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- 저자 : Keiichi Ohki (검색결과 1 건)
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Radioactive Waste Sampling for Characterisation - A Bayesian Upgrade
Caroline K. Pyke,Peter J. Hiller,Yoshikazu Koma,Keiichi Ohki 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.1
Presented in this paper is a methodology for combining a Bayesian statistical approach with Data QualityObjectives (a structured decision-making method) to provide increased levels of confidence in analyticaldata when approaching a waste boundary. Development of sampling and analysis plans for the characterisationof radioactive waste often use a simple, one pass statistical approach as underpinning for thesampling schedule. Using a Bayesian statistical approach introduces the concept of Prior informationgiving an adaptive sample strategy based on previous knowledge. This aligns more closely with theiterative approach demanded of the most commonly used structured decision-making tool in this area(Data Quality Objectives) and the potential to provide a more fully underpinned justification than themore traditional statistical approach. The approach described has been developed in a UK regulatorycontext but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station todemonstrate how the methodology can be applied in this context to support decision making regardingthe ultimate disposal option for radioactive waste in a more global context
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