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Related Experiment Video

Updated: Jun 12, 2025

Power Input Measurements in Stirred Bioreactors at Laboratory Scale

Power Input Measurements in Stirred Bioreactors at Laboratory Scale

Published on: May 16, 2018

A Computer Vision Approach toward Verifying CFD Models of Stirred Tank Reactors.

Calum Fyfe¹, Henry Barrington¹, Charles M Gordon²

¹Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow G1 1XL, U.K.

Organic Process Research & Development

|September 26, 2024

Summary

This summary is machine-generated.

This study demonstrates using colorimetric imaging and Kineticolor software to verify computational fluid dynamics (CFD) models for mixing processes. This noninvasive approach captures mixing dynamics in tank reactors, aiding process design and scale-up.

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Last Updated: Jun 12, 2025

Power Input Measurements in Stirred Bioreactors at Laboratory Scale

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Published on: May 16, 2018

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Published on: February 21, 2017

Area of Science:

Chemical Engineering
Fluid Dynamics
Process Intensification

Background:

Mixing is crucial for scalable chemical processes, yet its dynamics are complex to quantify.
Noninvasive imaging offers potential for understanding mixing, but its use in validating computational fluid dynamics (CFD) models is underdeveloped.
Accurate CFD models are essential for optimizing reactor design and ensuring process efficiency.

Purpose of the Study:

To explore the correlation between kinetic imaging data and traditional pH probe measurements.
To investigate the sensitivity of mixing to feed point location using competing parallel reactions.
To assess the utility of experimental imaging data for qualitatively validating CFD models of mixing.

Main Methods:

Utilized colorimetric reactions and custom kinetic imaging software (Kineticolor) for data acquisition.
Employed Villermaux-Dushman-type competing parallel reactions to probe mixing sensitivity.
Correlated imaging-derived kinetic data with pH probe measurements and visually assessed CFD model predictions.

Main Results:

Demonstrated a strong correlation between imaging kinetics and pH probe measurements.
Showcased how stirring rate, baffle presence, and feed position significantly impact mixing dynamics.
Provided experimental evidence supporting the qualitative assessment and verification of CFD models using imaging data.

Conclusions:

Kinetic imaging, coupled with software like Kineticolor, offers a viable noninvasive method for studying mixing.
This approach provides valuable experimental data for validating CFD models in tank reactors.
The findings advance the use of computer vision techniques for verifying fluid flow models in chemical engineering.