Risk Informed Asset Management (RIAM)

This project started in October 2018 in response to the need to develop data analytics tools coupled with risk-informed methods to manage plant assets over periods of extended operation (including license renewal and second license renewal). The first application of this project targets replacement/refurbishment expenditures of plant capital assets (i.e., Structures, Systems and Components - SSCs) as part of the plant license renewal process. The objective is to optimize the SSC replacement/refurbishment schedule based on economic constraints, data uncertainties and SSC reliability data. We started our work by formalizing, from a mathematical perspective, the SSC optimization replacement schedule by identifying its requirements, degrees of freedom and constraints. We then proceeded to develop computational tools able to solve this class of problems. We proceeded in two development directions: the first direction consists of stand-alone algorithms designed to optimize the SSC replacement/refurbishment schedule while the second one consists of methods that evaluate the impact of data uncertainties (e.g., budget and costs) on the replacement/refurbishment schedule. The outcome of this project during FY19-20 has been the creation of a software tool which contains a library of methods that can be employed to solve SSC replacement/refurbishment schedule optimization problems. The developed methods integrate both safety/reliability and cost models in a single decision making tool, which also provides the user with data analysis capabilities to explore and analyze the generated solution. Several examples with increasing levels of complexity are presented and analyzed in detail in order to demonstrate the developed capabilities and tools. The objective is to present a pragmatic workflow that can be followed by plant management workers, which considers the type of analysis, the type of constraints, data uncertainties and provides the most suited method and computational tool to be employed. The intent of this workflow and tool is to provide nuclear plant and fleet decision-makers with the capability to effectively and efficiently evaluate long-term asset management strategies to select the most effective and profitable investment portfolio.

RIAM overview 2021

RIAM and PHM projects overview



Stochastic optimization
C. Wang, D. Mandelli, M. Abdo, A. Alfonsi, J. Cogliati, P. Talbot, S. Lawrence, C. Smith, D. Morton, I. Popova, S. Hess, C. Pope, J. Miller, S. Ercanbrack
Development and Release of the Methods and Tools for Risk-Informed Asset Management
Idaho National Laboratory Technical Report INL/EXT-21-63255 (2021)

Stochastic optimization
D. Mandelli, C. Wang, M. Abdo, K. Vedros, J. Cogliati, J. Farber, A. Al Rashdan, S. Lawrence, D. Morton, I. Popova, S. Hess, C. Pope, J. Miller, S. Ercanbrack
Industry Use Cases for Risk-Informed System Health and Asset Management
Idaho National Laboratory Technical Report INL-EXT-21-64377 (2021)

Stochastic optimization
D. Mandelli, C. Wang, S. StGermain, C. Smith, D. Morton, I. Popova, S. Hess
Combined Data Analytics and Risk Analysis Tool for Long Term Capital SSC Refurbishment and Replacement
Tech. Rep. INL-EXT-19-55819, Idaho National Laboratory (INL), 2019

Distributionally robust optimization
D. Mandelli, C. Wang, M. Abdo, A. Alfonsi, P. Talbot, J. Cogliati, C. Smith, D. Morton, I. Popova, S. Hess
Development and Application of a Risk Analysis Toolkit for Plant Resources Optimization
Tech. Rep. INL-EXT-20-59942, Idaho National Laboratory (INL), 2020

Methods overview