Analysis of Crystal Size Dispersion Effects in a Continuous Coiled

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Analysis of Crystal Size Dispersion Effects in a Continuous Coiled Tubular Crystallizer: Experiments and Modelling Lukas Hohmann, Thorsten Greinert, Otto Mierka, Stefan Turek, Gerhard Schembecker, Evren Bayraktar, Kerstin Wohlgemuth, and Norbert Kockmann Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.7b01383 • Publication Date (Web): 24 Jan 2018 Downloaded from http://pubs.acs.org on January 29, 2018

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Crystal Growth & Design

Analysis of Crystal Size Dispersion Effects in a Continuous Coiled Tubular Crystallizer: Experiments and Modelling Lukas Hohmann1,‡, Thorsten Greinert1,†,‡, Otto Mierka2, Stefan Turek2, Gerhard Schembecker3, Evren Bayraktar2, Kerstin Wohlgemuth3, Norbert Kockmann1,* 1

TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory

of Equipment Design, Emil-Figge-Str. 68, D-44227 Dortmund, Germany 2

TU Dortmund University, Institute of Applied Mathematics (LSIII), Vogelpothsweg 87,

D-44227 Dortmund, Germany 3

TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory

of Plant and Process Design, Emil-Figge-Str. 70, D-44227 Dortmund, Germany KEYWORDS Batch-to-continuous, Continuous seeded tubular crystallizer, Coiled flow inverter (CFI), Suspension flow regimes, Population balance equation (PBE) modelling, Growth rate dispersion (GRD), Solid phase residence time distribution (RTDS)

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ABSTRACT

Continuous processing gains in importance in the fine chemical and pharmaceutical industry where crystallization is an important downstream operation. Seeded cooling crystallization of the L-alanine/water

system was investigated under similar conditions, i.e. temperature interval,

cooling rate, and seed material, both in a stirred batch vessel and in a continuous plug flow crystallizer in the coiled flow inverter (CFI) design with horizontal helical tube coils (ID = 4 mm) and frequent 90° bends of the coils. Short-cut calculations based on characteristic time scales and the Damköhler number allow for comparing the batch and continuous crystallization processes. The experimental results reveal crystal growth and growth rate dispersion to be dominating on the product crystal size distribution (CSD). However, at low flow rates of approximately 31 g min-1 a moving sediment flow of the slurry was present in the CFI crystallizer, resulting in further size dispersion effects. Elevated flow rates of approximately 40 g min-1 resulted in a more homogeneous suspension flow and a product CSD comparable to batch quality. Simulation studies based on a population balance equation model strengthen the hypothesis of the solid phase residence time distribution (RTDS) to be more spread in the moving sediment flow regime, leading to a wider product CSD.

1. INTRODUCTION The development of continuously operated fine chemical and pharmaceutical processes is widely discussed in current literature. Multiple drivers are claimed in comparison with conventional batch processes, such as constant product quality in steady-state operation, inherent process safety due to lower hold-up volumes, increased resource/energy efficiency by utilizing


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process-intensified equipment and integrated processes, and a shorter time-to-market for new products, when utilizing versatile equipment concepts.1–7 Various studies proof the general applicability of small-scale equipment for continuous manufacturing in dedicated examples8–13. Johnson et al.8, Mascia et al.9, Roberge et al.10 and Cole et al.13 describe typical processes and unit operations for continuous manufacturing of pharmaceuticals. Klutz et al.11 present a fully continuous biopharmaceutical pilot plant for the production of monoclonal antibodies. Adamo et al.12 report on a combination of continuous and (semi-)batch unit operations for end-to-end production of various pharmaceuticals with a modular equipment setup. However, multi-purpose equipment toolboxes12,14,15, equipment databases16,17, and concepts for module-based plant design and integrated product/process development9,12,16–20 are still under investigation rather than being in widespread industrial application. Due to the typical properties of fine chemical and pharmaceutical products, handling of solids often becomes crucial, when dealing with small-scale flow processes. Solids can be involved both during synthesis21–23 and during downstream operation, as crystallization from solution is a common unit operation for purification and final product isolation from the synthesis solvent8,9,12,13,24–27. This work focuses on cooling crystallization. Concerning continuous cooling crystallization the fully back-mixed ‘mixed suspension mixed product removal crystallizer’ (MSMPR) and the ‘ideal plug flow (PF) crystallizer’ are discussed as basic equipment concepts28,29. In small-scale processes with suspension throughputs in the g h-1 to g min-1 range MSMPR behavior is typically realized by stirred vessels with both a continuous feed and a product stream27,30–32. A robust slurry mixing can be achieved, as stirrer type and stirrer speed are


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degrees of freedom and can be adjusted accordingly to the product properties30. MSMPR crystallizers are usually self-seeded due to secondary nucleation by collision effects and surface breeding, which stabilizes both the process and the product crystal size distribution (CSD)33. However, a broad product CSD is usually expected due to back-mixing and mixed product removal28,29. Equipment concepts for PF crystallizers gain increasing interest as an ideal PF crystallizer might offer process conditions and a product CSD comparable to an ideal batch crystallizer. However, the slurry handling is an issue as tubular devices are prone to settling of solids, wall scaling and clogging23,25,30. Moreover, concerning cooling crystallization, the temperature control along the axial coordinate is challenging25. A common concept to approach PF behavior is to connect multiple MSMPR devices in series. Each stage can be operated at another temperature level in case of cooling crystallization, but usually multiple cooling thermostats are required27,32,34. However, the number of stages is practically and economically limited due to equipment number and control. Solution and slurry transfer to the following vessel can be achieved, e.g. by peristaltic pumps27,32, interconnected pressurized vessels30,35, or vacuum-driven semi-continuous suspension transfer34,36–38. A renowned approach to a tubular PF crystallizer is the continuous oscillatory baffle crystallizer (COBC)39. The net suspension flow through horizontal baffled tubes is superimposed by a forward-backward piston pulsation. Thus, eddies evolve at the internal baffles intensifying radial mixing and heat transfer, while decoupling the slurry movement from the net flow. Kacker et al.40 and Ejim et al.41 recently investigated the slurry transfer in COBC devices and proved the residence time distribution of the solid phase (RTDS) to differ from the one of the liquid phase (RTDL) over a range of operational conditions and equipment arrangements. The measured mean


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residence time of the solid phase ̅ was observed to be higher than the calculated hydraulic mean

residence time of the slurry . This was mainly attributed to gravitational settling of the solid

phase in the horizontal tubes. The axial temperature profile for cooling crystallization in a COBC is typically achieved by multiple cooling thermostats being attached to the jackets of the crystallizer segments42,43. Apart from active mixing in the COBC, passive mixing was reported in static mixer44 and coiled tube45–54 PF crystallizers. Coiled tubes are usually operated without intense pulsation, except a moderate forward pulsation of the peristaltic feed pumps which are usually applied. Thus, the slurry transfer is solely achieved by the net fluid flow. Typically, those devices are operated in the laminar flow regime. In order to overcome the limitations of the laminar flow regime with respect to the radial heat and mass transfer and wide RTDL, coiling of the tubes induces centrifugal forces leading to a secondary flow profile. These so-called ‘Dean vortices’ significantly enhance radial mixing leading to a narrower RTDL compared to straight tubes55. Moreover, particle fluidization56,57 is intensified and wall scaling can be decreased58. The positive effect of coiling on the RTDL can be further enhanced by frequent bending of the coils with an angle of 90° in so-called ‘coiled flow inverter’ (CFI) devices59–62. Hence, the directions of the centrifugal forces change and the orientations of the Dean vortices reorganize, while breaking-up stagnant regions in the center of the vortices. This can lead to an RTDL close to ideal plug flow60,63,64. PF crystallizer designs with vertical helical coils45,51–54, horizontal helical coils46 and a CFI crystallizer with horizontal helical coils and eight 90° bends50 were reported. In an investigation of Eder et al.48, the orientation of helical coils was found to have no significant impact on the


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crystallization process and product quality. However, solids with a higher density compared

with the solution density are known to segregate from the solution in vertical helical coils due to the gravitational force, forming a sediment on the lower surfaces of the tubes57,65. Wiedmeyer et al.53,54 recently reported the mean residence time ̅ of crystals in a vertical helical

coil to be higher than the calculated hydraulic residence time of the slurry . Furthermore a size-dependent RTDS was observed, with larger particles moving significantly faster through the tube compared to smaller particles. Applying coiled tubular devices for cooling crystallization, axial temperature profiles are typically achieved by placing the coils into one47,51,53,54 or multiple48,52 cooling bathes at different temperature levels, or by tube-in-tube counter-current cooling with a significantly higher flow rate of the cooling agent compared to the crystallizing solution45. In our previous studies50,66

counter-current cooling with similar heat capacity flows for both the crystallizing solution/slurry and the cooling agent has been shown to allow for adjusting smooth linear and curved axial temperature profiles in a lab-scale CFI cooling crystallizer with a single cooling thermostat. In addition to the helical coil crystallizers with a single continuous fluid phase, tubular twophase slug flow crystallizers with a crystallizing solution/slurry and a second immiscible gas67–74 or liquid phase75–77 are reported. The second phase supports the slurry transfer in the tubes and provides a narrow RTD of both phases. Regardless of the equipment concept, spontaneous nucleation was found to be less operable in tubular cooling crystallizers. Either rapid wall scaling and clogging42,47,50 was promoted by heterogonous nucleation on the tube walls or a clear supersaturated (metastable) solution was


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obtained at the crystallizer outlet45,50. Therefore, spontaneous nucleation is to be avoided either by inducing primary nucleation68–70 or by applying continuous seeding42,47–49,52,74. In this work, the seeded cooling crystallization of L-alanine from an aqueous solution was investigated both in a stirred batch vessel crystallizer and in the continuous horizontal CFI crystallizer50. Experimental CSD data from both setups are compared by short-cut modeling and are further used within a population balance equation (PBE) model in order to analyze and quantify the impact of crystal growth, growth rate dispersion (GRD)28,78 and RTDS on the width of the product CSD of the continuous crystallizer. Thereby, the suitability and the limitations of the CFI crystallizer technology for the transfer from batch to continuous processing is evaluated.

2. EXPERIMENTAL METHODS 2.1. Materials For all experiments in this study L-alanine/water was used as a binary model system. Saturated solutions were prepared from crystalline L-alanine (purity ≥ 99.6 %, Evonik Rexim (Nanning) Pharmaceutical Co., Ltd., China) and deionized water (conductivity ≤ 5 µS cm-1) which was obtained on-site. Seed crystals were prepared from crystalline

L-alanine

(purity ≥ 99 %,

Sigma-Aldrich Chemie GmbH, Germany).

2.2. Equipment The flow diagram of the entire experimental setup is depicted in Figure 1-a). In this setup both batch and continuous mode experiments can be performed. For the batch cooling crystallization experiments, a cylindrical jacketed vessel (0.1 L, glass), equipped with a heating/cooling thermostat (Ministat 125, Peter Huber Kältemaschinenbau AG, Germany), a magnetic stirrer


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(MR Hei-Tec, Heidolph Instruments GmbH, Germany), and a double cross-head magnet stirrer bar (PTFE, 13 x 17 mm, VWR international, USA) was used, see Figure 1-b). The temperature in the batch crystallizer was measured via a Pt100 resistance thermometer (RÖSSEL Messtechnik GmbH, Germany). A lab-scale CFI cooling crystallizer50 with 9 helical segments and eight 90° bends, being equipped with a tube from anti-adhesive, chemical resistant fluorinated ethylene propylene (FEP,

Bohlender GmbH, Germany, inner diameter = 4 mm, coil diameter = 41 mm, total tube length = 6.54 m), was used for continuous cooling crystallization experiments see Figure 1-c).

A heating/cooling thermostat (Ministat 125, Peter Huber Kältemaschinenbau AG, Germany) generated precooled air as a cooling agent for counter-current cooling of the slurry. The axial temperature profiles of the crystallizing slurry and the gaseous cooling agent were measured by five thermocouples (OMEGA Engineering GmbH, Germany) and five Pt100 resistance thermometers (RÖSSEL Messtechnik GmbH, Germany), respectively. For each sensor a twopoint calibration was carried out with a temperature-controlled water bath. A non-invasive temperature measurement of the slurry was performed from the outer side of the tube. Further details on the design of the CFI crystallizer50 and the temperature measurement technique66 are described elsewhere.


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Figure 1. Experimental setup, a) flow scheme, switching of the 3-way valve allows for performing either batch or continuous experiments, b) setup for batch cooling crystallization, c) setup for continuous crystallization The seed crystal suspension for both the batch and the continuous crystallization experiments was prepared in a jacketed storage vessel (3.5 L, glass), equipped with a heating thermostat (CC 304, Peter Huber Kältemaschinenbau AG, Germany), another magnetic stirrer (MR HeiTec, Heidolph Instruments GmbH, Germany), and a triangular magnetic stirrer bar (PTFE, = 50 mm, Bohlender GmbH, Germany). A further jacketed vessel (1.0 L, glass) was used to provide pre-heated solvent for the start-up and shut-down procedures of the CFI crystallizer. A peristaltic pump (LabDos, HiTec Zang GmbH, Germany) equipped with a 3 roller pump head (Masterflex EasyLoad L/S, Cole-Parmer, USA) and PVC tubing (Tygon® R-3603,

= 3.2 mm, Saint-Gobain Performance Plastics, France) was used to transfer the seed crystal suspension from the storage vessel to the batch crystallizer and to feed the CFI cooling


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crystallizer in batch and continuous experiments, respectively. In case of the continuous experiments, the product was captured on scales (572, Kern & Sohn GmbH, Germany, accuracy ± 0.01 g) in order to measure and control the mass flow rate of the slurry. The process automation, the monitoring of sensor data and the control of thermostats and the peristaltic pump were performed by means of a process automation system (LabManager® and LabVision® software, HiTec Zang GmbH, Germany).

2.3. Preparation of Seed Crystal Suspension Seeded batch and continuous crystallization experiments were designed in order to analyze and characterize the performance of the CFI cooling crystallizer. Due to the given design of the crystallizer prototype a maximum cooling interval of ∆,, = 4.7 – 6.0 K was achieved,

see chapter 3.2. The temperature for the seed suspension was set to = 30 °C in order to gain a product suspension temperature after cooling close to room temperature = 24.2 ± 0.1. The same seed suspension was used both for batch and continuous experiments.

Initially, a saturated aqueous L-alanine solution at = 30 °C was prepared for the seed

suspension. The saturation mass fraction of the binary system and the corresponding

saturation mass loading of the solvent were calculated according to experimental solubility data79, eq. (1).

gala ·g-1 ! = 1.12381∙10-1 ∙ exp'9.08492·10-3 ∙ (°C)* sol gala ·g-1 ! solv

= 1 −

(1)

Deionized water and crystalline L-alanine were weighed into the 3.5 L seed suspension storage

vessel, to achieve = 3.5 kg of solution. The mixture was heated to = 35 °C, and stirred at - = 400 min-1 for at least 2 h in order to completely dissolve the crystalline

L-alanine.


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Afterwards, the solution was filtered with an immersion filter (ROBU® Glasfilter Geräte GmbH, Germany, pore width 10 - 16 µm) by pumping it through the filter into a second identical jacketed vessel to remove insoluble impurities. The solution was subsequently cooled to the saturation temperature of = 30 °C and kept at this temperature for at least 30 min.

Seed crystals were dry-pestled and sieved on a vibrating sieve tower (Retsch GmbH, Germany)

for 30 min. A sieve fraction of 125 µm ≤ /0 ≤ 160 µm was obtained. The seed crystals were added to the solution by manually sieving the material through a 160 µm test sieve directly into the seed suspension storage vessel in order to disaggregate crystals, while stirring the suspension

at - = 400 min-1 in order to homogeneously suspend the slurry. A mass fraction of dissolved L-alanine

in the saturated solution of , = 0.148 gS gsol-1 and a seed crystal mass fraction

of , = 0.01 gS gsusp-1 in the seed suspension were prepared, see Table 1 and Table 2. The amount of seed crystals was chosen both to suppress undesired secondary nucleation, see chapter 2.4, and to enable a reliable CSD measurement, see chapter 2.6. In this study, the seed suspension preparation was executed twice on different days with separately pestled and sieved crystals (batch ‘a’ and ‘b’). From each suspension batch, various batch and continuous experiments were conducted, see Table 1 and Table 2. The seed crystal CSD was analyzed for each batch and continuous experiment after feeding the suspension to the corresponding crystallization devices by means of sedimentation analysis, see chapter 2.6.

2.4. Batch crystallization experiments For each batch experiment a defined amount of seed suspension (1 = 0.1 L) was fed from the

seed suspension storage vessel to the stirred batch crystallizer (- = 400 min-1) by means of the peristaltic pump, whereas the jacket temperature of the batch crystallizer was kept constant at


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= 30 °C. The flow rate of the pump was similar to the later continuous experiments at approximately 30 g min-1, refer to chapter 2.5.

The batches were subsequently cooled down from 2 = 30 °C over 3 = 27 °C to room

temperature 4,53 = 24 °C or 4,54 = 23 °C, respectively, with two different linear set cooling rates 6 , while controlling the temperature of the suspension via the heating/cooling thermostat.

Thereby, a temperature interval and cooling rates comparable to the continuous crystallization experiments were covered. The batch time is defined by temperature interval and cooling rate. The experimental conditions of the batch experiments are summarized in Table 1. For each set cooling rate and each seed batch 78 = 2 experiments were conducted.

Table 1. Set process conditions of batch cooling crystallization experiments B1 and B2; errors on weighing solids and solvent for seed suspension preparation are given in parentheses.

9 [L]

B1

:; 3È

3 4 4 3 4

'Ý]ÝX *

Ý

(26)

v 4 − Û]3 ≤ 3È

(27)

∙ ©Ý ÚØ jTÛ W ∙ Û Û = 'ÝX ]ÝX *

4

ÚØ

3È

Ý Þ ⁄4

4 ÚØ ∙ v4 ∙ TÛÙ3 − Û4 W − TÞÙ4W∙T ⁄4Wè ]ÝX *

4∙ç¡R¢ ÚØ

3

'Ý èÚX ]Ý èÚX *

T ⁄4WX

(28) (29)

Figure 10 depicts analytical and numerical solutions from discretization for the effective radial

velocity profiles of the solid phase and corresponding RTDS for various values of . Due to the different effective flow velocities in the annular sections, various mean residence times of the crystals in the crystallizer can be represented. On this base, the PBE model was solved simultaneuously for each annular section. An ideal mixing in the radial direction was assumed for the liquid phase, but in contrast no transfer of crystals between the annular sections was considered. The overall product CSD in the outlet was calculated by superimposing the resulting CSD from each annular section, by means of the weight function é , eq. (29).


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Figure 10. a) Effective radial velocity profiles of the solid phase for various curvature

parameters ; b) corresponding step response function for the RTDS; the solid lines depict the discretized numerical solution, dashed/dotted lines depict the analytical solution.

4.2. Simulation results (batch) 4.2.1. Size-independent growth According to the presented hypothesis of a non-negligible RTDS in the CFI crystallizer the model parameters for the BCF growth kernel, eq. (22), were estimated from batch results in a first step. Four different scenarios were used, incorporating both the first part of the batch

cooling crystallization processes from 2 to 3 (B1-1, B2-1), and the entire processes from 2 to

4 (B1-2, B2-2), see chapters 2.4 and 3.1. The parameter estimation was performed

simultaneously for all of the four scenarios by means of the least-square-method with regard to the CSD. The ‘simulated annealing algorithm’ was used to find the region of the global optimum of the problem, followed by the ‘Nelder-Mead simplex algorithm’ for identifying a local optimum, according to a method being proposed by Besenhard et al.95. Both algorithms are pre-


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implemented to MATLAB®. The parameter estimation was carried out eight times. All batch scenarios can be covered by a single parameter set É5ÊË and ê5ÊË , see Table 5. However, a

slight deviation in É5ÊË was found between individual estimations, as various local solutions

close to the global optimum were identified by the algorithms.

The estimated parameter É5ÊË is significantly higher compared to the results of

Wohlgemuth et al.79, who estimated the BCF parameters in the range of É5ÊË ~ 100 µms-1 for the

L-alanine/water

system and crystals resulting from primary nucleation and growth in

solution. This difference can be attributed to the mechanical pre-processing of the seed crystals in this work, leading to a rough surface of the crystals, which provide numerous dislocations for growth. Concerning the parameter ê5ÊË the same order of magnitude was found compared to

Wohlgemuth et al79. The hyperbolic tangent term of the BCF model, eq. (22), which incorporates

the parameter ê5ÊË , limits the growth rate at high supersaturation levels. As the maximum

supersaturation level KL8 is small in this work, see chapter 2.4, the impact of this term is rather low, leading to an elevated variance when estimating this parameter.

Table 5. BCF parameters for the four batch scenarios, deviations of the parameter estimation are given in parentheses.

ìíîï [µms-1]

ðíîï [-]

58.57 (± 0.03)

0.913 (± 0.370)

Comparing experimental and simulated CSD results with this parameter set and modeling solely size-independent growth, the product mode size is well represented, see Figure 11 and Table 6 However, the width of the CSD is underestimated, except experiment B1-1. This


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indicates further phenomena to be present in addition to crystal growth, influencing the width of the CSD. The good agreement of model and experiment B1-1 should not be overinterpreted concerning the uncertainties of the CSD measurement, see chapters 2.6 and 3.1.

Figure 11. Comparison of experimental and simulated CSD for the batch crystallization scenarios. The PBE model incorporates size-independent growth, only. Table 6. Regression coefficients Û 4 and mean growth rates ̅ from simulating the four batch crystallization scenarios incorporating size-independent growth, only.

ñB [%]

µ ´ [µms-1]

B1-1

99.82

0.0075

B1-2

82.12

0.0176

B2-1

98.14

0.0074

B2-2

86.33

0.0262


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4.2.2. Growth rate dispersion From the slope ½¾p of the batch crystallization experiments, see Figure 9, and the mean

growth rate ̅ resulting from simulation of size-independent growth, see Table 6, the PBE model

incorporating GRD can be parametrized. The results for the crystallization batch scenarios are summarized in Figure 12 and Table 7. The simulation gives a good approximation of the experimental data, taking the uncertainty of the CSD measurement into account, see chapters 2.6 and 3.1.

Figure 12. Comparison of experimental and simulated CSD for the batch crystallization scenarios. The PBE model incorporates size-independent growth and GRD.


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Table 7. GRD coefficient £ and regression coefficients Û 4 from simulating the four batch crystallization scenarios incorporating size-independent growth and GRD.

º´ [µm2s-1]

ñB [%]

B1-1

0.1743

98.70

B1-2

0.4110

96.88

B2-1

0.1727

99.52

B2-2

0.6114

97.09

4.3. Simulation results (continuous flow) To simulate the continuous flow scenarios C1, C2, and C3, see chapters 2.5 and 3.2, the growth kernel parameters from the batch crystallization scenarios and the GRD coefficient based on the batch experiments, see Figure 9, were utilized. No additional parameter estimation according to these phenomena was applied. The results incorporating size-independent growth and GRD give a proper approximation of the experimental CSD data with respect to mode and width, see Figure 13 and Table 8. This result indicates crystal growth and GRD to be the major crystallization phenomena in continuous mode, as well. Furthermore, equal growth kinetics of the seed crystals both in the stirred batch and the continuous CFI crystallizer are indicated. This implies the mean residence time of the crystals in the CFI crystallizer to be approximately equal to the overall mean residence time of the slurry, even when the moving sediment flow regime is present. This might be an advantage of horizontal coil devices for suspension application in comparison to COBC or vertical coils, see chapter 1. However, the fit is worse compared to the

batch experiments regarding to the regression coefficient Û 4 , compare Table 7 and Table 8. A


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flow regime depended RTDS in the continuous mode experiments could explain residual differences between experiments and model.

In order to incorporate the RTDS in the model the parameter was estimated while minimizing

the residual errors between experimental and simulation results. The ‘Nelder-Mead simplex algorithm’ was used. As it was expected from the flow profile observation the scenarios C1 and

C2 yield in significantly lower values of than scenario C3, see Table 8. This indicates a broader RTDS in the moving sediment flow regime compared to the homogeneous suspension flow regime, which approaches ideal plug flow even for the solid phase.

Figure 13. Comparison of experimental and simulated CSD for the three continuous flow experiments. The simulations incorporate size-independent growth, GRD, and RTDS. Table 8. Model parameters and regression coefficients Û 4 from simulating the three continuous flow scenarios incorporating size-independent growth, GRD, and RTDS. growth

µ ´ [µms-1]

growth, GRD

º¹ [µm2s-1]

ñB [%]

growth, GRD, RTDS

ñB [%]

ñB [%]

» [-]

C1

91.44

0.0294

0.6869

96.98

98.76

2.438

C2

89.25

0.0397

0.9266

96.73

97.55

3.037

C3

94.30

0.0406

0.9468

98.24

98.66

10.160


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The simulation results with the PBE model incorporating crystal growth, GRD, and RTDS agree well with the experimental results, especially for scenario C3 with small deviations from plug flow, see Figure 13. The simulation results for C1 and C2 reveal a bimodal CSD due to the relatively wide RTDS, which results from the crystal fractions in the annular sections close to the wall with disproportionally high mean residence times. Nevertheless, a bimodal CSD can be neither resolved by the LND, nor by the utilized analytical method for the CSD measurement. As the utilized RTDS model is based on an effective radial velocity profile, rather than modeling the true 3D fluid and particle dynamics, the predicted bimodal CSD could be an artifact of the RTDS model, as well. As discussed in chapter 3.2, future research should focus on the fluid dynamic fundamentals of the solid/liquid suspension flow in horizontal helical coils and CFI, incorporating the RTDS of a particle population.

5. CONCLUSION Continuous solution crystallization is an active field of research and various equipment concepts are under investigation in order to overcome major technical challenges, such as sedimentation, wall scaling, equipment plugging, residence time distribution of the solid phase (RTDS) and temperature control. The presented horizontal coiled flow inverter (CFI) crystallizer with counter-current cooling offers nearly ideal plug flow of the liquid phase and a smooth linear axial temperature profile. In seeded operation two different flow regimes with respect to the slurry were observed as the crystals tend to sediment in the tubes due to the gravitational force, which can be overcome by sufficient fluid drag force. In contrast to various literature studies on vertical coil devices, an


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approximately homogeneous distribution of crystals over the tube cross section was achieved at sufficiently high flow rates, here: = 39.9 g min-1 in the CFI crystallizer with an inner tube

diameter of = 4 mm and L-alanine crystals with a median size of /q2,2 = 98.3 – 108.5 µm.

As the CFI crystallizer equipment addresses the field of transferring processes from batch to continuous operation, the batch product quality, here the width of the product crystal size distribution (CSD) was regarded as a benchmark. Comparing the results from batch and continuous operation, crystal growth and growth rate dispersion (GRD) were both dominating the product CSD. However, a flow regime dependent RTDS in the CFI crystallizer was indicated by the wider product CSD in the moving sediment flow regime compared to the homogeneous flow regime and the batch results. When operating the CFI crystallizer in the homogenous suspension flow regime, a narrow product CSD comparable to the products from batch operation was indicated by the accompanying simulation study. Therein, a population balance equation (PBE) model with a numerical solution method was proposed for the seeded continuous tubular crystallizer, incorporating an RTDS model based on the convection model of the laminar flow. A simulation study accompanying to the experiments proved that the growth rate and GRD kinetics are transferable from batch to continuous operation, when using seed crystals with similar mechanical pretreatment. Furthermore, the results indicated the mean residence time of the crystals in the CFI crystallizer to be approximately equal to the overall mean residence time of the slurry for both flow regimes. Nevertheless, different RTDS in the tubular crystallizer affect the CSD. Therefore, the PBE study helps analyzing local phenomena in the tubular crystallizer while experimental investigations are integral. A CFI crystallizer being operated in the homogeneous flow regime is a promising


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equipment concept for transferring crystallization processes from batch to continuous mode while maintaining the product quality with respect to the width of the CSD. In a commercial application of CFI crystallizer equipment with a larger cooling interval and a longer residence time, the homogeneous flow regime needs to be maintained not only for the seed crystals but as well for the growing product crystals. Current research work focusses on a deeper understanding of the solid/liquid suspension flow characteristics in horizontal helical coils and CFI. The equipment and process design for such crystallizers needs to incorporate geometry parameters of the device, process parameters such as flow rate and temperature level, as well as the system properties such as particle sizes, densities of the involved phases and the solution viscosity. The process and equipment design further requires the time scales of the system to be balanced. The presented concept using the Damköhler number of the crystallization process can aid such a design on the base of short-cut calculations and few experimental studies from laboratory scale.

ASSOCIATED CONTENT Supporting Information. The following files are available free of charge: (1) Supporting information on the utilized physical properties data of the

L-alanine/water

system with empirical correlations for the

dynamic viscosity and density of the solution and details on the CSD measurement (PDF). (2) Video clip of the suspension flow behavior in a helical coil in the middle of the CFI cooling crystallizer during the experiments C2 and C3. The moderate forward pulsation of the peristaltic pump is apparent (AVI).


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AUTHOR INFORMATION Corresponding Author *Prof. Dr.-Ing. Norbert Kockmann, TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Equipment Design, Emil-Figge-Str. 68, D-44227 Dortmund, Germany, [email protected] Present Addresses †TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Thermodynamics, Emil-Figge-Str. 70, D-44227 Dortmund, Germany Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ‡These authors contributed equally. Funding Sources This research was funded by the German Federal Ministry of Economic Affairs and Energy (BMWi) and the Project Management Jülich (PtJ) as part of the ENPRO initiative (ENPRO-SMekT grant number: 03ET1254D). ACKNOWLEDGMENT The authors acknowledge Martin Matuschek, Lukas Bittorf, and Oliver Klaas for their experimental support. Prof. Dr. Andreas Seidel-Morgenstern (Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany) is acknowledged for the fruitful discussion on the Damköhler number in crystallization processes at the ‘2017 ProcessNet Crystallization Annual Meeting’ in Cologne, Germany.


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ABBREVIATIONS BCF CFI COBC CSD FEP GRD LND MSMPR ODE PBE PDE PF RTD

Burton Cabrera and Frank coiled flow inverter continuous oscillatory baffle crystallizer crystal size distribution fluorinated ethylene propylene growth rate dispersion log-normal distribution mixed suspension mixed product removal ordinary differential equation population balance equation partial differential equation plug flow residence time distribution


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SYMBOLS

É5ÊË ê5ÊË ¾p l 7 7 ½¾p d2 2 Û Û4 6 K ew4V

∆N ̅ j 1 1 é / /q2,2

growth kernel parameter, ms-1 model parameter, effective velocity profile, growth kernel parameter, Damköhler number, dispersion coefficient of GRD, m2s-1 extinction, diameter, m growth rate, ms-1 tube length, m mass flow rate, kgs-1 number of samples, crystal loading of the solvent, #Sgsolv-1 revolution speed, s-1 slope in size dispersion plot, m cumulative number-weighted CSD, number-weighted density function of CSD, m-1 tube radius, m regression coefficient, time-based cooling rate, Ks-1 supersaturation, variance of the CSD, m2 temperature, K batch process time, s experiment runtime, s measured mean residence time, s characteristic time scale of crystal growth, s flow velocity, ms-1 volume, m3 volumetric flow rate, m3s-1 weight function mass fraction of component Ç in mixture ò, gigj-1 solvent mass loading of component Ç , gigsolv-1 particle size, m median particle size, number-based, m


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Greek symbols

k mnop

dynamic viscosity, Pas density, kgm-3 model parameter of the LND, hydraulic mean residence time, s

Indices c cool exp i L max prod S sat sed seed set sol solv susp ∞

coil cooling agent experiments inner tube liquid phase maximum product material solid phase saturation conditions sedimentation process seed material setpoint solution solvent suspension, slurry environment

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Analysis of Crystal Size Dispersion Effects in a Continuous Coiled Tubular Crystallizer: Experiments and Modelling

Lukas Hohmann, Thorsten Greinert, Otto Mierka, Stefan Turek, Gerhard Schembecker, Evren Bayraktar, Kerstin Wohlgemuth, Norbert Kockmann

Experimental investigations on the seeded cooling crystallization of L-alanine from aqueous solution in a coiled tubular plug flow crystallizer were benchmarked with batch experiments and analyzed by means of the Damköhler number. Population balance equation modeling was used to characterize size dispersion effects, i.e. growth rate dispersion, and solid phase residence time distribution.


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Analysis of Crystal Size Dispersion Effects in a Continuous Coiled

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