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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.
Taek Kyum Kim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 127 | Number 3 | November 1997 | Pages 346-357
Technical Paper | doi.org/10.13182/NSE97-A1940
Articles are hosted by Taylor and Francis Online.
A new optimization method is presented for determining the optimized pressurized water reactor (PWR) fuel-loading pattern in the maximization principle of the end-of-cycle (EOC) core reactivity. The new method utilizes the point reactivity model in deriving the objective function corresponding to the EOC core reactivity as a linear function of the fuel-loading binary variables. It also uses a mixed integer programming algorithm consisting of the branch and bound method and dual linear programming algorithm in order to maximize the EOC core reactivity. The utility of the new optimization method is discussed in terms of numerical examples for the fuel-loading-pattern optimization of the cycle 4 core of the Yonggwang unit 2 PWR plant.
