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Related Experiment Videos

[Modelling a penicillin fed-batch fermentation using least squares support vector machines].

Yi Liu1, Hai-Qing Wang

  • 1National Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China.

Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
|April 1, 2006
PubMed
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This study introduces a novel modeling approach for biochemical processes using least squares support vector machines (LS-SVM) and the Pensim simulator. The method accurately predicts key concentrations in penicillin fermentation with limited data, enhancing process control.

Area of Science:

  • Biochemical Engineering
  • Nonlinear Dynamic Systems Modeling
  • Machine Learning Applications

Context:

  • Biochemical processes are complex, time-varying, and nonlinear systems.
  • First-principle modeling is costly and difficult due to lack of mechanistic understanding and sensors.
  • Existing complex models may not ensure practical performance.

Purpose:

  • To propose a novel modeling approach for penicillin fed-batch fermentation using least squares support vector machines (LS-SVM).
  • To present a parameter adjustment strategy for LS-SVM within the Pensim simulator.
  • To develop predictive models for penicillin, biomass, and substrate concentrations.

Summary:

  • A least squares support vector machine (LS-SVM) approach, integrated with the Pensim simulator, is proposed for modeling penicillin fed-batch fermentation.

Related Experiment Videos

  • This method effectively models time-varying, nonlinear biochemical dynamics using limited on-line measurements.
  • Predictive models for penicillin, biomass, and substrate concentrations were successfully established.

    • Impact:

    • Achieved accurate and efficient predictive models for penicillin fermentation.
    • Demonstrated the suitability of the LS-SVM approach for biochemical process control and optimization.
    • Overcame limitations of traditional first-principle modeling in complex biological systems.