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Determining Phase Separation Dynamics with an Automated Image Processing Algorithm.

James Daglish1, A John Blacker2, Gregory de Boer1

  • 1School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, U.K.

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Summary
This summary is machine-generated.

Efficient product extraction is crucial for manufacturing. A new automated image processing method quantifies liquid-liquid separation, improving process development and detecting inefficiencies early.

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Area of Science:

  • Chemical Engineering
  • Process Chemistry
  • Analytical Chemistry

Background:

  • Inefficient product extraction during reaction workups leads to significant manufacturing delays.
  • Emulsions and rag layers formation are common issues that prolong separation times and reduce production efficiency.
  • Current methods for assessing separation efficiency are often time-consuming and lack quantitative data.

Purpose of the Study:

  • To develop an automated image processing methodology for tracking liquid-liquid interfaces.
  • To provide a quantitative measure of separation rates for immiscible liquids.
  • To support process development by enabling early detection of separation issues.

Main Methods:

  • Development of an automated image processing algorithm to track the interface between liquid phases.
  • Application of the algorithm to 29 diverse cases involving various liquid mixtures.
  • Validation of the algorithm's robustness across a wide range of separation behaviors.

Main Results:

  • The developed algorithm successfully automated the tracking of liquid-liquid interfaces.
  • Quantitative separation rates were obtained, enabling precise assessment of separation efficiency.
  • The method demonstrated robustness with varied liquid mixtures, indicating suitability for high-throughput experimentation.
  • Early identification of potential separation problems was achieved during process development.

Conclusions:

  • The automated image processing methodology offers an efficient and quantitative approach to assess liquid-liquid separation.
  • This tool aids in optimizing reaction workup processes, reducing manufacturing inefficiencies.
  • The algorithm's applicability to high-throughput experimentation supports faster and more reliable process development.