Sampling strategy and statistical analysis for radioactive waste characterization

10/18/2019
by   Nadia Perot, et al.
0

This paper describes the methodology we have developed to define a sampling strategy adapted to operational constraints in order to characterize the dihydrogen flow rate of 2714 nuclear waste drums produced by radiolysis reaction of organic mixed with α-emitters. The objective was to perform few but relevant measurements. Thus, a sample of only 38 drums has been selected to be measured. Statistical analysis of drum measurement data of dihydrogen rate provided an estimation of the mean and the upper bound of the physical quantity of interest which gave a good convergence with global measurements from the ventilation system of the facility. Thereafter, performing a factorial data analysis has demonstrated the representativeness of the measurement data set and the sampling strategy assumption validity. Moreover, it provided information that has been used for a regression analysis to develop a linear prediction model of dihydrogen flow rate production for the waste drum characterization.

READ FULL TEXT
research
12/21/2018

Uncertainty evalutation through data modelling for dimensional nanoscale measurements

A major bottleneck in nanoparticle measurements is the lack of comparabi...
research
07/17/2019

Optimal Sampling for Generalized Linear Models under Measurement Constraints

Suppose we are using a generalized linear model to predict a scalar outc...
research
02/15/2019

A Performance Study of a Fast-Rate WLAN Fingerprint Measurement Collection Method

Indoor positioning systems exploiting WLAN signal measurements such as R...
research
12/09/2020

A Sampling Type Method in an Electromagnetic Waveguide

We propose a sampling type method to image scatterer in an electromagnet...
research
07/08/2012

Keeping greed good: sparse regression under design uncertainty with application to biomass characterization

In this paper, we consider the classic measurement error regression scen...
research
04/08/2019

Generalized active learning and design of statistical experiments for manifold-valued data

Characterizing the appearance of real-world surfaces is a fundamental pr...
research
05/31/2017

The ALAMO approach to machine learning

ALAMO is a computational methodology for leaning algebraic functions fro...

Please sign up or login with your details

Forgot password? Click here to reset