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Control of continuous fed-batch fermentation process using neural network based model predictive controller.

A Uma Maheshwar Kiran¹, Asim Kumar Jana

¹Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada.

Bioprocess and Biosystems Engineering

|March 5, 2009

Summary

This summary is machine-generated.

A novel controller improved baker's yeast fed-batch fermentation by precisely managing glucose feed rates. This strategy optimizes cell growth and metabolite production using a unique control variable for enhanced fermentation performance.

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Area of Science:

Biotechnology
Industrial Microbiology
Biochemical Engineering

Background:

Fed-batch fermentation is critical for cell growth and metabolite production.
Nutrient feeding strategies significantly impact fermentation outcomes.
Optimizing glucose feed rate is essential for baker's yeast fermentation efficiency.

Purpose of the Study:

To develop and evaluate a novel controller for glucose feed rate in baker's yeast fed-batch fermentation.
To assess the effectiveness of a neural network-based model predictive controller and optimizer.
To improve set-point tracking performance in fermentation processes.

Main Methods:

Utilized the difference between specific carbon dioxide evolution rate and oxygen uptake rate (Qc - Qo) as the controller variable.
Implemented a neural network-based model predictive controller and optimizer.
Evaluated controller performance through set-point tracking analysis.

Main Results:

The novel controller demonstrated good performance in set-point tracking.
The strategy effectively controlled the glucose feed rate during fermentation.
The results indicate successful optimization of the fed-batch process.

Conclusions:

The developed controller is effective for managing glucose feed rates in baker's yeast fed-batch fermentation.
The use of (Qc - Qo) as a controller variable shows promise for fermentation control.
This approach can enhance cell growth and metabolite production efficiency.