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Dynamic re-optimization of a fed-batch fermentor using adaptive critic designs.

M S Iyer¹, D C Wunsch

¹Dresser-Rand Control Systems, Houston, TX 77043, USA.

IEEE Transactions on Neural Networks

|February 6, 2008

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This study introduces adaptive critic designs for real-time optimization and control of fed-batch fermentors. These methods outperform traditional optimizers in producing monoclonal antibodies.

Area of Science:

Biochemical Engineering
Control Systems
Artificial Intelligence

Background:

Traditional fed-batch process optimization relies on complex, offline methods lacking real-time adaptation.
Existing control strategies struggle with dynamic changes inherent in bioprocesses.

Purpose of the Study:

To demonstrate the effectiveness of adaptive critic designs for online re-optimization and control of aerobic fed-batch fermentors.
To compare the performance of various adaptive critic designs against traditional optimization techniques.

Main Methods:

Implementation of heuristic dynamic programming, dual heuristic programming, and generalized dual heuristic programming.
Application of these adaptive critic designs to an aerobic fed-batch fermentor producing monoclonal antibodies.

Related Experiment Videos

Comparison with a heuristic random optimizer for performance evaluation.

Main Results:

Adaptive critic designs showed superior performance in optimizing the fed-batch fermentor operation.
Heuristic dynamic programming, dual heuristic programming, and generalized dual heuristic programming effectively enabled online re-optimization.
Significant improvements in process control and optimization were observed compared to the random optimizer.

Conclusions:

Adaptive critic designs offer a powerful and adaptable approach for real-time fed-batch bioprocess control.
These AI-driven methods provide a viable alternative to conventional, static optimization techniques.
The study highlights the potential of adaptive critic designs for enhancing biopharmaceutical production, specifically monoclonal antibodies.