Airâliquid segmented continuous crystallization process optimization

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Air–liquid segmented continuous crystallization process optimization of the flow field, growth rate, and size distribution of crystals Min Su, and Yanyan Gao Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b05236 • Publication Date (Web): 25 Feb 2018 Downloaded from http://pubs.acs.org on February 25, 2018

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Industrial & Engineering Chemistry Research

Air–liquid segmented continuous crystallization process optimization of the flow field, growth rate, and size distribution of crystals Min Su*, Yanyan Gao School of Marine Science and Technology, School of Chemical Engineering, Hebei University of Technology, 300130 Tianjin, China *E-mail: [email protected]; Fax: 86-22-60204274; Tel: 86-22-60202812 ABSTRACT The continuous crystallization behavior of an air–liquid segmented tubular crystallization process was investigated using glycine as a model compound. Process parameters such as aspect ratio of the liquid slug, Reynolds number, seed loading, tubing material, slug shape, and tube crystallizer arrangement were optimized experimentally for their effect on the growth rate, size distribution, and morphology of the produced crystals. An Eulerian liquid–solid two-phase model coupled with a Realizable k–ε model in a computational fluid dynamics platform was also used to simulate the suspension state and the flow trajectory of crystals in a liquid slug. The results of our study indicated that a uniform crystal size benefiting from a heightened flow dynamics of crystals can be achieved under the following conditions: a slug aspect ratio of 1:1, 2 wt% seed loading, a Reynolds number higher than 22.87, a tubing material with highly hydrophobic properties and a resulted highly spherical slug shape. The growth rate of crystals was the highest when the slug aspect ratio of the slug was 1:1. Horizontal arrangement of the crystallizer has a more homogeneous slug velocity and crystal velocity in slug though the crystal growth rate and crystal size were not significantly affected by different arrangements. These results give an insight into the air–liquid segmented tubular continuous crystallization process. Keywords: Continuous crystallization; Segmented; Slug; Tubular; Crystal size; CFD 1. INTRODUCTION Crystallization plays an important role in the separation and purification methods used in the pharmaceutical industry. Crystal growth rate has a significant impact on the quality of the resulting crystal (i.e., purity and crystal size distribution (CSD)), which further influences downstream operations, stability, in vivo dissolution, and bioavailability of pharmaceutical products1. Continuous crystallization has drawn great attention in the pharmaceutical industry and academia alike, because it is considered to have the potential to reduce manufacturing costs by eliminating downtime, reducing contamination risks, and enhancing consistent control of crystal quality at the steady state2-4. Currently, continuous crystallization is achieved mainly by two popular approaches: mixed suspension mixed product removal (MSMPR) crystallizers and tubular crystallizers. MSMPR is an approach in which one or more well-stirred crystallizers are connected in series for multiple-stage operations. Multistage MSMPR crystallizers have been used to continuously produce active pharmaceutical ingredient (API) crystals under similar residence times (usually hours) and other process conditions5,6 and have achieved comparable yields5,7,8 and 1

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purities9,10 to the batch process. Recycling of the mother liquor has been shown to improve the yield and purity in MSMPR continuous processes. Seeding with the previous MSMPR products during startup can assist in reaching the steady state sooner, but no difference in chord length distribution (CLD) was observed compared with other seeding strategies11. However, the heat transfer in MSMPR is slow due to the low surface-to-volume ratio, which may generate a nonuniform temperature profile and can lead to growth dispersion inside the vessel12. Furthermore, a larger and wider CSD is obtained due to backmixing, dispersed residence time (RT), and strong agitation of crystals in the vessel12-14. Another method is to use a tubular crystallizer in order to approach plug flow. In tubular crystallizers, which have a high surface-to-volume ratio, uniform supersaturation and RT for crystal growth are achievable. Compared with the MSMPR crystallizer, the use of a plug flow crystallizer (PFC) can produce crystals with a narrower size and shape distribution and has the potential for high reproducibility and process efficiency at low capital and production costs13,14. A PFC can be operated using mixing parts inside the tube in an effort to resist crystal sedimentation. For example, a continuous oscillatory baffled crystallizer contains periodically spaced orifice baffles with oscillatory motion, providing efficient mixing while offering more control over the crystallization process15. Here, the use of a PFC combined with the Kenics-type static mixers provides smaller crystals with a narrower size distribution compared with the MSMPR crystallizer 16 . Another type of PFC is the coiled tube with a millimeter-scale inner diameter (ID). The concept of a continuous tubular crystallizer for the production of API was demonstrated to be feasible due to the significantly increased growth of acetylsalicylic acid crystals in a recent study17. Additionally, the potential for highly accurate crystal size tuning in a tubular crystallizer was demonstrated in the seeded cooling crystallization of acetylsalicylic acid18. Another potential method to reduce CSD is through the use of a coiled flow inverter19. The helically coiled flow tube crystallizer can potentially narrow the crystal size and shape distribution during growth, because small crystals flow more slowly and have more time to grow compared with larger crystals20. Maintaining a temperature profile along the tube is useful in controlling nucleation and growth rate21 and in removing fine crystals22 in order to achieve narrower size distribution. Under sonication, the crystal size of an API that crystallized from a non-Newtonian fluid was controlled to the desired range of 1–7 µm23. However, the PFC is prone to clogging issues when driven by only net flow24 or to generate a wide CSD due to breakage with the use of additional mixing parts inside the tube. By introducing another phase (air or an immiscible liquid) rather than moving parts into the solution flow in a tube crystallizer, the mass and heat transfer can be enhanced due to the formation of an internal circulation flow inside each slug that operates like a small well-mixed crystallizer. As a result, narrower RT distribution, narrower CSD, and less particle attrition/breakage can be achieved. Slug flow crystallization split by an immiscible fluid has been used in nanoparticle synthesis processes25,26 and pharmaceutical model compound processes27. As a separation unit is usually needed to separate the aqueous suspension and the immiscible fluid for environmental and cost considerations, this method can be quite complex26. Slug flow crystallization that uses air as the carrier fluid does not need a separating unit, making the process much simpler. The use of an air–liquid segmented slug flow crystallizer has already been applied in the field of nanoparticle synthesis28-32 but is relatively new to the field of pharmaceutical crystallization17,33,34. Large uniform crystals of L-asparagine monohydrate were 2


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generated in less than 5 min from a segmented air–liquid tube crystallizer33. Lysozyme crystals were also produced via slug flow crystallization, ranging from 15 to 40 µm in size34. Sonication application in the segmented tubular process has its merits too. Ultrasound-assisted seed generation incorporated with the segmented gas-slurry flow successfully yielded constant seed quantity and quality and more uniformly sized crystals35,36. Thus, it is clear that the gas–liquid slug flow crystallization process decreases crystal breakage, enhances heat and mass transfer, narrows RT distribution, and improves CSD. Some parameters that influence the nucleation and growth of crystals in a segmented tubular device have been found in the literature. Controlling the temperature profile along the tube is the main factor in controlling the nucleation and growth of crystals, especially in the cooling method. Modeling results suggest that a maximum of three heat exchangers is necessary for optimal design along the segmented tube37. A slug aspect ratio near 1 was found to produce better mixing than larger ratios33, however, in a similar gas–liquid microfluidic system, effective mixing was achieved in slugs regardless of their length38. This is probably due to the size difference of the tubing. A higher flow rate reduces the tendency of agglomeration and RT needed for crystal growth, and thus results in reduced overall size and narrow size distribution of the crystals17,34,39. By contrast, slow flow rates increase the risk of crust buildup and blockage of the tubing21. An increase in seed loading led to a decrease in the difference in the seed and product crystal sizes, allowing for a narrow CSD to be achieved21. Altering the arrangement of the coiled crystallizer with respect to spatial directions did not lead to a significantly different crystal size21. Although some results of the effects of specific process parameters on crystal size are found in the literature, the crystal mixing/suspension condition, flow trajectory, growth rate of crystals inside the slugs, final CSD, and even their balance in the process under various process conditions still need to be systematically understood and investigated. Additionally, the formation of crusts in the millimeter-scale tubing presents a risk of blockage. As Eder et al. has noted, the choice of the surface material can have positive effects on crust reduction21. Therefore, the objective of this work is to systematically investigate the effect of process factors, including the slug aspect ratio, Reynolds number, seed loading, tubing material, slug shape, and crystallizer arrangement, on the flow and mixing conditions of the slurry inside the liquid slug, growth rate, and size distribution of the crystal products. The flow trajectory, suspension state, and mixing homogeneity of crystals in the liquid slug were simulated using ANSYS® Fluent 15.0 (Fluent). The crystal growth rate and size distribution were characterized using a designed experimental method for an optimal air–liquid slug flow crystallization process. A continuously seeded cooling crystallization of glycine from water was used as the model that was studied for the segmented flow crystallization. 2. MATERIALS AND METHODS 2.1. Materials. For all the experiments, α-glycine (purity ≥99 wt%) and ninhydrin of reagent grade were purchased from Tianjin Kwangfu Fine Chemical Research Institute and used as received. Distilled water was used to prepare aqueous glycine solutions. 2.2. Crystallization procedures. As shown in Figure 1, the gas/liquid slug flow crystallization setup was comprised of three main sections: feeding slurry preparation, crystallization, and filtration. A jacketed beaker (500 mL) was used to dissolve glycine crystals 3



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and contain the feed slurry under constant stirring (KENUO, Magnetic stirrer, CJB-S140). Two water baths (XINZHI, DC-2015, ±0.2℃) were used to control the temperature of the feed and the tube crystallizer, respectively. The tube crystallizer (20 m in total length, including three sampling points at positions of 13, 15, and 18 m) had an ID of 3 mm and was connected to a Y-shaped mixer with the same ID. The contact angle of each flattened section of tubing was measured using a contact angle meter (DSA100, KRUSS). The liquid and air feeds were pumped through the Y-shaped mixer into the tube crystallizer through a peristaltic pump (LongerPump, BT100-2J). Vacuum filtration (Great Wall, SHB-ⅢA) for the separation of solid crystals was performed after the crystallization step. A predetermined mass of glycine crystals was first dissolved in distilled water (331 g/kg of water) at 47℃, higher than the saturation temperature (42℃) to guarantee that all the crystals were adequately dissolved. Once completely dissolved, the solution was slowly cooled to 42℃ to reach a supersaturation state in which primary nucleation did not occur. The theoretical solubility of α-glycine for the purpose of this study was taken to be 313 g/kg of water at 42℃40. Seeds (1–3 wt% of the solute) with a main particle size of 8 µm were added into the supersaturated solution as nuclei and remained in a uniform suspended state.

1,9-water bath, 2-magnetic rotor, 3-jacketed beaker, 4-magnetic stirrer, 5-air pump, 6-slurry pump, 7-Y shape mixer, 8-tube crystallizer, 10-sand core funnel, 11-suction flask, 12-vacuum pump Figure 1. Schematic representation of the experimental setup. Next, gas–liquid cooling crystallization was performed from 42 to 20℃. The slurry feed and air were passed through the two branches of the Y-shaped mixer at a set flow rate into a tube using peristaltic pumps, calibrated prior to the beginning of the experiments. The total flow rate in the tube crystallizer was thus the sum of the two pump flow rates. The relationship between rotation rate and volume flow rate is shown below in Eq. (1) for the air pump (Figure 1,5) and in Eq. (2) for the slurry pump (Figure 1,6). 1.8306 － 0.2581 0.9988 (1) 1.8354 0.1048 R 0.9992 (2) Here, , and , are the volume flow rate (mL/min) and rotation speed (rpm) of the air pump and the slurry pump, respectively. The aspect ratio (L /D) can be obtained after calculating the length ( ) of the liquid slug from Eq. (3), shown below.

t

(3)

Here, denotes the number of liquid slugs generated within a certain period of time t (min), L 4


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is the length of the liquid slug generated (mm), and D is the ID of the tube (3 mm). L /D is then defined as the ratio between the liquid slug length and the ID of the tube. The slurry slugs were constantly collected into a filter as they exited the tube, where they were then separated into aqueous mother liquor and solid crystals under vacuum. The concentration of the mother liquid was determined using a double-beam UV–visible light spectrophotometer (wavelength = 570 nm, YoKe Instrument, N6000plus) at room temperature. The morphologies of the crystals were characterized by scanning electron microscopy (SEM, FEI, Nova Nano SEM450) and CLD was measured using a focused beam reflectance measurement (FBRM, METTLER-TOLEDO, G400). The contact angle of the tube material was measured using the contact angle meter (KRUSS, DSA100). 2.3. Determination of the Concentration of Glycine Solution. A calorimetry method was used to determine the concentration of the glycine solution41-43. The first step was to establish a standard curve from which the concentration of glycine solution at the end of the crystallization process could be determined. Considering the existence of a detectable limit and a linear dynamic range of the quantitative concentration41, a glycine solution with a concentration of 0.4280 g/L was prepared. Different volumes (0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00, 1.05, 1.10, 1.15 mL) of the prepared glycine solution were then removed and placed in separate test tubes that were filled to 1.50 mL using distilled water. The diluted concentration values were calculated and used for correlation with future measured absorbance values. In all, 1.5 mL of HAc– NaAc buffering solution (pH 5.5) and 1.5 mL of a ninhydrin (3 wt%) reagent were then added into each test tube and mixed extensively after each addition. The solution was then incubated in a boiling water bath for 15 min, during which point the solution color changed from canary yellow to Ruhemann’s purple. Once the solution was removed from the bath and cooled to room temperature, 4.50 mL of 60 vol% ethanol was added into each test tube. The resulting solution was measured for λ = 570 nm absorbance using a UV–visible spectrophotometer. All the absorbance values were averaged from triplicate sets of data obtained from three independent sets of experiments. The data were then graphed to form a standard curve to which an equation was fit. On the basis of the standard curve, the concentrations of the glycine samples in the following experiments were determined using the method described for absorbance determination. 2.4. Computational Fluid Dynamics (CFD) Simulations. CFD modeling was used to formulate and solve the basic mass and momentum balances through numerical techniques. The Navier–Stokes (NS) fluid-flow equations are instationary and nonlinear, making solution computation difficult and time-consuming. To facilitate faster computation, the equations were linearized and solved over many small volume meshes (Figure 2). Assuming that the liquid ﬂow is in the steady state, the solution of the hydrodynamic is not time dependent, and the variations in viscosity and density are negligible, the NS model44 used is

ρu · ∇u ∇ · [pI + μ∇u + ∇u) ] + F (4) where ρ represents the density of the ﬂuid (kg/m3), u represents the velocity vector (m/s), p represents the pressure (Pa), µ represents the dynamic viscosity (Pa·s), T represents the absolute temperature (K), and F represents the volume force vector (N/m3). The flow fields of the liquid and crystals were simulated for each flowing liquid slug. As each liquid slug has the same conditions, one slug was used as the geometric modeling object. The 5



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geometry of the liquid slug was constructed in 3D using the software ICEM CFD 15.0 to reduce computational time. As depicted in Figure 2, the slug was represented by a column with an ID of 3 mm and was uniformly filled with hexahedral mesh cells. The high orthogonal quality allowed for ease in the follow up computation. The geometry was then imported into Fluent to simulate the flow field using numerical and computational methods.

Figure 2. Computational mesh established in ICEM CFD v15.0. CFD simulations were performed using a two-phase Eulerian–Eulerian model coupled with the Realizable k–ε model. The Realizable k–ε turbulent model and standard wall function submodels were used to describe the turbulence conditions of the liquid and solid phases. In the computing process, use of the Realizable k–ε turbulent model requires the turbulent kinetic energy and dissipation rate equations to be solved. The turbulent kinetic energy transport equations were derived by exact equations whereas the dissipation rate equations were obtained by means of physical reasoning and the mathematical simulation of similar prototype equations. The turbulent kinetic energy k and the dissipation rate equations ε are as follows: ,-

/

5

/-

ρ ,. /0 234 + 7 6 9 /0 : + ;- + ;< => ?@ 1

,A

8

/

5

AE

/A

ρ ,. /0 234 + 76 9 /0 : + =C D> =C -F 1

B

(5)

1

√A

1

A

+ CA - CHA ;

Airâliquid segmented continuous crystallization process optimization

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Airâliquid segmented continuous crystallization process optimization