key for most intermediate and end product processes. In practice, however, crystallization process design often omits a sound design of the process due to short term time limits. In this paper, we present an automated approach to design and run relative supersaturation control for a cooling crystallization. A two step procedure is applied to run a supersaturation controlled crystallization, using an automated crystallizer with real-time analysis of both, solid and liquid phase. The aim of this paper is to underline, that understanding and designing a robust crystallization process can be achieved rather rapidly when using an automated approach based on real-time analytics combined with accurate temperature and process control. 2 Introduction The concept of designing and controlling batch crystallization processes based on the knowledge of the metastable zone is widely discussed. In many cases, a robust design of crystallization processes is obtained by keeping the concentration in the liquid phase at a constant level above solubility, i.e. keeping supersaturation low. Staying within the metastable zone typically leads to crystallizations dominated by crystal growth and reduced secondary nucleation within the ongoing crystallization [Barrett, 2002], [Dunuwila, 1997]. Reasons to keep the crystallization within a robust path can be various, such as defined crystal growth to a desired particle size, obtaining narrow particle size distributions, improving downstream processes, or reducing the risk of creating the wrong polymorph [Groen, 2003], [Matthews, 1997], [Worlitschek 2003]. In this paper, we define supersaturation control as the mechanism to control the trajectory of a batch crystallization in such a way, that the supersaturation stays at a constant level within the metastable zone throughout the whole crystallization. Though widely discussed [Groen, 2003], [Lewiner, 2001], supersaturation control is almost not applied in the daily practice of crystallization design [Liotta, 2004]. Short time frames for optimizing a crystallization step often do not allow for a detailed study of the process thus leading to problems in the later stages of scale-up and downstream processing. 1/13

2 Using the METTLER TOLEDO crystallizer, a two step procedure is applied to run a supersaturation 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 reflectance (ATR) probe. Based on that data, a crystallization with approximately constant supersaturation profile is designed. Within a second experiment, the lab reactor uses the information of the mid-infrared spectra to control the temperature in such a way, that the supersaturation 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 emphasize, 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 coolingheating-dilution cycles. Experiment 2 consists of the steps a) cooling to achieve a supersaturated 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. 2/13

3 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 Reflectance (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 concentrations 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. 3/13

4 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 suspension 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 information. Typically around 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 measurement (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 respect to vertical in a plane parallel to the reactor wall. 4/13

5 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 connected and interact with respect to display, process control, and data management (see figures 4 and 5). Fig. 4: Left: Setting up the crystallizer; the process diagram of the WinRC for ALR 7.5 software including 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. 5/13

6 Fig. 5: Implementing a control of the temperature of the reactor contents Tr by means of a control variable. 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. 6/13

7 4 Results The design of the crystallization process is applied for a crystallization of paracetamol in ethanol. 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 metastable 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 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 dissolution 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 particles 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 reduction when running the experiment in a fully automated manner. 7/13

8 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 determination, 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 measurement 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 triangles and cubes show the values of estimated solubility and MSZW limit, respectively. The values indicated by crosses are equilibrium literature data determined at constant temperature and a liquid phase analysis [Worlitschek, 2002]. 8/13

9 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 cm -1 and 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 straightforward approach can be used to successfully run robust crystallization processes. a b s o r b a n c 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 measurement. Right: The peak between cm -1 and 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. 9/13

10 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 dissolution 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 metastable zone is clear as well in the peak area/temperature plane. For the design of the crystallization control, the process trajectory shown as a dotted line was chosen. The process trajectory 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. 10/13

11 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 µm, respectively, the temperature of the reactor contents Tr, and the peak area of chosen infrared spectra. The temperature of the reactor 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 measurement highlights the desired crystallization behavior. While the coarse particles increase significantly 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. 11/13

12 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 experiment 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 resulting 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 experiment 2. The graph underlines solubility and supersaturation within the peak area / Tr plane. 12/13

13 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, Dunuwila, D. D., Berglund, K. A., ATR FTIR spectroscopy for in situ measurement of supersaturation, Journal of Crystal Growth 179, (1997), Fevotte, G., New perspectives for the online monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy, International Journal of Pharmaceutics, 241, (2002), Groen, H., Mougin, P., Alistair T., White, G., and Wilkinson, D., Dynamic In-Process Examination of Particle Size and Crystallographic Form under Defined Conditions of Reactant Supersaturation as Associated with the Batch Crystallization of Monosodium Glutamate from Aqueous Solution, Ind. Eng. Chem. Res., 42, (2003), 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, Chemical Engineering Science, 56, (2001), 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), Matthews, H., Model identification and control of batch crystallization for an industrial-chemical system (slurry filtration), Ph.D. Thesis, University of Wisconsin-Madison, 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, Worlitschek, J., Mazzotti, M., Monitoring, Modeling, and Optimization of Batch Cooling Crystallization, Cryst. Growth and Des., (2003), 4, /13