1. From Design of Experiments to closed loop control
Petter Mörée & Erik Johansson
Umetrics

2. Umetrics, The Company Part of ~1Billion conglomerate
The market leader in software for multivariate analysis (MVDA) & Design of Experiments (DOE)
25+ years in the market
Off line analysis tools
On-Line process monitoring and fault detection
700+ companies, 7,000+ users
Pharmaceutical, Biotech, Chemical, Food, Semiconductors and more
Worldwide Presence with MKS
Offices:
Umeå, Malmo, Sweden
York, England
Boston, San Jose, USA
Singapore
Frankfurt, Germany
Close collaboration with universities in USA, Sweden, UK and Canada
Partnership with Sartorius; global marketing, distribution, development and integration.

3. Building a capable process
Manufacturing
DOE
Control Strategy
Knowledge building
DOE Analysis
Design Space
Error detection/ Knowledge building
QRA: Quality Risk Assessment
MVDA
QFD
Quality Function Deployment
DOE is a knowledge building tool for process development
MVDA is used both for process understanding and process monitoring

4. Processes and their data are never perfect Delegates at this meeting are of course excluded
Multivariate data analysis (MVA) is a tool to learn from data
Marek used MVA and NIR to predict glucose nad other parameters inside the reactor
This talk will focus on process parameters
Tightly controlled
pH, pO2, Temperature
Parameters used for keeping tightly controlled at their sepoint
Stirring, airation, cooling, base addition ..
Commonly measured
CER, OUR …
Monitor, interpret, control

5. Is this chart familiar?
DJIA = x1*Merck + x2*J&J + x3*Pfizer + x4*DuPont

6. t1= x1*Temperature + x2*Pressure + x3*Agitation speed + x4* pO2.
MSPC – Multivariate Statistical Process Control Evolution Level – Monitoring
Example of a fermentation
t1= x1*Temperature + x2*Pressure + x3*Agitation speed + x4* pO2.
Control limits
Average (signature)
of all good experiments
New run/experiment assessed by the model

7. Statistical Process Control MULTIVARIATE CONTROL CHART
Signature
average of all good runs
control limits (± 3s from avg.)

8. MVDA Objectives for the pharmaceutical & biopharmaceutical industry
Increase of process understanding
Identification of influential process parameters
Identification of correlation pattern among the process parameters
Generation of process signatures
Relationship between process parameters and quality attributes
Increase of process control
Efficient on-line tool for
Multivariate statistical control (MSPC)
Analysis of process variability
Enabling on-line early fault detection
Support for time resolved design space verification
real time quality assurance
Predicting quality attributes based on process data
Excellent tool for root cause, trending analysis and visualization
Fundament for Continued Process Verification (CPV)
Development
Production
Mechanistic understanding on how formulation and process parameters affect product performance

9. Work and Data flow For Method Development
Reduction
of Dimensionality
Batch Level
Evolution
Level
Aims:
- Creation of batch signature
Identify correlation patterns
Fundament for CPV
All Process Parameters
Individual Probes
Individual Probes …

10. Work and Data flow For Routine Use in Production
Batch Level
Evolution
Level
Aims:
Conformity check
Real time release testing
- Trend analysis
- Root cause analysis
Identification of responsible Parameter(s)
Increased of level of detail
Answers: What? When? How?
Investigation on process data

11. What makes Multivariate-SPC so powerful?
The SIMCA product family uses a data compression technique
Multivariate data analysis
PCA and or PLS
Data from all relevant process parameters are concentrated to a few highly informative graphs
Simplifies overview, analysis and interpretation
Enable use of data by increasing ease of use
Simple drill-down functionality to transfer compressed information back to raw data for analysis
The upper plot shows how 7 measured parameters change over the batch evolution for one batch. In the lower figure the 7 parameters have been concentrated into one.
Using all data for analysis is one of the keys to success in Early fault detection. An issue with control of a CPP will show up earlier in the behavior of the actuators than in the CPP itself. The fault will be identified, analyzed and acted upon quicker to prevent effects on product quality.

12. Drill-down for analysis

13. Monitor Early fault detection Project dashboard Knowledge building
SIMCA-online technology is acknowledged for its ability to detect process issues before they become critical
Project dashboard
Full drill-down to raw data for cause analysis
Knowledge building
Instant analysis of process changes improves understanding
Process visibility
Easy-to-grasp graphics makes the process status accessible to colleagues at all levels

14. Prediction and Continued Process Verification
Product quality information
Indirect information based on process behavior
As long as a process behaves well, product should be according to specification
Soft sensor modeling
Predict hard-to-get process properties from online process data, spectral data etc.
Predictive analytics
Online prediction of product quality and properties
Continued Process Verification
Ongoing assurance is gained during routine production that the process remains in a state of control.

15. Motivation for QbD
Reducing process variability is not necessarily desirable
With variation in inputs
Initial material qualities
Environment
Equipment
Static process
Results in variability in outputs

16. QbD and PAT Strategies Control strategy b) feedforward control
Adjusting the process based on variations in the input
Media and feed composition
Used in pulp and paper and other industries with natural products with high variability
Cheese production

17. QbD and PAT Strategies Control strategy c) PAT control
Adjusting the process based on measurement of quality in the process
Used in many processing industries using various methods
Direct measurement of material quality
Inferential control – estimation of quality from process measurements
Spectral calibration

18. Important Process Parameter
Monitoring
Important Process Parameter
Monitoring is used to detect and diagnose process deviations
UMETRICS CONFIDENTIAL

19. Model Predictive Control (MPC)
Important Process Parameter
MPC is used to predict
UMETRICS CONFIDENTIAL

20. Model Predictive Control (MPC)
Important Process Parameter
MPC is used to predict and optimize the process
UMETRICS CONFIDENTIAL

21. Manipulated Variables
Model Based Control
Manipulated Variables
Important Process Parameter
UMETRICS CONFIDENTIAL

22. Novartis Biopharmaceutical

27. Chemometric portfolio

28. Thank you for your attention!