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Akimoto, Maya; Horita, Takuma; Nagai, Anna; Oki, Keiichi; Pyke, C.*; Hiller, P.*; Koma, Yoshikazu
no journal, ,
We investigated the planning method for analysis consistent with the characteristics of the Fukushima Daiichi NPS Wastes (1F wastes), and suggested that the method combining the Data Quality Objectives (DQO) process, which is widely adopted in environmental remediation, and the Bayesian inference, is effective. To evaluate the applicability of the method to the 1F waste, we tried to apply the method by setting the objective to classify rubble, which has large individual differences and diverse properties. In this study, we calculated the sample size required to determine the classification by difference of contamination by alpha nuclides for the rubble collected from the reactor buildings. We report on analysis planning for the rubble from the reactor buildings using the DQO process and the Bayesian inference.
Nagai, Anna; Horita, Takuma; Akimoto, Maya; Oki, Keiichi; Hiller, P.*; Pyke, C.*; Koma, Yoshikazu
no journal, ,
We investigated the planning method for analysis consistent with the characteristics of the Fukushima Daiichi NPS Wastes (1F wastes), and suggested that the method combining the Data Quality Objectives (DQO) process and the Bayesian inference, is effective. To investigate the usefulness of the method, it was applied to the study of the analysis plan for 1F wastes. In this study, we assume the case of disposal of the sludge generated from the decontamination device, and discussed for the method with the objective of understanding the properties necessary for disposal. The sludge was assumed that after dewatering temporarily stored and then stabilization and sample size calculated considering the change in population due to the dewatering. We report on the study on analysis planning for the sludge generated from the decontamination device using the DQO process and the Bayesian inference.
Horita, Takuma; Akimoto, Maya; Nagai, Anna; Oki, Keiichi; Pyke, C.*; Hiller, P.*; Koma, Yoshikazu
no journal, ,
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.
Oki, Keiichi; Horita, Takuma; Nagai, Anna; Akimoto, Maya; Hiller, P.*; Pyke, C.*; Koma, Yoshikazu
no journal, ,
no abstracts in English
Hiller, P.*; Pyke, C.*; Koma, Yoshikazu; Oki, Keiichi
no journal, ,
Bayesian statistics is complementary to the DQO approach due to their underlying iterative principles. For waste characterisation this provides an opportunity for greater information for decision makers when analytical data approaches a waste boundary. The Bayesian t-test is analogous to the current statistical approach advised by CL:AIRE with the benefit of more completely using Prior information and allowing for the introduction of adaptive sampling strategies based on developing knowledge. This iterative approach provides a more fully underpinned justification for sampling numbers and provides increased flexibility for the DQO team than the traditional statistical approach. Developed in a UK regulatory context and translated to fallen trees from the Fukushima Daiichi NPS, this paper demonstrates potential benefits of this methods for a waste nearing the characterisation boundary and shows how the approach can be used to support decision making on waste disposal in a global context.