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APV 2nd Experts’ Workshop
Hot melt extrusion and its use in the manufacturing of pharmaceutical dosage forms
December 1-2, 2009
Modeling of the extrusion process and prediction of scale-up and transfer behaviour
Adam DreiblattExtrusioneering International, Inc.
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Presentation outline
Modeling strategySimulation of hot melt extrusion process1D simulation case studyTransfer technology (i.e. “scale-up”)Summary
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Why modeling ?
Limited availability and cost of API’sEvaluate alternate machine configurationsEvaluate process variables (virtual DOE)
Obtain data not otherwise availableTroubleshoot problemsScale-up
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Simulation strategy
Several options available3D modelingResponse surface methodology1D modeling
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3D finite element modeling2D flow analysis network
Rigorous treatmentAccurate, detailedLimited to unit opsResource intensive
Image courtesy Century Extrusion, with permission
3D modeling
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Response surface methodology
Rigorous treatmentAccurateLimited extrapolationResource intensive
Image courtesy Bernhard Van Lengerich, with permission
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1D simulation
ApproximationsVersatileCost effectiveIntegrated cross-section
Image courtesy Mahesh Gupta (Peldom), with permission
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Presented by: Adam Dreiblatt
Modeling basis
Extruders cannot differentiate between pharma polymers and “traditional”thermoplastics
API’s that do not melt are no different than filled thermoplastic compounds.Pharma solid dispersion is no different than polymer alloy.
The extruder can only detect viscosity, degree-of-fill, pressure, etc…
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Modeling basis
Hot melt extruder geometry is identical to “traditional” polymer machinery – from the perspective of the melt in the screw channel (intermeshing, co-rotating Erdmenger self-wiping profile). Hot melt extrusion applications can use existing modeling and simulation tools available for “traditional” polymer processing.Interpretation of results is critical to the successful use of these tools…
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Complex geometry+
Complex rheology
Precludes comprehensive treatment of complete process
Why Not Simulate Twin Screw Extrusion ?Modeling challenge
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1D simulation example
TF = Feed temperature of melt
TB = Inner barrel surface temperature
Density, Specific Heat = constant, independent of melt temp.
Melt viscosity = strong function of temperature and shear rate
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q = Volumetric melt flow (assumed uniform melt density)
N = Screw rotation speed
A, B = Characteristic for each screw component, function of (z)
Melt viscosity = strong function of temperature and shear rate
, 0B dpq AN z Ldzη
= + ⋅ < <
Mass, momentum balance
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φ = Screw fill level
U = Heat transfer coefficient between melt and barrel
TB = Inner barrel surface temperature
C, D = Known functions of geometry and physical properties
( )2 , 0BdTq C N DU T Tdz
z Lφη= − − < <
Energy BalanceEnergy balance
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Calculate p(z) and T(z) , 0 < z < L
Assumes p, T are function of z only
T = “cross-section average temperature”
Extruder geometry
Material properties
Operating conditions
Defined Values
Calculate axial temp, press
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Problems:
A,B = Complex function of (z), different for each screw component
P(z) = Function of viscosity (strong function of temperature)
T(z) = Function of screw fill level (φ = 1 if p > 0, φ < 1 if p = 0)
Boundary condition 1 p = p DIE at z = L
Boundary condition 2 T = TF at z = 0
Solving balance equations
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Divide each screw component into computational elements
More subdivisions assigned to “active” screw types
N = total number of computational elements
Alternative solution
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All coefficients, processing variables are a function of (z)
Continuous Variables p (z), 0 < z < L
T (z), 0 < z < L
Discrete Point Values pi , i = 0, 1, 2, …N
Ti , i = 0, 1, 2, …N
Alternative solution
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, for 1,2,3,... i ii
i i
B pq AN i Nzη
∆= + ⋅ =∆
( )2 , for 1,2,3,... ii i i i i i B
i
Tq C N DU T T i Nz
φη∆ = − − =∆
Boundary condition 1 pN = p DIE
Boundary condition 2 T0 = TF
Alternative solution
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Assume temperature profile Ti , (i = 0, 1, 2, …N)
Iteration procedure – step 1
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Compute pressure drop through die/orifice (PDIE)
Iteration procedure – step 2
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Pressure equation solved from i = N-1 to i = 1
Initial condition at i = N
Compute viscosity at assumed T
Compute screw fill level
Iteration procedure - step 3
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Step 4: Temperature equation solved from i = 1 to i = N – 1
Initial condition at i = 0
Based on computed screw fill level
Iteration procedure – step 4
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Computed T(z) compared to assumed T(z)
If T, P profiles are within specified tolerance criteria
Iteration ends
Compute residence time, power, etc.
Compare computed results
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Computed T(z) compared to assumed T(z)
If T, P profiles do not meet specified tolerance criteria
Assume new temperature profile
Repeat calculations until convergence
Compare computed results
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Simulation case study
34mm lab-scale extruderLeistritz LSM34, L/D = 35300 rpm screw speed12.5 kg/hr feed rate
Raw materials ‘pre-granulated’PolymerPlasticizerSurfactantAPI
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Define geometry
Machine typeFree volumeAvailable powerGeometric parameters
Feeding and venting positionsScrew configurationDie geometry
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Define materials
PolymersSolid state thermal and physical propertiesMelt thermal and rheological propertiesRheological model
Solid additivesSolid state thermal and physical propertiesNon-melting “inert” filler as API placeboRheological model
Liquid additivesPlasticizing effect
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Define extrusion process
Screw speedFeed positionFeed temperatureFeed rateTemperature profile
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Analyze results
Specific energyDischarge temperatureDischarge pressureResidence time
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Calibration of model
Practical use of 1D simulation requires “calibration” of model results using empirical coefficients to match actual process data
MeltingSpecific energyMelt temperature
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Axial profiles
Degree-of-fillMeltingPressureTemperatureSpecific energyResidence timeViscosityMixing
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Physical PropertiesCrystallinityMorphologyBioavailability
RheologyMol. weightMw Distribution
OtherDissolutionColor
Product Quality Attributes
Specific EnergyMechanicalThermal
Melt TemperatureResidence TimePressure
Key SystemParameters
Machine ParametersFree VolumeScrew ConfigurationDie Geometry
Process ParametersScrew SpeedFeed RateBarrel Temperature
ExtrusionParameters
Correlation of results
Ref: Bernhard Van Lengerich, PhD Thesis, Technical University of Berlin
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Scale-up
Lab-scale process from 34mm machine was transferred to 50mm production line
Leistritz ZSE50 extruder, L/D=36Volume difference = 4X = 50 kg/hrProduction machine different Do/Di
Product requirement = 100% amorphousAPI has extreme thermal sensitivity, degradation level not to exceed lab scale
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Prediction of position where melting is completed = 547.5mm
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Material temperature where melting is completed = 136 °C
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Melt residence time can be identified (23 sec)
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Prediction of position where melting is completed = 920mm
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Material temperature where melting is completed = 135 °C
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Melt residence time can be identified (33.6 sec)
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Summary
Commercial process successfully launchedLower discharge temperature and narrower RTD than lab-scale process (also lighter color)Different extruder geometry and screw designSpecific energy same as lab-scale
Product specifications (crystallinity, degradation products, dissolution) can be correlated with specific energy, residence time and thermal history
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Summary
1D process simulation for hot melt extrusion applications is commercially available and provides a cost-effective tool to probe deeper inside the extruder
Expensive and scarce API’s (kg quantities)Scale-up (translation between OEM machinery)Product and process optimization
These simulation tools can be used to model both solid dispersion and controlled release oral solid dosage forms
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Summary
Rheological characterization for polymer/API compositions remains a challenge for any simulation/modelingtechnique
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Thank You !