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A Novel Fermentation Control Method Based on Neural Networks

  • Xuhua Yang
  • Zonghai Sun
  • Youxian Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

This paper proposes a novel fermentation control method. Two stages are involved. First, propose the fermentation time model and the optimal fermentation temperature model based on RBF Neural networks. Second, on the base of the two models, propose the novel fermentation control method by which different fermentation batch can adopt different optimal fermentation temperature trajectory which fits itself. Using this method, each fermentation batch can be fermented at optimal fermentation temperature trajectory and will improve average product proportion. The practical application showed that this method can improve average product proportion 3% effectively.

Keywords

Fermentation Process Fermentation Batch Fermentation Time Radial Basis Function Network Fermentation Temperature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Leal Ascencio, R.R., Reynaga, F., Herrera, E., Gschaedler, A.: Artificial neural networks as a biomass virtual sensor for a batch process. In: Proceedings of the 2001 IEEE International Symposium on Intelligent Control, pp. 246–251 (2001)Google Scholar
  2. 2.
    Xiong, Z., Zhang, J.: Modeling and optimal control of fed-batch processes using control affine feedforward neural networks. American Control Conference 6, 5025–5030 (2002)Google Scholar
  3. 3.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xuhua Yang
    • 1
  • Zonghai Sun
    • 1
  • Youxian Sun
    • 1
  1. 1.National Laboratory of Industrial Control Technology, Institute of Modern Control EngineeringZhejiang UniversityHangzhouP.R. China

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