Fuzzy clustering based models applied to petroleum processes

Miguel Angel Ramirez Canelon, Eliezer Colina Morles

Research output: Contribution to journalArticlepeer-review


The application of fuzzy clustering techniques has recently become in a very useful alternative in the area of modeling and identification of complex industrial processes. In particular, fuzzy clustering techniques such as Fuzzy C-Means and the Gustafson-Kessel (GK) algorithms will be analyzed and applied in details in this paper. These algorithms will be implemented in the construction of Takagi-Sugeno fuzzy models for the gas-liquid separation process, the water-oil separation process and the oil-heating process, which are important processes in the oil industry. Validations of the obtained fuzzy models will be performed and some conclusions will be established.

Original languageEnglish (US)
Pages (from-to)159-171
Number of pages13
JournalWSEAS Transactions on Systems and Control
Issue number3
StatePublished - 2008
Externally publishedYes


  • Artificial lift production methods
  • Fuzzy C-means
  • Fuzzy clustering
  • Gustafson-Kessel (GK) algorithm
  • Least-squares method
  • Production separator
  • Washing-tanks and fired heaters

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization


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