Abstract
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 language | English |
|---|---|
| Title of host publication | 22nd International Conference on Nuclear Engineering |
| Subtitle of host publication | Nuclear Education, Public Acceptance and Related Issues; Instrumentation and Controls; Fusion Engineering; Beyond Design Basis Events |
| Publisher | American Society of Mechanical Engineers (ASME) |
| Number of pages | 5 |
| Volume | 6 |
| ISBN (Print) | 978-0-7918-4596-7 |
| DOIs | |
| Publication status | Published - 2014 |
| MoE publication type | A4 Article in a conference publication |
| Event | 22nd International Conference on Nuclear Engineering, ICONE 22 - Prague, Czech Republic Duration: 7 Jul 2014 → 11 Jul 2014 |
Conference
| Conference | 22nd International Conference on Nuclear Engineering, ICONE 22 |
|---|---|
| Abbreviated title | ICONE22 |
| Country/Territory | Czech Republic |
| City | Prague |
| Period | 7/07/14 → 11/07/14 |
Keywords
- data reconciliation
- estimation uncertainty