In-Process ATR-FTIR Spectroscopy for Closed-Loop Supersaturation

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In-Process ATR-FTIR Spectroscopy for Closed-Loop Supersaturation Control of a Batch Crystallizer Producing Monosodium Glutamate Crystals of Defined Size Heidi Gro1 n,†,‡ Antonia Borissova,§ and Kevin J. Roberts*,§ Centre for Molecular and Interface Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K., and Institute of Particle Science and Engineering, Department of Chemical Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Closed-loop computer control of solution supersaturation based on in-process measurements of the concentration using ATR-FTIR spectroscopy and adaptive determination of the corresponding temperature profile has been applied to control the crystallization of monosodium glutamate from an aqueous solution. The crystal size and its distribution are varied and optimized by applying different control strategies such as rapid desupersaturation, constant supersaturation, and step-changing supersaturation profiles. 1. Introduction Crystallization is an important industrial process used in the manufacture of a wide range of chemicals producing high-purity products through processes having low energy demands compared to other unit operations such as distillation. Ideally, crystallization processes should be optimized so that the crystalline product can be produced with predefined properties (e.g., crystal size distribution (CSD), purity, morphology, yield) targeted to meet specific customer requirements. Direct control of batch crystallizers to meet product specifications requires a combination of accurate online measurements of reactant supersaturation together with the use of an appropriate control model to sustain the required levels of supersaturation for the following nucleation and crystal growth processes. In this paper, this overall theme is addressed through the application of in-process attenuated total reflection (ATR) FTIR spectroscopy coupled to an appropriate process control algorithm. This setup enables a better operation of batch crystallization via the use of in-process supersaturation measurements in a closed-loop process control setup. Through this, an outline process control methodology for the production of crystals with desired particle properties has been developed and demonstrated experimentally. 2. Control of Supersaturation 2.1. Control Strategies. The formation, modification, and distortion of particulate structures depend on a number of thermodynamic, kinetic, and fluid dynamic factors. The formation of desirable product properties is not always primarily driven via variation of process state functions such as pressure, volume, and temperature but more by process parameters that are directly dependent on the degree of supersaturation in the * Corresponding author. Telephone: +44 (0) 113 343 2404. Fax: +44 (0) 113 343 2405. E-mail: [email protected]. † Heriot-Watt University. ‡ Present address: Degussa AG, Germany. § University of Leeds.

crystallizing solution. A key fundamental barrier to reliable control is given by the competing demands of crystal nucleation and growth and the fact that their relative balance is dependent on scale, system, equipment, and process conditions. Different strategies have been reported1-12 to control supersaturation and are used in industry. Some of the most important factors are seeding, programmed cooling, programmed feeding rates of reactants (in precipitation), and solvent evaporation. This work has focused on the use of controlled cooling modes to achieve the required supersaturation, which was first proposed by Mullin and Ny´vlt1 and refined by Jones and Mullin.2,3 A method to calculate optimal cooling profiles, based on moment transformation of the population balance coupled with material and energy balances, has been presented by Jones.3 Because of complex processes inherent in batch crystallization, such as the interaction of process effects based on mass- and heat-transfer phenomena or the interdependence of crystal properties, it is not possible to ensure successful operation of the crystallizer by incorporating a control strategy, which is solely based on crystallization kinetics and process dynamics. This results from the fact that the theoretical temperature profiles are not directly applicable to industrial control systems because optimal cooling profiles cannot be determined in advance for a specific solute-solvent system under specific experimental conditions. Thus, preprogrammed temperature profiles cannot always guarantee the desired product because of the inherent specific process characteristics. In addition, the values of the kinetic constants for nucleation, crystal growth, and other related processes are often not known, thus making precise calculation of the desired temperature profiles problematic. A potential solution to this is provided through the use of closed-loop (direct) computer control of supersaturation based on online measurements of the solution concentration linked to an adaptive determination of the associated reactor temperature control. Measurement-based control strategies to produce unimodal CSDs have been examined,4-7,10 with supersaturation being monitored online through conductivity,4 calorimetric,8 or ATR-FTIR measurements.9,13-16

10.1021/ie020346d CCC: $25.00 © 2003 American Chemical Society Published on Web 11/28/2002

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Turbidity5 and pH monitoring7 have also been applied to measure CSD. Similarly, strategies to produce unimodal CSDs via suppression of secondary nucleation,4 based on the fact that the solute is consumed by growth of a sufficient number of seeds and subsequent agglomeration of small particles onto the growing seeds, have been proposed. Various other studies have demonstrated the application of a swing mode in either the temperature-time or pH-time profile including time steps with negative supersaturation to dissolve fines and improve the CSD.5-7 Closed-loop control applies a feedback strategy to calculate the desired control effect. Two approaches are possible in the realization of the feedback control strategy: measurement-based or model-based. The measurement-based approach uses online measurements of process parameters (concentration, temperature, etc.) to calculate the control effect (e.g., temperature profile). The model approach is based on the use of a mathematical model of the crystallization process including mass, heat, and population balances, in which the parameters are updated on the basis of measurements and the new control variables are calculated. A general closed-loop strategy to control the crystal size and size distribution by controlling the supersaturation profile within a batch crystallizer has been developed. It has been based on the in situ monitoring concentration and therewith supersaturation in the crystallizer itself and on control of the reactor temperature profile in order to be able to manipulate the balance between the nucleation and growth processes. Through this, the production of the crystalline product with desired properties, for example, uniform crystals (by size and shape) and particles of specific CSD, is possible. 2.2. Control Model. The supersaturation of the solution is expressed as a ratio between the concentration of the solution c and the equilibrium concentration c* at the corresponding temperature in the crystallizer S ) c/c*. Strategies to obtain uniform crystal products, for example, can be mathematically expressed on the basis of the first derivative of the supersaturation with respect to temperature. The condition that needs to be satisfied to obtain uniform crystals is to maintain a constant level of supersaturation in the crystallizer. Thus, the control strategy for obtaining uniform crystals should be based on the realization of process conditions leading to a zero derivative of supersaturation with respect to temperature:

d ln c d ln c* ) dT dT

(1)

The control strategy according to eq 1 is to change the logarithms of c and c* equally. Time variation of supersaturation can be derived considering the complexcomposed function S[T(t)] and the corresponding partial derivatives in the differentiation. If the solubility is expressed as a second-order polynomial with respect to temperature, 2

c* ) acT + bcT + cc

(2)

where ac, bc, and cc are constants, and then the control

strategy (eq 1) can be expressed by the following equation:

2acT + bc d ln c ) dT acT 2 + bcT + cc

(3)

The rate of change of concentration with temperature and hence time should satisfy eq 3 in order to keep supersaturation constant. This strategy to obtain uniform crystals is presented by the flowchart in Figure 1. It demonstrates the iterative algorithm used to maintain constant supersaturation and the resultant modification of the temperature profile. The task is to keep the level of supersaturation within a defined range between a minimal Smin and a maximal value of the supersaturation Smax. If that range is small enough, the controlled supersaturation could be considered constant. There are two iteration loops in the algorithm: the time change of the process variables and a temperature gradient change, which is initiated when the control condition (presented by eq 1) is violated. The time values ti (i ) 1, 2, 3, ...) define the time intervals for the change of the temperature gradient. Once the process is designed, initial conditions (T0 and c0) together with the constraints on the process conditions, the respective ranges of temperature (Tmin and Tmax) and concentration (cmin and cmax), are defined. Numerical parameters, which need to be defined beforehand, are the time interval, ∆t, and the increment of change of the temperature rate, . The initial temperature and concentration gradients are calculated on the basis of the mathematical model for the given initial conditions or are measured online, depending on the control approach applied (model- or measurements-based). New values of process variables (c and T) are calculated or measured and constraints on them checked. For the case of constant supersaturation control, the control condition requires equivalent concentration and solubility gradients with respect to temperature. If this condition is satisfied, there is no need to change the temperature control, and the process time increases with the time step, thus initiating a new iteration. If this control condition is not satisfied, then the temperature gradient changes according to a predefined algorithm. It can be seen in Figure 1 that the temperature gradient changes with positive or negative increment , depending on the deviation from the optimal value of the control condition. After the new temperature gradient is determined, new values for the temperature T and concentration c are found. The value for T is set on the basis of the already defined new gradient for the temperature. The constraint on the temperature interval is then checked again. If it is satisfied, a new iteration in the control procedure starts, and if it is not, the increment decreases and a new temperature gradient is determined. The procedure stops if the increment is approaching zero or another small value. 3. Instrumentation and Methodology The closed-loop measurement-based control strategy described above, utilizing an ATR-FTIR in-process spectrometer for supersaturation monitoring, was applied to the crystallization of monosodium glutamate monohydrate (MSG; formula, C5H8NO4Na‚H2O; molec-

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Figure 1. Flowchart of the reactor control strategy to maintain a constant level of supersaturation within a batch crystallizer based on a mathematical model in the form of temporal variations of concentration and temperature.

ular weight, 187.14). The material was purchased from BDH Chemical Co. (purity 99.969%). Aqueous solutions of MSG were prepared with distilled water. 3.1. Instrumentation. Experiments were carried out using a HEL Autolab reactor system comprised of a 500 mL jacketed glass reactor, a Julabo FP50-HD thermostated bath, a data interface board (A/D), and a PC running WinNT with WinISO process control software (HEL Ltd.). Reactor stirring was provided using a pitched blade stirrer rotating at a constant speed of 330 rpm. The temperature and turbidity (to detect crystallization onset) were measured using a platinum resistance thermometer (PT100) and an in-house-built turbidimetric fiber-optic probe system, respectively, with both signals logged by the control computer. In situ measurements of supersaturation were carried out using a Dipper-210 ATR immersion probe equipped with a ZnSe conical internal reflection element manufactured by Axiom Analytical Inc. together with a Bomen WorkIR FTIR spectrometer. The latter was connected to a PC equipped with Grams software (Galactic Industries Corp.) linked via a RS232 link to the reactor control PC. Figure 2 shows a simple schematic of the data collection and control system as utilized for the closedloop supersaturation control experiments. The system allowed for all process parameters to be modified directly using the reactor control software (e.g., stirrer speed) or through cascade control (e.g., reactor and oil temperature). 3.2. Methodology. Because of the poor nucleation behavior of MSG, crystallization was initiated at very high levels of supersaturation, to which the solution was crash-cooled. The corresponding temperature profiles during the experiments carried out are illustrated in Figure 3. MSG solutions (980 g of MSG monohydrate/

1000 g of water; saturated at 65 °C) were prepared. The concentration was chosen to be low enough to avoid oiling out.16 The solution was heated to 70 °C in a controlled manner (at a rate of 1.5 K/min) to ensure reproducibility of all of the experiments and kept at this temperature for 180 min to ensure dissolution of all crystals. The solution was then cooled from 70 to 17.5 °C by applying a constant cooling rate of -1 K/min and kept constant at this temperature until crystallization occurred. For uncontrolled crystallization, the temperature was kept constant at 17.5 °C for the whole experiment (isothermal crystallization). For the supersaturation-controlled runs, the software was optimized to change the temperature according to the required supersaturation profile (closed-loop control). ATR-FTIR spectra were obtained using 38 scans/min, and the online data were accumulated, recorded, and controlled every minute during the batch crystallization process. The “as-grown” crystals were examined using a standard reflected-light Nikon microscope equipped with a video camera linked to a PC with a Leica Image Grabbing Software Package (Lida) for data storage and analysis. Samples of the crystals prepared were extracted from the crystallizer after a fixed time to provide representative data for comparison. Figure 4 shows a schematic of the closed-loop control strategy applied utilizing an ATR-FTIR in-process sensor for supersaturation monitoring. The underlying control algorithms are described below. The user can choose up to eight wavenumbers at which the intensity of ATR-FTIR peaks should be read into the reactor software for utilization of quantitative analysis of the concentration of the solute. The following macro has been developed and was implemented within the HEL WinISO reactor control software.


Figure 2. Representation of the data acquisition and control system as utilized for the in-process supersaturation control measurements.

Figure 3. Schematic representation of the temperature cooling profiles prior to the isothermal MSG crystallization experiments.

First, the peak ratios R (up to four ratios from eight peaks) were determined:

R ) P1/P2

(4)

with P1-P8 being the intensities of the ATR-FTIR peaks at a1-a8 wavenumbers within the spectral range of 4000-650 cm-1. From this peak ratio R and the current temperature T, the concentrations c were calculated by choosing one out of the three following options for the calibration relations: (a) Exponential calibration curves obeying the Lambert-Beer Law in the transmittance mode.

R ) (wT + x) exp[(yT + z)c]

(5)

(b) Linear calibration curves obeying the LambertBeer law in the absorption mode.

R ) (wT + x)c + (yT + z)

(6)

(c) Calibration power curves if the Lambert-Beer law is not applicable.

R ) (wT + x)cyT+z

(7)

The parameters of the above equations (5)-(7) w, x, y, and z are numbers to four decimal places (() to be given by the user. This general description of ATR-FTIR calibration relationships with respect to concentration makes the setup versatile for crystallization experiments of different solubilities and/or solute-solvent systems.

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Ind. Eng. Chem. Res., Vol. 42, No. 1, 2003 Table 1. Values of the Variable Parameters in the Control Algorithm (Equations 5-7 and 10) As Used for Crystallization of MSG from an Aqueous Solution calibration for R ) P1/P2 eq 7 (power) solubility eq 10 (second order)

Figure 4. Schematic diagram showing the basic features of the supersaturation control system developed.

Supersaturation S can be readily obtained by dividing the current concentration c as determined using eqs 5-7 by the corresponding solubility c* at the respective temperature given by eq 1 with the solubility c* given by a fourth-order polynomial function with respect to temperature T; thus,

c* ) jT4 + kT3 + lT2 + mT + n

(8)

with the parameters j, k, l, m, and n being numbers to four decimal places (() to be given by the user, thus enabling accurate approximation of the respective solubility curve. The supersaturation S, as defined in eq 1, is the controlling parameter; thus, a temperature control mechanism with respect to S was implemented as follows: If S > Smax, then change dT/dt by ([∆(dT/dt)]1; if S < Smin, then change dT/dt by ([∆(dT/dt)]2. The parameters Smax, Smin, [∆(dT/dt)]1, and [∆(dT/dt)]2 can be given by the user according to the required range of supersaturation and the change of supersaturation with respect to temperature. The above parameters can be defined independently in different steps of the experiment, allowing specific values for the individual nucleation or growth stages. Online data and respective plots of R, c, c*, and S over time were displayed, with changes to all of the process and control parameters being made online when needed. A cascade control system was created containing two control loops: reactor temperature control and circulating oil temperature control. The supersaturation control is realized through reactor temperature changes according to the selected control strategy. The latter is achieved via proportional-integral-derivative control of the temperature of the circulating oil. For the MSG crystallization experiments carried out, the constraints of supersaturation and temperature were specifically set to match the control strategy adopted for the respective batch experiments as detailed in sections 4.1-4.3. The variable parameters in the control algorithm are listed in Table 1. 4. Results and Discussion Controlled crystallization experiments to maintain a constant level of supersaturation and to realize alternative desupersaturation profiles were carried out. Uncontrolled crystallization experiments were also carried out as a reference point.

P1

P2

w

x

y

z

3279 1400 -0.2563 142.5 0.0001 0.7466 j k l m n 0 0 0.0529 0.5458 655.7

4.1. Uncontrolled Supersaturation Crystallization. Figure 5 shows the data obtained from measurements made without supersaturation control revealing the supersaturation to deplete from its initial value S0 (≈1.45) after an induction time of 370 min at a constant temperature of 17.5 °C. Induction times were found to vary slightly throughout the various runs (compare Figures 6 and 7), consistent with the presence of heteronuclei. It can be seen that after the onset of crystallization the level of supersaturation within the reactor depleted rather rapidly from its initially high level. The rate of desupersaturation, however, was found to decrease significantly with time, resulting in rather long crystallization times to reach equilibrium. The increase in solution transmittance (green line in Figure 5) just prior to crystallization was observed in all experiments. The reason for this is not clear but may be related to intense multiple light scattering between the nucleating particles prior to size enlargement via growth. A corresponding micrograph of the crystals thus produced is given in Figure 5 showing the mean crystal size to be ca. 100 µm albeit with a rather nonuniform CSD containing a significant fraction of fines of ca. 2540 µm size. 4.2. Crystallization under Controlled Constant Supersaturation. Three experiments were carried out (runs 1-3) in which a constant level of supersaturation was maintained during crystallization via closed-loop supersaturation control. The constraints for supersaturation and temperature in the control algorithm for the respective crystallization experiments are listed in Table 2. The supersaturation control step was arranged to start with the beginning of the constant temperature step at 17.5 °C (t ) 300 min). This meant that, if none of the reactor control constraints was violated, then the temperature would remain at 17.5 °C. Thus, supersaturation control was only initiated after the onset point of crystallization. The constraints on temperature were the same for all experiments. The minimum value was set to 1 °C, so as not to risk freezing of the solution. The upper limit was set to 50 °C to ensure that operation was permanently under the saturation temperature of 65 °C. The resulting desupersaturation and temperature profiles are given in Figure 6. In run 1 the supersaturation was kept constant at 1.20. The closed-loop control mechanism forced the temperature to be decreased only slightly as crystallization proceeded, reflecting the rather low levels of supersaturation resulting in low nucleation and growth rates. From run 2 (S ) 1.30) it can be seen that the temperature was automatically decreased in an approximately convex profile, being in pleasing agreement with the theoretically determined optimum cooling profiles described in the literature1-3 often used to obtain constant supersaturation levels throughout the run period. This effect was also reproduced in run 3, where supersaturation was kept constant at the higher


Figure 5. Desupersaturation profile (left) associated with the isothermal crystallization of MSG. Also shown is the solution transmittance profile indicating the onset of turbidity (green) together with micrographs (right) of the crystals produced.

Figure 6. Desupersaturation and corresponding temperature profiles (left) associated with the controlled crystallization of MSG (initial concentration: 980 g/1000 g of water) and the corresponding micrographs (right): run 1, S ) 1.2; run 2, S ) 1.3; run 3, S ) 1.44.

level of 1.44. However, after about 820 min, the minimum (safety) preset reactor temperature of 1 °C was reached, making it impossible to maintain the constantly high level of supersaturation beyond this point. The corresponding representative micrographs of the crystals prepared from these three runs are given in Figure 6. It can be seen that the crystals produced from

run 1 at S ) 1.20 have a mean length of around 300 µm with a significantly reduced aspect ratio compared to noncontrolled runs giving needlelike crystals that have a large width of around 35-50 µm. The crystalline product obtained from S ) 1.30 (run 2) shows a mean size of about 250 µm, with a bigger aspect ratio giving needles of about 15-20 µm width. The small size

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Figure 7. Experimental data resulting from two alternative desupersaturation profiles (left) as produced by imposing different constraints for the nucleation and crystal growth stages of the process together with correspondent micrographs of the crystals (right): crystals resulting from high nucleation rate (top); crystals resulting from high growth rate (bottom). Supersaturation ranges: run 4, 1.53 < Snucl < 1.56 and 1.10 < Sgrow < 1.14; run 5, 1.14 < Snucl < 1.18 and 1.34 < Sgrow < 1.38. Table 2. Constraints for Supersaturation and Temperature Values As Implemented in the Control Algorithm for the Crystallization Experiments with Controlled Constant Supersaturation Levels (Figure 6) run

Smin

Smax Tmin [°C] Tmax [°C] Toil,min [°C] Toil,max [°C]

1 2 3

1.18 1.28 1.39

1.22 1.32 1.44

1 1 1

50 50 50

-5 -5 -5

60 60 60

fraction of the crystals from both runs is negligible. Crystals produced at S ) 1.44 (run 3), however, are rather different from the crystals obtained at two lower levels of supersaturation, being very fine needles with a uniform mean size of about 70-80 µm. It was observed that the temperature gradient increases as crystallization progresses because of the fact that the thermodynamic capacity of the solution to create supersaturation decreases, consistent with the requirements of the criteria presented in eq 1. A comparison of these data with those prepared without supersaturation control (Figure 5) clearly shows that the crystals produced under constant levels of supersaturation are much more uniform than crystals produced by uncontrolled isothermal batch crystallization. Additionally, the constant supersaturation growth conditions effected a significant reduction in the amount of fines. It was also found that the run at lower supersaturation (S ) 1.20) yielded crystals with a smaller aspect ratio (6-8) compared to those prepared at the higher supersaturation (S ) 1.30), which yielded an aspect ratio of about 12-16. This observation is indicative of differing interface kinetic mechanisms between the different habit forms of MSG, interestingly suggesting

a possibility of control of particle shape and habit modification via control of the reactor desupersaturation profile. This effect has not been previously reported for MSG, which has been found to crystallize from an aqueous solution in the form of long needlelike crystals, having an aspect ratio of about 15.17 Previous work has shown that MSG can be crystallized in the presence of alanine [>0.1% (w/w)] as an additive at relatively low supersaturations (S < 1.15), resulting in rhombic prisms having an aspect ratio of only 5.17 However, this process demands induction times as long as 1 week, imposing unrealistic process conditions in terms of product manufacture. 4.3. Crystallization with Controlled Nucleation and Crystal Growth Stages. Two further experiments (runs 4 and 5) were carried out to test alternative desupersaturation profiles to improve the quality of the crystals produced. In the first (run 4) control strategy, supersaturation was kept constant at a very high level for the nucleation stage and thereafter at a low level for the crystal growth stage. This can be expected to yield very fine crystals because of enhanced nucleation and suppressed crystal growth. In the second (run 5) control strategy, supersaturation was kept constant at a rather low level for the first stage after the onset of crystallization and was then increased to a higher level for the crystal growth stage. This can be expected to yield larger crystals because nucleation is suppressed and crystal growth enhanced. The corresponding constraints on the control parameter supersaturation are given in Table 3, with the desupersaturation profiles being given in Figure 7.

Ind. Eng. Chem. Res., Vol. 42, No. 1, 2003 205 Table 3. Constraints on Supersaturation As Implemented in the Control Algorithm for the Two Crystallization Experiments Exhibiting an Alternative Supersaturation Profile (Figure 7) nucleation stage

growth stage

run

Smin

Smax

Smin

Smax

4 5

1.53 1.14

1.56 1.18

1.10 1.34

1.14 1.38

For run 4 the supersaturation was initially raised postnucleation to a level of 1.55 and after a further 100 min reduced to the lower level of 1.12. The resultant response in the temperature profile to achieve this supersaturation profile is given in Figure 7, revealing that for the nucleation stage the temperature was reduced at an increasing rate in order to obtain the constant level of supersaturation. Thereafter, the temperature was rapidly increased to deplete supersaturation to the desired level of 1.12. For run 5 the supersaturation profile was decreased immediately after the onset of crystallization to suppress nucleation, and then after a further 100 min, it was increased to higher levels. As can be seen in Figure 7, the mirror profile to run 4 is represented. However, within the growth stage the supersaturation could not be increased to a higher level than 1.38 because of the reactor control limitation on the temperature (Tmin ) 1 °C). This also caused the supersaturation to slowly deplete within the growth stage, reflecting the fact that it was not possible to maintain it at a constant level. A significant difference in the characteristics of the crystalline product of the two runs was observed, as shown in the corresponding micrographs (Figure 7). Crystals obtained from run 4 at very high supersaturation during the nucleation stage showed a large number of very fine needles having a length of about 25 µm. They were found to be much smaller than the ones obtained from run 3 at constant S ) 1.44 (compare Figure 6), where the mean size was more about 70-80 µm. This observation is consistent with two process steps: (i) The level of supersaturation in this alternative desupersaturation profile exceeded the initial value during the first stage of nucleation, encouraging massive nucleation. (ii) Subsequent crystal growth was hindered by depleting the level of supersaturation for the rest of the crystallization time. Quite pleasingly though, a uniformly sized product was obtained, reflecting the subsequently rapid reduction of supersaturation during the course of crystallization. The crystalline product obtained from run 5 shows a smaller number of very large crystals of about 300 µm length, comparable to those obtained by run 1 with a constant supersaturation throughout the crystallization of 1.2. However, these crystals show a slightly larger aspect ratio. It can also be seen that there is a considerable fraction of fines observed, indicating that secondary nucleation could not be completely avoided when increasing the level of supersaturation during the run. From this, it can be concluded that the second step boundary for supersaturation of 1.38 was chosen to be a little on the high side, too great to avoid secondary nucleation completely. Despite this, the main fraction of the crystal produced of 300 µm was found to be very uniform.

5. Conclusions The theoretical background for model-based control of supersaturation has been derived on the basis of the analysis of the first derivative of the supersaturation function with respect to temperature and applied together with measurement-based supersaturation control to the batch crystallization of MSG from an aqueous solution. A feedback strategy has been applied to control the level of supersaturation in the crystallizer via closedloop control as realized through a cascade of two loops: control of the temperature in the crystallizer itself and control of the temperature of the reactor coolant. Different control strategies were applied to obtain crystalline products with specific properties, i.e., mean size and crystal shape. The strategy of constant supersaturation throughout the batch has successfully yielded the desired product properties, notably uniformity in crystal size and shape. The size and shape of the crystals produced was found to be influenced in a significant manner, depending on the level of supersaturation. Alternative desupersaturation profiles were used to obtain distinct crystals. Altering the level of supersaturation during crystallization was chosen to enhance or suppress either nucleation or crystal growth. Realizing extremely high levels of supersaturation during the nucleation stage and rather low levels during the subsequent crystal growth stage yielded a uniform crystal product with a dominant fine fraction but very narrow CSD. The reverse supersaturation profile (low supersaturation for nucleation and high for crystal growth) has been successfully applied to obtain small numbers of uniform large crystals. The methodology applied here could be further developed and automated in order to routinely obtain a crystalline product with the required physical and chemical properties. Acknowledgment This work was carried out as part of the Chemicals Behaving Badly project financially supported by Grant GR/L/23055 provided by the U.K.’s Engineering and Physical Science Research Council (EPSRC) together with an industrial consortium including Astra Charnwood, BASF, Glaxo Wellcome, ICI, Malvern Instruments, Pfizer, SmithKline Beecham, and Zeneca. We gratefully acknowledge Chemicals Behaving Badly, notably industrial coordinator Leslie Ford, project team members for their interest in this work and for helpful discussions. A.B. also gratefully acknowledges the financial support of EPSRC/ROPA Grant GR/N/20300. Notation c ) solute concentration, kgsolute/kgsolution c* ) solute solubility, kgsolute/kgsolution ac, bc, cc ) constants in the second-order polynomial solubility equation (eq 2) j, k, l, m, n ) constants in the fourth-order polynomial solubility equation (eq 8) T ) temperature, °C P1, P2 ) intensities of ATR-FTIR peaks

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R ) calibration ratio w, x, y, z ) coefficients in the calibration curves (eqs 5-7)

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Received for review May 9, 2002 Revised manuscript received October 9, 2002 Accepted October 17, 2002 IE020346D

In-Process ATR-FTIR Spectroscopy for Closed-Loop Supersaturation

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