one dimensional process simulation for twin-screw ... · simulation will quantify changes in...
TRANSCRIPT
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One Dimensional Process Simulation For
Twin-Screw Compounding –
Limitations And Opportunities
Presented by
Adam Dreiblatt Director of Process Technology
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Presentation outline
o Extrusion modeling strategy
o Modeling methodologies
o Opportunities for 1D simulation
o Limitations
o Summary
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Why simulation ?
Increasing costs
• Raw materials, energy, labor
• Lost production time
Obtain data not otherwise available
• Alternative machine/screw configurations
• What-if scenarios (i.e. downstream feed, etc.)
Troubleshoot/diagnose problems
Accuracy in scale-up
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3D finite element (FEM)
2D flow analysis network FAN)
o Rigorous treatment
o Accurate, detailed
o Limited to unit operations
o Resource intensive
3D Modeling
Only valid for filled sections of the extruder !
(how to validate results?)
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Response surface methodology
o Rigorous treatment
o Accurate
o Limited extrapolation
o Resource intensive
Image courtesy Bernhard Van Lengerich, with permission
Only valid within experimental region
(e.g. cannot use for scale-up)
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o Approximations
o Versatile
o Cost effective
o Integrated cross-section
Image courtesy Mahesh Gupta (Peldom), with permission
Assumes there are no radial gradients
(e.g. at every position (z), temperature, pressure,
viscosity, etc. is constant)
One-dimensional modeling
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Complex geometry
+
Complex rheology
Precludes comprehensive
treatment of complete
process
Why Not Simulate Twin Screw Extrusion ?
Modeling challenges
Too many machine and formulation variables !
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Formulation
Polymer (s) + Additives + Filler(s)
Compound Properties
(Mechanical, Rheological, etc.)
We know much about the raw materials…
We know much about the compounded product…
We do not know much what happens in between…
“BLACK BOX”
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Characterization of Raw Materials
Modeling the compounding process requires comprehensive
data for the solid-state and melt properties of the polymer,
thermal characteristics of fillers, etc…
As a comparison, simulation
of injection molding must
model the melt cooling back
to a solid, whereas
compounding must model the
transformation of solid to melt
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Extruder type: Intermeshing, co-rotating, twin-screw
Diameter (mm), Do/Di, Length (L/D) = Free volume
Torque (Nm), Speed (rpm) = Available power
Screw design = Mixing, Specific Mechanical Energy
Die geometry = Size, shaping
What do we know about the extruder ?
“BLACK BOX”
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Barrel / die temperature setpoints (°C)
Feed rate (kg/hr)
We know what we want to occur inside the extruder (melt, mix, etc.)
We are not so sure where, when and how it occurs…if it does…
Screw speed (rpm)
Vacuum (mbar)
What do we know about the process ?
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What do we know about the process?
Temperature
Pressure
Barrel / die temperature (actual,°C)
We can measure average residence time, RTD
We can measure specific energy input (mechanical, thermal)
Motor Load (kW)
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There is no method or instrumentation to obtain this data directly…
1D simulation provides such insight !
Where is the
polymer
melting?
How much
mixing…
…where?
Why is the
melt temp
so high?
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Model for Twin Screw Compounding
Machine Parameters Free Volume
Screw Configuration
Die Geometry
Feeding Protocol
Process Parameters Screw Speed
Feed Rate
Barrel Temperature
Specific Energy Mechanical
Thermal
Melt Temperature
Residence Time
Pressure
Physical Properties Tensile
Impact
Rheology Mol. weight
Mw Distribution
Other Color
Electrical
Product Quality
Attributes
Key System
Parameters
Extrusion
Parameters
Molecular
Structure
Shear Rate
Shear Stress
Ref: Van Lengerich
1D simulation provides predictive capabilities for key system
parameters…that directly influence compound quality…
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1D simulation example
TF = Feed temperature
TB = Inner barrel surface temperature
Melt viscosity = strong function of temp, shear rate
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Divide each screw component into computational elements
More subdivisions assigned to “active” screw types
N = total number of computational elements
1D Modeling Approach
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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
1D Modeling Approach
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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 Temperature, Pressure Profiles
1D Modeling Approach
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Simulation results depend on accuracy of material data
Ability to validate (and calibrate) melting
Ability to validate (and calibrate) power, temperature
Once the model is ‘calibrated’, results are valid
Re-calibration may be required for different types of materials
Default model works well for “traditional” polymers
Needs “tuning” when lubrication effect of other
polymers and/or additives interferes with
melting efficiency
How accurate are 1D simulations ?
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I want to use any
polymer type or
formulation and get
accurate results
without having to
obtain actual
material rheology…
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The melting model has a significant impact on all results
Must account for all polymer types, presence of
additives, fillers, lubricants, impact modifiers, etc. to
accurately predict melting phenomena…
Once melt is established, program calculates viscous
heating, power consumption, etc…
The available 1D simulation programs each uses their
own ‘proprietary’ melting model…
Accuracy of simulation results depends on ability to
calibrate polymer melting !
Melting model
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Calibration of melting model
Where melting begins and ends
determines viscous energy
dissipation…predicting melting
sooner (or later) than reality will
result in over-estimating (or under-
estimating) torque, melt
temperature, etc.
Simulation program needs ability to adjust location and
extent of melting, for example, to correlate with presence of
low-melting additives feeding with polymer
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Calibration of energy input
Simulation program needs ability to adjust viscous dissipation, for
example, to compensate for viscoelastic behavior of polymer
and/or presence of lubricating additives
The alternative approach
to ‘calibration’ of the
melting model and energy
input is to alter the raw
material rheology…
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Calibration of energy input
Some polymer melts do not form a continuous melt film within
the barrel (e.g. fractional melt polyolefins), predicted frictional
heating will over-estimate (or under-estimate) resulting
torque and melt temperature
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In the absence of “good” material data, 1D
simulation is able to quantify trends
Simulation will quantify changes in calculated values
(melt temp, specific energy, backup length, residence
time, mixing quality, etc.) as a result of changes in
operating conditions and/or machine geometry
Ability to validate (and calibrate) power, temperature
1D simulation provides accurate trends (if not absolute
values) representing actual machine performance
How valid are 1D simulations ?
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1D Simulation - Trends
1D simulation provides
quantitative value of
“mixing” quality for given
set of operating
conditions…alternate
mixing designs can be
evaluated in terms of
increase (or decrease)
in mixing index value
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1D Simulation - Trends
1D simulation provides
quantitative value of
“stress” for given set of
operating conditions.
Stress Index values can
be maximized (i.e. for
increased dispersion of
filler) or minimized (i.e.
reduced fiber breakage)
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When comparing different screw designs and/or
machines
Simulation will quantify changes in calculated values
when using same material to challenge different machine
geometries (even if material is not actual material)
Can optimize screw configurations and scale-up to other
machine sizes using this approach
1D simulation provides quantitative measure of twin-screw
compounding process !
How valid are 1D simulations ?
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Compare different machine size, different OEM
Scale-up example, 70mm to 92mm
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Comparison of mixing phenomena
Scale-up example, 70mm to 92mm
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Comparison of melting process
Scale-up example, 70mm to 92mm
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1D simulation can predict process parameters that
correlate directly with product quality.
These programs cannot determine “good” or “bad” without
baseline reference data (e.g. actual machine data).
Simulation programs must have some ability to “calibrate”
the models with actual process data for accuracy and
validity.
Characterization of raw materials requires extensive data
to achieve accurate results
Summary
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In the absence of accurate raw material data, 1D
simulation results are valid for identifying trends and
comparative studies
Optimization of screw configurations
Scale-up
As operating costs continue to escalate, 1D simulation
provides an economical alternative to running plant trials
Summary - continued
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Thank You !