Data reconciliation of the turbine section: Evaluation of estimation uncertainty

Olli Saarela, Emil Wingstedt

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    Data reconciliation is technique for reducing measurement uncertainty by adjusting measured data to comply with a first principles process model, most importantly with mass and energy balances. It also provides estimates for modelled unmeasurable process variables and estimates for the uncertainties of the computed values. For computing these estimates the process model has to include estimates of measurement uncertainties defined a priori. A priori consideration of all potential sources of uncertainty is far from trivial. This paper discusses a data-driven approach of uncertainty evaluation, based on identifying and subtracting variability modes affecting multiple measurements. Possible bias in the measurements is not considered. The approach is applied to evaluate the uncertainties of estimates computed with a data reconciliation model of a turbine section of a nuclear power plant.
    Original languageEnglish
    Title of host publication22nd International Conference on Nuclear Engineering
    Subtitle of host publicationNuclear Education, Public Acceptance and Related Issues; Instrumentation and Controls; Fusion Engineering; Beyond Design Basis Events
    PublisherAmerican Society of Mechanical Engineers (ASME)
    Number of pages5
    ISBN (Print)978-0-7918-4596-7
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    Event22nd International Conference on Nuclear Engineering, ICONE 22 - Prague, Czech Republic
    Duration: 7 Jul 201411 Jul 2014


    Conference22nd International Conference on Nuclear Engineering, ICONE 22
    Abbreviated titleICONE22
    Country/TerritoryCzech Republic


    • data reconciliation
    • estimation uncertainty


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