A risk-informed safety significance categorization (RISSC) is used in risk-informed regulation and application (RIRA) to allocate the structures, systems, or components (SSCs) into two or more groups, according to their safety significance using both probabilistic and deterministic insights. In this work, both the probabilistic and the deterministic insights are based on level 1 and 2 internal event probabilistic safety assessments (PSAs) and normal operating conditions. We use both the importance analyses of PSAs and the qualitative assessments based on the deterministic insights as decision factors for the decision problem RISSC.
In this work, we proposed a method for risk-informed safety significance categorization using the analytic hierarchy process (AHP) and Bayesian belief networks (BBN). This method introduces early participatory of an expert panel by using the AHP and the BBN to improve the overall decision-making process and perform integrated evaluation quantitatively. The early participatory of the expert panel is applied to perform the RISSC in an early stage on the basis of the expert’s knowledge and experience; the AHP to structure the decision problem RISSC with the decision factors and to obtain the relative importance of the decision factors, that is, the weighting values of the decision factors; the BBN to evaluate the probabilities that a component falls in Hi (high safety significant category) in the respective decision factors as safety significances in the respective decision factors. The weighting values of the decision factors and the probabilities that a component falls in Hi in the respective decision factors are integrated into the total probability. The total probability is used as the final safety significance for the decision-making in the RISSC: if the total probability is greater than 0.6, the safety significance category of the component is High Safety Significance Category (HSSC); if the total probability is smaller than 0.6 and greater than 0.4, the safety significance category of the component is Potentially high Safety Significance Category (PSSC); if the total probability is smaller than 0.4, the safety significance category of the component is Low Safety Significance Category (LSSC).
The proposed method was applied to the components, such as CC-V073, CV-V530, and SI-V644 in Ulchin Unit 3. The expert panel consisted of two PSA experts and one system design expert. Before categorizing the components, the design basis functions, simplified P& IDs, and the FV and the RAW values of the PSA were prepared for the experts’ evaluations. By using this method, we could categorize the components quantitatively on the basis of experts’ knowledge and experience in an early stage. In addition, the quantitative results of the applications, that is, the weighting values of the decision factors and the probabilities used as safety significances in the respective decision factors were found to be useful to the presentation and documentation of the bases of the final decisions in the RISSC.
위험도 정보이용 안전 중요도 분류는 PSA의 확률론적 정보와 결정론적인 정보를 이용하여 구조, 계통 또는 기기를 안전중요도에 따라 2개 혹은 그 이상의 카테고리로 분류하는 것이다. 이 연구에서는 기존방법의 단점들을 개선하고 효과적으로 분류를 수행 할 수 있는 방법으로 AHP와 BBN을 이용한 위험도 정보이용 안전 중요도 분류 방법이 제안되었다. 이 방법에서는 전체 의사결정과정을 개선시키고 종합적인 평가를 정량적으로 수행하기 위하여 AHP와 BBN을 이용한 전문가들의 조기참여를 도입하였다. 제안된 방법의 합리성을 증명하기 위해 울진 3호기의 CC-V073, CV-V530, SI-644에 적용해 보았다. 이 과정에 참여한 전문가는 PSA 전문가, 계통설계 전문가 그리고 저자였으며, 분류를 시작하기에 앞서 전문가들의 평가를 돕고자 PSA정보와 결정론적인 정보가 준비되었다. 적용결과 제안된 방법은 합리적인 결과를 보였으며, 기존방법의 단점들도 개선시킬수 있음을 알 수 있었다. 추가적으로 제안된 방법의 정량적인 결과들은 최종 분류결과의 판단근거로써 유용하게 사용될 수 있을것으로 판단되었다.
