Knowledge-elicitation and data-mining: Fusing human and industrial plant information

  • Authors:

  • W. Browne;L. Yao;I. Postlethwaite;S. Lowes;M. Mar

  • Affiliations:

  • Department of Cybernetics, University of Reading, Reading, RG6 6AY, UK;Department of Engineering, University of Leicester, Leicester, LE1 7RH, UK;Department of Engineering, University of Leicester, Leicester, LE1 7RH, UK;Alcoa, Europe, Flat Rolled Products, Kitts Green, Birmingham, B33 9QR, UK;Alcoa, Europe, Flat Rolled Products, Kitts Green, Birmingham, B33 9QR, UK

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2006

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Abstract

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.