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Determining Phase Separation Dynamics with an Automated Image Processing Algorithm.

James DaglishA John BlackerGregory de BoerAlex CramptonDavid R J HoseAnna R ParsonsNikil Kapur
Published in: Organic process research & development (2023)
The problems of extracting products efficiently from reaction workups are often overlooked. Issues such as emulsions and rag layer formation can cause long separation times and slow production, thus resulting in manufacturing inefficiencies. To better understand science within this area and to support process development, an image processing methodology has been developed that can automatically track the interface between liquid-liquid phases and provide a quantitative measure of the separation rate of two immiscible liquids. The algorithm is automated and has been successfully applied to 29 cases. Its robustness has been demonstrated with a variety of different liquid mixtures that exhibit a wide range of separation behavior-making such an algorithm suited to high-throughput experimentation. The information gathered from applying the algorithm shows how issues resulting from poor separations can be detected early in process development.
Keyphrases
  • deep learning
  • machine learning
  • high throughput
  • liquid chromatography
  • mental health
  • neural network
  • public health
  • health information
  • high resolution
  • mass spectrometry