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Self-generation of Control Rules Using Hierarchical and Nonhierarchical Clustering for Coagulant Control of Water Treatment Plants

  • Hyeon Bae
  • Sungshin Kim
  • Yejin Kim
  • Chang-Won Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

Abstract

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator’s knowledge for plant control. In this study, statistical indices were used to determine cluster numbers and seed points from hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

Keywords

Water Treatment Plant Seed Point Control Rule Rule Extraction Aluminum Sulfate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Black, A.P., Hamnah, S.A.: Electrophoretic Studies of Turbidity removal Coagulant with Aluminum Sulfate. J. AWWA 53, 438 (1961)Google Scholar
  2. 2.
    Kwang, J.O.: Principle and Application of Physical and Chemical Water Treatment, pp. 192–209. Gisam publisher (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hyeon Bae
    • 1
  • Sungshin Kim
    • 1
  • Yejin Kim
    • 2
  • Chang-Won Kim
    • 2
  1. 1.School of Electrical and Computer EngineeringPusan National UniversityBusanKorea
  2. 2.Dept. of Environmental EngineeringPusan National UniversityBusanKorea

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