Semi-mechanistic models for state-estimation - Soft sensor for polymer melt index prediction

Balazs Feil, Janos Abonyi, Peter Pach, Sandor Nemeth, Peter Arva, Miklos Nemeth, Gabor Nagy

Research output: Contribution to journalConference article

14 Citations (Scopus)


Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models that can accurately describe the process. This paper presents a semi-mechanistic modeling approach where neural networks describe the unknown phenomena of the system that cannot be formulated by prior knowledge based differential equations. Since in the presented semi-mechanistic model structure the neural network is a part of a nonlinear algebraic-differential equation set, there are no available direct input-output data to train the weights of the network. To handle this problem in this paper a simple, yet practically useful spline-smoothing based technique has been used. The results show that the developed semi-mechanistic model can be efficiently used for on-line state estimation.

Original languageEnglish
Pages (from-to)1111-1117
Number of pages7
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - Dec 9 2004
Event7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004 - Zakopane, Poland
Duration: Jun 7 2004Jun 11 2004


ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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