A decision-support approach to severe accident management in nuclear power plants. (18th August 2020)

• Record Type: Journal Article
• Authors: Bohanec, Marko; Vrbanić, Ivan; Bašić, Ivica; Debelak, Klemen; Štrubelj, Luka
• Is Part Of: Journal of decision systems. Volume 29(2020)Supplement 1
• Page Start: 438
• Page End: 449
• Publication Date: 2020-08-18
• Subjects: Severe accident management -- nuclear power plant -- decision support -- decision model -- qualitative rule-based multi-criteria model -- method DEX; Decision support systems -- Periodicals; Management information systems -- Periodicals; Information resources management -- Periodicals; Information storage and retrieval systems -- Periodicals; Management -- Communication systems -- Periodicals; Decision support systems; Information resources management; Information storage and retrieval systems; Management -- Communication systems; Management information systems; Periodicals; 658.40305
• DOI: 10.1080/12460125.2020.1854426
• Languages: English
• ISSNs: 1246-0125

Abstract

ABSTRACT: Nuclear power plants produce electricity with relatively low operational costs and low impacts on the environment, but are also associated with the fear of severe accidents. These are extremely rare, but once they occur, they put an immense pressure on the accident management team. In this paper, we report about an ongoing development of a decision support system called Severa, aimed at supporting the decision-making team during the course of an accident or a training exercise. The software is being developed in the context of the EU H2020 project NARSIS. The system assesses the plant damage state, predicts possible accident progressions and assesses available management actions and their consequences. Two already implemented modules are presented: a monitoring and radioactive-release assessment module. The methodological approach primarily relies on qualitative rule-based multi-criteria models, but also includes other techniques: data analysis, probabilistic safety assessment and event-tree modelling.