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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.
Yanzi Liu, Xuegang Zhang, Gang Zhang, Jianjun Jiang, Li Zhang, Hong Hu, Tao Qing, Yanhua Zou, Dan Yang, Liaozi Xi, Fan Tang, Ming Jia, Yiqian Wu, Zhiyao Liu
Nuclear Technology | Volume 207 | Number 1 | January 2021 | Pages 74-93
Technical Paper | doi.org/10.1080/00295450.2020.1733376
Articles are hosted by Taylor and Francis Online.
The digital control system (DCS)+state-oriented procedure (SOP) system adopted by China’s Ling’ao Phase II nuclear power plant’s main control room requires changes to the cognitive process, behavior mode, and error mode while triggering new human factors. Therefore, in this paper we present a cognitive reliability model for the DCS+SOP system in the Ling’ao Phase II Nuclear Power Plant’s main control room and conduct a human reliability analysis. The model is based on the cognitive process with respect to considering the coordinator’s accident recovery effect and obtaining the method of calculating cognitive reliability. We determine impact factors for the three cognitive stages of the operator’s and the coordinator’s diagnosis, decision making, and operation. We obtain the operator’s and the coordinator’s weights for each process through an analytic hierarchy process. Using methods of simulation and analyzing the experiment data, we obtain revised coefficients for the cognitive reliability model. Additionally, the trend of the simulation curve indicates the rationality of the model. Finally, we provide an example based on the proposed cognitive reliability model. The process of analyzing the example demonstrates that this method provides a feasible analysis method for the cognitive reliability of the DCS+SOP system in the main control room.