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Subtraction analysis based on self-organizing maps for an industrial wastewater treatment process

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  • Heikkinen, M.
  • Poutiainen, H.
  • Liukkonen, M.
  • Heikkinen, T.
  • Hiltunen, Y.

Abstract

This paper presents an overview of an analysis method based on self-organizing maps (SOM) which was applied to an activated sludge treatment process in a pulp mill. The aim of the study was to determine whether the neural network modeling method could be a useful and time-saving way to analyze this kind of process data. The following analysis procedure was used. At first, the process data was modeled using the SOM algorithm. Next, the reference vectors of the map were classified by K-means algorithm into clusters, which represented different states of the process. At the final stage, the reference vectors of the map and the centre vectors of the clusters were used for subtraction analysis to indicate differences of the process states. The results show that the method presented here can be an efficient way to analyze the data of an activated sludge treatment process.

Suggested Citation

  • Heikkinen, M. & Poutiainen, H. & Liukkonen, M. & Heikkinen, T. & Hiltunen, Y., 2011. "Subtraction analysis based on self-organizing maps for an industrial wastewater treatment process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(3), pages 450-459.
    • Handle: RePEc:eee:matcom:v:82:y:2011:i:3:p:450-459
    DOI: 10.1016/j.matcom.2010.10.021
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    References listed on IDEAS

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    1. Räsänen, Teemu & Ruuskanen, Juhani & Kolehmainen, Mikko, 2008. "Reducing energy consumption by using self-organizing maps to create more personalized electricity use information," Applied Energy, Elsevier, vol. 85(9), pages 830-840, September.
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