Comparative Investigation of Supersaturation and Automated Direct

Article pubs.acs.org/crystal

Comparative Investigation of Supersaturation and Automated Direct Nucleation Control of Crystal Size Distributions using ATR-UV/vis Spectroscopy and FBRM Ali N. Saleemi, Chris D. Rielly, and Zoltan K. Nagy* Loughborough University, Loughborough, LE11 3TU, U.K. ABSTRACT: The paper presents a thorough evaluation of different control strategies used in cooling crystallization, namely, unseeded linear cooling, seeded linear cooling, supersaturation control, and automated direct nucleation control. The sensitivity and robustness of supersaturation and automated direct nucleation control approaches are evaluated by varying different process parameters, such as seed loading and heating and cooling rates for the crystallization of paracetamol in isopropyl alcohol. The supersaturation control approach uses concentration information provided by ATR-UV/vis spectroscopy, while the direct nucleation control approach is based on focused beam reflectance measurements. Various statistics were used to assess the performance of each approach and it has been shown that the automated direct nucleation control approach, which requires no prior information about the system, outperformed the other control strategies. For the paracetamol in isopropyl alcohol system, significant variations were found in the metastable zone width in the presence of crystals, which were responsible for the poor performance of the control strategies other than the automated direct nucleation control approach.

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INTRODUCTION Crystallization is a key purification and separation technique used in the pharmaceutical and fine chemicals industries. Crystal properties, such as crystal size distribution (CSD), habit, purity, and polymorphic form, greatly affect the downstream processing and bioavailability of the product.1−3 Obtaining a narrow and reproducible CSD is therefore important, as is underlined by the U.S Food and Drug Administration (FDA) that emphasizes the need for reduced batch to batch variability in the product. To achieve this, especially on an industrial scale, open-loop temperature control strategies are implemented, in which the system follows a preset profile. Typically a linear cooling profile is used because of its simplicity; however, it often results in a poor CSD.4 Alternatively, the so-called programmed cooling profiles can be used in which the temperature is decreased slowly at the beginning and at a faster rate toward the end of the batch to promote growth.5,6 Similar concepts apply to antisolvent crystallization systems, too.7 These simple open loop control approaches ignore any disturbances, as they are unable to respond to changes that occur in the system; resulting in a broad bimodal CSD and batch to batch variability. Several methodologies have been proposed and used to improve CSD. A frequently used method is fines destruction by using external heating loops.8,9 Some of the problems associated with this approach are clogging of pipes and temperature variations.10 Another method reported to improve CSD by eliminating fines is using a wetter/double-deck jacket crystallizer design.10 However, the complex design of this method makes it very difficult to be applied on the industrial scale. The FDA’s initiative to encourage the use of process analytical technology (PAT) and the availability of various robust in situ © 2012 American Chemical Society

sensors has greatly improved the monitoring and control of pharmaceutical crystallization processes.11 ATR-FTIR has been widely used for in situ concentration monitoring and control of both cooling and antisolvent crystallizations.12−22 In some of these studies closed-loop concentration feedback control has also been implemented. On the basis of the solubility and metastable zone width (MSZW) information, a suitable supersaturation set point is selected, so as to promote growth and at the same time to avoid any undesired nucleation events. Generally for this strategy a constant supersaturation is maintained during the crystallization process, hence the technique is often termed as supersaturation control (SSC) approach. Batch times can also be optimized to achieve the desired CSD, while maintaining a constant or variable supersaturation trajectory. Another advantage of the supersaturation control (in particular the adaptive supersaturation control approach) is its insensitivity to most of the process disturbances and variations in process parameters.23 Control strategies using ATR-FTIR to improve CSD by dissolving fines in situ have also been repoted in the literature.24,25 Unlike the use of ATR-FTIR, there is very little literature available on the application of ATR-UV/vis spectroscopy in pharmaceutical crystallization. Some of the earliest studies were carried out by Anderson et al.26 and Thompson et al.27 and recently ATR-UV/vis spectroscopy has been used as a monitoring and concentration measuring tool in pharmaceutical crystallization.28,29 To the authors’ best knowledge, this is the first time that the supersaturation control (SSC) approach Received: September 26, 2011 Revised: January 28, 2012 Published: February 7, 2012 1792

dx.doi.org/10.1021/cg201269c | Cryst. Growth Des. 2012, 12, 1792−1807

Crystal Growth & Design

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robustness of the various control strategies to obtain a desired CSD.

using ATR-UV/vis spectroscopy has been applied in a pharmaceutical crystallization process. There are several sensors available for in situ monitoring of solid phase during crystallization process. Ultrasound probe has been used for in situ solid and liquid phase monitoring mostly for inorganic systems.30−32 Another method comprising of bulk video imaging and image analysis has also been used for nucleation detection and process monitoring.33−35 Focused beam reflectance measurement (FBRM) is an extensively used technique for monitoring of crystallization processes, and provides qualitative and quantitative information about nucleation and crystal growth.36−42 The chord length distribution (CLD) measured by FBRM can be related to different phenomena yielding CSD change, for example, nucleation, growth, agglomeration, and attrition. Compared to ATR-FTIR, very few studies are available in which a control strategy based on FBRM alone is employed. Recently, Abu Bakar et al.43 proposed a direct nucleation control (DNC) approach in which FBRM was used in a closed loop feedback control strategy to improve the CSD, by achieving larger, more uniform and less agglomerated crystals. The DNC was applied for cooling, antisolvent and combined cooling/antisolvent crystallization systems. Hermanto et al.44 used a similar approach in antisolvent crystallization, however in their approach FBRM was used simply to trigger the switching between predetermined but still open loop control strategies. The current work describes a fully automated DNC approach in which the heating/cooling rates are adapted automatically to achieve increased robustness of the approach. For the first time the DNC approach is evaluated comprehensively and compared with other already established model-free control approaches such as linear and programmed cooling as well as supersaturation control, using a series of experiments on a laboratory scale crystallizer. In addition to these closed loop approaches, first principles model-based design and control approaches can also be developed, but these require exact information about kinetics and growth of the system, which can only be obtained through extensive experimentation.45,46 The kinetic parameters obtained may differ from batch to batch, for example, because of impurities and variable seed characteristics.47 As a result, the required temperature profiles generated through simulations cannot account for process variations that may exist in the system, thus leading to poor final product properties and batch to batch variability. The main benefit of closed loop approaches is that they require very little experimentation and information about the system. Virtually no a priori information is required for the automated direct nucleation control (ADNC) approach, whereas the SSC requires building of a calibration model for the solute concentration and the determination of a suitable supersaturation set-point. Ease of implementation of these approaches makes them more favorable and attractive for robust implementation on both laboratory and full scale operations. A comparison study has been carried out using various control strategies, including unseeded, linear seeded, SSC and ADNC approaches. The aim is to identify the advantages and disadvantages of each control strategy in terms of its ability to produce large, uniform crystals with minimized agglomeration and fines, while remaining insensitive to variations in process conditions. Paracetamol (PCM) in isopropyl alcohol (IPA) is used as a model system. The high solubility of PCM in IPA, slow growth, and significant variations in the MSZW for primary and secondary nucleation events make it a suitable system to test the

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METHODOLOGY Supersaturation Control Approach for Cooling Batch Crystallization. In the supersaturation control (SSC) approach, the system follows an operating curve in the phase diagram which generally corresponds to a constant supersaturation.48,49 The feedback control requires concentration measurement, which in this work is obtained using ATR-UV/vis spectroscopy used in conjunction with a suitable calibration model. The supersaturation is computed using the concentration measurement and the solubility information for the compound in the particular system. The solubility curve can be represented as any (nonlinear) function of the temperature Csol = Csol(T). This function can be expressed by the van’t Hoff solubility equation; however often simple empirical polynomial expressions are used. The solubility curve in this study was represented by a secondorder polynomial fitted to experimental data Csol(T ) = a0 + a1T + a2T 2

(1)

In the current study, the absolute supersaturation (S) has been used, which is the difference between the solution concentration (C) and the equilibrium concentration (solubility) at a particular temperature, given by S(T ) = C − Csol(T )

(2)

Introducing eq 1 into 2 and setting the supersaturation equal to the desired set point supersaturation (Sset), allows the calculation of the temperature (Tset) required to achieve the target supersaturation by simply solving the following generic nonlinear equation Tset = arg(C − Csol(T ) − Sset = 0) T

(3)

When the polynomial eq 1 is used for the solubility term, this reduces to the solution of a simple polynomial equation. The resulting temperature Tset is tested to be within the physical limits for the process and is used as the set point for the lower level temperature control system. The concentration is computed from the derivative of the absorbance at a characteristic wavelength for the compound corrected for the effect of the temperature, using the calibration model of the following form: C = b0 + b1d + b2T + b3dT

(4)

where C is the concentration in (g/g solvent), b0, b1, b2 and b3 are the regression coefficients, d is the derivative absorbance at the selected wavelength, and T is the process temperature. It should be noted that the term d can be absorbance or the derivative of the absorbance (first or second) depending on the model selected. The derivative absorbance was used in this work as it removes any baseline offsets in the spectrum. A simple nonlinear term, expressed as the product of the derivative absorbance and temperature, is also included to improve the accuracy of the calibration model.50 In this case, the temperature profile is a function of the measured concentration and the supersaturation set point. The SSC approach is implemented using feedback control and requires a chemometrics-based calibration model, but it does not need extensive experimentation to obtain kinetic model parameters, such as growth and nucleation rate constants. 1793



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Figure 1. Block diagram for (a) supersaturation control (SSC) approach for batch cooling crystallization processes (b) Schematic phase diagram for the SSC approach.

provided by the FBRM is the square weighted chord length distribution, which is defined by the weighted counts in each channel

A block diagram for the SSC approach is shown in Figure 1a, while a typical operating curve in the phase diagram for the SSC is shown in Figure 1b. If a slow growing system is used or the solid concentration is low, a supersaturation set point closer to the solubility curve should be selected and maintained throughout the process as shown in Figure 1b. The seed addition point is also critical. If the system is seeded at a higher supersaturation there is a possibility of nucleation in the system therefore it is preferred to seed the system at lower supersaturations. Solubility data, in the form of eq 1, for the specific system to be used and the calibration coefficients are inputs to the approach. Once the seeds have been added, or generated in situ, the SSC approach can be started. The set point is selected or can be adjusted depending on the number of particles, or growth characteristics of the system. Concentration is continuously measured and fed to the supersaturation controller, which then sends a signal for temperature manipulation based on the process conditions and obtained from eq 3. In this way a constant supersaturation may be maintained throughout the batch time. In practice, maximum and minimum temperature limits should also be specified to define the process boundaries, reflecting the operating range of the cooling/heating system. Automated Direct Nucleation Control Approach for Cooling Batch Crystallization. In this paper, a fully automated ADNC approach for controlling CSD is presented. This is a model-free feedback control approach, which makes use of FBRM to measure chord length distributions, which can be related to the number and size of the particles present in the system. The chord lengths given by FBRM are grouped into 90 size bins or channels from 0.8−1000 μm. From these raw data, weighted and unweighted statistics may be calculated, for example, total counts/s is the number of chord length measurements for the whole size range. An increase in the total counts/s indicates nucleation or breakage/attrition in the system. From the FBRM measurements the square weighted mean chord length (SWMCL) can also be computed, which is sensitive toward the larger particles and can be used as a growth/agglomeration and average particle size indicator in the system.43 Mathematically it can be defined as SWMCL =

SWCLD =

∑ik= 1 Mi 2

ni (6)

It has been shown that for some systems the information provided by the SWMCL resembles with a good approximation the CSD obtained from laser diffraction instruments and optical microscopy.4,51,52 These statistics were therefore used to monitor the changes in the CSD during the experiments. The ADNC approach is based on the fact that a major source of variability in the CSD comes from primary and secondary nucleation events that occur in the system. Therefore, in situ fines removal through heat addition can help in improving the CSD by reducing the number of fines. This also eliminates the use of external heating loops in some crystallizer designs that are used for removing fines. A schematic block diagram for the ADNC approach is shown in Figure 2a. The total counts/s measured by the FBRM is continuously sent to the nucleation controller, where the measurement is compared against the target counts/s. The nucleation controller sends a signal to the temperature controller which then varies the vessel jacket temperature (Tjacket) accordingly. The operating profile is therefore based on the real time detection of nucleation and dissolution events in the process and does not follow a predetermined temperature profile. A typical ADNC operating profile is shown in Figure 2b. It is a well-known fact that large variations exist in the MSZW because of the presence of impurities and particles. A control strategy based on keeping the operating curve within the metastable zone for primary nucleation may not detect changes in the MSZW, and hence it may result in multiple nucleation events during the process, yielding a broad and/or multimodal product CSD. The ADNC is based on the measurements related to variations in the number of particles in the system and hence is able to respond to any changes in the MSZW, for example because of the presence of impurities, attrition/breakage or particles from detachment of crust on the wall.53 The continuous heating and cooling cycles remove fines and promote growth; in addition these cycles also help in preventing agglomeration and solvent inclusion in the crystals. Thus, in principle, ADNC can be used to obtain a narrow CSD of high purity products.

∑ik= 1 niMi 3 ∑ik= 1 niMi 2

Mi 2

(5)

where k is the number of channels in FBRM, ni is the number of counts measured in an individual channel, and Mi is the midpoint length of a channel. Another important statistic 1794



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Figure 2. Block diagram for (a) automated direct nucleation control (ADNC) approach for batch cooling crystallization processes and (b) schematic phase diagram for ADNC approach.

for the cooling phase), and the counts/s measurement exceeds the upper bound of the target value; at that point the ADNC switches to heating rather than cooling and operates under proportional control. If the counts/s exceeds the upper limit, the heating rate is set to its maximum value. Fine particles are removed by dissolution and the counts/s returns toward its target value. When the counts/s falls below the upper bound during the dissolution phase the heating rate is controlled again by the proportional controller, but with gain chosen separately for the dissolution phase. Thus the controller requires specification of the set point or target number of counts/s with its minimum and maximum bounds, the upper limits for the heating and cooling rates, the minimum and maximum temperatures in the crystallizer and the adjustable kh and kc values (gains of the proportional controller for the slow heating and cooling phases corresponding to the boundaries around the target counts/s). From a clear solution, the ADNC process begins with cooling the system at a specified rate until the nucleation takes place and target counts are reached. During the process slow cooling (based on the kc value) starts once the counts have crossed the lower limit and continuous until the counts/s reaches the upper limit to avoid switching between heating and cooling cycles around the target counts/s due to measurement noise. Once the upper limit is crossed the maximum heating mode will be switched on. This will lead to the dissolution of fine particles and the decrease of the number of counts/s measurements. Slow heating (based on kh) will turn on if the number of counts reaches the range between the target counts and upper limit, and the system is in heating mode. Hence the system behaves differently in the zone when the counts/s are between the target value and the upper limit depending on whether the range was achieved during a cooling or a heating phase. The heating and cooling cycles will continue until the total counts have stabilized in the range around the target value and the crystallizer temperature at its minimum limit. The FBRM counts/s measurement can be influenced by the change in the shape of the particles during the crystallization process, however these changes are generally very slow and negligible relative to the changes in the counts/s owing to nucleation or dissolution events. Because of these slow changes in the counts/s which may occur due to the changes in the shape of the particles the approach controls the number of counts/s within certain limits instead of trying to follow exactly a predetermined counts/s set point as other typical control

Figure 3 illustrates the feedback control approach for the ADNC used to maintain the total counts/s at its target value.

Figure 3. Schematic representation of the working principle of the automated direct nucleation control (ADNC) approach.

In addition to the target set point, the method uses upper and lower limits and proportional gains (i.e., kh and kc) for the heating and cooling phases. When the counts/s fall between the lower and upper limits, proportional control is applied to the jacket temperature. When the counts/s measurement falls outside these lower and upper limits then the heating and cooling is achieved following linear heating and cooling curves with predetermined slopes. The switching between the heating and cooling phases also takes into account whether a particular number of counts/s range is achieved during a heating or a cooling stage. This allows the approach to accommodate to the different dynamics of the nucleation and dissolution and to avoid excessive switching between cooling and heating phases caused by simple variations due to noise in the FBRM counts/s measurements. Minimum and maximum temperature limits are also specified and the jacket temperature is set to these values, whenever these limits are achieved. This intelligent strategy, which applies a simple feedback control approach but tailored to the particularities of the crystallization mechanisms, allows a better control of the desired target counts/s, than simple switching between heating and cooling cycles with predetermined and constant rates. Figure 3 exemplifies a typical operation of the strategy. Initially the counts/s are below the lower limit and the temperature set point follows the predetermined fastest linear cooling profile. This should force nucleation, causing an increase in the counts/s. When the counts/s crosses the lower limit, the cooling rate is reduced. Typically, nucleation causes an overshoot (the amount of which can be controlled by adjusting the gain of the controller 1795



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coated 4-pitched blade turbine and thermocouple. The temperature was controlled by a thermo fluid circulator bath (Huber Variostat CC-415 VPC). An FBRM probe (model D600L, Lasentec) was used to measure chord length distributions. FBRM data collection and monitoring was carried out by the FBRM control interface software (version 6.7). The UV/vis spectra of the solution were measured using a Hellma 661.822 ATR probe connected to a Carl Zeiss MCS621 UV/ vis spectrometer. Software written in LabVIEW (National Instruments) using libraries provided by Carl Zeiss was used for spectra collection. The FBRM, UV/vis and temperature data were recorded every 10 s. The data collected by computers connected to FBRM and UV/vis were sent to a third computer, running the Crystallization Process Informatics System (CryPRINS) software (in-house developed software) written in LabVIEW. This software is capable of receiving and sending data through an RS232 interface, by file sharing, or using an OPC (OLE, Object Linking and Embedding, for Process Control) server. The software enables the simultaneous monitoring of the data from various PAT tools and the implementation of the required temperature profiles in an automated way. The experiments were carried out using paracetamol (4acetaminophenol, 98% purity, purchased from Aldrich), analytical grade 2-propanol (isopropanol, IPA). A schematic representation of the equipment is in Figure 4.

approaches, and takes into account the rate of change in the number of counts/s. The FBRM measurements are transmitted to the Crystallization Process Informatics System (CryPRINS) software, in which the various crystallization control approaches have been implemented. In the current work, ADNC using total counts/s as the target was implemented, but the software has the option of implementing ADNC using any other statistics. Various experimental investigations revealed that the use of the total number of counts/s is a simple statistics that offers robust and reproducible control of the CSD. Using other statistics (e.g., number of fines particles below a certain size) can lead to similar control performance in terms of maintaining the statistics around the desired value, but may result in significantly different overall product CSDs, due to variations in the number of larger particles. The total counts/s is proportional with the number of particles in the system. Maintaining the number of particles in a system at lower values will result in larger particles and vice versa. The correlation between the number of counts/s used as the target for the ADNC approach and the real CSD can be determined for a particular system using an experiment with stepwise increase of the temperature of a slurry with crystalline product, and equilibrating the system for approximately 30 min −1 h at each temperature. As the particles partially dissolve the counts/s measurement will change accordingly. The actual CSD of the solid samples taken at each temperature can be measured by using standard measurement techniques such as image analysis or laser diffraction. The FBRM counts/s measurement can then be correlated to this desired real CSD. In the case of seeded crystallization processes the set point of the ADNC typically would be the number of counts/s measured after the introduction of the seed particles in the system.53 The FBRM probe provides a signal (counts/s) that is proportional to the particle numbers in the system. This measurement is relatively independent (or weakly dependent) of the growth of the particles, making FBRM an ideal tool for the implementation of the ADNC approach. Note that while in principle other sensors that provide a signal which is a continuous function of the particle concentration (e.g., bulk video imaging, turbidity, or ultrasonic probes) could be considered for the implementation of the ADNC approach, if the signal from these is a strong function of both size and number of particles, these sensors would provide a continuously increasing signal owing to the increase in solid fraction due to growth. By selecting a suitable upper limit for the target, the ADNC approach can accommodate a certain level of increase in the measurement signal, for example due to growth. In the case of most of the aforementioned measurement devices, nucleation or dissolution generally causes much faster increase/decrease in the signal than growth or change in shape (e.g., due to polymorphic transformation). Therefore the ADNC approach also allows setting a minimum threshold on the rate of change of the signal to avoid detection of false nucleation events owing to slow increases in the signal (e.g. number of counts/s) due to growth or change in shape. Similarly an upper bound on the rate of change can be set, to filter out the potential detection of false nucleation events, owing to particles adhering to the probe window (which would result in a sudden increase of the counts in a relatively few size bins).

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Figure 4. Schematic representation of the experimental setup. Calibration Model Development. A simple nonlinear model was used for determining the concentrations from the ATR-UV/vis spectroscopy data. Spectra were recorded over a range of different concentrations and temperatures for undersaturated and supersaturated (single phase) solutions. A model using seven different concentrations (0.1106 to 0.1840 g/g solvent) across a range of temperatures was built, using first derivative spectra at 266 nm. The concentrations used are shown in Figure 5a. The nonlinear model consisted of an intercept term, derivative, temperature and interaction terms, as shown in eq 4. The model parameters were obtained using a standard nonlinear least-squares optimization implemented in Matlab. The parameters obtained were b0 = −0.029, b1 = −3.7889, b2 = −0.0002, and b3 = −0.0248, with the temperature in °C and the first derivatives in nm−1. The root-meansquare error of prediction, calculated using this model was 0.0013 g/g, while the maximum positive and negative relative errors were 1.73% and −2.41%, respectively. To check the robustness of the model a separate validation experiment was performed in which samples were withdrawn from the slurry in the vessel at different temperatures and checked by gravimetric analysis. The calibration model gave concentrations which were within ±3% of the gravimetric analyses. The results obtained from gravimetric analysis were compared with the results obtained from the model and are shown in Figure 5b.

EXPERIMENTAL PROCEDURES

Materials and Instrumentation. The experiments were carried out in a 500 mL jacketed glass vessel fitted with an overhead PTFE 1796



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Figure 5. (a) Experimental data points shown in the phase diagram used for the calibration model covering the under- and supersaturated regions of the operating zone. (b) Predicted against gravimetrically determined concentrations, indicating a linear correlation (with high regression coefficient) with slope close to one and intercept term to 0, indicating a good prediction ability of the calibration model, with relative errors (also indicated) below 3%.

Table 1. Summary of the Experimental Conditions for All Experiments no.

experiment

1 2 3 4 5 6 7 8 9 10 11 12 13

unseeded slow linear cooling unseeded fast linear cooling seeded linear cooling programmed cooling SSC1 SSC2 SSC3 ADNC1 ADNC2 ADNC3 ADNC4 ADNC5 ADNC6

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heating/cooling rate (°C/min) 0.05 0.5 0.132

amount of seeds (mass %)

5 5 5 5 10

SSC set point (g/g)

ADNC set point (total counts/s) and bounds

duration of batch (min)

8000 (±1000) 8000 (±100) 4000 (±1000) 4000 (±100) 4000 (±100) 4000 (±100)

535 190 461 404 356 241 213 829 844 1069 1358 1319 1600

0.010 0.012 0.010

0.2/0.4 0.2/0.4 0.2/0.4 0.2/0.4 0.2/0.2 0.4/0.4

RESULTS AND DISCUSSION Unseeded Linear Cooling Crystallization Experiments. Table 1 summarizes the conditions for all the experiments carried out, the durations shown are from the onset of nucleation or seed addition. Two unseeded linear cooling crystallization experiments were carried out to provide a base case and to check the maximum number of nuclei that could be generated by the system. A concentration of 0.206 g/g solvent was used for both runs, with cooling rates of 0.5 °C/min and 0.05 °C/min. The results are shown in Figure 6. For the fast

cooling experiment the nucleation took place at a lower temperature, 5 °C and hence at a higher supersaturation, resulting in a greater number of total counts/s than for slow cooling. For the latter, as expected, nucleation took place at a higher temperature thus a lower supersaturation, resulting in a smaller number of total counts/s. The SWMCL for both experiments is shown in Figure 7a and b. Clearly there is more growth during the slow cooling experiment as fewer particles were generated (approximately 13 000 counts/s compared to 26 000 counts/s). The SWMCL at the end of the slow cooling experiment is around 80 μm compared to approximately 58 μm for the fast linear cooling. Nucleation and rapid growth and agglomeration of the initial nuclei that occur at high supersaturation during the first few seconds of the batch leads to the high initial values for the SWMCL. This value then decreases during the first part of the batches as nucleation continues (however at an already decreased supersaturation level less favourable for agglomeration), and thus the increasing number of small particles formed outweighs in the SWMCL the contribution of the larger particles generated during the initial growth/agglomeration phase. After the supersaturation reduces and the number of particles generated stabilizes, the increasing trend in the SWMCL indicates the growth of the particles. As expected, in the case of the slower cooling rate the initial decrease in the SWMCL is smaller owing to the more moderate nucleation compared to the fast cooling experiment, while the subsequent increase in the SWMCL during the rest of the batch is more pronounced because of the smaller number of particles generated. On the basis of these results, the

Figure 6. Unseeded cooling crystallization experiments with slow and fast cooling rates. Slow run was selected for comparison with other approaches. The ADNC target counts were also selected based on the slow run. 1797



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Figure 7. SWMCL plots of the (a) slow cooling crystallization experiment and (b) fast cooling crystallization experiment.

Figure 8. Seeded crystallization experiments with 5% seed with (a) linear cooling profile and (b) programmed cooling profile.

also nucleated during the linear cooling run but the magnitude was smaller than in the former case. This is because linear cooling generated a small but continuous nucleation during the entire process, and hence the supersaturation was kept at a lower value, whereas in the case of the programmed cooling the number of particles was constant until the onset of the sudden nucleation, which occurred at a higher supersaturation. Additionally, the cooling rate during the last part of the programmed cooling profile is steeper than the linear cooling rate, hence the supersaturation generation is higher during the corresponding final period of the batch. The late nucleation event during the programmed cooling profile significantly affected the final CLD, as indicated in Figure 9, which shows

unseeded slow cooling crystallization experiment was selected for comparison with other control strategies. For the ADNC experiments targets of 8000 and 4000 counts/s were selected, since they were considerably below the counts/s recorded for either of the unseeded cooling crystallization experiments. Seeded Crystallization Experiments using Linear Cooling or Supersaturation Control. The aim of these experiments was to investigate the effect of different seed loadings and supersaturation set points on the final CLD using SSC. In all the experiments the saturation temperature was 50 °C (0.206 g/g) and seeds were added after cooling to 48 °C. Crystalline seeds in the size range of 125−185 μm obtained using sieve analysis were used in all these experiments, as they are less prone to agglomeration. Three SSC experiments were carried out using different seed loadings and supersaturation set points, with details given in Table 1. Seeded crystallization experiments with linear and programmed cooling were also conducted for comparison (see Table 1). The linear and programmed cooling profiles are shown in Figure 8a and b. The duration of these experiments was based on the SSC1 experiment, that is, the time from seed addition until the process reached 5 °C (Figure 10). In programmed cooling run nucleation was successfully suppressed until 350 min, contrary to the linear cooling profile where a slow and steady increase in the counts was observed, indicating continuous nucleation in the system. Note that hydrodynamic conditions, amount and quality of seed were the same in both cases, hence the increase in counts/s in the case of the linear cooling most likely is the result of nucleation and not breakage of particles. In the case of the programmed cooling profile significant nucleation took place at about 25 °C. The system

Figure 9. Comparison of CLD for programmed and linear cooling profiles. 1798



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Figure 10. (a) SSC1 experiment with 5% seed and 0.010 g/g as supersaturation set point and (b) the corresponding supersaturation profile for the SSC1 experiment.

Figure 11. (a) SSC2 experiment with 5% seed and 0.012 g/g as supersaturation set point and (b) SSC3 experiment with 10% seed and 0.010 g/g as supersaturation set point.

crystallization operation and scale up. Compared to the linear cooling profile, the temperature decreases slowly in the beginning for the SSC1 and the SSC2 experiments. This slow decrease in temperature can be related to the slow consumption of the supersaturation by the seed crystals. As the crystals grow and more surface area is available the temperature decreases faster to keep the supersaturation constant. However the slow growth of the particles ultimately resulted in significant nucleation events for both the SSC1 and SSC2 runs. In the SSC2 experiment higher supersaturation set point was used, which, as expected, led to more significant nucleation, which started at an earlier stage of the batch than in the case of the SSC1 experiment. When the concentration started to decrease due to the secondary nucleation event, the temperature in SSC2 run decreases even faster than SSC1, to try to keep a higher supersaturation, which in turn generates even more significant nucleation. The temperature was decreased rapidly after the nucleation event by the controller to maintain the supersaturation at the desired level. These results indicate that the SSC approach cannot distinguish between whether the decrease in supersaturation is due to nucleation or growth. Therefore when nucleation is the dominating phenomenon the SSC may lead to poor performance, due to the lack of detecting and taking into account in the control action variations in the number of particles.

the presence of higher number of fines. These results also indicate that the system is characterized by slow growth, which is not enough to deplete the increasing supersaturation as the cooling rate increases during the programmed cooling, leading to the significant secondary nucleation when the supersaturation exceeds a certain limit. Longer batch times would be needed to avoid secondary nucleation in this case. On the basis of this comparison, the linear cooling profile was selected for comparison with SSC and ADNC experiments. Total counts/s, temperature, and concentration profiles for all SSC experiments are shown in Figure 10a and Figure 11a and b. To demonstrate the control performance and show the ability of the system to keep the supersaturation at the desired level, a supersaturation plot for SSC1 is shown in Figure 10 (b). The controller throughout the run was able to keep the supersaturation at the desired level; similar profiles were obtained for the other SSC runs, too. The temperature profiles are obtained automatically from the SSC, as the controller manipulates the temperature to maintain constant supersaturation. The resulting temperature profiles could be implemented subsequently using simple temperature control systems only, providing an inferential open loop constant supersaturation control approach, according to the direct design concept proposed by Fujiwara et al.21 The direct design approach can provide a rapid development of 1799



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Figure 12. SWMCL plots for seeded experiments (a) linear seeded crystallization, (b) SSC1, (c) SSC2, and (d) SSC3. The inset for linear seeded run shows high levels of noise in SWMCL when the solids are completely dissolved; hence, only the SWMCL after nucleation is used for analysis.

To create more surface area to promote growth the amount of seed was doubled and another supersaturation control experiment was performed using the same, (lower) set point as for SSC1. This run (SSC3) resulted in a fast cooling profile since due to the larger number of particles the supersaturation generated was consumed rapidly. The amount of secondary nucleation was less in SSC3 compared to the other experiments. This is in correlation with other observations in the literature, which indicate that the large number of crystals suppresses subsequent nucleation events, due to their higher surface area which promotes growth.36,54 Secondary nucleation affected the average particle size, as shown in Figure 12a, b, c, and d. Only the data after nucleation is shown because of the presence of noise when the solution was clear. In all the experiments the average particle size decreased after nucleation as more fines were generated in the system, similarly as explained in the case of the slow and fast cooling experiments (Figure 7). As noted earlier, SSC3 was less affected by nucleation events indicated by the smaller decrease in the SWMCL. The results suggest that nucleation dominated the growth in this particular system. Paracetamol is a slow growing system, especially as the temperature is lowered while maintaining constant supersaturation. The SWMCL plots also show that the average crystal size remained more or less between 100−110 μm for all the experiments, before nucleation took place, showing very little growth. This is in agreement with growth kinetic studies,55,56 which showed the growth velocity of crystal surface steps decreased as the temperature was lowered at constant supersaturation. The comparison of the square weighted chord length distributions (SWCLD) indicate that a broader and bimodal distribution was obtained (Figure 13) for linear cooling and the SSC experiments with the exception of SSC3, the unimodal CLD for seeds is also shown. Bimodality is obvious in case of linear cooling, SSC1 and SSC2, as in these cases significant nucleation occurred and hence large number of small particles was generated. In case of SSC3 although nucleation did take

Figure 13. Comparison of CLDs (at the end of the batches) for seeded cooling crystallization experiments. The CLD of the seed crystals used in all cases is also shown.

place the amount of larger seed crystals is higher and the number of new particles generated is less compared to the other experiments; hence, bimodality is not observed, except for a small shoulder on the CLD at smaller sizes. Similar results are indicated by Figure 12d, too, where the SWMCL for SSC3 run does not decrease significantly compared to the other profiles after nucleation. Automated Direct Nucleation Control Experiments. Six ADNC experiments were carried out with the aim to investigate the effects of different target counts/s, different upper and lower bounds and different heating/cooling rates on the control of the CSD. The details for these experiments are presented in Table 1. Figures 14 and 15 show the variation of the FBRM counts/s, temperature, and corresponding concentration profiles during the ADNC1-4 experiments, whereas Figure 16 shows the corresponding operating profiles in the phase diagram. In all ADNC experiments, the cooling was initiated after complete dissolution. Cooling was continued until primary 1800



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Figure 14. (a) ADNC1 experiment with 8000 target counts and ±1000 limits and (b) ADNC2 with 8000 target counts and ±100 limits.

Figure 15. (a) ADNC3 with 4000 target counts and ±1000 limits and (b) ADNC4 with 4000 target counts and ±100 limits.

Figure 16. Phase diagrams for direct nucleation control (ADNC) experiments (a) ADNC1, (b) ADNC2, (c) ADNC3, and (d) ADNC4. 1801



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crystals (the product of ADNC3) were present during the first dissolution stage for ADNC4 than for ADNC2 (product of ADNC1). ADNC1 and ADNC3 both used fine powder as initial material, which dissolved quickly, thus no deviation in solubility can be observed in those experiments with the heating rates used. The unweighted CLD (UWCLD) for the ADNC1 experiment at different times during the initial heating phase after nucleation is shown in Figure 17. At approximately 300 min the

nucleation occurred (in situ seed generation). In all cases primary nucleation occurred at large supersaturation resulting in a significant overshoot in the FBRM counts/s. Several heating and cooling cycles were required in each case before the counts stabilized around the target value. The difficulty in maintaining the counts at smaller target values can also be seen: the number of heating/cooling cycles doubled for the target of 4000 counts/ s (Figure 15a and Figure 15b) compared to 8000 counts/s (Figure 14a and Figure 14b). This is understandable as the presence of a greater number of particles makes it more difficult for subsequent nucleation events to take place, as was observed in the case of SSC3. For both cases with narrow bounds, a greater number of heating/cooling cycles were required to maintain the system close to the target number of counts. The effect of narrow bounds is more pronounced in the case of 4000 target counts/s, where the largest number of heating/cooling cycles was required. The heating/cooling cycles for all ADNC experiments are shown in the phase diagrams in Figure 16a, b, c, and d. Analysis of these figures helps in understanding the reduction of MSZW in the presence of particles and how ADNC adapts to this change in the MSZW. In all four ADNC experiments, the primary MSZW was approximately 45 °C and nucleation took place at about 5 °C. The very large primary nucleation zone generated very high initial supersaturation, leading to a very fast and significant nucleation. This has led to an overshoot of the target number of counts and the ADNC system switched to the heating stage. This first cooling and heating stage is indicated by the first large cycle in the phase diagrams of all four experiments. Once the particles were generated, a significant reduction in MSZW was observed, as shown qualitatively by the lines in Figure 16 representing the MSZW for secondary nucleation (nucleation events in the presence of solid particles). In the second and subsequent cooling cycles particles were already present leading to nucleation to occur at much lower supersaturation than the primary MSZW. The ADNC automatically switches to the heating stage earlier as the increase in counts/s is detected. This leads to the smaller cycles in the phase diagram. These results demonstrate the main advantage of the ADNC approach, that it can detect any variations in MSZW and can change the operating conditions (heating or cooling rates) accordingly. This variation in the MSZW and the ability of the ADNC approach to adapt accordingly is evident in all the experiments. Additionally the ability of the ATR-UV/vis spectroscopy to automatically determine the solubility curve is also shown. Starting from the slurry and slowly heating the system, the calibrated ATR-UV/vis spectroscopy indicates the increase of the concentration with temperature in the suspension, which closely corresponds to the solubility curve of the system. The effect of crystal size on the dissolution rate can also be observed from these plots. The experiments ADNC1 and ADNC3 were started using the raw material paracetamol in powder form, whereas experiments ADNC2 and ADNC4 were subsequent runs of the ADNC1 and ADNC3 experiments, correspondingly. Therefore larger crystalline particles were present during the initial dissolution stage of ADNC2 and ADNC4, leading to the slight deviation in the solubility curves when the initial heating phase is compared with the subsequent ADNC heating/cooling cycles. The effect is more pronounced in case of ADNC4 after 40 °C and can be attributed to the presence of bigger crystals during the initial dissolution phase. Since this experiment was performed after ADNC3, which used a lower counts/s set point (4000 counts/s) compared to ADNC1 (8000 counts/s) larger

Figure 17. Evolution of the CLD during the first ADNC heating cycle. The time shown is in minutes.

CLD is shown shortly after the onset of nucleation just before the first heating stage started as shown in Figure 14a. As the temperature increases initially the system is still supersaturated and the concentration continues decreasing due to mainly additional nucleation and growth as indicated by the CLD at 360 min, until the operating curve crosses the solubility. At about 360 min, the dissolution started as shown by the increase in concentration. The subsequent CLDs indicate a significant decrease in the number of fine particles and a shift toward larger mean chord length. Additionally, the appearance of larger crystals can also be observed, which is in contrast to the dissolution of fines during this period as both growth and dissolution cannot take place simultaneously as the temperature is increased. This shift in the UWCLD toward larger chord lengths can be explained by the so-called “snowstorm effect”.57 This phenomenon takes place in the presence of large number of fine particles, which can hinder the FBRM ability to measure the larger crystals. When the dissolution starts the number of fine particles is decreased and as a result more large particles are counted by the FBRM. In all ADNC experiments after the initial nucleation the number of fine particles was very large (always greater than 10,000 counts/s), hence it is likely that the snowstorm effect is the cause of the shift in the UWCLD as the temperature was increased. This is also supported by the fact that after 480 min when the total counts/s decrease significantly below a certain limit, no additional large particles appear in the CLD, although the number of fines continues to decrease. Another possible reason for this shift could be the change in the surface features of the crystals as they are affected during heating/cooling cycles.57 The same observation is illustrated by the SWMCL results for the ADNC experiments shown in Figure 18. In case of the ADNC1 experiment (Figure 18a) the SWMCL increases continuously until it reaches its maximum value at 528 min, corresponding to the maximum concentration at the end of the first ADNC heating cycle as shown in Figures 14a and 16a. The subsequent fluctuations observed are due to dissolution and growth of crystals along with nucleation 1802



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Figure 18. SWMCL for ADNC experiments (a) ADNC1, (b) ADNC2, (c) ADNC3, and (4) ADNC4.

events. A thorough understanding of the process can therefore be obtained by studying the SWMCL plots with the time diagrams and the phase diagrams together. Despite the oscillations, overall significant growth is observed throughout the process and as a result much larger crystals are obtained compared to the previously described approaches. At end of the batch a SWMCL of 158 μm resulted from the ADNC4 experiment compared to 77 and 101 μm obtained from the unseeded, slow linear cooling and from the SSC3 experiments, correspondingly. The SWMCL for the seeded SSC experiments started to decrease toward the end of the batch because of nucleation, resulting in bimodal CLDs at the end of the batch. Figure 19 shows a comparison of the CLDs for the various ADNC and the SSC3 experiments. The ADNC results show unimodal CLDs for all cases.

Table 2. Statistical Evaluation of ADNC and SSC Experiments experiment

median, sqr. wt.

mean, sqr. wt.

coefficient of variation

ADNC1 ADNC2 ADNC3 ADNC4 SSC3

108.52 106.68 134.89 136.24 92.81

118.14 115.10 145.74 152.22 101.08

0.534 0.521 0.540 0.572 0.577

runs compared to the SSC3 experiment. Median values indicate that the ADNC runs had a higher percentage of larger crystals compared to the product resulting from the SSC experiment along with narrower CLD as shown by the coefficient of variance values (COV). The results also indicate that using a lower target counts/s yields larger crystals, but the system is more difficult to control. The bounds used for the target will influence the number of ADNC cycles but ultimately yield similar product CLDs (similar mean, median and COV). In the case of the higher target counts/s (8000 counts/s), the system is easier to control, and the CLDs resulting from ADNC1 and ADNC2 are practically overlapping. For the lower target counts/s (4000 counts/s) more cycles are required to control the system, which also leads to higher COV. The COV in case of ADNC4, which has the maximum number of cycles but also yields the largest crystals, is closer to the SSC3 run showing slightly wider distribution. These results nonetheless highlight the importance of different ADNC parameters and their effect on the final CLD. The difference in the quality of crystals obtained is evident from the microscopic images of samples of the product obtained from each experiment, shown in Figure 20. The unseeded cooling crystallization produced a broad size distribution and agglomerated particles, as shown in Figure 20a. Some agglomeration is evident in Figure 20b and c for the seeded linear cooling experiment, and SSC3, but it is considerably reduced compared to the unseeded experiment due to operating at much lower supersaturation. In contrast there is very little agglomeration seen for the ADNC runs as shown in Figure 20d, e, and f, despite the fact that the initial nucleation phases for the ADNC experiments

Figure 19. CLD (at the end of the batches) comparison of ADNC experiments with SSC3, the set points and bounds (in counts/s) are shown in the legend for each ADNC run. The CLDs resulting from ADNC1 and ADNC2 are practically overlapping.

Table 2 shows statistics that characterize the product CLDs from each run, to evaluate the performance of the approaches compared in Figure 19. As previously mentioned, the ADNC approach helps in obtaining bigger crystals and desired CSD, this can be seen by higher median and mean values for the ADNC 1803



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Figure 20. Microscopic images of selected experiments for comparison. Scale bar represents 500 μm. (a) Unseeded linear cooling experiment, (b) seeded linear cooling experiment, (c) SSC3 run, (d) ADNC2, (e) ADNC3, and (f) ADNC4.

are similar to the unseeded linear cooling experiment. The continuous heating/cooling cycles during the ADNC experiments help in breaking any agglomerates that exist in the system, thus also preventing solvent inclusion between the crystals. This is illustrated by Figures 14 and 15, which indicate consistently a sudden increase in the FBRM counts/s around its maximum peak at the moment when dissolution starts (indicated by the first increase in the concentration). This sudden increase in the counts/s is generated by the breaking of the agglomerates promoted by the dissolution. Since the first nucleation occurs at very high supersaturation this promotes the formation of small particles and agglomeration, however the bonds between the particles are dissolved during the dissolution cycles. The crystals resulting from the ADNC experiments are larger with a narrow size distribution. The CLD distribution analysis showed that ADNC1 and ADNC2 (with higher target of 8000 counts/s) generated smaller crystals than the ADNC3 and ADNC4 experiments (with target of 4000 counts/s); this is also evident from the microscopic images. Additionally despite the slight variations in

the CLDs of the products of ADNC3 and ADNC4 the crystals appear very similar in size and quality. These results also indicate that the main tuning parameter of the ADNC is the target counts/s whereas the other parameters although will have significant effect on the operating profile yield similar product properties. Moreover, the crystals obtained from ADNC runs have well-defined shapes and sharp edges, indicating fewer defects on the crystal surfaces. These results indicate the beneficial effect of temperature cycles resulting from the ADNC approach on the shape of the crystals in addition to the improved size distribution. An important feature of the ADNC approach is that it is designed to generate temperature cycles, rather than providing ideal control of the target counts/s without over- and undershoots, and hence to exploit the benefits of repeated dissolution and growth processes on the product properties (e.g., deagglomeration, elimination of type one and two solvent inclusion, high quality crystal surface for better flowability, better aspect ratio, etc.). In comparison with other methods the duration of the ADNC experiments are longer (see Table 1). However, while 1804



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Figure 21. (a) ADNC5 with maximum heating/cooling rate of 0.2 °C/min and (b) ADNC6 with maximum heating/cooling rate of 0.4 °C/min.

Figure 22. Phase diagrams for (a) ADNC5 and (b) ADNC6.

the increase of the duration of the batch may be considered as a disadvantage of the approach, the many advantages of the temperature cycles, such as consistent seed (in the case of internal seeding), elimination of agglomeration, (hence, for example, elimination of solvent inclusion) and increase in quality of crystals (more uniform size, better surface properties, aspect ratio, etc.), in many practical cases in pharmaceutical crystallization can outweigh significantly the disadvantage in the potential increase in duration. During the production of an API, crystallization precedes the other unit operations. Crystalline product with poor properties can substantially increase the filtration time and/or solvent inclusion, and may pose problems during drying, tableting and compaction. Furthermore it is much easier to rectify problems during crystallization than during the other downstream processes. It is therefore reasonable to allow a longer batch time during crystallization to avoid any problems later on. Additionally, if accidental seeding or crusting takes place during crystallization an open loop temperature control will not be able to rectify or even detect such events, ADNC on the other hand in most cases can detect and take appropriate actions in such conditions. Furthermore, ADNC should not be considered as a control only method; it can be used as a crystallization design methodology, too. In this direct design context, the temperature profile obtained from a successful ADNC run, which incorporates the many beneficial effects of temperature cycling, can be used to consistently produce high quality product, and thus avoiding batch to batch variability, and using a fixed batch time. The typically used ripening approach via temperature cycling is based on trial and error experimentation and can use significant amount of time and resources, whereas the ADNC

approach is readily applicable, requiring less resources for implementation, since it determines automatically the best number of temperature cycles and duration of batch required to achieve the desired product quality. Two further ADNC experiments were carried out to evaluate the effect of different heating and cooling rates on the crystal product. In both cases the target counts were 4000 counts/s and the upper/lower limits were set at ±100 counts/s. The heating/cooling rates were 0.2 °C/min for ADNC5 and 0.4 °C/min for the ADNC6 experiments. The results are shown in Figure 21a and b, respectively. Fewer heating/cooling cycles were required for the ADNC5 experiment, which shows that this particular system is better controlled with slower heating/ cooling rates, however, this may also depend on other factors such as the upper and lower bounds for the target counts/s. The greater number of cycles in the ADNC6 experiment also leads to longer batch times. The phase diagrams for both runs in Figure 22a and b show similar behavior in terms of the MSZW variation and subsequent detection of nucleation events by the ADNC approach. In both cases continuous growth of crystals was obtained, as was observed in the previous ADNC experiments. The ADNC6 experiment has a very high number of cycles, since the higher cooling rate leads to considerable overshoots of the target. Despite the faster cooling and heating rates used the ADNC6 run takes considerably longer than the other experiments, owing to the large number of cycles, but as expected produces similar crystals, since similar final counts/s were achieved. The CLDs obtained for the ADNC5 and ADNC6 (Figure 23) experiments are similar to each other and to the 1805



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choosing the ADNC configuration parameters. For example in the case of high nucleation rates and slow growth kinetics (similar to the system used in this work) slower cooling rate can be used (to decrease nucleation rate). For fast growing systems high maximum cooling rate can be used. For systems which tend to nucleate as very small uniform particles (e.g., slow growing systems with very broad primary nucleation zone) or in the case of seeded ADNC when the seed consists of the raw material in powder form it is important to apply small maximum heating rate to avoid complete dissolution during the cycles. Generally, the larger the ratio between the nucleation and growth rates the more cooling/heating cycles will occur during the process, and the batch time will thus automatically increase, however the ADNC approach is able to provide consistent product quality.

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CONCLUSIONS Two control approaches, namely the concentration feedback or supersaturation control (SSC) and the automated direct nucleation control (ADNC) approaches were compared using FBRM and ATR-UV/vis spectroscopy for the cases of seeded and unseeded cooling crystallizations. The results show that the direct nucleation control approach, which requires no a priori information about the system, outperformed the other control strategies. For the paracetamol in isopropyl alcohol system, significant variations were found in the metastable zone width (MSZW) in the presence of crystals, which were responsible for the poor performance of control strategies other than the direct nucleation control approach. The SSC experiments showed how the seed loading can help in controlling or suppressing nucleation events. A higher seed loading gave the best results compared to the other seeded crystallization approaches. Bimodal CLDs were observed for the SSC experiments (except when higher seed loading was used) and for the linear seeded experiments, indicating that the SSC may perform poorly for slow growing systems, when nucleation occurs during the crystallization, because of its inability of detection and reacting to changes in the number of particles. The ADNC approach was able to detect any changes in the MSZW caused by the presence of particles or impurities. The phase diagrams automatically generated during these experiments using ATR-UV/vis spectroscopy with a suitable calibration model, helped in understanding why the ADNC approach is superior to the other approaches used. The main advantage of the ADNC is that continuous in situ removal of fines occurs during the heating stages, whereas the cooling stages help in the growth of larger crystals. The largest crystals with unimodal distribution were obtained with the ADNC

Figure 23. Comparison of the product SWCLDs for ADNC5 and ADNC6.

previous ADNC runs with 4000 target counts/s. Microscopic images (Figure 24) also confirm that crystals obtained from these two batches are similar to each other and to the previous ADNC runs, that is, with minimal agglomeration, well-conditioned and unimodal distribution and similar average size. The yields in all experiments are similar and are fixed by using the same initial concentration and final temperature. These results demonstrate clearly the benefits of the ADNC approach to produce large, high quality crystals with narrow CSD with no agglomeration, and its ability to adapt to changes in the MSZW. The final CSD depends mainly on the target counts/s used, whereas the other parameters such as bounds, heating/cooling rates, and gains will influence the number of cycles and duration of the batch but provide similar yield and CSD with good consistency, demonstrating the high robustness of the ADNC approach to produce consistent crystallization products. The performance of the ADNC approach can be further improved by using statistical control charts for the systematic determination of the bounds around the target counts/s and early prediction of the onset of nucleation. The performance of the ADNC approach was illustrated in the case of paracetamol in IPA. This crystallization system is characterized by relatively slow growth and high nucleation rates, making it challenging to control using linear or programmed cooling or SSC approaches, whereas the ADNC provides excellent control performance using a broad range of tuning parameters. The approach is applicable to different crystallization systems with either growth or nucleation as the dominating phenomenon, by appropriately

Figure 24. Microscopic images of the products from (a) ADNC5 and (b) ADNC6; the scale bar represents 500 μm. 1806



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approach compared to all other operating policies. In the case of the ADNC approach the mean size of the crystals mainly depends on the target counts/s; that is, a lower target counts/s resulted in larger crystals, but at the same time, it was also more difficult to control the process at lower counts/s, and there was a need for more heating/cooling cycles, resulting in longer batch times. The configuration parameters of the ADNC approach can be relatively easily fine-tuned depending on the characteristics of the system, for example, for a very slow growing system with high nucleation rate slow cooling rates can be used. The continuous heating/ cooling cycles (alternating surface dissolution and regrowth) during the ADNC approach also helped in the deagglomeration of the particles, which can reduce solvent inclusion, and improve crystal shape and surface quality. The crystals obtained from the ADNC runs had well-defined shapes with fewer surface defects. The ADNC was used for cooling crystallization, but the same approach is applicable to antisolvent and combined cooling and antisolvent crystallization process.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding is acknowledged from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. [280106-CrySys].

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REFERENCES

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Comparative Investigation of Supersaturation and Automated Direct

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