Self-Organizing Maps for Analysis of Expandable Polystyrene Batch Process

  • Mikko Heikkinen University of Kuopio, Department of Environmental Sciences P.O. Box 1627, FIN - 70211 Kuopio, Finland
  • Ville Nurminen StyroChem Ltd P.O. Box 360, FIN - 06101 Porvoo, Finland
  • Yrjö Hiltunen University of Kuopio, Department of Environmental Sciences P.O. Box 1627, FIN - 70211 Kuopio, Finland E-mail:


Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of SOM-based method for the analysis of Expandable PolyStyrene (EPS) batch process. To this end, a data set of EPS-batch process was used for training a SOM. Reference vectors of the SOM were then classified by K-means algorithm into six clusters, which represent product types of the process. This SOM could also be used for estimating the optimal amounts of the stabilisation agent. The results of a validation data set showed a good agreement between the actual and estimated amounts of the stabilisation agent. Based on this model a Web application was made for test use at the plant. The results indicate that the SOM method can also be efficiently applied to the analysis of the batch process.


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How to Cite
HEIKKINEN, Mikko; NURMINEN, Ville; HILTUNEN, Yrjö. Self-Organizing Maps for Analysis of Expandable Polystyrene Batch Process. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 2, n. 2, p. 143-148, apr. 2007. ISSN 1841-9844. Available at: <>. Date accessed: 02 july 2020. doi:


Neural networks, self-organizing maps, process control, batch process