model based innovation.pdf

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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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2009 Process Systems Enterprise Limited

Model-Based Innovation

Innovation, risk and modeling


4/412009ProcessSystemsEnterpriseLimited

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

. . . . . . . . . . . . . . . . . . . . . . . . .



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



Impediments

Innovation,risk&modelling


Imperatives


InnovationHigher

risks

Effective risk

management

Limited scope

for evaluation

of alternatives

Modelling

Integratedexperimental

&

modelling

methodologies



Impediments

Innovation,risk&modelling


Imperatives


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



Technological basis of


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






2. Model validation






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 ?



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2. Model validation





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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?



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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



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Model of

experimental

rig

Accurate model parametersof quantified uncertainty

with minimum experimentation

Experimental

rig



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2. Model validation




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



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g



2. Model validation




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

f / /


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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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2. Model validation






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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?


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


In summary


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The Objective The Technology


2. Model validation



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

[email protected]

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