Technology Extraction of Expert Operator Skills from Process Time Series Data

  • Setsuya Kurahashi
  • Takao Terano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)


Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain complicated and active plant behaviors. To uncover complex plant behaviors, this paper proposes a new method of developing a process response model from continuous time-series data. The method consists of the following phases: (1) Reciprocal correlation analysis; (2) Process response model; (3) Extraction of control rules; (4) Extraction of a workflow; and (5) Detection of outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from a Learning Classifier System using the Minimum Description Length criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown its validity and effectiveness.


Time Series Data Improvement Rate Distillation Tower Tabu List Control Rule 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Setsuya Kurahashi
    • 1
  • Takao Terano
    • 2
  1. 1.University of TsukubaTokyoJapan
  2. 2.Tokyo Institute of TechnologyYokohamaJapan

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