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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.
Aku Itälä, Markus Olin
Nuclear Technology | Volume 174 | Number 3 | June 2011 | Pages 342-352
Technical Paper | TOUGH2 Symposium / Radioactive Waste Management and Disposal | doi.org/10.13182/NT11-A11744
Articles are hosted by Taylor and Francis Online.
Finnish spent nuclear fuel final disposal is planned to be based on the Kärnbränslesäkerhet 3-Vertical concept, which was originally planned for fractured crystalline bedrock. Within this concept, the role of the bentonite buffer is considered central. The aim of the study was to model the evolution of the final repository during the thermal phase (heat-generating period of spent fuel) when the bentonite is initially only partially saturated. There is an essential need to determine how temperature influences saturation and how both of these factors affect the chemistry of bentonite.In this study the Long-Term Test of Buffer Materials A2 parcel test at the Äspö hard rock laboratory in Sweden was modeled using TOUGHREACT code. The results focused on the following phenomena occurring in the bentonite: cation exchange, changes of bentonite pore water, mineral alterations, saturation, and pressure changes in bentonite buffer.The results show similarity with experimental data. However, the results are open to questions, and further study is needed to confirm the validity of the results. Differences between modeled and experimental results can be explained, for example, so that the experimental results are not from the fracture position as our one-dimensional model assumes.