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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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RIC session focuses on interagency collaboration
Attendees at last week’s 2026 Regulatory Information Conference, hosted by the Nuclear Regulatory Commission, saw extensive discussion of new reactor technologies, uprates, fusion, multiunit deployments, supply chain, and much more.
With the industry in a state of rapid evolution, there was much to discuss. Connected to all these topics was one central theme: the ongoing changes at the NRC. With massively shortened timelines, the ADVANCE Act and Executive Order 14300, and new interagency collaboration and authorization pathways in mind, speakers spent much of the RIC exploring what the road ahead looks like for the NRC.
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.