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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.
Yasushi Seki, Isao Aoki, Shuzo Ueda, Satoshi Nishio, Ryoichi Kurihara, Takashi Tabara
Fusion Science and Technology | Volume 34 | Number 3 | November 1998 | Pages 353-357
Fusion Economics and Reactor Studies | doi.org/10.13182/FST98-A11963639
Articles are hosted by Taylor and Francis Online.
The radwaste generated from three fusion power reactors using ferritic steel, V-alloy and SiC/SiC composite were classified into low level waste (LLW) which can be disposed by shallow land burial (SLB) and medium level waste (MLW) which cannot be disposed by SLB because one or more of the radionuclides exceeds the derived limiting concentration value. When the recently developed FENDL/A2.0 library is used, the SLB fraction became 91% for ferritic steel, 36% for V-alloy and 65% for SiC/SiC. It is found that if the Nb impurity content in V-. alloy and N impurity content in SiC/SiC could be reduced to 1/100 (0.15 Wt.ppm) and 1/20 (5times10−4 Wt.%), respectively, the SLB fraction becomes nearly 100% for both materials. On the other hand, the alloying element W content needs to be reduced to further increase the SLB fraction in case of the ferritic steel F82H.