1. Innovation in Spectroscopic Sensor Design Enables in-line Large-Scale Hazardous Reaction Analysis
Dr. Jim Rutherford
3rd Annual Hazardous Chemistry for Streamlined Large-Scale Synthesis
29 Nov 2017 │ Antwerp, Belgium

2. Challenge to industry A real-time multi component analyser
Instrumentation
Performance
Process NIR
Great, but not got the resolving power
Process GC
Lengthy, very expensive
Process Mid IR
To date limited by the technology
Raman
Good, suffers sensitivity, noise, lifetime
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3. FTIR Spectrometer www.keit.co.uk www.keit.co.uk 14 November 20183

4. Scale
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5. Power of Mid-Infrared 800-4000 cm-1 / 12.5-2.5 µm www.keit.co.uk
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6. Certified for explosion risk areas
Class 1 Div 2 to follow by Q4
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7. Using mid-infrared light to measure water
Detector
Infrared (IR) Source
Broadband IR light is shone through the sample
Some of the light is absorbed by the water. The light pattern is confirmed by the detector.
linear relationship between amount of light absorbed and concentration of water.
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8. Sagnac Interferometer
Interferogram
Curved mirror
Processing
Curved mirror
Interference image
Fourier Transform
Beam splitter
Detector array
How the Sagnac works
Sample
Spectrum
Infrared source (emitter)
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9. NB: To watch the light path, view slide in presentation mode
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10. Data Flow Head End Unit DCS
Local Configuration via keyboard, video, mouse.
Head End Unit
OPC over Ethernet
Spectra
DCS
Modbus over RS-485
Chemometrics
Profibus
Concentrations
4-20mA
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11. Typical data
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12. IRmadillo in situ
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13. Challenge to industry
Producers struggle to safely monitor real-time* reactions
The need for in situ monitoring
Material hazard: The reaction is exothermic Examples:
Production of polyols
Water in solvents
Water sensitive substances such as NaH, LiAlH4 ‒ processes where it is essential to confirm dryness of solvents
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14. Challenge to industry
Producers struggle to safely monitor real-time reactions
2. Toxicity: The substances are toxic – Limit exposure to humans*
3. Operating conditions*
4. Closed loop feed back control. ROI.
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15. Current solutions Summary
Innovation! Keit in situ spectrometer – ATEX certified safe to use, solid-state instrument using mid-infrared spectral analysis (an information rich area of spectrum)
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16. Working Examples
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17. Example – Catalytic reactions
Catalytic reaction monitoring
Example acetic acid
IRmadillo currently developed to monitor process in line
NB: Fibre instruments cut off at approx. 2,100-2,200 wave numbers. MCO (metal carbonyl) band is higher than this - approx. 2,400. The IRmadillo is the only in situ FTIR instrument that can see this
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18. Example 1 – Catalytic reactions
Acetic acid
1 plant producing 650,000 tonnes per year
Cost of production is $560/tonne
Not a nice reaction methyl halides elevated temp and pressure and expensive Rhodium catalyst
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19. Acetic acid production
Exothermic in process plant
Elevated temperature
Utilises Rh catalyst
180 0C
10 Barr
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20. Acetic acid production
Elevated temperature
Utilises catalyst
Currently process GC monitoring over 10 minutes and cost of $750 k
Process MID IR gives a 15 second analysis of 7 components.
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21.
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22. Mid-IR vs fibre probe vs Raman
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23. Enzymatic production of Propanediol
Maize (sugars) => Poly ols
Use for Polymers PET
to Dairy thickener in yoghurt
Dextrose concentration is crucial
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24. Heat control is critical 2 g /l increase in sugar leads to a thermal runaway. Individual batch loss $200k
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25. Executive Summary
Trial has successfully observed production of glycerol and propanediol simultaneously
Separate model has been made to observe consumption of dextrose
Current detection limits are:
Glycerol – 3.99 g L-1
1,3-propanediol – 7.37 g L-1
Dextrose – 1.45 g L-1
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26. All spectra gathered during trial
Top: NIST reference spectra for glycerol, 1,3-propanediol and water vapour.
Bottom: All the spectra used to build the model (both fermentation references and water spectra were used (as these show natural variation in water vapour in the air as well as “0 g L-1 values” for organic species.
Results show good correlation with the NIST reference spectra for the alcohols. Liquid water is present in all samples (not surprising as this is an aqueous fermentation).
There is possible evidence of water vapour in the spectra, which could fluctuate with time of day, weather etc…and adds noise into the measurement.
Potential evidence of water vapour
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27. Glycerol summary
Top left: Beta coefficients (spectral features used to build model).
Top right: Predicted vs actual plot.
Bottom: plot of predicted values and HPLC measured value for each sample.
Conclusion: The model shows a good correlation with the reference spectra, but there is a little bit of noise in the model (possibly caused by water vapour).
The LOD for glycerol is 3.99 g L-1
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28. 1,3-propanediol summary
Top left: Beta coefficients (spectral features used to build model).
Top right: Predicted vs actual plot.
Bottom: plot of predicted values and HPLC measured value for each sample.
Conclusion: The model shows a good correlation with the reference spectra, with very little noise in the model.
The LOD for 1,3-propanediol is 7.37 g L-1, which could be decreased further by analysing more samples with a lower alcohol content.
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29. Dextrose summary
Top left: Beta coefficients (spectral features used to build model).
Top right: Predicted vs actual plot.
Bottom: plot of predicted values and HPLC measured value for each sample.
Conclusion: Dextrose required a separate model to be built because of its low concentration and noise in the data (probably caused by water vapour).
The correlation is reasonable, especially with the lower concentrations of sugar.
The LOD for dextrose is 1.45 g L-1.
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30. Propanediol production
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31. Propanediol production
Hazard
2g /l increase in dextrose leads to thermal runaway and subsequent loss of 2 – 3 days production with a cost of $200k
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32. Why does NIR struggle?
Glucose/dextrose
Fructose
Sucrose
All sugars are made up of C-C bonds, C-O bonds, C-H bonds and O-H bonds.
The arrangement is fairly similar in all cases.
Raman and FTIR both look at fundamental vibrations, so can detect these changes.
NIR looks at combinations and overtones of fundamentals, so struggles to detect these.
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33. Hydrogen peroxide Hydrogen peroxide
IRmadillo can monitor down to <0.1%
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35. Water absorption
Un agitated solution absorbing water from 100ppm to 2.5% in less than 2 hours.
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36. Fermentation: Sugars to Ethanol Real-time Concentration Monitoring
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37. Summary
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38. Summary
Industry need of safe, real-time monitoring of hazardous chemistry
Challenges and options
Advantages of in situ monitoring
Future of technology in process monitoring
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39. In measuring any reaction hazardous or not.
Instrument stability is key and mustn’t contribute variation in data.
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40. Having static optics means that the spectral signal does not drift over time and therefore does not need re-baselining or re-calibrating. If it doesn’t move, it can’t go out of spec
Measuring the absorbance at a given wavenumber over a period of 10 months
When did you last recalibrate your bathroom mirror, or your camera lens?
Customer measured the same mixture 6 times over the course of 3 months and superimposed the plots

41. Calibration Transfer
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42. Hazardous is in the eye of the beholder
What's the worst we can analyse
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43. Hazardous is in the eye of the beholder
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44. Municipal Solid Waste MSW
Keit Fit for Process Environment
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45. Rugged Solid-state sensor Compact
Safe for use in hazardous environments
Inert Hastelloy probe
Solid-state sensor
No moving parts
In-line process monitoring of liquids
Real-time reaction analysis
Compact
Probe length 180mm / 7”
Body length 310mm / 12.2”
Placed at point of generation
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46. Any questions? jim.rutherford@keit.co.uk www.keit.co.uk
Solid-state FTIR sensor for
real-time process monitoring
Follow Keit Spectrometers
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