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Cluster Computing

, Volume 22, Supplement 3, pp 6019–6030 | Cite as

Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process

  • Yu-mei Sun
  • Ni Du
  • Qiao-yan Sun
  • Xiang-guang ChenEmail author
  • Jian-wen Yang
Article
  • 116 Downloads

Abstract

The potency of fermentation broth is one of the key parameters reflect the yield and quality of fermentation products of chlortetracycline (CTC). But so far, there is no instrument available on-line detection of CTC potency. All the tests are done by manual off-line testing, it takes several hours to sample and analyze the results from a fermentation tank (the production process usually has dozens of small, large fermenters). The use of analytical results to control the amount of operation will lead to severe lag. This paper combines self-organizing feature map (SOM) neural network with accurate classification of data and least squares support vector machine (LSSVM) algorithm with strong described the nonlinear characteristics. The SOM–LSSVM global modeling method of forecasting CTC fermentation potency is establish in this paper. According to the characteristics of nonlinear CTC fermentation process, just-in-time learning-recursive least squares support vector regression (JITL–RLSSVR) is used to perform local real-time modeling and 10-folding cross validation, and a hybrid soft sensor modeling method (JITL–RLSSVR + SOM–LSSVM) for online prediction of CTC fermentation potency is proposed in this paper. Field experiments show that this method can obtain more accurate potency prediction value, and it can meet the requirements of the production process.

Keywords

Potency of chlortetracycline (CTC) fermentation broth Hybrid soft sensor models SOM (self-organizing feature map) JITL (just-in-time learning) LSSVM (least squares support vector machine) RLSSVR (recursive least-SOM-LSSVM-JITL-RLSSVR) 

Notes

Acknowledgements

This work is financially supported by Natural Science Foundation (No. ZR2016FM28) of Shandong Province in 2016. We also thank Charoen Pokphand Group for their financial support and for providing the industrial datasets offed-batch CTC fermentation process.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yu-mei Sun
    • 1
  • Ni Du
    • 2
  • Qiao-yan Sun
    • 1
  • Xiang-guang Chen
    • 2
    Email author
  • Jian-wen Yang
    • 2
  1. 1.College of Electronic EngineeringYantai Nanshan UniversityLongkouPeople’s Republic of China
  2. 2.School of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijingPeople’s Republic of China

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