State Estimation in Biotechnological Processes Using a Software-Sensor Combining Full-Horizon Observer and Neural Networks

Joachim Hörrmann (Corresponding Author), Dorothee Barth, Michael Kräling, Helmut Röck

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

1 Citation (Scopus)

Abstract

This paper presents an innovative method for the online determination of biomass in fermentation, using a combination of model-based full-horizon observer and Neural Network. The performance of the Neural Network depends highly on correct initial conditions of the unknown process values. Unfortunately, in biological processes it is impossible to guarantee reproducible initial conditions. On the contrary, the variations in the innoculated amount of bacteria oscillate between 30% in lab scale and more than 100% in industrial applications. To reduce the effect of these variations to the Neural Network, we herein propose the use of an optimization based estimator to determine the initial conditions of the process values online in early process stages in order to improve the estimation results of the Neural Network.

Original languageEnglish
Pages (from-to)9655-9660
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume41
Issue number2
DOIs
Publication statusPublished - 2008
MoE publication typeA4 Article in a conference publication
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008

Keywords

  • Modelling and identification
  • Parameter and state estimation
  • Neural network
  • Full-horizon observer
  • Biotechnology
  • Streptococcus thermophilus

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