Improving feedwater cross-correlation flow measurements in nuclear power plants with artificial neural networks

Da Ruan, Davide Roverso, Paolo F. Fantoni

    Research outputpeer-review


    One of the primary objectives of the power plant industry has long been the efficient operation of plant systems, thus reducing the cost of electricity production. The operating thermal power of a nuclear reactor is limited by the Nuclear Authority licensing requirements. Therefore, the thermal power of the plant should be determind very accurately. The feedwater flow rate is one of the major quantities used in determining the thermal power, and Venturi meters are traditionally used to measure this flow rate. Venturi flow meters are subject to known accuracy problems, mainly originating from the gradual build-up of fouling in the Venturi constriction, which leads to an overestimation of the produced thermal power, and a consequenct forced "derating" of the plant in order to stay within authority limits. This paper reports on early progress of a feasibility study of a computational intelligence approach to the enhancement of the accuracy of cross-correlation flow measurements as a potential solution to the problem of Venturi fouling.
    Original languageEnglish
    Title of host publicationComputational Intelligent Systems for Applied Research
    Subtitle of host publicationProceedings of the 5th International FLINS Conference , Gent, Belgium , 16 – 18 September 2002
    PublisherWorld Scientific Publishing
    Number of pages9
    ISBN (Electronic)9789814488167
    ISBN (Print)9789812380661
    StatePublished - 2002
    Event2002 - 5th International FLINS Conference: Computational Intelligent Systems for Applied Research - Ghent university, Gent
    Duration: 16 Sep 200218 Sep 2002


    Conference2002 - 5th International FLINS Conference

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