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Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation.

Hui Li1, Jiajun Chen1, Xingyan Li1

  • 1College of Biotechnology and Pharmaceutical Engineering, State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing, 211816, China.

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|December 4, 2023
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Summary

This study introduces an artificial neural network (ANN) and genetic algorithm (GA) for optimized lysine fermentation control. The novel approach enhances lysine production to 213.0 g·L⁻¹, improving bioprocess regulation.

Keywords:
Artificial neural networkFermentation controlFermentation kineticsGenetic algorithmL-lysine

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Area of Science:

  • Biotechnology
  • Industrial Microbiology
  • Process Engineering

Background:

  • Fermentation is crucial for bioproducts but lacks efficient process regulation.
  • Optimizing fermentation control is essential for industrial bioprocesses.

Purpose of the Study:

  • To develop an innovative lysine fermentation control strategy using artificial neural networks (ANN) and genetic algorithms (GA).
  • To predict and optimize key fermentation kinetics: specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (μ).

Main Methods:

  • Coupling of GA with ANN for integrated control strategy development.
  • Utilizing a three-layer feed-forward back-propagation ANN model (4:10:1).
  • Optimization of fermentation parameters via GA.

Main Results:

  • The ANN-GA model successfully predicted and optimized fermentation kinetics.
  • Optimal control parameters were determined for key factors including carbon to nitrogen ratio, residual sugar, ammonia nitrogen, and dissolved oxygen.
  • Peak lysine concentration reached 213.0 ± 5.10 g·L⁻¹ under optimized conditions.

Conclusions:

  • The developed ANN-GA control strategy significantly enhances lysine fermentation efficiency.
  • This novel approach demonstrates potential for optimizing the fermentation of various other bioproducts.