Markov Decision Processes for Intelligent, Risk-Informed Asset-Management Decision-Making

Conference · Sun Jun 09 04:00:00 EDT 2024

DOI:https://doi.org/10.13182/T130-43354· OSTI ID:2356793

Grabaskas, David; Zhao, Yunfei; Smidts, Carol; Moiseytseva, Vera; Ponciroli, Roberto; Brocheny, Pascal

Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Nuclear Energy - Nuclear Energy Enabling Technologies (NEET)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 2356793
• Country of Publication: United States
• Language: English