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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...
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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...
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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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Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
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Optimization of microalgal photobioreactor system using model predictive control with experimental validation.

Sung Jin Yoo1, Dong Hwi Jeong1, Jung Hun Kim1

  • 1Institute of Chemical Processes, School of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.

Bioprocess and Biosystems Engineering
|April 21, 2016
PubMed
Summary

Model predictive control (MPC) significantly improved microalgal biomass and lipid productivity in photobioreactors. However, large input manipulations during optimization can cause lag phases, requiring further study for enhanced microalgal cultivation.

Keywords:
Lag phaseMicroalgaeModel predictive controlPhotobioreactorUnscented Kalman filter

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Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases
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Area of Science:

  • Biotechnology
  • Algal Cultivation
  • Process Optimization

Background:

  • Maximizing biomass and lipid concentrations is crucial for microalgal applications.
  • Mixotrophic cultivation in photobioreactors offers potential for enhanced productivity.
  • Advanced control strategies are needed to optimize microalgal growth and lipid accumulation.

Purpose of the Study:

  • To investigate optimization methods for microalgal photobioreactor systems under mixotrophic conditions.
  • To apply model predictive control (MPC) for maximizing biomass and lipid concentrations.
  • To evaluate the effectiveness of MPC in tracking optimized biomass and lipid trajectories.

Main Methods:

  • Lipid concentration estimation using unscented Kalman filter (UKF).
  • Model predictive control (MPC) implementation using UKF-estimated lipid data.
  • Open-loop optimization for target trajectory generation and MPC tracking.
  • Simulation studies and experimental validation of the control strategy.

Main Results:

  • Significant improvements in biomass and lipid productivity were achieved with MPC application.
  • MPC successfully tracked the maximized biomass and lipid trajectories.
  • A lag phase was observed during feed flow rate manipulation due to large input volumes.

Conclusions:

  • Model predictive control (MPC) is an effective strategy for enhancing microalgal biomass and lipid productivity.
  • The observed lag phase indicates a potential model-plant mismatch, necessitating further research.
  • Further studies are required to optimize microalgal photobioreactor operation and mitigate input-induced lag phases.