Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production

Janos Abonyi, Sandor Nemth, Csaba Vincze, Peter Arva

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33 Citations (Scopus)

Abstract

The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing (SC)-based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation of the data distribution, efficient process monitoring can be performed in the two-dimensional projection of the process variables. For the estimation of the product quality, multiple local linear models are identified, where the operating regimes of the local models are obtained by the Voronoi diagram of the prototype vectors of the SOM. The proposed approach is applied to the analysis of an industrial polyethylene plant. The detailed application study demonstrates that the SOM is very effective in the detection of the typical operating regions related to different product grades, and the model can be used to predict the product quality (melt index and density) based on measured process variables.

Original languageEnglish
Pages (from-to)221-234
Number of pages14
JournalComputers in Industry
Volume52
Issue number3
DOIs
Publication statusPublished - Dec 1 2003

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Keywords

  • Operating regime-based modeling
  • Process monitoring
  • Product analysis and design
  • Self-Organizing Map (SOM)
  • Voronoi diagram

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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