Impact of Bias-Correction on Measurement Uncertainties in the Statistical Evaluation of Material Balances

摘要

Nuclear material is repeatedly measured as it moves within and between material balanceareas (MBA’s). Since different measurement instruments or methods might be used on the samematerials at different measurement points, relatively small measurement biases can haveconsiderable impact on material balances, in particular, for MBA’s that process significant materialamounts. Such MBA’s might realize statistically significant inventory differences (ID’s), orunacceptably large ID uncertainties, as a result of accumulated differences due to minor biasesbetween measurement methods.Bias-correction can be used to reduce such measurement biases. How this bias-correction affectsthe uncertainties of the measurement process should be understood and incorporated into theevaluation of the statistical significance of ID’s. In this work, the impact of bias-correction onmeasurement uncertainty is discussed for several types of measurement error models.Of primary interest, a relatively complex case is considered where a more capable, more expensivemeasurement method is used to estimate the bias of a less expensive, less capable method. Thissituation can arise when the more capable method is simply too expensive in terms of cost or timefor routine use while the less capable method, when used in conjunction with the superior method,is sufficient to meet accuracy and precision requirements. In addition, the lack of applicablemeasurement standards for the inferior method necessitates the use of measurements of actualprocess material for establishing instrument performance.In this latter application, several initial measurements of the same process materials are made withboth the more capable and less capable methods. The estimated bias between the methods is thenused to bias-correct subsequent process material measurements made using the less capable method.The measurement uncertainties associated with both measurement methods are then incorporatedinto the uncertainty associated with the bias-corrected result.