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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Tsutomu Hoshino*
Nuclear Science and Engineering | Volume 49 | Number 1 | September 1972 | Pages 59-71
Technical Paper | doi.org/10.13182/NSE72-A22527
Articles are hosted by Taylor and Francis Online.
The optimization of the multistage refueling decision process was studied by the heuristic approach for four-region batch refueling including shuffling. The current state of the process is assumed to be well characterized by so called heuristic features, as excess reactivity or peaking factor. The features are summed up with each weight, defining the decision evaluation function which should be maximized at each refueling by the best decision. Thus the final criterion, i.e., the average discharge burnup at the end of the whole reactor life can be regarded as a function of the weight and is maximized in the weight space by the hill climbing algorithm. The approach can also be interpreted as an attempt to determine, through learning, the general importances of the rules of thumb in the refueling policy as maximization of the excess reactivity or power flattening. A numerical simulation is given, and the maximum burnup, the refueling scheme, and the optimal weight are discussed in relation to the power-peaking factor constraint. Though the method is not guaranteed as for the optimality, reasonable solutions are obtained and the intuitive understanding of the process is possible by discussing the optimum weight of each rule.