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Related Concept Videos

Bioreactor Controls-I01:28

Bioreactor Controls-I

Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly monitored using...
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Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
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In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the fermentor via a sparger...
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Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
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Published on: May 18, 2020

Adaptive on-line optimizing control of bioreactor systems.

Z Shi1, K Shimizu, N Watanabe

  • 1Department of Chemical Engineering, Nagoya University, Chikusa, Nagoya 464, Japan.

Biotechnology and Bioengineering
|March 1, 1989
PubMed
Summary

New control strategies enhance bioreactor efficiency by optimizing operations. Adaptive control systems effectively manage time-varying, nonlinear dynamics for improved production processes.

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Control Systems

Background:

  • Bioreactor operation demands efficient control strategies to manage complex dynamics.
  • Time-varying and nonlinear characteristics are inherent challenges in bioreactor systems.
  • Optimizing operating points is crucial for maximizing product yield and process efficiency.

Purpose of the Study:

  • To propose and evaluate on-line optimizing control strategies for efficient bioreactor operation.
  • To develop a hierarchical control structure with distinct upper and lower layers for set-point optimization and process tracking.
  • To investigate the application of adaptive control for handling the nonlinear and time-varying nature of bioreactors.

Main Methods:

  • Computer simulations were employed to test proposed control strategies.
  • A two-layer control system was designed: an upper layer for optimal set-point searching and a lower layer for process output tracking.
  • Polynomial representation of the objective function was used for optimal operating point determination.
  • Discrete type self-tuning PID controllers and optimal controllers with interaction compensation were utilized.
  • Decoupling strategies were incorporated into the lower-layer closed-loop system.
  • A rough mathematical model was used for initial startup optimization.

Main Results:

  • Expressing the objective function as a polynomial proved effective for finding optimal operating points.
  • Adaptive control systems, specifically discrete self-tuning PID controllers, were effective for managing bioreactor dynamics.
  • The hierarchical control structure successfully separated optimization and tracking tasks.
  • Application to lactic acid production via cell recycle demonstrated significant improvement in control quality using decoupling strategies.
  • Application to baker's yeast cultivation showed a significant reduction in the initial startup period by using a rough mathematical model.

Conclusions:

  • On-line optimizing control strategies, particularly adaptive and hierarchical approaches, are effective for efficient bioreactor operation.
  • The proposed control framework successfully addresses the challenges of time-varying and nonlinear bioreactor dynamics.
  • Decoupling strategies and the use of mathematical models significantly enhance control performance and reduce startup times in specific bioreactor applications.