model based innovation.pdf
TRANSCRIPT
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2009ProcessSystemsEnterpriseLimited
Model-Based Innovationin Process Development and Design
Costas PantelidesCentre for Process Systems Engineering Managing Director
Imperial College London Process Systems Enterprise Ltd.
AAPS Workshop on
QbD-based Drug Development & ManufacturingBaltimore, 27 April 2009
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Overview
Model-based Innovation
Technological basis of Model-Based Innovation
Model-Based Innovation in Practice
Concluding remarks
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Model-Based Innovation
Innovation, risk and modeling
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Impediments
Whymodel?
Competitive advantage
Imperatives
Speed(time-to-market)
InnovationHigher
risks
Effective risk
management
Limited scope
for evaluation
of alternatives
Modelling
New equipment & process designs New chemistry & catalysts
New materials of construction
. . . . . . . . . . . . . . . . . . . . . . . . .
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Model-Based Innovation is
the use of validated, predictive models for
Quantifieduncertainty in the
model predictions
1. the optimization of process
design & operation
via comprehensive explorationof the space of alternatives
2. the quantification of the
technological risk involved
in model-based decisions
3. the effective targeting of
experimental R&D towards
minimization of this risk
Quantitative prediction of the
effects of design & operatingdecisions on KPIs,
within the accuracy necessary to
achieve the business objectives
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Impediments
Innovation,risk&modelling
Competitive advantage
Imperatives
Speed(time-to-market)
InnovationHigher
risks
Effective risk
management
Limited scope
for evaluation
of alternatives
Modelling
Integratedexperimental
&
modelling
methodologies
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Impediments
Innovation,risk&modelling
Competitive advantage
Imperatives
Speed(time-to-market)
InnovationHigher
risks
Effective risk
management
Limited scope
for evaluation
of alternatives
Modelling
Integratedexperimental
&
modelling
methodologies
Effective
management
oftechnologyriskin
innovation
Model
Based
Innovation
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Technological Basis forModel-Based Innovation
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Technological basis of
Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
f
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Technological basis of
Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
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Modeling of processing equipment
Significant progress in understanding & quantificationof basic physics and chemistry
large proportion of key unit operations can be
described in terms of detailed fundamental
mathematical models
models predictive over wide ranges of conditions
Greatly increased ability to model transient systems
which are distributed with respect to 2 or moredimensions
spatial and non-spatial distributions
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Modelingofdistributedunitoperations
Crystallization Particle size
Co-polymerization
Gasification
( )( ) ( )
( ) ( )
( ) ( )
1 2
1 12 2
1 1 1 2
1
1 12 2 2
2
,, ,
,
, ,
=
cat cat catin
cat
cat
nM Fin n Fout n
t
G nM
G nM
Molecular weights
MW1 + MW2
Coking+Temperature
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Some key unit operations in pharma
Reactions
homogeneous
heterogeneous
Separations
Solution crystallization cooling
evaporative
precipitation Chromatography
. . . . . . . . . . . . . . . . .
Filtration
Milling/Grinding
Granulation
Drying/Freeze Drying
Coating
Solids transportation . . . . . . . . . . . . . . . .
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Detailed modeling of solution crystallization
Size-dependent kineticsfor nucleation, growth,
attrition
Mass & energy balances
multiple liquid-phase
species
Population balance(s)
multiple solid phases
different polymorphs,
enantiomers,
chemical species
VDBnfnfLnGV
tnV outoutVininV )(,, ++=
Accuracy
of prediction ?
Technological basis of
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Technological basis of
Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
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Model identification & validation
Most equipment modelscontain parameters that
are not known a priori
thermodynamics
heat & mass transfer
kinetics
Need to be estimated from
multiscale modeling
experimental data
Experimentation often the
bottleneck in terms of time
& costNeed carefully targeted experiments
Karamertzanis et al.,
J. Chem. Theo. Comput.,
accepted for publication, 2009)
Brix Sensor
K-Patents Process Instruments Inc.
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The Model Validation Cycle
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The Model Validation CycleModel-based Model-targeted experimentation
Model of
experimental
rig
Experimental
rig
Statistical significance analysis:
Is the model valid in principle?
Successive refinement of
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Successive refinement of
solution crystallization model
WR = 331
WR = 301
WR = 43
account for
measurement
bias
replace traditional power
law growth kineticswith combined description
of mass transfer
and surface integration
Parameters accurate enough?
The Model Validation Cycle
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The Model Validation CycleModel-based Model-targeted experimentation
Model of
experimental
rig
Experimental
rig
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Model-Based Experiment Design
Minimize (estimated) error inparameter values following nth
experiment
taking account of previous
experiments 1n-1
Determine optimal
initial conditions for experiment
controls during the experiment
sampling times
A complex optimization
problem, but
can be solved routinely
leads to significant benefitsExperiment Number
P
arameterError(%
)
random design
optimal design
human expert designer
The Model Validation Cycle
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The Model Validation CycleModel-based Model-targeted experimentation
Model of
experimental
rig
Accurate model parametersof quantified uncertainty
with minimum experimentation
Experimental
rig
Technological basis of
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Technological basis of
Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
Example #1:
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a p e #
Optimization of batch crystallization recipe
Objective minimize batch time; or
minimize width of PSD; or
Constraints temperature at end of batch
growth rate during entire batch
average size at end of batch width of PSD at end of batch
Decision variables
amount and PSD of addedseeds
seed addition time
cooling profile
1 mm
Large space of time-varying decisions
subject to many constraints
Need formal mathematical techniques to search it
Dynamic Optimization
Example #1:
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p
Optimization of cooling crystallization profile
Original Recipe Optimal Recipe
New temperature profile
growth rate now below
0.01 m/s at all times
Example #2:
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Objective: maximize production rate while maintaining product quality
p
Batch-to-continuous process conversion
35% improvement in throughput; product quality at least as good
Model tracking
~10,000,000polymeric species
Kinetics extensively
validated againstbatch pilot plant
experiments
Technological basis of
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g
Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
Equipment scale up
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Equipment scale-up
Key challenge: predict interactions between detailed equipment geometry and design
fluid flow/mixing
other key phenomena
chemical reaction
homogeneous/heterogeneous mass transfer
nucleation, crystal growth
heat transfer
Scale-up may be difficult even in smaller equipment
depending on desired degree of control on product quality
A systematic approach to scale-up is needed
[Equipment-scale dependent]
[Equipment-scale independent]
Hybrid gPROMS/CFD equipment modeling
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Hybrid gPROMS/CFD equipment modeling
Computational Fluid Dynamics (CFD)
Fluid mechanics (single/multiphase)
Mixing
gPROMS
Heterogeneous reaction
Heat & mass transferNucleation & growth
Electrochemistry
Concept: combine different descriptions of processing equipmentwithin single, fully coupled model
Hybrid gPROMS multizonal/CFD models
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Hybrid gPROMS multizonal/CFD modelsUrban & Liberis (1999), Bezzo, Macchietto & Pantelides (2000, 2004)
Multizonal model (gPROMS) zone population balances growth, nucleation/attrition kinetics
network mass/energy balances
CFD model (Fluent
) total mass conservation momentum conservation
CFD Multizonal
phase mass fluxes between zones
volume-averaged zone energydissipation rate
Multizonal CFD
zone density/viscosity
Standardized, routine technology
Example
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Detailed design of gas/liquid/solid reactors
CH3
CH3
p-xylene4-CBA
O OH
OOH
PTA
O OH
O
+ O2 + HCO
2CH3
O
OH0.5
2+
O
OCH3CH3
k3
CH3
CH3
8CO2 +10.5 O2+ 5H2Ok
5
CH3
CH3
8CO +6.5 O2+ 5H2O
K4
+ 4.5O2 3CO2 3H2O+
O
O
CH3
CH3
Metyl acetate
+ 3O2 3CO 3H2O+
O
O
CH3
CH3
Metyl acetate
Model accurate enough to identify
improvements of
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Model-Based Innovation
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in model-based decisions
The Model Validation Cycle
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Model-based Model-targeted experimentation
Model of
experimental
rig
Accurate model parametersof quantified uncertainty
with minimum experimentation
Experimental
rig
How accurate isaccurate enough ?
Model-based technological risk management
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Three key questions
Given a certain level of model accuracy,what is the resulting uncertainty in the key
performance indicators (KPIs) of a process designed
using this model?
If the risk associated with this uncertainty is
unacceptable, which are the critical model aspects
on which further R&D needs to focus?
If the risk is, in principle, acceptable, then what is the
best design that can deal with the residual model
uncertainty?
Quantification & management of technological risk
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Quantification & management of technological risk
2 1
Riskacceptable
?
ProcessOptimization
[scenario-based]
Yes
Determine
KPI probability
distributions[Low-Discrepancy
Sequence samplingtechniques]
Determine critical uncertainparameters
[based on global sensitivity
measures for KPIs]
No
R. Blanco-Gutierrez,
PhD Thesis,
Imperial College London, 2007
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Model-Based Innovation in Practice
Success or Failure?
Model-Based Innovation
Case Studies
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Case Studies
Energy & environment Fuel cells & fuel cell systems
Polysilicon production
Waste gasification LNG storage
Gas-to-Liquids conversion
Safety analysis
. . . . . . . . . . . . . . . . . . .
Chemistry High-value polymers
New partial oxidation processes
New homogeneous catalyticroutes
. . . . . . . . . . . . . . . . . . . . . . . . . .
Health & Lifestyle
Pharmaceutical & fine chemicals
crystallization
Granulation
Suppression of impurities in APIs
Fermentation
. . . . . . . . . . . . . . . . . . . . .
Model-Based Innovation in Practice:
S F il ?
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Success or Failure?
1. What is the business objective? What benefit(s) are we expecting?
What payback period are welooking for?
How much are we prepared/able to
change current operations ordesigns to achieve this objective?
2. Can modeling help us achievethis objective?
What degree of modeling accuracywill be necessary?
What level of modeling detail willbe needed to deliver this accuracy?
Do we understand the physics/chemistry to the necessary extent?
Do we have sufficient experimentalmeasurements and/or capability toquantitatively characterize thesephysics/chemistry?
3. What modeling technologies/tools can deliver this project? Functionality: can the tool
deliver in principle?
Complexity: can the tool deliver
in practice?
4. Can we use these tools todeliver this project?
Skills?
Time frame?
Quality of delivery?
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Concluding remarks
Model-Based Innovation
In summary
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The Objective The Technology
1. Modeling of complex unit operations
2. Model validation
3. Model-based optimization
4. Model-based scale-up
5. Quantification of risk in
model-based decisions
R&D productivity not R&D investment is the real challenge for global innovation
Michael Schrage, MIT Media Lab
In summary
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Thank you for your attention
Mark Matzopoulos Chief Operating Officer