[2A16-19] Waste Management 3

[2A17] A Study of Statistical Planning Method for Analysis Consistent with the Characteristics of the Fukushima Daiichi NPS Wastes

(2)Calculation of the Optimal Sample Size Using the Bayesian Inference

*Takuma Horita^1,2, Maya Akimoto^1,2, Anna Nagai^1,2, Keiichi Ohki^1,2, Caroline Pyke³, Peter Hiller³, Yoshikazu Koma^1,2 (1. JAEA, 2. IRID, 3. National Nuclear Laboratory Limited)

Keywords:Data Quality Objectives Process, Bayesian Inference, Fukushima Daiichi Nuclear Power Station, Radioactive Waste Analysis Planning, Sample Size, mcmc Sample

In order to streamline the characterization of radioactive wastes generated by the decommissioning of the Fukushima Daiichi Nuclear Power Station (1F) for the treatment and disposal, we are considering an efficient planning method for analysis. The planning method is combined the Data Quality Objectives Process (DQO process), which defines the quality control method for collecting environmental analysis data, and the Bayesian inference, which is a statistical method. Calculations using the Bayesian inference allow us to probabilistically evaluate the sample size required to obtain the desired results. In other words, unlike conventional tests conducted with frequency statistics, the sample size can be obtained along with the probability, and thus the sample size can be flexibly planned based on the probability. In this presentation, we will report on a method for calculating the sample size for two purposes: (1) comparison with the reference value for disposal, and (2) population classification according to disposal properties.