Evolving Fuzzy Inferential Sensors for Process Industry.

Angelov, Plamen and Kordon, Arthur and Zhou, Xiao (2008) Evolving Fuzzy Inferential Sensors for Process Industry. In: 3rd International Workshop on Genetic and Evolving Systems, 2008. GEFS 2008. IEEE, Witten-Bomerholz, Germany, pp. 41-46. ISBN 978-1-4244-1612-7

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This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts ageing, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by The Dow Chemical Company, USA. (c) IEEE Press

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29 Oct 2008 14:20
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20 Sep 2023 02:13