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

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

The Twelve Principles of Green Chemistry

1

Green Chemistry and Continuous or Bio Process

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

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

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


- 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

Outline

 Case Studies





 Conclusion

9

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

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


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)

Outline

 Case Studies





 Conclusion

13

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


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


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



 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


Outline

 Case Studies





 Conclusion

18

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

 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


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

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


 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


Outline

 Case Studies





 Conclusion

24

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

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

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

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