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equipment design methodology case study: Cyclone design to optimize spray-dried-particle collection efficiency
Computers and Chemical Engineering 34 (2010) 1041–1048
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Pharmaceutical process/equipment design methodology case study: Cyclone design to optimize spray-dried-particle collection efficiency Lisa J. Graham a,∗ , Rebecca Taillon a , Jim Mullin a , Trevor Wigle b a b
Bend Research Inc., 64550 Research Road, Bend, OR 97701, United States Bend Research Pharmaceutical Process Development Inc. (BRPPD), 20503 Builders Street, Bend, OR 97701, United States
a r t i c l e
i n f o
Article history: Received 12 September 2009 Received in revised form 1 April 2010 Accepted 5 April 2010 Available online 13 April 2010 Keywords: Cyclone Spray-drying Computational fluid dynamics Particle collection
a b s t r a c t This paper describes a case study using a cyclone-design methodology to increase cyclone collection efficiency for spray-dried dispersions (SDDs) for pharmaceutical applications. The six-step methodology combines the use of classical cyclone design (CCD) correlations and computational fluid dynamics (CFD) modeling techniques. By combining these techniques, the methodology avoids the limitations inherent with the use of either technique alone and represents an improved alternative to conventional trial-and-error methods. Specifically, the methodology increases the efficiency of the design process by reducing (1) computational time; (2) experimental time (i.e., the need for numerous development runs); and (3) the use of costly active pharmaceutical ingredient (API). The case study shows how the methodology was used to quickly and accurately design a cyclone with improved performance for a desired product characteristic (improved collection efficiency for small particles rather than broad particle-size distribution). Validation data are presented demonstrating the accuracy of the approach. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Spray-drying is a well-characterized, widely used pharmaceutical operation that is used to produce particles of uniformly dispersed drug in a polymer matrix. The drug and polymer are dissolved in solvent and atomized in a high-temperature, highvelocity gas stream, producing small spray-dried dispersion (SDD) particles that have been used to increase the bioavailability of more than 200 low-solubility pharmaceutical compounds (Friesen et al., 2008; Vodak & Friesen, 2009). The general process train for spray-drying operations is shown in Fig. 1. As the figure shows, a cyclone separator is used to collect the SDD particles, which are entrained in the gas outlet stream from the drying chamber (Dobry et al., 2009). The efficiency of product collection – particularly for small particles – is a major cost factor affecting the economics of the spray-drying process, since increased collection efficiency maximizes the yield of drug products that are typically made from expensive active pharmaceutical ingredients (APIs). The size and geometry of the cyclone can have a major impact on maximizing collection efficiency of spray-drying processes. The optimum design will differ based on the product profile or on process parameters (i.e., constraints). For instance, various gas flow
∗ Corresponding author. Tel.: +1 541 382 4100. E-mail address: [email protected] (L.J. Graham). 0098-1354/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.compchemeng.2010.04.004
rates may require cyclone size changes, whereas brittle particles require low shear exposure to prevent particle attrition. This paper describes a case study using a methodology to optimize cyclone design to increase the collection efficiency of small-particle SDDs. The methodology combines the use of classical cyclone design (CCD) correlations with computational fluid dynamics (CFD) models, capitalizing on the strengths of both techniques. CCD correlations are semi-empirical in nature and are widely used in chemical industry for design purposes (Cortes & Gil, 2007) because they are easy to use and based on extensive experimental data collected over a wide range of operating conditions and cyclone geometries. As a result, they are computationally inexpensive, resulting in rapid design times. Correlations for calculating pressure drop and collection efficiency are of particular importance from the engineering point of view. The classic correlation proposed by Lapple (1951) is simplest to use. Lapple (1951) used the concept of time-of-flight and a series of simplifying assumptions to arrive at an expression for the particle-diameter cut fraction (dpc )—the particle size that is separated with 50% efficiency. Barth (1956) proposed an improved correlation that accounts for the effect of the geometry of the vortex finder. A model proposed by Leith and Licht (1972) introduced the concept of using the natural vortex length instead of using the geometric length (height) of the cyclone. This helped in correctly calculating residence time and, hence, accurately predicting collection efficiencies. Dietz (1981) developed a
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Nomenclature Ai (in.) area of inlet D (in.) diameter of barrel Dd (in.) diameter of powder collector De (in.) diameter of gas exit dpj (m) diameter of j particle size dpc (m) diameter of particle collected with 50% efficiency H (in.) height of inlet duct Lb (in.) length of barrel Lc (in.) length of cone mj (g/s) mass flow of j particle size Ne number of gas turns in the outer vortex S (in.) length of gas collector Vcyclone (m3 ) effective volume of the cyclone Vexit (m3 ) volume of the exit duct Vi (m/s) cyclone inlet velocity W (in.) width of inlet duct Greek letters P (inH2 O) pressure drop across cyclone t (s) residence time fractional efficiency of j particle size j (%) o (%) overall cyclone collection yield g (g/cm3 ) density of gas p (g/cm3 ) density of particle (kg/m s) gas viscosity
compartmentalized cyclone model based on the concept of natural vortex length. Recently, Gimbun, Chuah, Choong, and Fakhru’l-Razi (2005) showed that the model proposed by Li and Wang (1989) did a better job of predicting collection efficiencies than other traditional models. Even though these models offer a faster, simpler way of evaluating cyclone performance, such correlations are valid only with a limited range of operating conditions and particle sizes and often can be used only for a particular cyclone shape or geometry without adjusting the geometric-based constants.
With the advancement in computational power, CFD modeling is emerging as a practical alternative to the CCD approach. CFD models offer the freedom to evaluate the performance of cyclone designs that differ significantly from classical cyclone geometries. CFD modeling also allows independent variation of aspect ratios and processing conditions. Recent advances in CFD modeling have addressed the challenge of capturing the highly anisotropic swirling flow in the cyclone’s confined geometry. The accuracy of CFD predictions depends strongly on the correct choice of turbulence models (Gimbun et al., 2005; Jakirlic & Hanjalic, 2002; Slack, Prasad, Bakker, & Boysan, 2000); steady- versus unsteady-state treatment; discretization schemes; two-phase flow models (Chiesa, Mathisen, Melheim, & Halvorsen, 2005; Elghobashi, 1994); and solid-phase boundary conditions (Shi & Bayless, 2007). A detailed account of various CCD and CFD methods and current progress in cyclone CFD modeling is described in a review by Cortes and Gil (2007). As described below, the methodology presented here builds on the strengths of the CCD and CFD approaches, avoiding limitations inherent with either method and offering significant improvements over trial-and-error methods that are traditionally used. The case study describes how the methodology was used to arrive at an optimized cyclone design and how the results were used to fabricate, test, and validate the cyclone design. Validation data are provided, demonstrating the accuracy of the methodology. The methodology was used to produce a cyclone that increased collection efficiency from 74% (using a standard 12-in.-diameter cyclone) to 86%, using identical spray-drying conditions. The validation run confirmed that the methodology produced a design that maintained an appropriate cyclone pressure drop throughout the validation run—which was not predicted based on CCD calculations alone. While the improvement in collection efficiency may appear modest, it was achieved after only a single iteration and, because that collection efficiency was adequate for the stage of development for the compound in question, no further iterations were performed. Based on extensive experience with this methodology, further improvements in yield can be expected (1) by performing further iterations with the methodology and (2) by increasing the spray-drying run time (yields are typically lowest for shortduration runs).
Fig. 1. General process train for spray-drying.
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Based on the work presented here, the methodology represents an attractive approach to cyclone equipment design for pharmaceutical processes. Use of this six-step process produced a cyclone design that improved collection yields without the need for extensive development runs or design iterations and was more accurate than if CCD or CFD methods alone were used. The cyclone design met the practical needs for spray-drying processing with a minimum of development time, cost, and API consumption.
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drying run. Based on previous experience, further improvements in product yield are expected with larger batch sizes and longer spray-drying runs. In addition, improvements are expected with further iterations of the cyclone-design methodology, yielding progressively higher product yields until the targets for later stages of development are reached. The cyclone-design methodology is described in detail below. 3. Rationale for methodology for cyclone design
2. Background/physical situation 2.1. The importance of cyclone design in spray-drying Cyclones are ideal for use in spray-drying processes due to their low operating cost and low maintenance requirements. Cyclones consist of two major sections: (1) the upper cylindrical section known as the barrel and (2) the lower conical section known as the cone. During operation, the particle-laden gas stream enters tangentially at the top of the barrel and travels downward into the cone forming an outer vortex. Centrifugal force causes the particles to collide with the cyclone wall. When this occurs, the particles drop into the collection bin, which is attached to the bottom of the cone. When the outer vortex gas reaches the bottom of the cone, an inner vortex is created, reversing the direction of the gas, which exits the top of the cyclone. Uncollected particles exit with the gas outlet stream. For a given cyclone, collection efficiency decreases as particle size and solids loading in the inlet gas stream decrease. Smallparticle SDDs (or “fines”) remain entrained in the effluent gas stream exiting from the cyclone and are captured in a downstream baghouse filter. It is difficult to collect fines that adhere to the surface of the baghouse filter, so process yields decrease when heavy loadings of fines on the baghouse filter occur. In addition, heavy loadings of fines can create large pressure drops across the baghouse, which in extreme cases may lead to process shutdown. The optimization of cyclone design is particularly important for pharmaceutical applications that require spray solutions with low solids loadings (due to low API solubility) and collection of smallparticle SDDs. For the methodology that is the focus of this case study, an overall product yield of 74% was achieved with a standard 12-in.diameter cyclone. As is described below, the methodology was used to design a cyclone that increased the overall product yield to 86%. While this increase in yield may appear modest, it represents the results of a single iteration of the methodology. In this case, no further iterations were required, because this yield was acceptable for (1) the stage of development of the compound being tested, (2) the small batch size, and (3) the limited duration of the spray-
The cyclone-design methodology described here builds upon the strengths of two approaches – the CCD method and CFD modeling – to achieve more-accurate results than is possible with either technique alone. The methodology, which is illustrated in Fig. 2, is an iterative approach based on spray-drying operating parameters and particle properties. As the figure shows (and is described below), the methodology consists of the following six steps: (1) definition of problem, establishing design goals; (2) determination of operating constraints, based on identification of key process parameters (e.g., mass flow rate, density, viscosity, pressure drop) and solid-particle properties (e.g., density, particle-size distribution); (3) application of CCD calculations to estimate new cyclone geometry with acceptable pressure drop and improved efficiency; (4) use of CFD modeling to determine the particle efficiency curve and test the pressure drop; (5) manufacture of the optimized cyclone design built using the design; and (6) experimental validation, testing pressure drop and determining actual yield. The methodology uses CCD calculations as a first step in designing a high-efficiency cyclone. The CCD method is a stepwise approach based on the design engineer’s knowledge of operational flow conditions, solid-state density, and the type of cyclone (e.g., high-efficiency, conventional, or high-throughput) (Shepherd & Lapple, 1939). The CCD method uses the cyclone geometry, operating conditions, and particle properties to calculate the number of effective turns (Ne) in the cyclone, dpc , and the cyclone collection–efficiency curve. However, the CCD method for calculating dpc neglects the effects of the inlet particle-size distribution and concentration on cyclone collection efficiency (Wang, 2000). Wang has shown that the cyclone collection–efficiency curve is strongly affected by particle-size distribution and concentration (Wang, Parnell, & Shaw, 2000).
Fig. 2. Methodology for cyclone design optimization.
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ment begins, can save time and money in the manufacturing of custom components. In addition, it reduces the need for multiple development runs (as is common in trial and error), which can save experimental time and use of valuable API.
4. Case study In this section, the technical steps in the methodology (Fig. 2) are described in detail. The goal of this work was to increase the particle-collection efficiency of a small-particle SDD prepared from Compound A, a low-solubility API, on a PSD-2 spray dryer. 4.1. Define problem statement (Fig. 2, Box 1)
Fig. 3. Cyclone collection–efficiency curves from experimental, CCD calculations, and CFD model outputs using the standard PSD-2 cyclone.
Cyclone geometries resulting from CCD analysis are then tested using CFD models to yield a more-accurate prediction of collection–efficiency. Specifically, Fluent discrete-phase model (DPM) software (ANSYS® Inc., Canonsburg, PA) is used to predict the trajectory of particles in the cyclone flow field, generating fractional efficiency curves (ANSYS, 2009). These curves account for the particle-size distribution and solids loading at the cyclone inlet and result in predictions that simulate experimental observations much more accurately than the CCD method does alone. Iteration can occur at any point in the cyclone-design methodology, but most often occurs between the third and fourth steps (i.e., CCD calculations and CFD modeling). For instance, design constraints may be identified based on CCD calculations that may not prove accurate based on CFD modeling (as was the case for pressure-drop predictions in this case study). Based on the CFD results, the cyclone geometry can be iterated multiple times until the desired collection efficiency is attained. Iteration is stopped at the point that the desired process and product requirements are met—in this case study, the desired product yield was achieved after a single iteration, so no further work was required. The effectiveness of the methodology is illustrated in Fig. 3, which shows cyclone collection–efficiency curves based on the experimental, CCD, and CFD modeling for the current spray-drying process using the standard PSD-2 cyclone (pharmaceutical spray dryer [PSD] model, GEA Process Engineering Inc. [Niro], Columbia, MD). As the figure shows, the CFD calculations for dpc were more accurate than the CCD results alone. The shaded band indicates the target particle size for this case: 10–20 m. To predict the pressure gradients, velocities, and vortex flow through the core of the cyclone accurately, CFD methods require several days of computational time for acceptable convergence of the three-dimensional flow field. Therefore, the use of CCD methods prior to CFD modeling makes it possible to minimize the time used when running the CFD models. When scaling pharmaceutical processes, timelines are often accelerated, requiring rapid design optimization for equipment manufacturing and testing before implementation. Therefore, a coupled approach using CCD calculations to provide an initial estimate of cyclone geometry and using CFD models to ensure efficient collection can result in significant savings in design time compared with the traditional trial-and-error approach. Our experience has shown that the methodology offers several other advantages. The optimized process for cyclone design, which addresses the need for maximum yields before fabrication of equip-
The first step in the methodology is to define the problem statement. This case study defined the problem being addressed in spray-drying Compound A, a low-solubility API, from a feed solution with a solids loading of <1%, leading to a small-particle, high-bulk-density SDD. For large-particle, high-density SDDs, cyclone collection efficiencies of more than 95% have been achieved with a PSD-2 spray dryer equipped with a standard 12-in.-diameter cyclone barrel. However, when spray-drying 1.3 kg of Compound A using optimized process conditions, a cyclone collection yield of only 74% was obtained using the standard 12-in.-diameter cyclone. In addition, baghouse pressure increased steadily during the run. Fig. 4 shows particle-size distributions for material collected in the cyclone versus material trapped in the baghouse filter. Fig. 5 shows the cyclone collection efficiency as a percentage of total particles sprayed, assuming that all the SDD particles not collected in the cyclone were trapped in the baghouse filter. This assumption was validated by visual observations; no build-up in the spray chamber or spray-dryer outlet piping was observed. Based on particle-size analysis of the SDD collected from the cyclone and baghouse, an improved cyclone is needed that improves the collection of particles less than 10 m in size. This improvement in cyclone design is needed to achieve acceptable collection yields for this pharmaceutical process, while also staying within the confines of the process operating space.
Fig. 4. Particle-size distribution of SDD collected in the cyclone versus baghouse filter, as measured by Malvern laser diffraction.
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Table 1 Design parameters for a standard PSD-2 cyclone. Source
Ne a
Experimental CCD CFD a
NA 6 14
dpc (m)
o (%)
5 9 7
74 54 73
NA: not available.
An overall cyclone collection yield (o ) can be calculated from Eq. (4): o =
Fig. 5. Cyclone collection efficiency calculated as the percentage of particle size.
4.2. Determine operating constraints (Fig. 2, Box 2) The second step in the methodology is to determine the operating constraints due to process parameters and desired solid particle properties. In this case, the gas flow rate and overall pressure drop in the spray-drying process were restricted by the available load on the gas recirculation fans. The maximum acceptable cyclone pressure drop was determined to be 20 in. of water (inH2 O). The particle-size distribution and SDD properties were fixed for this process. 4.3. Perform CCD calculations (Fig. 2, Box 3) The third step of the methodology is to perform the CCD calculations based on the operating constraints identified in the second step. The CCD correlations were developed based on time-of-flight approach developed by Lapple (1951). The calculation assumes that particles with a radial displacement equal to the half width of the cyclone inlet (W) within the residence time of the particle in the cyclone will be collected at 50% efficiency (i.e., the dpc ). The equation for dpc states that the particles’ terminal velocity is achieved when the opposing drag force equals the centrifugal force. The drag force for each individual particle is determined by Stokes law. The dpc is affected by the gas viscosity (), gas density (g ), and the cyclone inlet velocity (Vi ). Eq. (1) shows the resulting force-balance equation:
dpc =
9W 2NeVi (p − g )
1/2 ,
1 Lc L + , H b 2
(2)
where the H is the inlet height, Lb is the barrel length, and Lc is the cone length. Based on the dpc , Lapple developed an empirical correlation to predict the fractional efficiency curve (j ) (Lapple, 1951): j =
1 1 + (dpc /dpj )
j mj ,
where mj is the mass flow rate of the particle size of interest. The CCD method for calculating the particle-diameter cut fraction neglects the effects of the inlet particle-size distribution and concentration on cyclone collection efficiency. Wang has shown that the cyclone collection–efficiency curve is strongly affected by particle-size distribution and concentration (Wang, 2000). Several papers in the current literature indicate that the Lapple model greatly underestimates the actual cyclone collection efficiency (Barth, 1956; Gimbun et al., 2005; Li & Wang, 1989). Experimental results from our current process verify this finding. Fig. 3 and Table 1 compare CCD parameters, Fluent model predictions, and experimentally derived values for a standard PSD-2 cyclone. 4.3.1. Calculate cyclone pressure drop The pressure drop across the cyclone is constrained by the available load on the gas recirculation fans. A maximum acceptable pressure drop (P) of 20 inH2 O was determined for the current process. Pressure drop across the cyclone can be estimated from Eq. (5) (Shepherd & Lapple, 1939):
P =
0.003g Vi2
where dpj is the diameter of the particle of interest.
8(Vexit + 0.5Vcyclone ) D × De2
,
(5)
where Vexit is the annular-shaped volume above the exit duct and Vcyclone is the effective volume of the cyclone. The cyclone pressure drop for the new cyclone design is defined by the previous step in the methodology. Several other methods for calculating pressure drop have been developed as summarized by Cortes and Gil (2007). Pressure-drop results from the Shepard and Lapple correlation versus experimental data and Fluent results are presented in Table 2. CCD calculations overpredicted the pressure drop, whereas the CFD predictions were close to the experimental results demonstrating the utility of using both CCD and CFD analysis. 4.3.2. Optimize cyclone geometry To optimize cyclone geometry, dpc and pressure drop were calculated using Eqs. (1)–(5). The dimensions of the cyclone were modified and the new theoretical efficiency and pressure drop were determined iteratively until the optimized configuration was determined based on a desired dpc of 4 m and desired pressure drop of 20 inH2 O.
Table 2 Comparison of pressure drop across cyclone from experimental data, CCD literature correlations, and Fluent CFD modeling. Cyclone
, 2
(4)
(1)
where p is the particle density and Ne is the number of revolutions in the outer vortex of the gas flow. Ne is approximated from Eq. (2): Ne =
(3) Standard High-efficiency
Pressure drop (inH2 O) Experimental
CFD
CCD
6 8
7 9
9 20
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Fig. 6. Estimated efficiency improvement from CCD literature calculations.
Based on this work, the round gas inlet pipe of the standard cyclone was replaced with a rectangular duct. The rectangular duct allows an entire side in which particles enter the cyclone tangential to the wall. To maximize the number of particles entering close to the wall, an H/W ratio >1 is preferred (Cooper & Alley, 1986). Reducing the cyclone barrel diameter (D) reduces the radial distance a particle must travel before colliding with the wall. By increasing the length of both the barrel (Lb ) and cone (Lc ), a longer particle-residence time in the cyclone is expected, allowing more time for the particle to collide with the cyclone wall. Table 2 shows the CCD results for (1) the standard PSD-2 cyclone configuration, (2) the first high-efficiency optimization, and (3) the second high-efficiency optimization. The theoretical dpc for the current process operating conditions and cyclone geometry using Eqs. (1) and (2) predicted a 9-m cut. Using the iterative methodology described here, a final theoretical dpc of 3 m and pressure drop of 20 inH2 O was obtained. Table 3 shows the CCD calculation results and the changes in cyclone geometry. The expected fractional efficiency curve versus particle diameter is shown in Fig. 6. 4.3.3. Apply CFD modeling (Fig. 2, Box 4) In the fourth step of the methodology, after CCD correlations are used for an initial optimization, CFD modeling is used as a second model-verification tool to predict the particle-collection efficiency and verify the cyclone pressure drop. This is done to refine the accuracy of cyclone-efficiency and pressure-drop predictions, which
account more accurately for the flow field in the cyclone (Cortes & Gil, 2007). For this work, a 400,000-hexahedral mesh was used for the barrel and inlet pipe of the cyclone. A small volume where the inlet pipe intersects the barrel of the cyclone required a special fine tetrahedral mesh to properly converge the cases. The main barrel of the cyclone was split into two separate volumes. The inner core of the cyclone required a fine hexahedral mesh to resolve the large velocity/pressure gradient in the core due to vortex flow. Fluent’s Gambit Version 2.4.6 software was used. The Reynolds stress turbulence model was used to converge the swirling flow field in using Fluent Version 6.3.26 software (Slack et al., 2000). Convergence of the flow field required the use of an unsteady–state calculation method and second-order discretization solver (Gimbun et al., 2005). At convergence, the vortex flow was resolved at the inner core of the cyclone. The turbulence model was then turned off and particle tracking was performed using Fluent DPM capabilities. A 14-bin particlesize distribution was used to approximate the SDD product. Fourteen DPM surface injections at various particle diameters and flow rates were introduced at the cyclone inlet. The cyclonecollection boundary condition was set as a DPM particle-trap surface. The cyclone-pressure outlet boundary was set as a DPM particle-escape surface. The walls of the cyclone were set as reflection surfaces. Further investigation on appropriate selection of boundary conditions has been done by Shi and Bayless (2007). In this work, particle coordinates versus time were written to a file using a userdefined function (UDF) and MATLAB® (The MathWorksTM , Natick, MA) was used to process data and determine which particles exited the domain from the trap boundary or the pressure outlet. Particles in the bottom half of the cyclone cone were considered collected. CFD results for the fraction efficiency curve are shown in Fig. 7. 4.3.4. Manufacture optimized cyclone (Fig. 2, Box 5) In the fifth step of the methodology, the optimized highefficiency cyclone is fabricated using the second optimization based on CCD and CFD results. As described above, the combination of CCD and CFD methods ensures that the design will function as desired, optimizing the collection efficiency for the small-particle SDDs. Fig. 8 shows the optimized high-efficiency cyclone after construction. This case study is the first use of this methodology for cyclone design and manufacture. 4.3.5. Perform experimental validation (Fig. 2, Box 6) In the sixth step of the methodology, experimental validation is performed. Initially, pressure drop across the cyclone was tested
Table 3 CCD results for the standard PSD-2 cyclone configuration, first optimization, and second optimization. Cyclone design parameters Barrel diameter (in.) Barrel length (in.) Cone length (in.) Inlet width (in.) Inlet height (in.) Gas outlet length (in.) Powder outlet diameter (in.) Gas outlet diameter (in.) Inlet area (in.3 ) Inlet velocity (m/s) Effective turns Gas residence time (s) Smallest particle collected (m) Diameter of particle collected with 50% efficiency (m) Pressure drop
D Lb Lc W H S Dd De Ai Vi Ne t dp dpc P
Standard cyclone
First optimization
Second optimization
12 11 21 3.3 3.3 11.00 3.75 6.38 12.57 13.2 6 0.94 13.2 9.3 3.4
10 15 25 2.0 5.1 5.09 3.82 5.09 10.35 16.0 6 0.56 10.1 7.2 4.4
8 20 60 1.6 4.0 4.02 4.02 4.02 6.32 26.2 12 0.61 4.6 3.3 19.0
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Fig. 7. Estimated fractional collection–efficiency curves calculated from Fluent CFD modeling. Fig. 9. Experimental validation of Fluent CFD model-pressure drop tested after manufacture of the high-efficiency cyclone at an operating flow rate of 475 kg/h.
by connecting it to a blower and generating the curve of pressure drop versus flow rate. This was done to ensure the cyclone would operate at acceptable flow rates, which were identified in Step 2. As shown in Fig. 9, the pressure test validated the CFD pressuredrop predictions for the cyclone. Results indicate that at the operating flow rate of 475 kg/h, the pressure drop is approximately 8 inH2 O—which is very close to the Fluent-predicted pressure drop of 9 inH2 O. After the cyclone was installed, a process-development spray run was performed to validate the modified design. Fig. 10 shows the improved collection efficiency using the optimized high-
Fig. 8. Optimized high-efficiency cyclone after construction.
efficiency cyclone. As the figure shows, a 3-m decrease in dpc was achieved. The final product yield was 86%. Fig. 11 compares the Fluent-predicted fractional collection– efficiency curve against experimental data measured by Malvern laser diffraction (Malvern Instruments Ltd., Worcestershire, U.K.) for the final validation development run. The validation run was performed with a placebo SDD to save the cost of using expensive API. Our experience has shown that runs with placebo SDDs mirror those of active SDDs with the same particle size and density. Finally, Table 2 compares CCD, Fluent, and experimental data. As the table shows, the CFD model improved on CCD pressure-drop calculations, resulting in an optimized cyclone design that achieved high collection efficiencies for small-particle SDDs.
Fig. 10. Collection efficiency of the standard cyclone versus the high-efficiency cyclone.
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for spray-drying processing with a minimum of development time, cost, and API consumption. Acknowledgments We gratefully acknowledge the contributions of our fellow Bend Research colleagues: Rick Falk, Doug Millard, Yogesh Waghmare, Ann Malkin, Dwayne Friesen, and Rod Ray. We also wish to acknowledge the support of Pfizer during this work. References
Fig. 11. Comparison of Fluent prediction versus experimental collection efficiency for the high-efficiency cyclone.
5. Conclusion This paper describes a case study using an efficient methodology for cyclone design to increase the collection efficiency for smallparticle SDDs. The methodology is based on the combined use of CCD calculations and CFD modeling techniques. The case study, which includes experimental validation after the fabrication and installation of the optimized high-efficiency cyclone, demonstrates the effectiveness of this methodology. Collection efficiency increased from only 74% using a standard 12-in.-diameter cyclone to 86% after a single iteration of the methodology to design a high-efficiency cyclone. Further improvements in efficiency are expected with a longer duration manufacturing run. Significantly, the methodology allowed design of a cyclone that was more efficient than would have been designed using CCD calculations alone, since those calculations alone would not have predicted pressure drop accurately. The validation run confirmed that the appropriate cyclone pressure drop was maintained throughout the validation run. Based on the work presented here, the methodology represents an efficient approach to equipment design for pharmaceutical processes. Use of this six-step process produced an optimized design quickly and efficiently, without the need for extensive development runs or design iterations. The final design meets the practical needs
ANSYS® . (2009). ANSYS® Fluent 12.0 user’s guide. Canonsburg, PA: ANSYS® Inc. Barth, W. (1956). Berechnung und Auslegung von Zyklonabscheidernauf Grund neuerer Untersuchungen. Brennst-Waerme-Kraft, 8, 1–9. Chiesa, M., Mathisen, V., Melheim, J. A., & Halvorsen, B. M. (2005). Numerical simulation of particulate flow by the Eulerian–Lagrangian and the Eulerian–Eulerian approach with application to fluidised bed. Computers and Chemical Engineering, 29(2), 291–304. Cooper, C. D., & Alley, F. C. (1986). Air pollution control: A design approach. Prospect Heights, IL: Waveland Press, Inc. Cortes, C., & Gil, A. (2007). Modeling the gas and particle flow inside cyclone separators. Progress in Energy and Combustion Science, 33, 409–452. Dietz, P. W. (1981). Collection efficiency of cyclone separators. AIChE Journal, 27, 888–892. Dobry, D. E., Settell, D. M., Baumann, J. M., Ray, R. J., Graham, L. J., & Beyerinck, R. A. (2009). A model-based methodology for spray-drying process development. Journal of Pharmaceutical Innovation, 4, 133–142. Elghobashi, S. E. (1994). On predicting particle-laden turbulent flows. Applied Scientific Research, 52, 309–329. Friesen, D. T., Shanker, R. M., Crew, M. D., Smithey, D. T., Curatolo, W. J., & Nightingale, J. A. S. (2008). Hydroxypropyl methylcellulose acetate succinate-based spraydried dispersions: an overview. Molecular Pharmacology, 5(6), 1003–1019. Gimbun, J., Chuah, T. G., Choong, T. S. Y., & Fakhru’l-Razi, A. (2005). A CFD study on the prediction of cyclone collection efficiency. International Journal for Computational Methods in Engineering Science and Mechanics, 6, 161–168. Jakirlic, S., & Hanjalic, K. (2002). Modeling rotating and swirling turbulent flows: a perpetual challenge. American Institute of Aeronautics and Astronautics Journal, 40, 1984–1996. Lapple, C. E. (1951). Processes use many collector types. Chemical Engineering, 58(5), 144–151. Leith, D., & Licht, W. (1972). The collection efficiency of cyclone type particle collectors—A new theoretical approach. AIChE Symposium Series, 68(126), 196–206. Li, E., & Wang, Y. (1989). A new collection theory of cyclone separators. AIChE Journal, 35, 666–669. Shepherd, C. B., & Lapple, C. E. (1939). Flow pattern and pressure drop in cyclone dust collectors. Industrial and Engineering Chemistry, 31(8), 972–984. Shi, L., & Bayless, D. J. (2007). Comparison of boundary conditions for predicting the collection efficiency of cyclones. Powder Technology, 173, 29–37. Slack, M. D., Prasad, R. O., Bakker, A., & Boysan, F. (2000). Advances in cyclone modeling using unstructured grids. Transactions of the Institution of Chemical Engineers, 78, 1098–1104. Vodak, D. T., & Friesen, D. T. (2009). Dispersions deliver results. European Biopharmaceutical Review, (April), 78–81. Wang, L. (2000). A new engineering approach to cyclone design for cotton gins. M.S. thesis. TX: Department of Agricultural Engineering, Texas A&M University, College Station. Wang, L., Parnell, C. B., & Shaw, B. W. (2000). 1D2D, 1D3D, 2D2D cyclone fractional efficiency curves for fine dust. In Proceedings of the 2000 Beltwide cotton production conferences. San Antonio, TX: National Cotton Council.
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Computers and Chemical Engineering journal homepage: www.elsevier.com/locate/compchemeng
Pharmaceutical process/equipment design methodology case study: Cyclone design to optimize spray-dried-particle collection efficiency Lisa J. Graham a,∗ , Rebecca Taillon a , Jim Mullin a , Trevor Wigle b a b
Bend Research Inc., 64550 Research Road, Bend, OR 97701, United States Bend Research Pharmaceutical Process Development Inc. (BRPPD), 20503 Builders Street, Bend, OR 97701, United States
a r t i c l e
i n f o
Article history: Received 12 September 2009 Received in revised form 1 April 2010 Accepted 5 April 2010 Available online 13 April 2010 Keywords: Cyclone Spray-drying Computational fluid dynamics Particle collection
a b s t r a c t This paper describes a case study using a cyclone-design methodology to increase cyclone collection efficiency for spray-dried dispersions (SDDs) for pharmaceutical applications. The six-step methodology combines the use of classical cyclone design (CCD) correlations and computational fluid dynamics (CFD) modeling techniques. By combining these techniques, the methodology avoids the limitations inherent with the use of either technique alone and represents an improved alternative to conventional trial-and-error methods. Specifically, the methodology increases the efficiency of the design process by reducing (1) computational time; (2) experimental time (i.e., the need for numerous development runs); and (3) the use of costly active pharmaceutical ingredient (API). The case study shows how the methodology was used to quickly and accurately design a cyclone with improved performance for a desired product characteristic (improved collection efficiency for small particles rather than broad particle-size distribution). Validation data are presented demonstrating the accuracy of the approach. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Spray-drying is a well-characterized, widely used pharmaceutical operation that is used to produce particles of uniformly dispersed drug in a polymer matrix. The drug and polymer are dissolved in solvent and atomized in a high-temperature, highvelocity gas stream, producing small spray-dried dispersion (SDD) particles that have been used to increase the bioavailability of more than 200 low-solubility pharmaceutical compounds (Friesen et al., 2008; Vodak & Friesen, 2009). The general process train for spray-drying operations is shown in Fig. 1. As the figure shows, a cyclone separator is used to collect the SDD particles, which are entrained in the gas outlet stream from the drying chamber (Dobry et al., 2009). The efficiency of product collection – particularly for small particles – is a major cost factor affecting the economics of the spray-drying process, since increased collection efficiency maximizes the yield of drug products that are typically made from expensive active pharmaceutical ingredients (APIs). The size and geometry of the cyclone can have a major impact on maximizing collection efficiency of spray-drying processes. The optimum design will differ based on the product profile or on process parameters (i.e., constraints). For instance, various gas flow
∗ Corresponding author. Tel.: +1 541 382 4100. E-mail address: [email protected] (L.J. Graham). 0098-1354/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.compchemeng.2010.04.004
rates may require cyclone size changes, whereas brittle particles require low shear exposure to prevent particle attrition. This paper describes a case study using a methodology to optimize cyclone design to increase the collection efficiency of small-particle SDDs. The methodology combines the use of classical cyclone design (CCD) correlations with computational fluid dynamics (CFD) models, capitalizing on the strengths of both techniques. CCD correlations are semi-empirical in nature and are widely used in chemical industry for design purposes (Cortes & Gil, 2007) because they are easy to use and based on extensive experimental data collected over a wide range of operating conditions and cyclone geometries. As a result, they are computationally inexpensive, resulting in rapid design times. Correlations for calculating pressure drop and collection efficiency are of particular importance from the engineering point of view. The classic correlation proposed by Lapple (1951) is simplest to use. Lapple (1951) used the concept of time-of-flight and a series of simplifying assumptions to arrive at an expression for the particle-diameter cut fraction (dpc )—the particle size that is separated with 50% efficiency. Barth (1956) proposed an improved correlation that accounts for the effect of the geometry of the vortex finder. A model proposed by Leith and Licht (1972) introduced the concept of using the natural vortex length instead of using the geometric length (height) of the cyclone. This helped in correctly calculating residence time and, hence, accurately predicting collection efficiencies. Dietz (1981) developed a
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Nomenclature Ai (in.) area of inlet D (in.) diameter of barrel Dd (in.) diameter of powder collector De (in.) diameter of gas exit dpj (m) diameter of j particle size dpc (m) diameter of particle collected with 50% efficiency H (in.) height of inlet duct Lb (in.) length of barrel Lc (in.) length of cone mj (g/s) mass flow of j particle size Ne number of gas turns in the outer vortex S (in.) length of gas collector Vcyclone (m3 ) effective volume of the cyclone Vexit (m3 ) volume of the exit duct Vi (m/s) cyclone inlet velocity W (in.) width of inlet duct Greek letters P (inH2 O) pressure drop across cyclone t (s) residence time fractional efficiency of j particle size j (%) o (%) overall cyclone collection yield g (g/cm3 ) density of gas p (g/cm3 ) density of particle (kg/m s) gas viscosity
compartmentalized cyclone model based on the concept of natural vortex length. Recently, Gimbun, Chuah, Choong, and Fakhru’l-Razi (2005) showed that the model proposed by Li and Wang (1989) did a better job of predicting collection efficiencies than other traditional models. Even though these models offer a faster, simpler way of evaluating cyclone performance, such correlations are valid only with a limited range of operating conditions and particle sizes and often can be used only for a particular cyclone shape or geometry without adjusting the geometric-based constants.
With the advancement in computational power, CFD modeling is emerging as a practical alternative to the CCD approach. CFD models offer the freedom to evaluate the performance of cyclone designs that differ significantly from classical cyclone geometries. CFD modeling also allows independent variation of aspect ratios and processing conditions. Recent advances in CFD modeling have addressed the challenge of capturing the highly anisotropic swirling flow in the cyclone’s confined geometry. The accuracy of CFD predictions depends strongly on the correct choice of turbulence models (Gimbun et al., 2005; Jakirlic & Hanjalic, 2002; Slack, Prasad, Bakker, & Boysan, 2000); steady- versus unsteady-state treatment; discretization schemes; two-phase flow models (Chiesa, Mathisen, Melheim, & Halvorsen, 2005; Elghobashi, 1994); and solid-phase boundary conditions (Shi & Bayless, 2007). A detailed account of various CCD and CFD methods and current progress in cyclone CFD modeling is described in a review by Cortes and Gil (2007). As described below, the methodology presented here builds on the strengths of the CCD and CFD approaches, avoiding limitations inherent with either method and offering significant improvements over trial-and-error methods that are traditionally used. The case study describes how the methodology was used to arrive at an optimized cyclone design and how the results were used to fabricate, test, and validate the cyclone design. Validation data are provided, demonstrating the accuracy of the methodology. The methodology was used to produce a cyclone that increased collection efficiency from 74% (using a standard 12-in.-diameter cyclone) to 86%, using identical spray-drying conditions. The validation run confirmed that the methodology produced a design that maintained an appropriate cyclone pressure drop throughout the validation run—which was not predicted based on CCD calculations alone. While the improvement in collection efficiency may appear modest, it was achieved after only a single iteration and, because that collection efficiency was adequate for the stage of development for the compound in question, no further iterations were performed. Based on extensive experience with this methodology, further improvements in yield can be expected (1) by performing further iterations with the methodology and (2) by increasing the spray-drying run time (yields are typically lowest for shortduration runs).
Fig. 1. General process train for spray-drying.
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Based on the work presented here, the methodology represents an attractive approach to cyclone equipment design for pharmaceutical processes. Use of this six-step process produced a cyclone design that improved collection yields without the need for extensive development runs or design iterations and was more accurate than if CCD or CFD methods alone were used. The cyclone design met the practical needs for spray-drying processing with a minimum of development time, cost, and API consumption.
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drying run. Based on previous experience, further improvements in product yield are expected with larger batch sizes and longer spray-drying runs. In addition, improvements are expected with further iterations of the cyclone-design methodology, yielding progressively higher product yields until the targets for later stages of development are reached. The cyclone-design methodology is described in detail below. 3. Rationale for methodology for cyclone design
2. Background/physical situation 2.1. The importance of cyclone design in spray-drying Cyclones are ideal for use in spray-drying processes due to their low operating cost and low maintenance requirements. Cyclones consist of two major sections: (1) the upper cylindrical section known as the barrel and (2) the lower conical section known as the cone. During operation, the particle-laden gas stream enters tangentially at the top of the barrel and travels downward into the cone forming an outer vortex. Centrifugal force causes the particles to collide with the cyclone wall. When this occurs, the particles drop into the collection bin, which is attached to the bottom of the cone. When the outer vortex gas reaches the bottom of the cone, an inner vortex is created, reversing the direction of the gas, which exits the top of the cyclone. Uncollected particles exit with the gas outlet stream. For a given cyclone, collection efficiency decreases as particle size and solids loading in the inlet gas stream decrease. Smallparticle SDDs (or “fines”) remain entrained in the effluent gas stream exiting from the cyclone and are captured in a downstream baghouse filter. It is difficult to collect fines that adhere to the surface of the baghouse filter, so process yields decrease when heavy loadings of fines on the baghouse filter occur. In addition, heavy loadings of fines can create large pressure drops across the baghouse, which in extreme cases may lead to process shutdown. The optimization of cyclone design is particularly important for pharmaceutical applications that require spray solutions with low solids loadings (due to low API solubility) and collection of smallparticle SDDs. For the methodology that is the focus of this case study, an overall product yield of 74% was achieved with a standard 12-in.diameter cyclone. As is described below, the methodology was used to design a cyclone that increased the overall product yield to 86%. While this increase in yield may appear modest, it represents the results of a single iteration of the methodology. In this case, no further iterations were required, because this yield was acceptable for (1) the stage of development of the compound being tested, (2) the small batch size, and (3) the limited duration of the spray-
The cyclone-design methodology described here builds upon the strengths of two approaches – the CCD method and CFD modeling – to achieve more-accurate results than is possible with either technique alone. The methodology, which is illustrated in Fig. 2, is an iterative approach based on spray-drying operating parameters and particle properties. As the figure shows (and is described below), the methodology consists of the following six steps: (1) definition of problem, establishing design goals; (2) determination of operating constraints, based on identification of key process parameters (e.g., mass flow rate, density, viscosity, pressure drop) and solid-particle properties (e.g., density, particle-size distribution); (3) application of CCD calculations to estimate new cyclone geometry with acceptable pressure drop and improved efficiency; (4) use of CFD modeling to determine the particle efficiency curve and test the pressure drop; (5) manufacture of the optimized cyclone design built using the design; and (6) experimental validation, testing pressure drop and determining actual yield. The methodology uses CCD calculations as a first step in designing a high-efficiency cyclone. The CCD method is a stepwise approach based on the design engineer’s knowledge of operational flow conditions, solid-state density, and the type of cyclone (e.g., high-efficiency, conventional, or high-throughput) (Shepherd & Lapple, 1939). The CCD method uses the cyclone geometry, operating conditions, and particle properties to calculate the number of effective turns (Ne) in the cyclone, dpc , and the cyclone collection–efficiency curve. However, the CCD method for calculating dpc neglects the effects of the inlet particle-size distribution and concentration on cyclone collection efficiency (Wang, 2000). Wang has shown that the cyclone collection–efficiency curve is strongly affected by particle-size distribution and concentration (Wang, Parnell, & Shaw, 2000).
Fig. 2. Methodology for cyclone design optimization.
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ment begins, can save time and money in the manufacturing of custom components. In addition, it reduces the need for multiple development runs (as is common in trial and error), which can save experimental time and use of valuable API.
4. Case study In this section, the technical steps in the methodology (Fig. 2) are described in detail. The goal of this work was to increase the particle-collection efficiency of a small-particle SDD prepared from Compound A, a low-solubility API, on a PSD-2 spray dryer. 4.1. Define problem statement (Fig. 2, Box 1)
Fig. 3. Cyclone collection–efficiency curves from experimental, CCD calculations, and CFD model outputs using the standard PSD-2 cyclone.
Cyclone geometries resulting from CCD analysis are then tested using CFD models to yield a more-accurate prediction of collection–efficiency. Specifically, Fluent discrete-phase model (DPM) software (ANSYS® Inc., Canonsburg, PA) is used to predict the trajectory of particles in the cyclone flow field, generating fractional efficiency curves (ANSYS, 2009). These curves account for the particle-size distribution and solids loading at the cyclone inlet and result in predictions that simulate experimental observations much more accurately than the CCD method does alone. Iteration can occur at any point in the cyclone-design methodology, but most often occurs between the third and fourth steps (i.e., CCD calculations and CFD modeling). For instance, design constraints may be identified based on CCD calculations that may not prove accurate based on CFD modeling (as was the case for pressure-drop predictions in this case study). Based on the CFD results, the cyclone geometry can be iterated multiple times until the desired collection efficiency is attained. Iteration is stopped at the point that the desired process and product requirements are met—in this case study, the desired product yield was achieved after a single iteration, so no further work was required. The effectiveness of the methodology is illustrated in Fig. 3, which shows cyclone collection–efficiency curves based on the experimental, CCD, and CFD modeling for the current spray-drying process using the standard PSD-2 cyclone (pharmaceutical spray dryer [PSD] model, GEA Process Engineering Inc. [Niro], Columbia, MD). As the figure shows, the CFD calculations for dpc were more accurate than the CCD results alone. The shaded band indicates the target particle size for this case: 10–20 m. To predict the pressure gradients, velocities, and vortex flow through the core of the cyclone accurately, CFD methods require several days of computational time for acceptable convergence of the three-dimensional flow field. Therefore, the use of CCD methods prior to CFD modeling makes it possible to minimize the time used when running the CFD models. When scaling pharmaceutical processes, timelines are often accelerated, requiring rapid design optimization for equipment manufacturing and testing before implementation. Therefore, a coupled approach using CCD calculations to provide an initial estimate of cyclone geometry and using CFD models to ensure efficient collection can result in significant savings in design time compared with the traditional trial-and-error approach. Our experience has shown that the methodology offers several other advantages. The optimized process for cyclone design, which addresses the need for maximum yields before fabrication of equip-
The first step in the methodology is to define the problem statement. This case study defined the problem being addressed in spray-drying Compound A, a low-solubility API, from a feed solution with a solids loading of <1%, leading to a small-particle, high-bulk-density SDD. For large-particle, high-density SDDs, cyclone collection efficiencies of more than 95% have been achieved with a PSD-2 spray dryer equipped with a standard 12-in.-diameter cyclone barrel. However, when spray-drying 1.3 kg of Compound A using optimized process conditions, a cyclone collection yield of only 74% was obtained using the standard 12-in.-diameter cyclone. In addition, baghouse pressure increased steadily during the run. Fig. 4 shows particle-size distributions for material collected in the cyclone versus material trapped in the baghouse filter. Fig. 5 shows the cyclone collection efficiency as a percentage of total particles sprayed, assuming that all the SDD particles not collected in the cyclone were trapped in the baghouse filter. This assumption was validated by visual observations; no build-up in the spray chamber or spray-dryer outlet piping was observed. Based on particle-size analysis of the SDD collected from the cyclone and baghouse, an improved cyclone is needed that improves the collection of particles less than 10 m in size. This improvement in cyclone design is needed to achieve acceptable collection yields for this pharmaceutical process, while also staying within the confines of the process operating space.
Fig. 4. Particle-size distribution of SDD collected in the cyclone versus baghouse filter, as measured by Malvern laser diffraction.
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Table 1 Design parameters for a standard PSD-2 cyclone. Source
Ne a
Experimental CCD CFD a
NA 6 14
dpc (m)
o (%)
5 9 7
74 54 73
NA: not available.
An overall cyclone collection yield (o ) can be calculated from Eq. (4): o =
Fig. 5. Cyclone collection efficiency calculated as the percentage of particle size.
4.2. Determine operating constraints (Fig. 2, Box 2) The second step in the methodology is to determine the operating constraints due to process parameters and desired solid particle properties. In this case, the gas flow rate and overall pressure drop in the spray-drying process were restricted by the available load on the gas recirculation fans. The maximum acceptable cyclone pressure drop was determined to be 20 in. of water (inH2 O). The particle-size distribution and SDD properties were fixed for this process. 4.3. Perform CCD calculations (Fig. 2, Box 3) The third step of the methodology is to perform the CCD calculations based on the operating constraints identified in the second step. The CCD correlations were developed based on time-of-flight approach developed by Lapple (1951). The calculation assumes that particles with a radial displacement equal to the half width of the cyclone inlet (W) within the residence time of the particle in the cyclone will be collected at 50% efficiency (i.e., the dpc ). The equation for dpc states that the particles’ terminal velocity is achieved when the opposing drag force equals the centrifugal force. The drag force for each individual particle is determined by Stokes law. The dpc is affected by the gas viscosity (), gas density (g ), and the cyclone inlet velocity (Vi ). Eq. (1) shows the resulting force-balance equation:
dpc =
9W 2NeVi (p − g )
1/2 ,
1 Lc L + , H b 2
(2)
where the H is the inlet height, Lb is the barrel length, and Lc is the cone length. Based on the dpc , Lapple developed an empirical correlation to predict the fractional efficiency curve (j ) (Lapple, 1951): j =
1 1 + (dpc /dpj )
j mj ,
where mj is the mass flow rate of the particle size of interest. The CCD method for calculating the particle-diameter cut fraction neglects the effects of the inlet particle-size distribution and concentration on cyclone collection efficiency. Wang has shown that the cyclone collection–efficiency curve is strongly affected by particle-size distribution and concentration (Wang, 2000). Several papers in the current literature indicate that the Lapple model greatly underestimates the actual cyclone collection efficiency (Barth, 1956; Gimbun et al., 2005; Li & Wang, 1989). Experimental results from our current process verify this finding. Fig. 3 and Table 1 compare CCD parameters, Fluent model predictions, and experimentally derived values for a standard PSD-2 cyclone. 4.3.1. Calculate cyclone pressure drop The pressure drop across the cyclone is constrained by the available load on the gas recirculation fans. A maximum acceptable pressure drop (P) of 20 inH2 O was determined for the current process. Pressure drop across the cyclone can be estimated from Eq. (5) (Shepherd & Lapple, 1939):
P =
0.003g Vi2
where dpj is the diameter of the particle of interest.
8(Vexit + 0.5Vcyclone ) D × De2
,
(5)
where Vexit is the annular-shaped volume above the exit duct and Vcyclone is the effective volume of the cyclone. The cyclone pressure drop for the new cyclone design is defined by the previous step in the methodology. Several other methods for calculating pressure drop have been developed as summarized by Cortes and Gil (2007). Pressure-drop results from the Shepard and Lapple correlation versus experimental data and Fluent results are presented in Table 2. CCD calculations overpredicted the pressure drop, whereas the CFD predictions were close to the experimental results demonstrating the utility of using both CCD and CFD analysis. 4.3.2. Optimize cyclone geometry To optimize cyclone geometry, dpc and pressure drop were calculated using Eqs. (1)–(5). The dimensions of the cyclone were modified and the new theoretical efficiency and pressure drop were determined iteratively until the optimized configuration was determined based on a desired dpc of 4 m and desired pressure drop of 20 inH2 O.
Table 2 Comparison of pressure drop across cyclone from experimental data, CCD literature correlations, and Fluent CFD modeling. Cyclone
, 2
(4)
(1)
where p is the particle density and Ne is the number of revolutions in the outer vortex of the gas flow. Ne is approximated from Eq. (2): Ne =
(3) Standard High-efficiency
Pressure drop (inH2 O) Experimental
CFD
CCD
6 8
7 9
9 20
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Fig. 6. Estimated efficiency improvement from CCD literature calculations.
Based on this work, the round gas inlet pipe of the standard cyclone was replaced with a rectangular duct. The rectangular duct allows an entire side in which particles enter the cyclone tangential to the wall. To maximize the number of particles entering close to the wall, an H/W ratio >1 is preferred (Cooper & Alley, 1986). Reducing the cyclone barrel diameter (D) reduces the radial distance a particle must travel before colliding with the wall. By increasing the length of both the barrel (Lb ) and cone (Lc ), a longer particle-residence time in the cyclone is expected, allowing more time for the particle to collide with the cyclone wall. Table 2 shows the CCD results for (1) the standard PSD-2 cyclone configuration, (2) the first high-efficiency optimization, and (3) the second high-efficiency optimization. The theoretical dpc for the current process operating conditions and cyclone geometry using Eqs. (1) and (2) predicted a 9-m cut. Using the iterative methodology described here, a final theoretical dpc of 3 m and pressure drop of 20 inH2 O was obtained. Table 3 shows the CCD calculation results and the changes in cyclone geometry. The expected fractional efficiency curve versus particle diameter is shown in Fig. 6. 4.3.3. Apply CFD modeling (Fig. 2, Box 4) In the fourth step of the methodology, after CCD correlations are used for an initial optimization, CFD modeling is used as a second model-verification tool to predict the particle-collection efficiency and verify the cyclone pressure drop. This is done to refine the accuracy of cyclone-efficiency and pressure-drop predictions, which
account more accurately for the flow field in the cyclone (Cortes & Gil, 2007). For this work, a 400,000-hexahedral mesh was used for the barrel and inlet pipe of the cyclone. A small volume where the inlet pipe intersects the barrel of the cyclone required a special fine tetrahedral mesh to properly converge the cases. The main barrel of the cyclone was split into two separate volumes. The inner core of the cyclone required a fine hexahedral mesh to resolve the large velocity/pressure gradient in the core due to vortex flow. Fluent’s Gambit Version 2.4.6 software was used. The Reynolds stress turbulence model was used to converge the swirling flow field in using Fluent Version 6.3.26 software (Slack et al., 2000). Convergence of the flow field required the use of an unsteady–state calculation method and second-order discretization solver (Gimbun et al., 2005). At convergence, the vortex flow was resolved at the inner core of the cyclone. The turbulence model was then turned off and particle tracking was performed using Fluent DPM capabilities. A 14-bin particlesize distribution was used to approximate the SDD product. Fourteen DPM surface injections at various particle diameters and flow rates were introduced at the cyclone inlet. The cyclonecollection boundary condition was set as a DPM particle-trap surface. The cyclone-pressure outlet boundary was set as a DPM particle-escape surface. The walls of the cyclone were set as reflection surfaces. Further investigation on appropriate selection of boundary conditions has been done by Shi and Bayless (2007). In this work, particle coordinates versus time were written to a file using a userdefined function (UDF) and MATLAB® (The MathWorksTM , Natick, MA) was used to process data and determine which particles exited the domain from the trap boundary or the pressure outlet. Particles in the bottom half of the cyclone cone were considered collected. CFD results for the fraction efficiency curve are shown in Fig. 7. 4.3.4. Manufacture optimized cyclone (Fig. 2, Box 5) In the fifth step of the methodology, the optimized highefficiency cyclone is fabricated using the second optimization based on CCD and CFD results. As described above, the combination of CCD and CFD methods ensures that the design will function as desired, optimizing the collection efficiency for the small-particle SDDs. Fig. 8 shows the optimized high-efficiency cyclone after construction. This case study is the first use of this methodology for cyclone design and manufacture. 4.3.5. Perform experimental validation (Fig. 2, Box 6) In the sixth step of the methodology, experimental validation is performed. Initially, pressure drop across the cyclone was tested
Table 3 CCD results for the standard PSD-2 cyclone configuration, first optimization, and second optimization. Cyclone design parameters Barrel diameter (in.) Barrel length (in.) Cone length (in.) Inlet width (in.) Inlet height (in.) Gas outlet length (in.) Powder outlet diameter (in.) Gas outlet diameter (in.) Inlet area (in.3 ) Inlet velocity (m/s) Effective turns Gas residence time (s) Smallest particle collected (m) Diameter of particle collected with 50% efficiency (m) Pressure drop
D Lb Lc W H S Dd De Ai Vi Ne t dp dpc P
Standard cyclone
First optimization
Second optimization
12 11 21 3.3 3.3 11.00 3.75 6.38 12.57 13.2 6 0.94 13.2 9.3 3.4
10 15 25 2.0 5.1 5.09 3.82 5.09 10.35 16.0 6 0.56 10.1 7.2 4.4
8 20 60 1.6 4.0 4.02 4.02 4.02 6.32 26.2 12 0.61 4.6 3.3 19.0
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Fig. 7. Estimated fractional collection–efficiency curves calculated from Fluent CFD modeling. Fig. 9. Experimental validation of Fluent CFD model-pressure drop tested after manufacture of the high-efficiency cyclone at an operating flow rate of 475 kg/h.
by connecting it to a blower and generating the curve of pressure drop versus flow rate. This was done to ensure the cyclone would operate at acceptable flow rates, which were identified in Step 2. As shown in Fig. 9, the pressure test validated the CFD pressuredrop predictions for the cyclone. Results indicate that at the operating flow rate of 475 kg/h, the pressure drop is approximately 8 inH2 O—which is very close to the Fluent-predicted pressure drop of 9 inH2 O. After the cyclone was installed, a process-development spray run was performed to validate the modified design. Fig. 10 shows the improved collection efficiency using the optimized high-
Fig. 8. Optimized high-efficiency cyclone after construction.
efficiency cyclone. As the figure shows, a 3-m decrease in dpc was achieved. The final product yield was 86%. Fig. 11 compares the Fluent-predicted fractional collection– efficiency curve against experimental data measured by Malvern laser diffraction (Malvern Instruments Ltd., Worcestershire, U.K.) for the final validation development run. The validation run was performed with a placebo SDD to save the cost of using expensive API. Our experience has shown that runs with placebo SDDs mirror those of active SDDs with the same particle size and density. Finally, Table 2 compares CCD, Fluent, and experimental data. As the table shows, the CFD model improved on CCD pressure-drop calculations, resulting in an optimized cyclone design that achieved high collection efficiencies for small-particle SDDs.
Fig. 10. Collection efficiency of the standard cyclone versus the high-efficiency cyclone.
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for spray-drying processing with a minimum of development time, cost, and API consumption. Acknowledgments We gratefully acknowledge the contributions of our fellow Bend Research colleagues: Rick Falk, Doug Millard, Yogesh Waghmare, Ann Malkin, Dwayne Friesen, and Rod Ray. We also wish to acknowledge the support of Pfizer during this work. References
Fig. 11. Comparison of Fluent prediction versus experimental collection efficiency for the high-efficiency cyclone.
5. Conclusion This paper describes a case study using an efficient methodology for cyclone design to increase the collection efficiency for smallparticle SDDs. The methodology is based on the combined use of CCD calculations and CFD modeling techniques. The case study, which includes experimental validation after the fabrication and installation of the optimized high-efficiency cyclone, demonstrates the effectiveness of this methodology. Collection efficiency increased from only 74% using a standard 12-in.-diameter cyclone to 86% after a single iteration of the methodology to design a high-efficiency cyclone. Further improvements in efficiency are expected with a longer duration manufacturing run. Significantly, the methodology allowed design of a cyclone that was more efficient than would have been designed using CCD calculations alone, since those calculations alone would not have predicted pressure drop accurately. The validation run confirmed that the appropriate cyclone pressure drop was maintained throughout the validation run. Based on the work presented here, the methodology represents an efficient approach to equipment design for pharmaceutical processes. Use of this six-step process produced an optimized design quickly and efficiently, without the need for extensive development runs or design iterations. The final design meets the practical needs
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