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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...
Batch vs Continuous Culture01:14

Batch vs Continuous Culture

Fermentation is a foundational biotechnological process used to produce pharmaceuticals, biofuels, enzymes, and food additives. Among industrial strategies, batch and continuous fermentation are the two most widely applied. Although both rely on microbial conversion of substrates into desired products, they differ markedly in operation, productivity, and suitability for specific applications.Batch fermentation occurs in a closed system in which nutrient media and inoculum are added at the...
Fed-Batch Culture01:23

Fed-Batch Culture

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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Related Experiment Video

Updated: Jul 11, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
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Published on: January 22, 2018

[Neural network detection of abnormalities in fed-batch fermentation].

Yun-Feng Li1, Jing-Qi Yuan

  • 1Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China.

Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
|April 30, 2005
PubMed
Summary

This study introduces an early detection method for fermentation abnormalities using a BP network. The approach successfully identifies abnormal fermentation batches, enabling timely intervention and improved process control.

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Artificial Intelligence

Background:

  • On-line detection of fermentation abnormalities is challenging, often leading to late-stage fault identification.
  • Traditional off-line analysis methods are reactive, limiting the ability to correct issues during the process.

Purpose of the Study:

  • To develop an early warning system for detecting abnormal fermentation batches.
  • To improve process control and product yield by enabling timely intervention.

Main Methods:

  • Utilized a simple three-layer Backpropagation (BP) network for predictive modeling.
  • Incorporated prediction errors, profit function, and pH values as input features.
  • Integrated characteristic indicators for abnormal charge detection in practical operations.

Main Results:

  • Successfully predicted product amount for early detection of abnormalities.
  • Demonstrated effective early discovery of abnormal charges in Cephalosporin C Fed-batch Fermentation.
  • The method proved successful in a real-world pharmaceutical factory setting.

Conclusions:

  • The developed BP network-based method enables successful early detection of fermentation abnormalities.
  • This approach facilitates proactive management of fermentation processes, preventing significant losses.
  • The study highlights the potential of AI in enhancing bioprocess monitoring and control.