1. Sampling strategy and statistical analysis for radioactive waste characterization
- Author
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Nadia Perot, Dominique Carré, Ingmar Pointeau, Alexandre Le Cocguen, Anne Duhart-Barone, Hervé Lamotte, Laboratoire d'études et de modélisations des systèmes (LEMS), CEA Cadarache, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service d'Etudes des Systèmes Innovants (SESI), Département Etude des Réacteurs (DER), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Laboratoire des Sciences de l'Information et de la Communication (LabSIC), Université Paris 13 (UP13)-Université Sorbonne Paris Cité (USPC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de Modélisation des Transferts dans l'Environnement (LMTE), Service Mesures et modélisation des Transferts et des Accidents graves (SMTA), Département Technologie Nucléaire (DTN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Département Technologie Nucléaire (DTN)
- Subjects
FOS: Computer and information sciences ,Nuclear and High Energy Physics ,Factorial ,risk analysis ,020209 energy ,FOS: Physical sciences ,Sample (statistics) ,02 engineering and technology ,Drum ,01 natural sciences ,Upper and lower bounds ,010305 fluids & plasmas ,Methodology (stat.ME) ,statistical analysis ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,Safety, Risk, Reliability and Quality ,Process engineering ,Waste Management and Disposal ,Statistics - Methodology ,business.industry ,Mechanical Engineering ,Radioactive waste ,Sampling (statistics) ,Regression analysis ,sampling strategy ,radioactive waste characterisation ,Data set ,Nuclear Energy and Engineering ,Physics - Data Analysis, Statistics and Probability ,Environmental science ,business ,Data Analysis, Statistics and Probability (physics.data-an) ,[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an] - Abstract
International audience; 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.
- Published
- 2019