Coupling a Continuous Flow Reactor System to a Platform for … · 2012. 11. 27. · Reactor System to a Platform for Improved Process Development and Optimization ... Acetic Acid

Coupling a Continuous Flow Reactor System to a Platform for Improved Process Development and Optimization - An Approach toDefining FDA's QbD

Brian Marquardt, Wesley Thompson, Michael Roberto, Charles Branham and Thomas DearingApplied Physics LaboratoryUniversity of WashingtonSeattle, WA 98105

U.S. Food andDrug Administration

Analytical Sampling for Online Applications

High Throughput Experimentation For… Discovery and screening Process development Process optimization Process control Production

Eliminate chemical engineering production problems related to scaling up batch systems

Increase production through use of many parallel microreactors to achieve volume

Why Use Continuous Flow Reactors?

Example: Esterification of Methanol

Standard Raman spectra

Acetic Acid + Methanol Methyl Acetate + H2OH+

Raman Shift (cm-1)

Inte

nsity

Acetic AcidMethanol Methyl Acetate

0

5000

10000

15000

20000

400 600 800 1000 1200 1400 1600 1800

ROI

• PCA analysis of the formation of acetate monitoredby Raman spectroscopy – reaction time 1.5 hours

0 30 600

50

100

150

200

250

Rel

ativ

e In

tens

ity

90Reaction Time (min.)

5º

15º25º

35º55º 45º

65º

Batch Runs at Different Temperatures

Total experiment time ~ 1 week (includes charging reactors, cleaning, …)

Continuous Rxn. with Temp. Step

• with residence time module• flow rate: 10.56 ml/min (residence time ~ 2.5 min)

acetic acid

methyl acetate

Temp 40°C

Temp 25°C (25°C – 40°C)

With control of flow reactor parameters and analytics, fast optimization is possible

1 week of batch data reproduced in less than three hours by continuous flow

• without residence time module• flow rate: 0.89 ml/min (residence time ~ 5 min)

PCA Analysis on data after mixing:1st PCA scores Increase in reaction yield after each temperature step

Product Yield vs. Temperature

Range = 30-60°C

Result: Estimated Response Surfaces

Challenges To Using AF Reactors

EDUCATION!!!!!! Interfacing modular units Sampling and screening Analytical characterization Data handling Process modeling and feed back control

(particularly if they are used for production)

Analysis of Advanced Flow Reactors Problems with performing online measurements

Gas formation in sample lines Temperature change before reaching analyzer Phase change between reactor and analyzer Sensor placement at optimal position Automated flow and pressure control

Most PAT problems are due to sampling not measurement device

Need better systems to sample processes

Application of Sampling Systems To Microreactors

Example of bringing analytics to a process and the challenges with integrating them

What is NeSSI?• Industry-driven effort to

define and promote a new standardized alternative to sample conditioning systems for analyzers and sensors Standard fluidic interface for

modular surface-mount components ISA SP76

Standard wiring and communications interfaces

Standard platform formicro analytics

What does NeSSI™ Provide Simple “Lego-like” assembly

Easy to re-configure No special tools or skills required

Standardized flow components “Mix-and-match” compatibility between vendors Growing list of components

Standardized electrical and communication (Gen II) “Plug-and-play” integration of multiple devices Simplified interface for programmatic I/O and control

Advanced analytics (Gen III) Micro-analyzers Integrated analysis or “smart” systems

NeSSI Raman Sampling Block

• Parker Intraflow NeSSI substrate• Sample conditioning to induce backpressure to

reduce bubble formation and the heated substrate allows analysis at reactor conditions

Reactor Feed 1

Reactor Feed 2

Product Stream

Real-timeCalibration

waste

prod

AnalyzerSuite

Pump 1

Pump 2

NESSI AF Reactor Sampling/Calibration

• Application of sampling systems and analytics to optimize and control AF reactor

Phase I

Background and Past Accomplishments

Goal: to improve reaction monitoring and optimization through the use of continuous glass flow reactors, NeSSI and analytics

Funded by the FDA to demonstrate the benefits of improved reactor design, effective sampling and online analytics to increase process understanding (QbD)

Partners: FDA, Corning, CPAC, MEPI, Kaiser, Parker

QbD Project began November 2008 Process Reactions – June 2009

Demonstrating QbD - Phase I

CF Reactor and Raman Analyzer

4 channel, 785 nm Kaiser Optical Systems Rxn2 probes placed at different reactor zones

Raman Analysis of CF Reactor

3 4

1

2

• Monitor reaction with 4 channel 785 nm Raman system• NeSSI sampling systems (1-4) equipped with Raman ballprobes• Online GC also used as post quench online analyzer (4)

Chloroformate Chemistry

Carbonate and dimer formation

HO

OH pyridine

toluèneO

O

Cl + O

O

O

OH

2-ethylhexyl chloroformate butane-1,2-diol 2-ethylhexyl 2-hydroxybutyl carbonate

O

O

Cl O

O

O

OH

+ O

O

O O O

O

dimmer2-ethylhexyl chloroformate 2-ethylhexyl 2-hydroxybutyl carbonate

+N

.HCl

dimer

Organic Acid Chloride Organic Carbonate + dimer

NeSSI Ballprobe - Raman/NIR/UV

Matrix Solutions: www.ballprobe.com

Ballprobe Specs.•Hastelloy C-276

Ti, SS, Monel•Sapphire optic•Std. temp range:

-40 – 350° C•Pressure:

0-350 Bar

NeSSI™ Sampling System for Reactor

Raman Probe

monitor

bypass

clean

NeSSI™ and Raman Probe Images

Design of Experiments Information

31 Experiments total Temperature steps Reaction with no toluene Changes in butanediol ratio Changes in pyridine ratio Propanediol instead of butanediol Simulated Reactor problems

Pump failure Less heat exchange Poor dilution of chloroformate

Raman Peaks of Interest for Rxn.

Chloroformate

TolueneDimerCarbonateToluene

3D Plot of Raman Reaction Data (Low cm-1)

ChloroformateToluene

Toluene

Ch. 1GC Results (%)

Test R-OH 2EHCF R-Cl Carbonate Dimer1 1.17 0.00 0.26 96.17 2.402 2.16 45.84 0.32 51.42 0.59

0 10 20 30 40 50 600

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Nor

mal

ized

Sig

nal I

nten

sity

0 10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Sample Number

Nor

mal

ized

Sig

nal I

nten

sity

Reaction Profiles for 2 DoE StepsGC Results (%)

Test R-OH 2EHCF R-Cl Carbonate Dimer1 1.17 0.00 0.26 96.17 2.402 2.16 45.84 0.32 51.42 0.59

Test 1

Test 2

---- Carbonate---- Chloroformate

DoE Run1 Channel 1 Data (Reactor)

20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

0.12

Run1Ch1LowPCADensity 1Density 2Density 3

20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Run1Ch1LowPCAFeed 1Feed 2Feed 3

20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Run1Ch1LowPCAPressure 1Pressure 2Pressure 3

20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

0.12

Run1Ch1LowPCAAmbient Temp.Quench Temp.Reactor Temp.

Phase I Summary Data collected and organized

17 days in Toulouse France Analysis and modeling

Evaluation of various modeling protocols PCA, MCR, ALS

Calibrate to GC results (PLS) Phase II of project

Acquire reactor at CPAC Focus on reactor control Implement more sensors Real-time product work-up

Begin to implement models for process feedback control

Phase II

Continuous Process Understanding and Control

Process Understanding Chemistry

Determine, evaluate and test reactions in CF reactors Pharmaceutical industry relevant chemistry Emphasis on process not specific chemistry

Determine CRF (Critical Response Factors) and Levels Sampling & DoE

Coupling analytics at mL scale Reaction monitoring w/online analytics

Reaction/Process Modeling and Control Determine Control Mechanisms Initiation of Process Control

FDA Project - Phase II Dec. 20, 2010 – Dec. 19, 2011

Esterification of Benzoic Acid and Subsequent Hydrolysis

Acid and alcohol form an ester Benzoic acid and methanol or ethanol Protection technique for carboxylic acids

Flow rate, temperature dependant 1.0 to 20.0 µL/min 25 to 175 degrees C

Forms water as a product

Esterification

C. Wiles, 2009

Esters undergo hydrolysis in basic conditions Possible to implement as 2nd rxn after esterification

Deprotection technique suitable for continuous flow conditions

Again, flow and temperature dependant Slower flow rate, higher temp for best results

Hydrolysis of Esters

C. Wiles, 2009

Analytical Control System Design

Control

Process Modeling

Data Fusion

Analytics and Data Handling

Hardware Control and Monitoring(Software)

Hardware – NeSSI, Raman, Other PAT, Reactor

Optimization

Corning® Advanced-FlowTM LFLow-Flow Capability (1-10 mL/min)

Corning has introduced a reduced flow-rate reactor that retains the outstanding mixing and heat exchange performance of its Advance-FlowTM glass reactors while providing:

LF platform will be the larger reactor platform for our Phase II FDA project

Low internal volume (2 mL flow)

High flexibility

Metal-free reaction path

Scalability

Compatibility with analytics

T, Flow and Pumping control

Reactor with NeSSI and Analytics at CPAC

Set control variables Flow rates Pressure Temperature

Monitored variables Flow rates Pressure Temperature Raman

Hardware Control and Monitoring

HPLC Pump(Flow Rate)

Back Pressure Regulator Flow Meter Pressure

Gauge

•Reactant 1

Thermocouple Raman Other PAT

Flow Meter Pressure Gauge Thermocouple

•Product

Raman Other PAT Needle Valve

Temperature Control

Control Analytics

System Control and Analytics

HPLC Pump(Flow Rate)

Back Pressure Regulator Flow Meter Pressure

Gauge

•Reactant 2

Thermocouple Raman Other PAT

NeSSI Reagent Sampling System

NeSSI Product Sampling Sys.

Continuous Flow Large Scale Goals

Software / Control

Step Temp & Flow

Linear Step

Custom StepContinuous Temp & Flow

Feedback Mechanisms

Monitoring System

Flow

Temperature

Pressure

RamanCalibration of

Flow and Temp

Chemistry / Production

Determine Yield

Cost-effective Flow Rate

Optimize system

Flow Rate

Pressure

TemperatureAutomated Process?

Completed

Awaiting Hardware

Q2+3 2011

Reactor, Sampling and Analytics

Full System w/ Control

Possibility for multiple analytical techniques Raman ATR InfraredMicro-GCMany others (refractive index, LC, fluorescence, etc.)

Modular system All techniques compatible with NeSSI hardware All measurements performed serially in NeSSI system System allows for screening of effective analytics Interchangeable

Process Analytical Technology

Kaiser Multi-Channel Raman

Mettler Toledo - React-IR

Thermo/C2V Fast Micro-GC

http://www.c2v.nl/ as well as http://www.thermo.com

Mechatest Sampling Solutions

NeSSI Liquid Sampler Compliant with ANSI/ISA 76.00.02 38.2 mm

(1.5 in.) footprint. Three Port configuration: Sample Inlet,

Bypass and Vent. Quick and safe operation, low dead volume

design with bypass. Manual or Automatic sampling In-line Sampling Closed Loop and Emission Free Sampling Easy in operation

http://www.mechatest.nl/

Validation will be performed off-line with gas chromatography-mass spectrometry (GC-MS)

Offline validation by GC-MS will allow for quantitative multivariate modeling of the online PAT suite This will provide the data for data fusion, reaction

modeling and process control

Analytics Validation

Monitoring of flow, temperature, and pressure at multiple points will allow for complete understanding of physical system

Matching Raman data with CRF’s to investigate product formation under varying conditions

Precise temperature information across the entire reactor space will allow for effective characterization of reaction thermodynamics Thermal mass balance Heat capacity

Reaction Modeling/Control

Organizing publication of Phase I results Custom designing NeSSI sampling and analytics

apparatus for Phase II Designing LabVIEW software interface for

hardware control Interfacing control software with new analytical

hardware Perform batch chemistry of reactions for

validation and comparison to CF results

In Progress

Pure product Can workup be done on-line?

Neutralization Salt generation Separation of phases

Continuous generation of products Esters and carboxylic acids Loop through with continuous workup

Add excess acid or base to catalyze next step? Continuous, excessive salt generation may degrade

equipment (plugging???)

Challenges

U.S. Food and Drug Administration Moheb Nasr Christine Moore David Morley Sharmista Chatterjee

Corning Glass Pierre Woehl Sergio Pissavini Jérémy Jorda

Parker Mike Cost

Kaiser Optical Systems Ian Lewis Hervé Lucas Bruno Lenain

CPAC University of Washington Applied Physics Lab

La Maison Européenne des Procédés Innovants Annelyse Conté

Thanks

MEPI

U.S. Food andDrug Administration