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Updated: Aug 19, 2025
07:33
Quantifying Mixing using Magnetic Resonance Imaging
Published on: January 25, 2012
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Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena
Henry Barrington1, Alan Dickinson2, Jake McGuire1
1Department of Pure & Applied Chemistry, University of Strathclyde, Royal College Building 204 George Street, Glasgow G1 1XW, U.K.
Organic Process Research & Development
|November 28, 2022
Summary
A new software platform uses computer vision to analyze mixing phenomena for process scale-up. This tool enables novel comparisons of mixing metrics across various chemical and non-chemical processes, aiding reaction progress prediction.
Area of Science:
- Chemical Engineering
- Process Chemistry
- Computational Fluid Dynamics
Background:
- Accurate analysis of mixing is crucial for chemical process scale-up.
- Current methods for assessing mixing can be limited in scope and application.
- Time-resolved metrics offer deeper insights into dynamic mixing processes.
Purpose of the Study:
- To introduce a software platform for computer vision-enabled analysis of mixing phenomena.
- To enable comparisons of pixel-derived mixing metrics across diverse processes.
- To demonstrate the application of these methods from development to process scale-up.
Main Methods:
- Development of a unified software platform integrating known and novel time-resolved mixing metrics.
- Utilizing camera-based analysis for pixel-derived metric quantification.
- Application across various reactor scales and process types (chemical and non-chemical).
Main Results:
- Demonstrated hitherto unavailable comparisons of mixing metrics derived from pixel data.
- Showcased applicability across a range of reactor scales and process types.
- Validated camera data correlation with offline concentration analyses in a case study.
Conclusions:
- The described analytical methods are versatile, applicable with any camera across development to process scale-up.
- Camera data analysis provides powerful insights into mixing phenomena.
- Camera data can potentially predict reaction progress, optimizing process control.
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