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Related Experiment Video

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Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Published on: December 6, 2013

Feedback identification of continuous microbial growth systems.

D G O'Neil¹, G Lyberatos

¹Department of Chemical Engineering, University of Florida, Gainesville, Florida 32611.

Biotechnology and Bioengineering

|September 1, 1986

Summary

This study introduces feedback control to analyze continuous culture systems, revealing distinct dynamic behaviors for different models. This method aids in selecting the best model for process control and optimization.

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Area of Science:

Bioprocess Engineering
Chemical Engineering
Systems Biology

Background:

Dynamic modeling is crucial for continuous culture systems in process control and optimization.
Distinguishing between different dynamic models is essential for accurate system representation.

Purpose of the Study:

To address the fundamental problem of dynamic modeling in continuous culture systems.
To utilize feedback control for model discrimination and identification.
To analyze and differentiate the dynamic behaviors of various chemostat models.

Main Methods:

Applying feedback control to force the system towards bifurcation.
Analyzing dynamic information obtained through this bifurcation technique.
Comparing dynamic behaviors predicted by unstructured, delayed, and structured models.

Main Results:

The feedback control method successfully exposed significant differences in the nonlinear dynamic structure of various models.
Distinct dynamic behaviors were observed for unstructured, delayed, and structured chemostat models.
The technique proved effective in discriminating between different potential models.

Conclusions:

Feedback control-induced bifurcation is a powerful technique for model discrimination in continuous culture systems.
This method provides valuable dynamic information for process control and optimization.
The study highlights the importance of selecting appropriate models for accurate bioprocess representation.