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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.
Man Gyun Na
Nuclear Science and Engineering | Volume 138 | Number 3 | July 2001 | Pages 305-314
Technical Paper | doi.org/10.13182/NSE01-A2216
Articles are hosted by Taylor and Francis Online.
A receding horizon control method is applied to the axial power distribution control in a pressurized water reactor. The basic concept of receding horizon control is to solve on-line, at each sampling instant, an optimization problem for a finite future and to implement the first optimal control input as the current control input. Thus, it is a suitable control strategy for time-varying systems. The reactor model used for computer simulations is a two-point xenon oscillation model based on the nonlinear xenon and iodine balance equations and a one-group, one-dimensional, neutron diffusion equation with nonlinear power reactivity feedback that adequately describes axial oscillations and treats the nonlinearities explicitly. The reactor core is axially divided into two regions, and each region has one input and one output and is coupled with the other region. Through numerical simulations, it is shown that the proposed control algorithm exhibits very fast tracking responses due to the step and ramp changes of axial target shape and also works well in a time-varying parameter condition.