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From Design of Experiments to closed loop control

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

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From Design of Experiments to closed loop control

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  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 perfectDelegates 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. 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 control limits (± 3s from avg.) average of all good runs Multivariate Process Signature

  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

  9. Work and Data flowFor 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 flowFor 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

  12. Drill-down for analysis

  13. Monitor • Early fault detection • 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 Results in variability in outputs • With variation in inputs • Initial material qualities • Environment • Equipment Static process

  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. Monitoring • Monitoring is used to detect and diagnoseprocess deviations Important Process Parameter UMETRICS CONFIDENTIAL

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

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

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

  22. Novartis Biopharmaceutical

  23. Chemometric portfolio

  24. Thank you for your attention!

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