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The Role of Process Analytical Technology (PAT) in
 Green Chemistry and Green Engineering – Part II


                    ...
The Twelve Principles of Green Chemistry




                     1
Green Chemistry and Continuous or Bio Process
Green Chemistry and Continuous or Bio Process
Green Chemistry and Continuous or Bio Process
Outline


   Case Studies

     - Monitoring of a Biotransformation using ReactIR™

     - Development of a Continuous Pr...
Case Study: FTIR as PAT tool for Biotransformation
Monitoring of Baeyer-Villiger bio-
transformation kinetics and finger-
...
Case Study: FTIR as PAT tool for Biotransformation
Results of CDD biotransformation as
a function of cell growth in a fed-...
Case Study: FTIR as PAT tool for Biotransformation
  - Better understanding of reaction
    kinetics

  - Original utiliza...
Outline


   Case Studies

     - Monitoring of a Biotransformation using ReactIR™

     - Development of a Continuous Pr...
Case Study: FTIR as PAT Tool for Continuous Process
Development and Scale-up of Three
Consecutive Continuous Reactions for...
Case Study: FTIR as PAT tool for Continuous Process

                                                                     ...
Case Study: FTIR as PAT Tool for Continuous Process
 Outcome
  - Ensure product quality via proper ratio
    and base fee...
Outline


   Case Studies

     - Monitoring of a Biotransformation using ReactIR™

     - Development of a Continuous Pr...
Case Study: Calo for Reaction Kinetics Screening
An Integrated Approach Combining                                         ...
Case Study: Calo for Reaction Kinetics Screening

RC1e allows precise measurement of                                      ...
Case Study: Calo for Reaction Kinetics Screening

                                                                        ...
Case Study: Calo for Reaction Kinetics Screening

 Results: Slow reaction                                                ...
Outline


   Case Studies

     - Monitoring of a Biotransformation using ReactIR™

     - Development of a Continuous Pr...
Case Study: RC1e Calorimetry for Biotransformation
Biocalorimetry and Respirometric
Studies on Metabolic Activity of
Aerob...
Case Study: RC1e Calorimetry for Biotransformation
 Results
                                                             ...
Case Study: RC1e Calorimetry for Biotransformation
                                                                       ...
Case Study: RC1e Calorimetry for Biotransformation
Oxycalorific coefficient determined from
the slopes of heat generated v...
Case Study: RC1e Calorimetry for Biotransformation
 Conclusion
  - Growth and activity of P. Aeruginosa
    monitored by ...
Outline


   Case Studies

     - Monitoring of a Biotransformation using ReactIR™

     - Development of a Continuous Pr...
Summary
Challenges of (bio)process development: ReactIR™, calorimetry, reactors
-   Did the reaction work?
      - Underst...
Software for Design, Data Acquisition and Analysis

 Reaction Progress Kinetic Analysis: A Powerful
                     ...
Questions and Answers
              For further information on products and applications:

                       Visit us...
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The role of process analytical technology (pat) in green chemistry and green engineering webinar

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The role of process analytical technology (pat) in green chemistry and green engineering webinar

  1. 1. The Role of Process Analytical Technology (PAT) in Green Chemistry and Green Engineering – Part II Tuesday December 1st 4am, 9am, and 2pm EST Presenter: Dominique Hebrault, Ph.D. Senior Technology and Application Consultant
  2. 2. The Twelve Principles of Green Chemistry 1
  3. 3. Green Chemistry and Continuous or Bio Process
  4. 4. Green Chemistry and Continuous or Bio Process
  5. 5. Green Chemistry and Continuous or Bio Process
  6. 6. Outline  Case Studies - Monitoring of a Biotransformation using ReactIR™ - Development of a Continuous Process with ReactIR™ - RC1e Calorimetry: a Tool for Continuous Process Development - Bioprocess Monitoring using RC1e Calorimetry  Conclusion 5
  7. 7. Case Study: FTIR as PAT tool for Biotransformation Monitoring of Baeyer-Villiger bio- transformation kinetics and finger- printing using ReactIR spectroscopy  Introduction Most fermentation monitoring concerns the determination of analyte concentrations ReactIR™ used for: - Measuring progress and kinetics - Conversion of cyclododecanone (CDD) into lauryl lactone (LL) - Catalyzed by a recombinant NADPH- dependent cyclopentadecanone monooxygenase Source: Peter C.K. Lau et al, Biotechnology Research Institute, National Research Council, Canada; Industrial Biotechnology 2006, 138–142; Applied and Environmental Microbiology, 2006, 2707–2720
  8. 8. Case Study: FTIR as PAT tool for Biotransformation Results of CDD biotransformation as a function of cell growth in a fed- batch culture Qualitative: 3-D spectral fingerprint of CDD conversion to LL shows: - Decrease of CDD absorbance at 1713cm-1 - Increase of LL absorbance at 1741cm-1 Quantitative: Peak profiling and quantitative calibration model using QuantIRTM to monitor - Use of authentic standards of CDD and LL - Detection sensitivity for LL: 0.2 mM Source: Peter C.K. Lau et al, Biotechnology Research Institute, National Research Council, Canada; Industrial Biotechnology 2006, 138–142; Applied and Environmental Microbiology, 2006, 2707–2720
  9. 9. Case Study: FTIR as PAT tool for Biotransformation - Better understanding of reaction kinetics - Original utilization of ReactIR™ technology for offline qualitative and quantitative monitoring of cyclododecanone biotransformation - Further development in online monitoring and automatic controlling - Initial expansion to a wider range of cycloketones Source: Peter C.K. Lau et al, Biotechnology Research Institute, National Research Council, Canada; Industrial Biotechnology 2006, 138–142; Applied and Environmental Microbiology, 2006, 2707–2720
  10. 10. Outline  Case Studies - Monitoring of a Biotransformation using ReactIR™ - Development of a Continuous Process with ReactIR™ - RC1e Calorimetry: a Tool for Continuous Process Development - Bioprocess Monitoring using RC1e Calorimetry  Conclusion 9
  11. 11. Case Study: FTIR as PAT Tool for Continuous Process Development and Scale-up of Three Consecutive Continuous Reactions for Production of 6-Hydroxybuspirone  Introduction Control base / buspirone stoichiometry is critical to product quality Optimization based on offline analysis is time consuming and wasteful Actual feed rate adjusted based on the feedback from inline FTIR: Flow cell and ReactIR™ DiComp probe Source: Thomas L. LaPorte,* Mourad Hamedi, Jeffrey S. DePue, Lifen Shen, Daniel Watson, and Daniel Hsieh, Bristol-Myers Squibb Pharmaceutical Research Institute, NJ, USA, Organic Process Research and Development, 2008, 12, 956-966; Mettler Toledo Real Time Analytics Users’ Forum 2005 - New York
  12. 12. Case Study: FTIR as PAT tool for Continuous Process KHMDS  Implemented startup strategy - Start with slight undercharge of base (feed rate) to reduce diol 8 - Flow rate increased at 1% increments until no decrease of Buspirone 1 signal is observed - Base feed rate was reduced 1-3% - Works well because enolization fast, equilibrium reached within minutes Source: Thomas L. LaPorte,* Mourad Hamedi, Jeffrey S. DePue, Lifen Shen, Daniel Watson, and Daniel Hsieh, Bristol-Myers Squibb Pharmaceutical Research Institute, NJ, USA, Organic Process Research and Development, 2008, 12, 956-966; Mettler Toledo Real Time Analytics Users’ Forum 2005 - New York
  13. 13. Case Study: FTIR as PAT Tool for Continuous Process  Outcome - Ensure product quality via proper ratio and base feed rate - Minimize waste of starting material - Faster reach of steady state via real- time detection of phase transitions - FTIR also used for enolization monitoring during steady state  Scale-up - Lab reactor: Over 40 hours at steady state - Pilot-plant reactor: Successful implementation (3-batch, 47kg/batch) Source: Thomas L. LaPorte,* Mourad Hamedi, Jeffrey S. DePue, Lifen Shen, Daniel Watson, and Daniel Hsieh, Bristol-Myers Squibb Pharmaceutical Research Institute, NJ, USA, Organic Process Research and Development, 2008, 12, 956-966; Mettler Toledo Real Time Analytics Users’ Forum 2005 - New York
  14. 14. Outline  Case Studies - Monitoring of a Biotransformation using ReactIR™ - Development of a Continuous Process with ReactIR™ - RC1e Calorimetry: a Tool for Continuous Process Development - Bioprocess Monitoring using RC1e Calorimetry  Conclusion 13
  15. 15. Case Study: Calo for Reaction Kinetics Screening An Integrated Approach Combining Type A: Very fast, t1/2< 1 s, controlled by Reaction Engineering and Design of mixing Experiments for Optimizing Reactions  Introduction Type B: Rapid, 1 s < t1/2< 10 min, mostly kinetically controlled Early phase RC1e experiments to obtain a basic understanding of: Type C: Slow, t1/2 > 10 min, safety issue in a batch mode - Enthalpy - Kinetics - Mass Balance - Type of phases 50% of reactions in the fine/pharmaceutical industry could benefit from a continuous process (microreactors) Source: D.M. Roberge, Department of Process Research, Lonza, Switzerland, Organic Process Research and Development, 2004, 8, 1049-1053; Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323
  16. 16. Case Study: Calo for Reaction Kinetics Screening RC1e allows precise measurement of Type A: Very fast, t1/2< 1 s controlled by mixing reaction enthalpy Instantaneous reaction heat is related to reaction rate  Results: Very fast reaction - No heat accumulation - Dosing controlled C=C double bond oxidized / cleaved by aqueous NaOCl catalyzed by Ru Source: D.M. Roberge, Organic Process Research and Development, 2004, 8, 1049-1053; Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323
  17. 17. Case Study: Calo for Reaction Kinetics Screening Type B: Rapid, 1 s < t1/2< 10 min, mostly  Results: Rapid reaction kinetically controlled - Heat signal function of dosing rate - Reagent accumulates and reacts after the end of the dosage - Lower temperatures favor high accumulation - Higher temperatures favor formation of side products Quench of ozonolysis into methanol / dimethyl sulphide Source: D.M. Roberge, Organic Process Research and Development, 2004, 8, 1049-1053; Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323
  18. 18. Case Study: Calo for Reaction Kinetics Screening  Results: Slow reaction Type C: Slow, t1/2 > 10 min, safety issue in a batch mode - Accumulation of energy > 70% - Most of the heat potential evolves after the end of addition - Typically initiated by temperature increase or catalyst addition - Autocatalytic reaction and / or induction period  Conclusion Real time RC1e calorimetry also for early Knoevenagel-type reaction catalyzed by NaOH: on kinetics and safety assessment intramolecular aromatic ring condensation Source: D.M. Roberge, Organic Process Research and Development, 2004, 8, 1049-1053; Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323
  19. 19. Outline  Case Studies - Monitoring of a Biotransformation using ReactIR™ - Development of a Continuous Process with ReactIR™ - RC1e Calorimetry: a Tool for Continuous Process Development - Bioprocess Monitoring using RC1e Calorimetry  Conclusion 18
  20. 20. Case Study: RC1e Calorimetry for Biotransformation Biocalorimetry and Respirometric Studies on Metabolic Activity of Aerobically Grown Batch Culture of Pseudomonas Aeruginosa  Introduction Goal is to select an enhanced culture, design a bioreactor, for treatment of saline wastewater (tanning industry) Metabolic efficiency of halobacterial strains evaluated by RC1e calorimetry Heat is a by-product of metabolic processes, nonspecific, non-invasive and insensitive to the electrochemical, and optical properties Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340- 347; Biochemical Engineering Journal 2008, 39, 149-156
  21. 21. Case Study: RC1e Calorimetry for Biotransformation  Results O2 uptake Good correlation of kinetic profiles by Glucose standard method (shaker), simulation, Growth, heat and reaction heat Biomass Concentration Substrate Concentration Heat rate follows growth curve at various glucose concentration Shows affinity of strain to glucose Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340- 347; Biochemical Engineering Journal 2008, 39, 149-156
  22. 22. Case Study: RC1e Calorimetry for Biotransformation Heat yield vs substrate Heat yield coefficient (kJ heat evolved per g dry cell formed) determined from total heat versus biomass concentration Heat yield vs biomass growth Heat yield coefficient (kJ heat evolved per g of glucose consumed) determined from total heat versus substrate concentration Substrate breakdown results in more heat evolution than biomass growth Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340- 347; Biochemical Engineering Journal 2008, 39, 149-156
  23. 23. Case Study: RC1e Calorimetry for Biotransformation Oxycalorific coefficient determined from the slopes of heat generated versus Heat vs colony forming unit cumulative oxygen uptake Literature reported aerobic tendency of P. Aeruginosa confirmed here Heat vs O2 uptake Cell number increases until substrate(s) depleted, then stops growing, and die Heat flux ideal candidate to monitor growth rate Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340- 347; Biochemical Engineering Journal 2008, 39, 149-156
  24. 24. Case Study: RC1e Calorimetry for Biotransformation  Conclusion - Growth and activity of P. Aeruginosa monitored by biocalorimetry, which fits biomass growth and oxygen uptake rates - Oxycalorific coefficient and heat yield values found matches theoretical values Better understanding of biokinetics of halotolerant P. Aeruginosa isolated from tannery soak liquor Helps efficient design of bioreactor Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340- 347; Biochemical Engineering Journal 2008, 39, 149-156
  25. 25. Outline  Case Studies - Monitoring of a Biotransformation using ReactIR™ - Development of a Continuous Process with ReactIR™ - RC1e Calorimetry: a Tool for Continuous Process Development - Bioprocess Monitoring using RC1e Calorimetry  Conclusion 24
  26. 26. Summary Challenges of (bio)process development: ReactIR™, calorimetry, reactors - Did the reaction work? - Understand selectivity and reactivity - Identify intermediates or by-products - How long did it take? - Endpoint, initiation-point, stall-point - Can this process be scaled-up? - Identify key control parameters - Understand, measure reaction - Will it be safe? kinetics - Measure reaction heat/enthalpy - Determine heat capacity, heat transfer coefficient - Worst case scenario estimation - Thermal accumulation and conversion
  27. 27. Software for Design, Data Acquisition and Analysis  Reaction Progress Kinetic Analysis: A Powerful Methodology for Mechanistic Studies of Complex Catalytic Reactions* Summary Data Reaction Progress Kinetic Fit Simulate Models Reaction Conditions Edit Model Parameter Axis Lo Hi Temperature Model Comment k: 1.00 A(0) X axis 10.0 20.0 a: 1.50 B(0) Constant 5.00 8.00 Only two data points. Rerun b: 0.01 T Y axis 40.0 60.0 E act: 24.3e-4 Apply Button/menu drop down – Simulation Output Options: 1) New Isothermal model Time to 95 % conversion of A  Early-on kinetic evaluation 2) New temp. depend. model 3) New from selected model Conversion of A at 60 minutes Q Peak during 60 minute reaction New Isothermal model Delete This point the user clicked on represents A(0)=15 and T=48 C. The entire reaction is shown at right using these reaction conditions.  Temperature dependence model Time to 95% conversion of A 16.000 T=48 C 14.000 12.000 10.000 [A],[B] 8.000 6.000 10.0 60.0 4.000 2.000 0.000  Catalyst stability evaluation 0.000 10.000 20.000 30.000 40.000 A(0) T 40.0 time 20.0 *Donna G. Blackmond, Angew. Chem. Int. Ed. 2005, 44, 4302 – 4320  Simulation
  28. 28. Questions and Answers For further information on products and applications: Visit us at www.mt.com/autochem OR Email us at autochem@mt.com OR Call us + 1.410.910.8500 Visit www.mt.com/ac-webinars for the current webinar schedule and access to the on-demand webinar library Don’t miss the 17th International Process Development Conference - May 16 to 19, 2010 in Baltimore, MD, USA – www.mt.com/ipdc 27 Internal usage only

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