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.

This content is only available via PDF.
You do not currently have access to this content.