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Fed-batch culture is a widely used bioprocessing strategy combining aspects of batch culture with controlled substrate feeding to optimize cell growth and product formation. In this semi-closed system, nutrients are strategically added during fermentation, while the accumulated products and biomass remain within the bioreactor until the end of the operation. This controlled addition of substrates allows for better management of growth kinetics, nutrient limitation, and metabolite...
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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
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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
PubMed
Summary

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.

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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.
  • 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.