Scale-Up of a Semi-Batch DTB Crystallizer for HNIW Based on

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Scale-Up of a Semi-Batch DTB Crystallizer for HNIW Based on Experiments and CFD Simulation Dong-Hoon Oh, Rak-Young Jeon, Jun-Hyung Kim, Chang-Ha Lee, Min Oh, and Kwang-Joo Kim Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.8b01237 • Publication Date (Web): 27 Dec 2018 Downloaded from http://pubs.acs.org on January 1, 2019

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Crystal Growth & Design

Scale-Up of a Semi-Batch DTB Crystallizer for HNIW Based on Experiments and CFD Simulation Dong-Hoon Oh 1, Rak-Young Jeon2, Jun-Hyung Kim3, Chang-Ha Lee1, Min Oh2,* , Kwang-Joo Kim2,* 1Department

of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu,

Seoul 120-749, Korea 2Department

of Biological and Chemical Engineering, Hanbat National University, 125 Dongseodaero,

Yuseong-gu, Daejeon 305-718, Korea 3Agency

for Defense Development, 462 Jochiwon-gil, Yuseong-gu, Daejeon 305-150, Republic of Korea

*Correspondence: Min Oh (E-mail: [email protected]), Kwang-Joo Kim (E-mail: [email protected]), Department of Biological and Chemical Engineering, Hanbat National University, Daejeon, Korea

ABSTRACT In this study, experimental work was carried out for the production of micro-sized HNIW in a 500 L DTB crystallizer. Acetone and isopropanol were used as solvent and antisolvent. From the experimental data, the kinetic parameters of the nucleation and growth rates were obtained by maximum likelihood method. The kinetics was then used in the computational fluid dynamics simulation of a 500 L crystallizer. The simulation results were compare figure 4d to the experimental data in terms of crystal size distribution, which showed a marginal error. Based on this mathematical model, the 500 L DTB crystallizer was scaled-up to 3000 L. Case studies were conducted on the 3000 L DTB crystallizer with one and two draft tubes. HNIW solid volume fraction, particle mean diameter, and particle size distributions were compared for both cases, which led to the conclusion that two draft tube crystallizer showed better performances. Finally, the result of this paper envisaged dealing with the scale-up of the DTB crystallizer based on the experimental results and CFD simulations.

1. Introduction Hexanitrohexaazaisowurtzitane (HNIW) is a nitramine class and one of the most powerful non-nuclear explosives. It is primarily used in propellants and has a reduced sensitivity that enhances easy handling and transportation. HNIW has four kinds of typical polymorphs (𝛼, 𝛽, 𝛾and 𝜀). The 𝜀 form of HNIW with the lowest sensitivity is the most thermodynamically stable and has the highest density.1 Hence, the 𝜀 form of HNIW is mostly used in energetic materials. To produce HNIW 20 micrometer crystals of the 𝜀 form, this study used a draft tube baffled (DTB) crystallizer. DTB crystallizer produces larger crystals by reducing the generation of fine crystals upon a combination of low mechanical impact and thermal fine destruction.2 Therefore, it can narrow

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down the HNIW particle size distribution (PSD). It is important to narrow the particle size distribution in crystallization experiments because poor PSD causes problems such as high crystal separation cost, and low product quality in manufacturing processes.3 Many researchers have studied DTB crystallization using experiments and Computational Fluid Dynamics (CFD) simulations. CFD is a powerful tool for simulating the behavior of systems involving fluid flow, heat transfer, and other physical processes. It does not require the production of a model or instrument and so it saves time and money. In recent times, many researchers are carrying out CFD simulations to complement their experimental results. Rane et al.4 studied the principles of various industrial crystallizers and their flow patterns and reported that adding a draft tube to a crystallizer reforms its flow field. A DTB crystallizer with an axial impeller could provide a suitable flow field for the vacuum evaporation crystallization of ammonium sulphate.5 By performing CFD scale-up, the results of the nucleation and crystal growth of the crystallizer can be obtained in three dimensions. The model also helps to improve the industrial process. However, there is a problem in scaling up a crystallizer. In a large-scale crystallizer, it is difficult to achieve mixed suspension because an enormous amount of energy is required to suspend the crystals uniformly.6 Also, a significant problem of scale-up is that different physical processes become limiting at different scales.7 Multiscale modeling is one of the efficient and promising strategies when the problem encompasses the physicochemical phenomena with different scales, which usually involves difficulties in characterization. For instance, in a batch stirred crystallizer, fluid dynamics operate on a much faster time-scale than other phenomena.8Hybrid CFD compartment modeling with process simulation was utilized for the scale-up of batch cooling crystallization processes.9 This study highlighted the influence of flow fields, kinetics and crystal size distribution on the design and scale-up of MSMPR crystallizer. Bezzo et al.10 employed multi-zonal approach as a multiscale strategy in bioreactor modeling with CFD to predict fluid flow and in process simulation for kinetic analysis. From the viewpoint of the crystallizer design, the most essential aspect of this approach is the capability to predict accurate flow field and crystallization kinetics at different time scales. In this study, we modeled the behavior of the flow field and the crystallization kinetics at different time scales with gPROMS FormulatedProducts 11 and FLUENT 12. In recent years, the scale-up of crystallizers has been largely studied with CFD simulations. The details of some of the recent researches are summarized in Table 1. Table 1. Summary of Researches on Crystallizer Scale-Up The above papers have been used to scale-up or optimize crystallizers by combining crystallization experiments with CFD simulations. Different types of crystallizers were used: 1) Wei et al13., Zauner et al16., Song et al17., Malysiak et al18., and Zhu et al5. used a draft tube crystallizer, 2) Kougoulous et al9. used a simple crystallizer, 3) Schmidt et al15. used a conical base reactor, 4) Oslo experiments and CFD simulations were performed on fluidized-bed crystallizers by Al-Rashed et al19, 5) Bell21. explained crystallization and scale-up criterion. Table 1 above discusses the scale-up of crystallizers using experimental outcomes and software by several authors. This research covered experiments on 500 L DTB crystallizer and the analysis of crystallization kinetics in terms of parameter estimation. CFD simulation was carried out to confirm the kinetics, which was used for the


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scale-up of the 500 L DTB crystallizer to 3000 L. Section 2 of this research describes the 500L DTB crystallizer experiment from which the kinetic parameters of the nucleation and growth rates were obtained using the maximum likelihood method (MLM). Section 3 describes the CFD simulation of the 500 L DTB crystallizer. Section 4 covers the scale-up and optimization strategy used for the DTB crystallizer from 500L to 3000 L. It also explained the criteria required for the scale-up: constant tip-speed, constant Froude number, constant specific unit power, and geometric similarity.

2. Experiments 2.1. DTB crystallizer. The DTB crystallizer used in this work consists of a stainless-steel draft tube 8030 mm in height, 4515 mm in diameter, 400 L in volume, and surrounded by four inner baffles 100 mm in length. A 4022 mm diameter propeller was used to produce an axial flow in the crystallizer. It is equipped with a draft tube, four baffles, and a shaped bottom. The arrangement of the baffles is offset by 45°. A three-blade, marine-type propeller was used as a stirrer. A heat exchanger is used to maintain a constant temperature of 25°C. Water was used as the circulating medium for the experiments. The level of isopropyl alcohol (IPA) is sustained above the top end of the draft tube. A variable speed drive was used to control the speed of the propeller. The bottom mounted propeller generates circulation currents from the bottom to top inside the draft tube and top to bottom in the annular space. The schematic of the crystallizer and experimental set-up is shown in Figure 1a. The experimental setup also has a head tank, feeding pump, Raman probe, thermostatic bath, temperature controller, vacuum centrifugal filter, and vacuum dryer as shown in Figure 1a. Figure 1. (a) Schematic Diagram of Experimental Apparatus; (b) 500 L DTB Crystallizer Diagram for Experiment and CFD Simulation The concentration of the HNIW solutions is adjusted according to the stoichiometric ratios. From the storage tanks, the solutions are pumped into a head tank by passing through a filter. The head tank ensures a constant static pressure on the pump during the whole experiment in order to avoid variations in the feed rate and thus in the residence time. The centrifugal pump continuously feeds the solutions to the crystallizer. The solution was introduced into the crystallizer from the top. The ratio of HNIW to solvent varied from 0.8 to 1.1. The temperature was set from 283.15 K to 308.15 K. The acetone (ACT) mass fraction of the mixed solvent ranged from 0.08 to 0.09. The crystals were sampled at regular intervals using a solid-liquid separator with a glass filter and dried for 24 h at 50 °C for analysis. The size measurements of solid samples were carried out by a particle size analyzer (PSA) (CILAS-1064) in IPA. The products were identified using Raman spectroscopy (Kaiser Optical Systems, Ann Arbor MI, USA) equipped with a light-emitting diode laser (785 nm, 450 mW) as an excitation source.

2.2. Experimental results and kinetics analysis As indicated in Table 2, HNIW: ACT: IPA mass fraction ratio, seed, and temperature were tested under different operating conditions and the particle mean diameter was obtained. The polymorph of HNIW crystals obtained in these experiments was ε form.



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Table 2. Operating Conditions and Results for DTB Crystallizer Experiment These experiments were performed to generate a kinetic model of nucleation rate and growth rate. Case 1 and Case 2 were tested under the same conditions of HNIW: ACT: IPA mass fraction and temperature with different seed amounts. The particle mean diameter increased with increasing seed. Case 3 and Case 4 were carried out under the same seed and temperature conditions. As a result, particle mean diameter increased with increasing IPA mass fraction. Case 5 and Case 6 were tested under the same conditions of HNIW: ACT: IPA mass fraction seed. It was concluded that the particle mean diameter changed with temperature. From these experiments, it can be seen that HNIW: ACT: IPA mass fraction ratio, seed, and temperature are important parameters for particle mean diameter determination. Based on the experimental results, this study attempted to obtain kinetic parameter values. Kinetic parameters were considered for both kinetics and growth rates. In large crystallizers such as commercial crystallizers, the size of the impeller is increased, and the effect of secondary nucleation is increased. Consequently, one needs to determine the values of the unknown parameters: primary nucleation rate, secondary nucleation rate, and growth rate in order to maximize the probability that the model will predict the values obtained from the experiments.11 Parameter estimation technique was used to estimate the unknown parameter values, such as frequency factors, activation energies and mass transfer coefficients, based on the experimental results. Statistical methods (e.g. linear regression, least square methods) have usually been employed only on experimental data for this purpose. However, we argue that this approach sometimes shows undesirable effects since it does not consider the physicochemical phenomena taking place in the target process. The mathematical formulation for maximum likelihood method (MLM) in this study not only included the experimental data but also used the mathematical model of the crystallizer, objective function and the operational limitations as constraints.11 As a result, the formulation for MLM was solved in an optimization framework to maximize the objective function. From this approach, the parameter values were calculated considering the experimental data as well as the behavior of the target process. The equation related to this is given in eq 1. 

2  NE NVi NM ij   zijk  zijk  % N 1  ln  2   min    ln   ijk2  2 2   i 1 j 1 k 1    ijk2   

         

(1)

A crystallization kinetic model is required to obtain the crystallization kinetics with MLM. The crystallization kinetics model is reported in Table 3. We used gPROMS FormulatedProducts to calculate parameter values based on experimental data as well as mathematical formulation (e.g. objective function, mathematical model and constraints). The main features consist of comprehensive mathematical models, user interface allowing a user to build one’s own mathematical model, a library for the kinetics of nucleation, growth and breakage and parameter estimation calculator.11 The kinetics for nucleation and growth, and parameter values were then used for CFD calculation incorporated with UDF (see Figure 3). Table 3. The Crystallization Kinetic Model Used in gPROMS FormulatedProducts


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It satisfies a power law kinetic model at the primary nucleation rate, where kn, is the nucleation rate, ∆𝑐 is absolute supersaturation , 𝜌𝑐is crystal density, n is order of nucleation, EA,ns is the activation energy for nucleation, R is gas constant, and T is temperature.11 Secondary nucleation rate Jsec satisfies a power law kinetic model, and has a form similar to the primary nucleation rate but with an added energy dissipation rate,  , so that it can be expressed in terms of the impeller effect. G satisfies a power law kinetic model, and has a form similar to the primary nucleation rate, but has a constant growth rate and can express the kg growth rate. Since T , R, 𝜌𝑐, and ∆𝑐 are known values, parameter estimation is for other values (kn, n, kns, ns, EA,ns, kg, g, EA,g). As a result of parameter estimation, this study obtained the parameter values as shown in Table 2: kn=0.09 × 1016, n=2, kns=0.09 × 1016, ns=1, EA,ns=30000, kg=0.2, g=0.0901, and EA,g=30000. Figure 2 shows measured and predicted values and standard deviations for HNIW crystal mass, HNIW liquid mass concentration, and HNIW solution mass. The accuracy of the parameter estimation can be judged by the standard deviation between the experimental data and calculated results. The simulation reached a steady state at 1230s. The predicted HNIW crystal mass and liquid mass concentration agreed 100% with the measured value, while the solution mass agreed 99.8% with the measured value. The standard deviations of the predicted values were 0.159kg (1.06% ) for crystal mass, 0.01kg (2.77%) for liquid mass concentration, and 1.74kg (1%) for solution mass. It can be seen that the parameter estimation is accurate by observing the marginal errors recorded for the predicted value (< 1%) and the standard deviation (< 3%). Figure 2. Parameter Estimation of gPROMS FormulatedProducts

3. CFD simulation 3.1. solution strategy and CFD modeling. Figure 3 shows the procedure for the implemented solution strategy. After setting the initial feed conditions, rpm, and operating temperature, the experiment was run. Experimental data, particle size distribution, HNIW solid mass, and yield were obtained. The experimental results were used to obtain the crystallization kinetics. The crystallization kinetics—primary nucleation rate, secondary nucleation rate, and growth rate were obtained by the MLM. The calculated values of the crystallization kinetics were used in CFD for population balance routine (quadratic method of moment, QMOM) and user-defined function (UDF). UDF is a “User-Defined Function” which is used when some physicochemical phenomena cannot be described with the built-in functions. In this study, we used the UDF option to define functions for nucleation and growth kinetics. Figure 3 shows the conceptual information exchange between FLUENT and UDF written in C language. During the simulation, the UDF interacts with Fluent to call for process variables such as density, viscosity, and temperature from the conservation equations. The process variables provided are used to calculate Primary nucleation, Secondary nucleation and Growth rate. The Primary nucleation, secondary nucleation, and growth rate calculated by the UDF are sent to QMOM, a fluent solver, to calculate the mass source of HNIW crystals generated in the crystallizer. The resulting crystallization kinetic values are then sent back to the conservation equations as source terms that are used to calculate the Velocity, Number of particles, Mixing degree, and Particle size distribution of the HNIW crystals. CFD modeling and simulation results were compared to the experimental data to validate the mathematical model. The experimental and validated CFD models were used to



scale the DTB crystallizer up to 3000 L using the scale-up strategies presented in Section 4. In order to optimize the results, two simulations were run on the scaled DTB crystallizer with one conventional and two draft tubes.

Figure 3. Solution Strategy for DTB Crystallizer Scale-Up CFD Simulation with gPROMS FormulatedProducts and UDF

Figure 4 illustrated the geometry and dimension of the DTB crystallizer for CFD simulation based on Figure 1b It also detailed the values of the design parameters.

Figure 4. The Geometry of the DTB Crystallizer

Table 4 shows the Figure 5 design parameters and values. These figures are shown in Figure 1b. Table 4. Design Parameters and Values of the DTB Crystallizer in Figure 5 Multiphase model and population balance model for CFD simulation are illustrated in Table 5. The Eulerian multiphase model allows for the modeling of multiple separate and interacting phases. The phases can be liquids, gases, solids, or combinations of them.12 Realizable

k   model satisfies mathematical constraints on the

Reynolds stresses, and is consistent with the physics of turbulent flows [18]. Scalable wall functions avoid the deterioration of standard wall functions under grid refinement below. These wall functions produced consistent results for grids of arbitrary refinement.12 QMOM provides dynamic calculation of the size bins of six or eight moments.12 Table 5. Process Parameters and Model for CFD

3.2. CFD results and validation. Figure 5 shows the time transient contour of the volume fraction of HNIW solution of Case 1. At 5 s, the HNIW liquid was being fed through the inlet pipe into the crystallizer. The propeller then circulated the HNIW solution in the vessel. At 20 s, 28.8 kg of HNIW solution flowed through the pipe into the vessel. The marine impeller produces axial and downward flows which facilitated the upward movement of the HNIW solution in the crystallizer. At 40 s, the HNIW solution started flowing down the draft tube and was consequently transformed into crystals. At 80 s, all of the HNIW solutions were transformed into HNIW crystals. This indicates that a steady state was reached in 80 s. As shown in Table 2, the primary nucleation rate, the secondary nucleation rate, and the growth rate are strong functions of supersaturation. After the 80 s, supersaturation is nearly zero so there is no driving force for crystal formation as described in Table 2.

Figure 5. Time Transient Contour of the Volume Fraction of HNW Dissolved in the Solution for DTB Crystallizer when Flow Time is (a) 5 s, (b) 20 s, (c) 40 s, (d) 80 s


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Figure 6 shows the time transient contour of the volume fraction of HNIW crystal of Case 1. In Figure 6a, HNIW crystals were not formed because only a small fraction of the HNIW solution was injected in 5 s. At 20 s, all of the HNIW solutions were injected and a small amount of HNIW crystal was formed. At 40 s, HNIW crystals were formed along the flow of HNIW solution. At 80 s, all HNIW crystals were transformed from HNIW solution to HNIW crystal. Due to the draft tube, the flow direction of the HNIW solution was enhanced. Hence, the HNIW crystal was formed uniformly in the draft tube. The crystal volume fraction at 200 s and the crystal volume fraction at 80 s were the same which indicates that steady state had already been reached in 80 s. Figure 6. Time Transient Contour of the Volume Fraction of HNIW Crystal for DTB Crystallizer when Time is (a) 5 s, (b) 20 s, (c) 40 s, (d) 80 s, (e) 200 s Figure 7 shows the HNIW crystal velocity vector at the 80 s. Inside the crystallizer, the impeller is bent at 60 degrees and has a marine shape, which forms an axial flow and produces a downward flow. This causes the HNIW crystal to flow up the vessel and flow down through the draft tube. The flow direction of the HNIW crystal is enhanced by the draft tube and increases the impeller effect on the narrowing of the PSD. Hence, the HNIW crystal size becomes uniform and the particle size distribution becomes narrow. The baffles help in ensuring the proper blend of materials in the crystallizer, thereby maximizing the function of the draft tube.

Figure 7. HNIW Crystal Velocity Vector Distribution of DTB Crystallizer at 80 s Figure 8 shows the experimental and simulation results at a steady state based on the conditions of Case 1. The mean diameter of the HNIW crystals from the CFD simulation and the experiment are 22 μm (calculated from QMOM) and 20.83μm. The number density (#/m3) of the CFD results and the experiment at the mean diameter are 1.49 × 1018 and 1.47 × 1018. The error of the mean diameter and number density are 1.05 % and 1.4 %. These errors are less than 2%, which indicates a good match between simulation and experimental results. This confirms that the mathematical model is correct. Figure 8. PSD from Results at Steady State; (a) Experiment (b) CFD Simulation

A 14.4 kg HNIW solution and a 14.4kg ACT were injected into the crystallizer with an IPA level of 0.55 m (158 kg) through the pipe for 6.5 s. After which the simulation was performed in a batch state. As shown in Figure 9, as the crystallization progressed to about 6.5 s, the HNIW solution concentration began to decrease due to the formation of HNIW crystals. At 80 s, the crystallization was terminated due to very low supersaturation. It can be seen that the crystallization does not progress any longer when the yield reaches 93% in 80 s.

Figure 9. Time Transient Profile of the (a) Yield (b) HNIW Crystal Mass, HNIW Solution for DTB Crystallizer

4. Scale-up of DTB crystallizer



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4.1. Scale-up strategy. One main objective of this research is to combine experimental and simulation results to aid in the scale-up of DTB crystallizers. This section elaborates on the scale-up strategy adopted and implemented in this study. The scale-up of crystallization processes is difficult without experiencing deviations in the physical attributes that characterize the process.23 Hence, it is imperative to identify the most essential parameters that influence the fluid dynamics of the process.18 Important process conditions such as crystal growth, crystal size distribution, nucleation, and pumping capacity are largely influenced by specific power input (energy dissipation) and primary circulation time.18 In a stirred-tank crystallizer with or without a draft tube, a volumetric flow is circulated with the aid of an impeller or pump. The volumetric flow must be determined with a supersaturation ∆c that is not sufficiently reduced during a cycle. A sudden decrease in supersaturation would hinder the growth of crystals, thereby rendering the crystallizer economically unfeasible.24 A stirred-tank crystallizer with a draft tube and marine impeller, having the dimensionless geometric ratios shown in Table 6, has a minimum impeller speed (Nmin) given as: N min  28

mT c

(2)

Here, mT is the suspension density and τ is the residence time. Table 6. Dimensionless Design Parameters25 Crystallization in highly soluble systems occurs at low dimensionless supersaturation, ∆c ρC, to avoid excessive nucleation, while it occurs rapidly at high suspension densities (mT).24 This implies that scale-up should be implemented at approximately N = const (constant impeller speed) but such scale-up indicates that the specific power input, ε, increases according to:

  N 3 D2

(3)

This would result in undesirably large specific power input which is detrimental to crystal size and crystal suspension. It can, therefore, be inferred that although there is proper mixing in small crystallizers, crystal suspension is an issue while in large crystallizers, crystals are readily suspended but mixing is difficult. This is why eq 2 is highly relevant to the scale-up of crystallizers. Marine-type impellers are recommendable for the gentle circulation of the crystal suspension. A larger stirred tank with a high D/T ratio would consequently convert the specific power dissipated into a desirably large volumetric flow and less shear stress.24 This suggests that a minimal tip velocity that is large enough to uniformly suspend particles must be used on scale-up while avoiding the use of small diameter impellers in large crystallizers.26 It is often difficult to scale-up a crystallizer with geometric similarity (D/T=const).18,24 However, maintaining partial geometric similarity (same geometrical shape) with increased D/T and W/D ratios improves mixing and enhances scale-up efficiency. 25,26 A careful investigation of scale-up criteria using:

a. constant tip-speed giving N scale 

N

orig

 Dorig 

Dscale


(4)

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b. constant Froude number giving N scale 

N orig

(5)

Dscaled Dorig

3  c. constant specific unit power giving N scale  3 N orig

2 Dorig 2 Dscale

(6)

showed that a constant tip-speed assumption gives an impeller Reynolds number which is 10 times bigger than the original, while constant Froude number and specific unit power assumptions give 30 and 20 times bigger Reynolds numbers respectively. The scale-up strategy implemented in this research was therefore carried out by obtaining the minimum impeller speed Nmin according to eq 2 and assuming constant tip-speed and partial geometric similarity with larger D/T and W/D ratios. Table 7 shows the design parameters and values for the scale-up. Scale-up-Case1 (DTB-1) is a conventional scale-up of 500L DTB crystallizer to 3000 L by the scale-up rule. Scale-up-Case2 (DTB-2) added one draft tube to the conventional scale-up to allow for crystals with narrow-size distribution.

Table 7. Operating Conditions and Design Parameters and Values for Scale-Up

Figure 10 shows the meshed structure of the DTB crystallizer. The number of meshes for the DTB-1 and DTB-2 is about is 400000 and 550000.

Figure 10. Meshed Structure of 3000 L DTB Crystallizer: (a) Scale-Up-Case 1 (DTB-1) (b) Scale-Up-Case 2 (DTB-2)

4.2. CFD simulation results for scale-up Figure 11 shows the HNIW crystal volume fraction of the steady state when DTB-1 is implemented. At 75 rpm, the HNIW solid was formed at the bottom of the tank due to the high HNIW density (2040 kg/m3). However, since the rpm is low, axial and downward flows are not welldeveloped by the marine impeller and so, the upward movement of the HNIW solid crystals was hindered. At 100 rpm, HNIW solid crystals were well-formed by the developed axial and downward flows. As a result, the HNIW solid crystals flowed through the draft tube and were influenced by the circulation generated by the impeller. Nonetheless, some of the HNIW solids were under influence by the generated axial and downward flows and, as a result, were partially submerged at the bottom of the tank. At 150 rpm, the axial and downward flows were welldeveloped, thereby enhancing the circulation of the HNIW solids in and out of the draft tube. The impeller agitation was fully converted into large volumetric flows which enhanced the growth of the HNIW crystals. Due to the large size of the scaled-up crystallizer (3000 L), large amounts of the HNIW solid crystals were formed. This resulted in relatively low axial and downward flows than in Case 1 (500 L crystallizer).

Figure 11. HNIW Crystal Volume of Fraction in the Scale-Up-Case 1 (DTB-1) with Different Impeller rpms at



80s: (a) 75 rpm, (b) 100 rpm, (c) 150 rpm Figure 12 shows the HNIW crystal volume fraction at steady state when DTB-2 is implemented. Unlike DTB1, when DTB-2 was run at 75 rpm, less HNIW solid crystals were deposited at the bottom of the tank. At 100 rpm, large amounts of HNIW solid crystals entered and exited the draft tubes compared to the DTB-1. In the crystallizer, the HNIW solid crystals were formed evenly from the draft tube side to the impeller side. At 150 rpm, the axial and downward flows were fully developed, thereby enhancing the formation of HNIW solid crystals in and out of the draft tube with high impeller rotational effect.

Figure 12. HNIW Crystal Volume Fraction in the Scale-Up-Case 2 (DTB-2) with Different Impeller rpms at 80 s: (a) 75 rpm, (b) 100 rpm, (c) 150 rpm Figure 13 shows the velocity vector field of the HNIW crystal of DTB-1 and DTB-2 at the steady state simulation time of 80 s. When the two results were compared, the flow direction of the HNIW solid crystals was seen to be the same. However, when the velocity vector fields were compared, DTB-2, which had two draft tubes, had more HNIW solid vectors entering its draft tubes than in DTB-1 which had only one draft tube. Figure 13 shows that for the same rpm, DTB-2 formed a well-developed axial flow, downward flow, and HNIW solid flow direction than DTB-1 (Figure 11). The results show that the DTB-2 is highly influenced by the impeller rpm than DTB-1. Figure 13. HNIW Crystal Velocity Profile of CFD Simulation at 80s (a) Scale-Up-Case1 (DTB-1) and (b) ScaleUp-Case2 (DTB-2) Figure 14 shows the particle mean diameter and particle size distribution according to the various rpms when the crystallization time is 80 s in Scale-up-Case2. At 75, 100, and 150 rpms, the number density of the particle diameters were 3.4 × 1017, 4.0 × 1017, and 5.0 × 1017 respectively. From this result, it can be seen that the number density of the particle diameter increases as the rpm increases. This result shows that the HNIW solid crystals entering the draft tube increase as rpm increases as shown in Figure 14. As such, the particle size becomes constant with a narrower particle size distribution.

Figure 14. PSD from the CFD Simulation Results of Scale-Up-Case 2 (DTB-2) with Different Impeller rpms at 80 s

Figure 15 shows the particle mean diameter for the various rpms from 0 to 80 s. A 23.46 μm particle mean diameter was obtained at 75 rpm, 22.4 μm at 100 rpm, and 14.3 μm at 150 rpm. As the impeller velocity increased, the particle mean diameter decreased. As can be seen in Table 3, the energy dissipation rate increased as the impeller speed increased; hence, the secondary nucleation rate, affects the particle mean diameter. A similar observation was made in an article wherein the nucleation rate and particle size distribution were seen to have


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been affected by agitator speed. The experimental particle size distributions showed a shift to smaller particle sizes due to the increase in the agitation rate and, hence, secondary nucleation and attrition.14

Figure 15. Time Transient Profile of the Particle Mean Diameter of HNIW Crystals with Different rpms of ScaleUp-Case 2 (DTB-2) A scaled-up crystallizer should satisfy the target product specification and performance. In this study, the target values are same as shown during the experiments. We, therefore, attempted to compare the CFD simulation results of the scaled-up crystallizer (3000 L) with the experimental results of the 500 L DTB crystallizer. As a result of the comparison between DTB-2 (3000 L and 100 rpm) and the Case 1 for 500 L, the mean diameters were 22.4 μm and 22 μm with 1.01% error at steady state. At this time, the number density of DTB-2 and Case 1 for 500L showed 25.8 % error with 3.8 × 1017 and 1.47 × 1018. This indicates that a successful scale-up was implemented with little variations in the small and large scale process outputs. The influence of turbulence on mixing and power consumption is another important issue for the scale-up procedure. Figure 16 and Figure 17 show the turbulent kinetic energy and turbulent dissipation in the crystallizers at different rpm at t=80 s.

Figure 16. Turbulent Kinetic Energy in the crystallizer at different rpm (75, 100 and 150) for t=80 s: (a) 500 L, (b) DTB-1 (c) DTB-2

Figure 17. Turbulent Eddy Dissipation in the Crystallizer at Different rpm (75, 100 and 150) for t=80 s: (a) 500 L, (b) DTB-1 (c) DTB-2

The energy dissipation rate increases with increasing rpm as seen in Figure 17 (a) of 500 L. A replica of this trend was observed in the scaled-up versions of the 3000 L crystallizers of DTB-1 and DTB-2. This proves that the scale-up versions give a true reflection of its standard or small-scale (500 L) version. This similarity in behavior is expected because power input from the impeller to the fluid is equivalent to the rate of dissipation energy distribution in the crystallizer. This improves fluid motion which translates into mixing and contacting necessary for the process objective.27 However, for any rpm, the scale up versions of the crystallizer had a welldistributed turbulent dissipation rate compared to the 500 L. This observation agrees with a report by27 wherein a larger D/T ratio on scale-up has been seen to improve the volumetric flow of the crystallizer thereby reducing the shear stress and enhancing the crystallization. From the same author, power transferred is also influenced by the geometry of impeller that is bigger impeller size tend to have a higher transfer of the dissipated energy and turbulence into the fluid thereby enhancing mixing. Relatively, the impeller size of the scaled-up versions is bigger than the 500L crystallizer and their better distribution of the rate of dissipation of the turbulence energy as shown in Figure 16 and Figure 17 respectively, confirm how the rpm was efficiently transferred into the fluid to aid crystallization. In this light, it can be



Page 12 of 42

concluded that the scaled-up versions efficiently used input power to improve the fluid motion and mixing since for any rpm their distributions were far better. Figure 18 below shows torque per volume variations for 75, 100 and 150 rpm over time of the 500 L with DTB-1 and DTB-2. Figure 18. Time transient torque per volume in the crystallizers for 500 L, 3000 L with one draft tube (DTB-1), 3000 L with two draft tubes (DTB-2) at different rpm (75, 100 and 150)

From Figure 18, the torque on impeller increases with increasing rpm for the same tank size in any given time. However, for any given impeller rpm, the torque variations with time showed a common trend. The torque increased at the start for some period and decreased with time to a point where it remained almost at the same value. Also, DTB-1 gave higher torque values than DTB-2 in all cases (for example, with 150 rpm, DTB-1: 68.4Nm, DTB-2: 64.1Nm) whiles the torque of 500 L in all cases is lower than the scaled-up impeller. The relation between the specific torque per volume and the specific power consumption per volume can be described as follows; P 2 N imp M  V V

(7)

This implies that for any given tank size, increasing the impeller speed will cause the torque on the impeller to increase thereby costing more of power consumption. This trend was observed in both the small-scale crystallizer and the scaled-up versions of single and double draft tube. However, for the scaled-up tank of the dual tube, the torque on the impeller was lower than its corresponding single draft tube. In the case of 150 rpm impeller speed, the power consumption of DTB-2 per volume is 9.1% less than that of DTB-1 (DTB-1: 35.52kW/m3, DTB-2: 32.29kW/m3). Similar scenarios repeated for all the rpm cases which imply that the DTB-2 for the scaled-up tank has lower power consumption than the DTB-1 although its distribution of the rate of turbulence energy as explained in 3000 L was better than the small scale.

5. Conclusions In this paper, the CFD simulation of an anti-solvent crystallizer with 500 L to produce 𝜀 type HNIW crystal was performed. Experiments were carried out to obtain the crystallization kinetics using the MLM. CFD modeling and simulations were performed and validated by comparing the obtained results to the experimental data. Using CFD modeling, the scale-up of a DTB crystallizer from 500 L to 3000 L was performed. The CFD model and crystallization kinetics were verified with an obtained marginal error (less than 2%). The main parameters obtained from the kinetic model were used for the scale-up of the CFD simulation. A comprehensive scale-up strategy was adopted and implemented. The scale-up results were optimized by running a two-case simulation: one in a conventional crystallizer (DTB-1) and the other in a two-draft tube crystallizer (DTB-2). The simulation results show that DTB-2 has a more evenly mixed flow field than DTB-2. It was further observed that the larger


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the impeller rpm, the narrower the particle size distribution as the HNIW solid crystals was more influenced by the impeller speed. This affected the unit energy dissipation of the secondary nucleation rate which decreased the growth rate and the particle mean diameter of the crystals. By comparing the particle mean diameter of the 100 rpm DTB-2 which is 22.4 μm to that of the small scale (500 L) which is 22 μm, a minimal error of 1.01% was observed. The number density obtained for the scale-up, 3.8 × 1017, and that for the 500 L, 1.47 × 1018, indicates a percentage error of 25.8%. The specific power consumption for DTB-1 and DTB-2 was 35.52kW/m3 and

32.29kW/m3, which showed 9.1 % less power consumption of DTB-2 than that of DTB-1. This study, therefore, concludes that the systematic scale-up methodology of the DTB crystallizer was established through this study.

Acknowledgement. This Research Was Supported by the Agency for Defense Development (ADD).



Symbols used



N  NE NVi NM ij

 ijk % zijk zijk J prim

kn

c c

n

E A, ns

R T



G kg

N min mT

 

P

V

Nimp M

The objective function in maximum likelihood method, Total number of measurements taken during all experiments, Set of parameters to be estimated, which may be subject to given bounds, Number of experiments performed, Number of variables measured in the ith experiment, Number of measurements of the jth variable in the ith experiment, Variance of the kth measurement of variable j in experiment I, kth measured value of variable j in experiment I, kth model/predicted value of variable j in experiment I, Primary nucleation rate, m-3s-1 Nucleation rate, m-3s-1 Supersaturation, J kg-1 Density of fluid phase, kg m-3 Order of nucleation rate, Activation energy for nucleation for nucleation, J ,mol-1 Ideal gas constant, J K-1 mol-1 Temperature, C 0 Energy dissipation rate m2 s-3 Growth rate Growth rate m s-1 Minimum impeller speed, rpm Suspension density, kg m-3 residence time, sec Specific power input, W kg-1 Power, W Volume, m3 Impeller speed, rpm Torque, Nm


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References (1) P. Goede.; N. V. Latypov.; H. Östmark. Fourier Transform Raman Spectroscopy of the Four Crystallographic Phases of a, b, g and e 2,4,6,8,10,12-Hexanitro2,4,6,8,10,12-hexaazatetracyclo[5.5.0.05,9.03,11]dodecane (HNIW, CL-20). Propellants, Explos., pyro tech. 2004, 29, 205-208. (2) https://www.youtube.com/watch?v=42BA1GWIWic, 2018 (accessed 18 July 2018) (3) W. Wirapong. Computational fluid dynamics simulation of a DTB. 2006. (4) C. V. Rane.; A. A. Ganguli.; E. Kalekudithi.; R. N. Patil.; J. B. Joshi.; D. Ramkrishna. CFD Simulation and Comparison of Industrial Crystallizers. Can. J. Chem. Eng. 2014, 92, 2138-2156. (5) Z. Zhu.; H. Wei. Flow Field of Stirred Tank Used in the Crystallization Process of Ammonium Sulphate. Science Asia. 2008, 34, 97-101. (6) Z. Sha.; S. Palosaari. Mixing and crystallization in suspensions. Chem. Eng. 2000, 55, 1797-1806. (7) M. Li.; G. White.; D. Wilkinson.; K. J. Roberts. Scale up study of retreat curve impeller stirred tank using LDA measurements and CFD simulation. Chem. Eng. J. 2005, 108, 81-90. (8) F. Bezzo.; S. Macchietto.; C. C. Pantelides.A general methodology for hybrid multizonal/CFD models Part 1. Theoretical frame work. Comput.Chem. Eng. 2004, 28, 501-511. (9) E. kougoulos.; A. G. jones.; M. W. wood kaczmar. A hybrid CFD compartmentalization modeling framework for the scaleup of batch cooling crystallization processes. Chem. Eng. Com. 2006, 193, 1008-1023. (10) F. Bezzo.; S. Macchietto.; C. C. Pantelides. General hybrid multizonal/CFD approach for bioreactor modeling. A.I.C.h.E. Journal. 2003, 49, 2133-2148. (11) gPROMS FormulatedProducts Doc., PA 2017 (12) ANSYS FLUENT User’s Guide 12.0, ANSYS Inc., Canonsburg, PA 2009. (13) Wei, H. Y. Computer-aided design and scale-up of crystallization processes: Integrating approaches and case studies. Chem Eng Res Des. 2010, 88, 1377-1380. (14) E. Kougoulos.; A. G. Jones.; M. W. Wood-Kaczmar. Process Modelling Tools for Continuous and Batch Organic Crystallization Processes Including Application to Scale-Up. Org. Process Res. Dev. 2006, 10, 739-760. (15) B. Schmidt.; J. Patel.; F. X. Ricard.; C. M. Brechtelsbauer.; N. Lewis. Application of Process Modelling Tools in the Scale-Up of Pharmaceutical Crystallisation Processes. Org. Process Res. Dev. 2004, 8, 998-1008. (16) R. Zauner.; A. G. Jones. Scale-up of Continuous and Semibatch Precipitation Processes. Ind. Eng. Chem. Res. 2000, 78, 894-902. (17) X. Song.; M. Zhang.; J. Wang.; P. Li.; J. Yu. Optimization Design for DTB Industrial Crystallizer of Potassium Chloride. Ind. Eng. Chem. Res. 2010, 49, 10297-10302. (18) P. M. Synowiec.; A. Małysiak.; J. Wójcik. Fluid-dynamics scale-up problems in the DTM crystallizer. Chem. Eng. Sci. 2012, 77, 78-84. (19) M. Al-Rashed.; J. Wójcik, R. Plewik.; P. Synowiec.; A. Kuś. Multiphase CFD modeling: Fluid dynamics aspects in scale-up of a fluidized-bed crystallizer. Chem. Eng. Process. 2013, 63, 7-15. (20) X. Liu.; D. Hatziavramidis.; H. Arastoopour.; A. S. Myerson. CFD simulations for analysis and scale-up of anti-solvent crystallization. A.I.C.h.E. Journal. 2006, 52, 3621-3625. (21) T. A. Bell. Challenges in the scale-up of particulate processes-an industrial perspective. Powder Technol.



2005, 150, 60-71. (22) Li. M.; White. G.; Wilkinson. D.; Roberts. K .J.; . Scale up study of retreat curve impeller stirred tank using LDA measurements and CFD simulation. Chem. Eng. 2005, 108, 81-90.


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For Table of Contents Use Only

Scale-Up of a Semi-Batch DTB Crystallizer for HNIW Based on Experiments and CFD Simulation

Dong-Hoon Oh 1, Rak-Young Jeon2, Jun-Hyung Kim3, Chang-Ha Lee1, Min Oh2,* , Kwang-Joo Kim2,*

TOC graphic

Synopsis Experimental work and CFD simulation were executed to produce micro-sized HNIW in a 500 L DTB crystallizer with acetone and isopropanol as solvent and antisolvent. Based on the validated mathematical model, the 500 L DTB crystallizer was scaled-up to 3000 L. Case studies were conducted on the 3000 L DTB crystallizer with one and two draft tubes, which demonstrated HNIW solid volume fraction, particle mean diameter, and particle size distributions.



Figure 1. (a) Schematic Diagram of Experimental Apparatus; (b) 500 L DTB Crystallizer Diagram for Experiment and CFD Simulation


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Figure 2. Parameter Estimation of gPROMS FormulatedProducts



Figure 3. Solution Strategy for DTB Crystallizer Scale-Up CFD Simulation with gPROMS FormulatedProducts and UDF


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Figure 4. The Geometry of the DTB Crystallizer



Figure 5. Time Transient Contour of the Volume Fraction of HNW Dissolved in the solution for DTB Crystallizer when Flow Time is (a) 5 s, (b) 20 s, (c) 40 s, (d) 80 s


Page 22 of 42

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Figure 6. Time Transient Contour of the Volume Fraction of HNIW Crystal for DTB Crystallizer when Time is

(a) 5 s, (b) 20 s, (c) 40 s, (d) 80 s, (e) 200 s



Figure 7. HNIW Crystal Velocity Vector Distribution of DTB Crystallizer


Page 24 of 42

at 80s

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Figure 8. PSD from Results at Steady State;

(a) Experiment (b) CFD Simulation



Figure 9. Time Transient Profile of the (a) Yield and (b) HNIW Crystal Mass, HNIW Solution for DTB Crystallizer


Page 26 of 42

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Figure 10. Meshed Structure of 3000 L DTB Crystallizer: (a) Scale-Up-Case 1 (DTB-1) (b) Scale-Up-Case 2 (DTB-2)



Figure 11. HNIW Crystal Volume of Fraction in the Scale-Up-Case 1 (DTB-1) with Different Impeller rpms at 80s: (a) 75 rpm, (b) 100 rpm, (c) 150 rpm


Page 28 of 42

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Figure 12. HNIW Crystal Volume Fraction in the Scale-Up-Case 2 (DTB-2) with Different Impeller rpms at 80 s: (a) 75 rpm, (b) 100 rpm, (c) 150 rpm



Figure 13. HNIW Crystal Velocity Profile of CFD Simulation at 80s (a) Scale-Up-Case1 (DTB-1) and (b) Scale-Up-Case2 (DTB-2)


Page 30 of 42

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Figure 14. PSD from the CFD Simulation Results of Scale-Up-Case2 with Different Impeller rpms at 80 s



Figure 15. Time Transient Profile of the Particle Mean Diameter of HNIW Crystals with Different rpms of Scale-Up-Case 2 (DTB-2)


Page 32 of 42

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Figure 16. Turbulent Kinetic Energy in the Crystallizer at Different rpm (75, 100 and 150) for t=80 s: (a) 500 L, (b) DTB-1 (c) DTB-2



Figure 17. Turbulent Eddy Dissipation in the Crystallizer at Different rpm (75, 100 and 150) for t=80 s: (a) 500 L, (b) DTB-1 (c) DTB-2


Page 34 of 42

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Figure 18. Time Transient Torque per Volume in the Crystallizers (500 L, DTB-1, DTB-2) at Different rpm (75, 100 and 150)



Page 36 of 42

Table 1. Summary of Researches on Crystallizer Scale-Up

Author

Contents

Wei 13

 Carried out an Aspen plus modeling and simulation on (NH4)2SO4 crystallization  Suggested an integrated approach for the scale-up of crystallizers using CFD and PSE software  Discussed the benefits of using CFD for crystallizer scale-up such as complex interactions between hydrodynamics,

physical processes

and

reducing

development time  Successfully implemented a scale up strategy to design a real process plant that was built but provided no details on the kinetics and geometry of the reported implementation Kougoulous 14

et al.

 Used CFX (CFD S/W) and gPROMS to model hydrodynamics and particle size distribution for lab-scale batch and MSMPR crystallizer (0.5 L)  Performed CFD simulations for 1L, 5L and 25L agitated vessels  The research aimed at ascertaining the influence of impeller configuration on crystallization kinetic phenomena by studying crystal size distributions using crystallization kinetics and solubility data.  A complex kinetic model to predict size-dependent growth and total nucleation was developed with an average growth assumption that particles are continuously removed from the crystallizer within 1 mean residence time.  From parametric studies, it was observed that a pitch blade impeller produced higher attrition and secondary nucleation rates due to high particle-impeller contact.  Hence, a flat blade impeller was considered a better choice and for further studies.  Scale up of the flat blade impeller was carried out with constant agitator speed, constant power input per unit mass and geometric similarity.  It was concluded that scale-up with constant agitator speed results in increased attrition, and higher power consumption whiles scale up with constant power input per unit mass offers good mixing but with inefficient heat transfer resulting in increased agglomeration.

Schmidt et 15

al.

 Applied process modeling technology to the scale-up of pharmaceutical crystallizer with API, Compound B and S (not specifically described) to evaluate the influence on crystallization  Used CFD tools (CFX-ProMixus, FLUENT) to performed case studies on


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geometrically similar lab-scale (2L), pilot-scale (50L, 250L) and commercialscale (630L) processes  Used a tank diameter of 1.8 m, and impeller diameters of 0.7 m, 0.8 m and 0.4 m in this report  A first order mass transfer desupersaturation model was considered with the assumption that all materials transported to the solid phase by desupersaturation is deposited on the crystal surface.  Scale up of the process was carried out at a constant tip speed with geometric similarity. A good solid suspension was observed but with an increase in shear rate.  Even though a successful scale up is reported in this study, scale up with geometric similarity and constant tip speed only ensures mesomixing of the particles with no information on macro and micromixing and therefore does not capture the complete details of a scale up process. Zauner

et

16

al.

 Executed experiments and CFD simulations on C2H2CaO5 which was precipitated from reacting supersaturated solutions of CaCl2 and Na2C2O4 for semi-batch (1L (d = 40 mm, D = 105 mm), 5L (d = 65 mm, D = 180 mm), 25L (d = 110 mm, D = 300 mm))  Suggested the segregated feed model (SFM) with population balance, which considers diffusive micro-mixing, convective meso-mixing to model and predict the mixing effects during precipitation  The results revealed that, constant impeller speed is not recommended for scale up as it only ensures macromixing which does not control the processes under investigation.  Constant tip speed has also been reported to ensure constant mesomixing but with high shear rate distribution.  On scale up with constant power input per unit volume, micromixing was achieved but with great deviation from mesomixing.  Hence, it can be concluded that, successful scale up is not possible with geometric similarity, constant impeller speed, constant tip speed, and constant power input per unit volume as stated by [24]

Song et al. 17

 Employed CFD techniques for the simulation and optimization of an existing

continuous DTB crystallizer for KCl production  Geometric dimensions considered are D = 12.2 m, d = 2.5 m and H = 14.8 m  Rosin-Rammler model was used to describe crystal size distribution in the feedstock stream  The study aimed at determining the impact of various impellers on KCl



production  Three different impellers; marine, Rushton, and pitch blade were studied.  The results showed that the marine impeller gives a low power consumption and less velocity fluctuation for the same impeller speed (70 rpm) which favors crystal precipitation and production.  The study concluded that a marine impeller is the best choice for crystallization processes with low power consumption and increase in product quality.  A marine impeller is therefore used in the present research. Malysiak et

 Modeled and simulated (NH4)2SO4 crystallization with a DTM crystallizer

al.18

 Investigated the effect of scale-up from 11L to 1,375L on (NH4)2SO4

crystallization with four different volumes, constant stirrer tip speed and constant turbulence dissipation rate using CFD software.  A Lightnin A100 propeller with a diameter ratio of 0.26 was used for this study  Different grid sizes of 600 k cells, 900 k cells and 1200 k cells were observed to have negligible effects on the results.  A scale up attempt with constant tip speed and geometric similarity failed when the volume was increased to about 2 orders of magnitude.  Another parametric study which considered a partial geometric similarity by maintaining a constant gap between the stirrer and draft tube resulted in a shorter circulation time by 1 orders of magnitude.  It was concluded that it is impossible to scale up a crystallizer whiles maintaining geometric similarity  Al-Rashed et al.19

 Performed CFD simulation on Oslo fluidized bed crystallizer in the production

of NaCl crystals by studying the scale-up in six volumes (0.039 m3, 0.33 m3, 2.64 m3, 29 m3, 97 m3, 230 m3)  Tank diameters of 0.35 m, 1.5, 3.0, 4.5, and 6.0 m were considered for this research with various number of computational cells from 20,000 to 600,000  The scale up strategy considered in the research is constant outlet velocity with geometric similarity  It was reported that compression of the fluidized bed at diameters greater than 5 m was inevitable on scale up  Due to sudden changes in suspension bed behavior on scale up, the researchers noted that it is risky to scale up a process directly from a laboratory scale (volume less than 1 m3) to an industrial scale  It was concluded that volume scale up factor should be less than 125


Page 38 of 42

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 Although fluidized bed crystallizers are not the focus of the present research, the information provided therein is vital for scale up processes 20

Liu et al.

 Executed CFD simulation of the anti-solvent crystallization of C2H5NO2 from a

water-ethanol mixture at different scale including lab-scale (1.06 dm3), pilotscale (24.1dm3) and industrial-scale (52.24 dm3)  In the report, the pilot scale crystallizer had 68,205 grid cells and the industrial scale crystallizer had 133, 082 grid cells with a diameter ratio of 0.33 used for the geometric design  The research discussed the effects of feed rate, feed position, and agitation speed on mixing which is not the focus of the present work  It was observed that optimum mixing is achieved at higher agitation rate, slow feed rate, and a feeding position at the same height as the impeller blade and close to the vessel wall.  The study roughly discussed scale up with constant tip speed and constant power input per unit volume and concluded that these strategies cannot ensure mixing similarity for antisolvent and solvent mixtures, since these techniques have been proposed for liquid-particle systems. A dimensionless parameter, NV/Q was however suggested for scale up whiles maintaining geometric similarity  The research is limited in information regarding the scale up of crystallizers, but the conclusions made are not farfetched when compared to the observations of other authors [20, 16, 17].  From the conclusion, a substantial research is required to discuss the factors that affect crystallizer scale up on a multiscale Bell

21

 Well written review paper which encompassed various particulate systems including crystallization, dewatering in centrifuges, grinding in mills and silo design.  Discussed various issues confronting the scale up of particle processes  In the submission, process factors such as attrition, agglomeration, and aggregation make it very difficult to handle particle systems  It was reported that simultaneously maintaining similarities in equipment shape, velocities, and power inputs, can be impossible in scale up and that there are no simple and constant scale-up rules for stirred vessels, since the actual power required will depend on many factors, including the particle and fluid properties as well as the vessel diameters.  In conclusion, a collaboration between industry and academia was proposed for solving the challenges of particle scale up and its related issues.



Page 40 of 42

 Investigated the scale-up effects of mixing vessels for three geometrically

Li Mingzhong 22

et al.

similar lab-scale vessels (0.5L (43003 cells), 2L (57140 cells), 20L (75917 cells)) with a diameter ratio of 0.59, using CFD simulation  The study aimed at investigating the changes in discharge flow number, secondary circulation flow number, pumping efficiency, and power number on scale up by conducting parametric studies on the three crystallizers at 100 rpm with a time step of 1/120 of the rpm.  Results of the scale up studies show that for geometrically similar reactor vessels, the discharge flow number, secondary circulation flow number and pumping efficiency decreased slightly with increasing vessel size, however, the power number is nearly constant for each scale  Although the report indicated that scale up was possible with geometric similarity, only macromixing was examined with no mention of meso and micromixing which are very important factors in crystallizer scale up.

Table 2. Operating Conditions and Results for DTB Crystallizer Experiment Case 1 2 3 4 5 6

HNIW (kg) 14.4 14.4 6.6 6.6 6.3 6.3

ACT (kg)

IPA (kg)

Seed (g)

Temperature (C)

Mean size (um)

14.4 14.4 11 11 10.5 10.5

158 158 165 198 189 189

150 300 300 300 300 300

10 10 30 30 10 5

22 35 39 29 16 21

Table 3. The Crystallization Kinetic Model Used in gPROMS FormulatedProducts Quantity

Kinetics

Parameter

n

Primary nucleation rate Secondary nucleation rate Growth rate

 ∆c   −E  J prim = kn   exp  A, n   RT   rc  ns

J sec

 ∆c   −E  = kns   exp  A, ns  e a  RT   ρc 

 − E A, g   ∆c  G = k g exp     RT  ρc 

kn = 0.09 × 1016 , n =2 kns = 0.09 × 1016 , ns =1, E A, ns =30000

g

k g =0.2, g =0.0901, E A, g =30000


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Table 4. Design Parameters and Values of the DTB Crystallizer in Figure 4 Parameters Impeller type

Values Marine

No. of Impeller blades

3

Baffle height, BH [m]

0.4

Height of tank, HT [m]

1.1

Diameter of tank, TD[m]

0.68

Draft tube height, DT[m]

0.4

Pipe height, Hp[m]

0.83

Table 5. Process Parameter and Model for CFD Values Multiphase

Eulerian 3-phase model

Turbulence

Realizable model

Turbulence multiphase model

k −ε

Mixture

Near wall treatment

Scalable wall function

Population balance

Quadrature model

Table 6. Dimensionless Design Parameters12 Parameters

Values

HT/T

0.8-1.4

D/T

0.25-0.5

L/D

0.2-0.25

W/D

0.12-0.25



C/T

0.25-0.5

Bw/T

0.1-0.12

B/T

0.015-0.02

BH/T

0.5-0.8

Table 7. Operating Conditions and Design Parameters and Values for Scale-Up Values (Scale-up-Case1) Impeller type

Values (Scale-up-Case2)

Marine

No. of Impeller blades

3

Baffle height BH[m]

0.6

Height of tank, HT[m]

2

Diameter of tank, TD[m]

1.4

Pipe diameter, Dp[m]

1.4

Draft tube , DT[m]

0.6

Draft tube number No. of meshes

1

2

About 400,000

About 550,000


Page 42 of 42

Scale-Up of a Semi-Batch DTB Crystallizer for HNIW Based on

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