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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.
A. V. Kiryukhin, E. P. Kaymin, E. V. Zakharova
Nuclear Technology | Volume 164 | Number 2 | November 2008 | Pages 196-206
Technical Paper | Tough206 | doi.org/10.13182/NT08-A4019
Articles are hosted by Taylor and Francis Online.
TOUGHREACT V1.0 modeling was used to reproduce laboratory tests involving sandstone samples collected from a deep radionuclide repository site at the Siberia Chemical Plant, Seversk, Russia. Laboratory tests included injection of alkaline fluids into sandstone samples at 70°C. Some minerals were constrained in the model to precipitate or dissolve, according to laboratory test results. Modeling results were compared with observed test data (mineral phase changes, transient concentration data at the outlet of a sample column). Reasonable agreement was obtained between calculated and measured mineral phases (Na-smectite and kaolinite precipitation, quartz, microcline, chlorite, and muscovite dissolution). After a cation exchange option was used in the model, the most abundant secondary mineral generated was dawsonite, which corresponds to sodium carbonates observed in the sample after an injection test. Time-dependent chemical concentrations (transient chemical concentration data) at the outlet of the sample column qualitatively matched the data observed.