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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NECX debut: Shaping the next era of energy
The sold-out inaugural Nuclear Energy Conference & Expo (NECX) got off to a roaring start in Atlanta, Ga., Tuesday morning with an opening plenary that was a live highlight reel discussing the latest industry achievements.
Starting with a lively promo video that left the audience amped up for Entergy’s CEO and NEI chair Drew Marsh, who welcomed everyone to the event, hosted jointly by the American Nuclear Society and the Nuclear Energy Institute. He spoke to a full house of more than 1,300 attendees, promising a blend of science, technology, policy, and advocacy centered around the future of nuclear energy.
Ryan M. Spangler, Mahsa Raeisinezhad, Daniel G. Cole
Nuclear Technology | Volume 210 | Number 12 | December 2024 | Pages 2331-2345
Research Article | doi.org/10.1080/00295450.2024.2377034
Articles are hosted by Taylor and Francis Online.
This paper presents research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance aimed at reducing lifetime maintenance and repair costs. Additionally, a focal point of this research is to make the decisions explainable to operators, improving the trustworthiness of the decisions from what can be considered a black box model. In this work, we develop and evaluate an explainable, online asset management methodology to help reduce lifetime maintenance and repair costs. Using the latest advancements in condition monitoring, inventory management, deep reinforcement learning, and explainable artificial intelligence methods, we create a predictive maintenance methodology that can optimize the maintenance and spare part management of a repairable nuclear power plant system.
To demonstrate these methods, preliminary studies were conducted on a representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Using deep reinforcement learning, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies for the chosen system. A key component of our methodology is the integration of Shapley values to quantify the contribution of various factors to the decision-making process. This addition enhances the explainability and trustworthiness of our decisions, providing operators with transparent and understandable insights into the rationale behind maintenance strategies. The robustness and resiliency of our decision policy against observation noise were also thoroughly evaluated, demonstrating its effectiveness in uncertain operational environments.