ispe cop kvg 20081001...ispe cop meeting, malmö, sweden 1 october 2008 11 dtu chemical engineering,...

1

Application of mechanistic models within a PAT framework

Krist V. Gernaey

Gürkan Sin, Erez Albo, John Woodley

Ravendra Singh, Rafiqul Gani

ISPE CoP meeting, Malmö, Sweden1 October 2008

2 DTU Chemical Engineering, Technical University of Denmark

Outline

• Introduction - PAT

• Mechanistic model – development

• Standardised protocol

• Results

• Design of a PAT system

• Conclusions

2



Outline

•Introduction - PAT• Mechanistic model – development


• Results


• Conclusions



What is PAT?

• Process Analytical Technology guideline, defined by USA FDA in 2004

• One of the central issues in the FDA PAT guideline is the transition from a fixed process to a robust and adjustable manufacturing process

Input OutputProcess

Fixed process

Input OutputProcess

Robust and

adjustable

processMC

3



A traditional fermentation (antibioticproduction by S. coelicolor)

Glucose, air, base

On-linemeasurement:

CO2, dissolved O2, temperature, pH, Base addition, etc.

Off-lineanalysis:

Biomass, Glucose, NH4

+



PAT approach to fermentation: on-line NIR + control

Glucose, air, base

On-linemeasurement:

CO2, dissolved O2, temperature, pH, Base addition, etc.

NIR measurements:

Biomass, glucose, NH4

+

4



PAT projects

• Mostly with pharmaceutical companies, increasingly also in food industry

• Emphasis in many projects is on the analytical aspects:

– Development and implementation of on-line analysers

– Chemometric models

– Data-mining methods

• The DTU-KT approach to PAT:

– Process knowledge is the key to developing PAT applications

– Mechanistic modelling = collection of process knowledge

– On-line analysis is excellent, but not a goal by itself. On-line analysisis to be supplemented by appropriate control strategies!

– Applied to:

• Fermentation (Danisco, Novozymes)

• Organic synthesis based pharmaceutical production(Lundbeck)

• Design of PAT systems (DTU PhD project)



Quoting from the PAT guideline

• For a process control system to qualify as a PAT process it mustincorporate an appropriate combination of some, or all, of the following basic PAT tools as listed in the PAT Guidance for Industry

– Multivariate tools for design, data acquisition and analysis

– Process analyzers

– Process control tools

– Continuous improvement and knowledge management tools (AI, supervisory control)

Mechanistic models

5



Outline


• Mechanistic model – development• Standardised protocol

• Results


• Conclusions



Mechanistic model - development

• Model development – equations are structured in a matrix

– Example: matrix description of Monod-Herbert aerobic growth model

-1-1/gX02. Decay

1-1/YX,O-1/YX,S1.Growth

Process, j

C-mol X/(L.h)C-mol/Lmol/LC-mol/LUnits

XSOSSSymbols

Rates, rjC3C2C1Component, i

XKS

S

SS

S

+maxµ

Xkd

Sin et al. (2008). Biotechnol. Bioeng., 101:153-171

6



Matrix, model of S. coelicolor fermentation

Sin et al. (2008). Biotechnol. Bioeng., 101:153-171

Equations biological processes

Equations chemical equilibria

Equations mass transfer

Conservative properties



Mechanistic modelling + uncertainty analysis

Sin et al. (2008), Biotechnol. Bioeng., 101:153-171

Sin et al. (2008), Biotechnol. Prog., revisedversion submitted

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Uncertainty analysis

• uncertainty in input � uncertainty in output

Monte Carlo simulations

Output uncertainty

Input uncertainty



Sensitivity analysis, e.g. via SRC or Morris screening

-0.03mS0.05gX10

-0.03pKH2PO40.07tlag9

0.04gX0.08pKNH8

-0.06mmax-0.12iNX7

0.06iPX-0.13pKH2PO46

0.08KP-0.21mmax5

0.09KLaO20.30KS4

0.49PO20.30KP3

0.52KHO2-0.30mS2

0.54Pin0.75iPX1

m*qm*qRank

OxygenGlucose

Sin et al. (2008), Biotechnol. Prog., revised version submitted

Decreasingimportance

8



Outline



•Standardised protocol• Results


• Conclusions



System and process description:

Define appropriate boundaries and constraints

Reactor, e.g. volume (start, max)

Foaming, anti-foaming agents

Feed

Aeration

Heat

Control system

Define objective of the simulation study

A standardised protocol

9



Standardised protocol … supported by appropriate software tools

• Development of a matlab toolbox including standard procedures for:

– Model definition

– Parameter estimation

– Uncertainty analysis

– Sensitivity analysis



Outline




•Results• Design of a PAT system

• Conclusions

10



Following the protocol

• Define objective of the study:

– Maximize the volumetric productivity of the fed-batch fermentation with S. coelicolor.

• Define boundaries and constraints, assumptions:

– We only consider 3 limiting nutrients (glucose, ammonia and phosphate)

– Phosphate is the limiting nutrient triggering production

– Reactor: V(0) = 0.2 * Vmax;

– Feed: Glucose: 600 g/L; etc.

– Aeration: Air flow rate can vary between 0.5 and 2 vvm

– …



Implementing controllers - Simulink

11



Example of dynamics during a simulation

• Operation with two different set points for phosphate

• Batch phase ends after about 60 hours



Mass transfer limitations

12



Comparison between simulated controlstrategies

0.2141.28PI 2 (P set point dependson DO)

0.316.9PI 1 (2 P set points)

0.090.433Exponentialfeed

RED Productivity

(mg/L.h)

ACT Productivity

(mg/L.h)

Controlstrategy

ACT = actinorhodin; RED = undecylprodigiosin



What we really would like to do

• Lab work!

– Compare a number of control strategies

– Compare experimental data (delays, uncertainties) with model predictions (ideal world)

– Planned for next year

• Software

– Extend functionality

– Apply sensitivity/uncertainty analysis to the controlled process

• Controlled process is more robust, considering input uncertainty! *

• Provide more examples (e.g. spray-drying process)

– Knowledge transfer: Offer a Phd course in sensitivity and uncertaintyanalysis

* Flores et al. (2008) Water Research (in press, DOI: 10.1016/j.watres.2008.05.029)

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Outline




• Results

•Design of a PAT system• Conclusions



Introduction: Objective of the work

To develop a software for design of PAT systems

Design of a PAT system involves:

� Selection of critical process variables

� Selection of measurement methods

� Implementation of the control system

V2 V3V1

V5 V6 V7

V9 V10 V11

V4

V8

V12

Production process

Process variables Critical process variables

T2

T5

T7

T12

Measurement methods

C2

C5

C7

C12

Control system Predefined product quality

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Systematic Framework: overview

Product

QualitySpecifications

ProcessSpecifications

Problem

definition

Knowledge

base

Literature Industrial survey

ICAS-MoT

Modelling ToolModel

library

Predefined

product

qualityDesign

algorithm

Proposed

process

monitoring and

analysis system

(PAT system)

Validation

Final process

monitoring

and analysis

system

(PAT system)

No

Yes

Design methodology

Does the process performance comply

with the process specifications ?

Singh et al. (2008). Computers and Chemical Engineering (in press)



Problem definition

1. product property

specifications

2. Process

specifications

3. Process analysis

Supporting Tools

Knowledge base

7. Proposed process monitoring and analysis system (PAT system)

8. Validation

Flow Diagram of theDesign Methodology

Not accepted

9. Final process monitoring and analysis system (PAT system)

Acceptable

5. Interdependency

analysisModel libraryPairing of critical

variables & Actuators

6. Performance analysis

of monitoring toolsKnowledge base

Selected

Monitoring tools

Outcome

Process points

Process variables

4. Sensitivity analysisModel libraryCritical process points

Critical process variables

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*(reference: Petrides et al., Biotechnology and Bioengineering, Vol. 48, pp. 529-541 (1995))

Application of the software to a fermentation process



Step 4: Sensitivity analysis

Operational objective

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 2 4 6 8 10 12 14 16 18

Time (hr)

mu

e (

pe

r h

r)

Achieved prof ile

Low er limit

DO (Dissolved oxygen)

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0 2 4 6 8 10 12 14 16 18

Time (hr)

O (K

g/m

3)

Achieved profile

Lower limit

Upper limit

Objective is not achieved

DO is a critical variable

16



Step 5: Interdependency analysis

More sensitive

Air flow rate

Air flow rate

Stirrer speed

Retrieved fromknowledge base



Step 6: Performance analysis of monitoring tools

Retrieved from knowledge base

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Step 9: Final PAT system: one feasible alternative

R: response time

T90: time for 90% response [ ]: reference number

Heat

Sterilization

Centrifugal Compression

Air Filtration

Air Filtration

Storage

Ammonia

Air

Fermentation

Air

Water

Media

Mixing

Thermocouple

R = 1 ms [51]CTemp.

Coolant flow rate

Electrochemicalsensor

C

pH

Ammonia flow rate

R <15 s [9]

Air flow rate

Dissolved oxygen

Optical sensorC

T90 = 12 s[50]

Temperature

Thermocouple

C

Steam flow rate

R = 1 ms [51]

NIR C

Homogeneity

Stirring duration

0.5 s[60]

NIR

C

Homogeneity

Stirrer speed

0.5 s [60]



Outline




• Results


•Conclusions

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Conclusions

• Modelling:

– Matrix notation simple and efficient to describe complex systems

– Specific for PAT applications: include all measured variables in the model (model outputs)

– Model deficiencies are an indication of a lack of process knowledge

– Sensitivity analysis: a means to focus experimental efforts!

– PAT specific: Perform the modelling in a structured way (software, all steps should be documented)

• Process:

– The dynamic model describes the process dynamics sufficiently well

– The model can be used for process control and optimisation purposes

– Mass transfer is main limitation

– Experimental validation is needed



Conclusions

• A framework and methodology for the design of a PAT system have been developed

– The framework has been implemented in a software prototype

– The application of this software prototype has been demonstratedthrough the fermentation process case study

– Further case studies under development

• Tablet manufacturing process

• Crystallisation process

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Acknowledgements

• Fermentation data were obtained from the Innovative Bioprocess Technology Research Consortium. The consortium is funded by:

– The Danish Council for Technology and Innovation

– Chr. Hansen A/S

– Danisco A/S

– Novozymes A/S

• H.C. Ørsted postdoctoral scholarship awarded by the Technical Universityof Denmark (DTU) to Gürkan Sin (01/02/2007-31/01/2008)

• FTP postdoctoral project from the Danish Council for Technology and Innovation (01/02/2008 - …)

• DTU PhD scholarship awarded to Ravendra Singh (01/09/2006-31/08/2009)

ispe cop kvg 20081001...ispe cop meeting, malmö, sweden 1 october 2008 11 dtu chemical engineering,...

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