Applications of guided microwave spectroscopy in process analysis

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Applications of guided microwave spectroscopy in
process analysis

• Vicki Loades and Tony Walmsley
• Analytical Science Group
• Department of Chemistry
• University of Hull
• v.c.loades_at_chem.hull.ac.uk

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• Aim
• To demonstrate that guided microwave spectroscopy
  (GMS) is an alternative method for process
  analysis.
• Introduction
• Background and Benefits of GMS
• Brief Description of some multivariate methods
• Some Examples of Research in the area.
• Binary solutions (low and high levels of
  components)
• Analysis of a multiphase industrial samples

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Ideal Process Spectrometer

• Wish List For a Process Spectrometer
• Non-invasive
• Non-destructive
• Suitable for solids/liquids, gases and
  suspensions
• Suitable for dark coloured samples
• Analyses the whole sample
• Large sample Volumes

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Microwave Spectroscopy

• Microwave region Approx. 200MHz and 80GHz.
• Microwave spectra are a result two properties.
• Dielectric constant (e) - Reduction in velocity
• As the electromagnetic wave passes through the
  sample it causes an alternating polarization.
  This polarization and depolarization reduces the
  wave velocity across the chamber during analysis.
  
• Dielectric loss (e) - Reduction in magnitude
• As the molecules orientate in the electric field
  energy is lost to friction. This causes the waves
  magnitude to reduce across the sample.

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Epsilon Industrial Guided Microwave SpectrometerTM
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Waveguide Frequency Cut - off
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Example GM Spectra
Cut-off
Response
Frequency MHz
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Principal Component Analysis

• This is a method that decomposes the spectra into
  principal components (PC) each PC has score and
  loading.
• By plotting the scores it is possible to
  visualise trends in spectral data that might have
  been difficult to see in the original spectra.
• Loadings plots can show where the main regions of
  variation in the spectra occur and when the
  Number of PCs start to represent noise in the
  data rather than useful information.

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Partial Least Squares

• This is an extension to PCA, but this time
  reference measurements (e.g. concentration,
  density or pH) for the spectral data are also
  included in the calculations.
• This allows calibration models to be generated
  which can then be used to predict the levels of
  unknown samples.
• Prediction ability is determined by the Root Mean
  Squared Prediction Error (RMSPE).

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Spectral Pre-treatment

• Pre-treatment methods can be used to improve
  correlation between reference and spectral data.
• Background subtraction removes the pure spectra
  of a component from the data.
• Useful when components are masked by a more
  responsive component which is not of interest.
• Mean centring subtracts the mean from the data
  set.
• This removes a large chunk of the magnitude
  leaving the true variation in the data.
• Orthogonal signal correction removes variables
  which are not orthogonal to the reference data.
• A more involved method useful for highly
  co-linear and overlapping spectra

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Binary Solutions

• Aim
• To build calibration models which can accurately
  predict components of interest.
• Different sample sets
• Aqueous samples at levels below 30.
• Alcohol solutions above 30.
• Solvents are fairly difficult to analyse by
  non-destructive methods such as spectroscopy.
• The standard method for analysis is Gas
  Chromatography

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Sample Sets
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Acetonitrile Samples Spectra
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Acetonitrile Samples Spectra Background Subtracted
RMSPE 1.07
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Ethanol Samples Spectra
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Ethanol Samples Spectra Background Subtracted
RMSPE 0.28
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Methanol and Ethanol
RMSPE 1.1
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Methanol and Propanol
RMSPE 0.35
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Ethanol and Propanol
RMSPE 0.95
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Industrial Analysis

• Aim
• To be able to monitor a multiphase sample of an
  industrial process as it is converted to product.
  
• Investigate effect of phase variations to the GM
  Spectra of the samples.
• Ultimately use GMS to control the conversion
  process between reactor vessels.
• Sample is difficult to analyse by standard
  methods It is dark in colour, has high level of
  solids and also has organic and aqueous phases.

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On-line Analysis
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Industrial Samples

• Sample composition
• 50 Aqueous
• 30 Solid
• 20 Organic
• Pale brown paste when mixed.
• The electromagnetic properties vary with sample
  phase.

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Typical Sample
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GM Spectra of Industrial Samples
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Signal Corrected GM Spectra
Increasing Oxidation
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PCA of Signal Corrected GM Spectra
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Sample Separation

• Record GM spectra as the mixed sample separates
  back into the original phases
• As shown on left
• Takes approx. 3mins

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GM Spectra of sample phase separation
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Principal Component Analysis
Time
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Summary

• We have shown that Guided Microwave Spectroscopy
  (GMS) when combined with multivariate methods can
  be used as an alternative for process analysis.
• The main advantage is the suitability of this
  method for samples which are of multiple phases
  or that are dark in colour.
• In particular the applications of the analysis of
  some binary mixtures and more importantly samples
  taken from an industrial process have been
  demonstrated.

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On Going Work

• Currently monitoring beer fermentation as a batch
  process taking place inside the guided microwave
  spectrometers sample chamber.
• This is a living system containing dissolved
  gases, particulates.
• Initial results are promising with visible trends
  in the spectra and scores plots.

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Acknowledgements

• Chris Walker and Steward MacKenzie, ThermoONIX.
• Sylvia Ewans, Ewan Polwart and Ian Wells, Avecia.
• This research is funded by
• EPSRC, Engineering and Physical Sciences Research
  Council.
• CPACT, Centre for Process Analytics Control
  Technology.