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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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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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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...
Bioreactor Controls-II01:18

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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...
Bioreactor Controls-III01:22

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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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Updated: May 15, 2026

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

Automatic control of bioprocesses.

Marc Stanke1, Bernd Hitzmann

  • 1Process Analytics and Cereal Technology, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 23, 70599, Stuttgart, Germany.

Advances in Biochemical Engineering/Biotechnology
|January 12, 2013
PubMed
Summary
This summary is machine-generated.

Bioprocess automation requires diverse control strategies, not a single algorithm. Hybrid systems combining methods like soft sensors and PID controllers are common due to measurement challenges.

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Area of Science:

  • Bioprocess Engineering
  • Control Systems Theory

Background:

  • Bioprocess automation relies on sophisticated control strategies.
  • While closed-loop control research is abundant, real-world bioprocess applications remain limited, with many studies focusing on simulations.
  • The inherent diversity of bioprocesses necessitates varied control approaches.

Purpose of the Study:

  • To review and discuss various open-loop and closed-loop control methodologies in bioprocess automation.
  • To highlight the practical challenges and limitations of implementing advanced control techniques in real bioprocesses.
  • To analyze the trend towards hybrid control systems and the role of soft sensors and model predictive control.

Main Methods:

  • Review of existing literature on bioprocess control strategies.
  • Analysis of the application of different control algorithms, including PID controllers, soft sensors, and model predictive control.
  • Discussion of hybrid systems combining theoretical models with fuzzy logic and artificial neural networks.

Main Results:

  • A significant gap exists between simulated and real-world applications of closed-loop control in bioprocesses.
  • Soft sensors combined with proportional-integral-derivative (PID) controllers are prevalent due to limitations in direct online measurement systems.
  • Model predictive control (MPC) shows increasing importance but requires robust models and computational power.
  • Hybrid systems are often employed to compensate for the lack of theoretical bioprocess models.

Conclusions:

  • No single control algorithm is universally applicable to all bioprocesses; tailored and hybrid approaches are essential.
  • The development of effective control strategies is often constrained by the availability and cost of reliable online measurement systems.
  • Control algorithms are frequently specialized for specific organisms, conditions, or measurement setups, limiting broader transferability.