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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.
E. A. Schneider, U. B. Phathanapirom
Nuclear Technology | Volume 193 | Number 3 | March 2016 | Pages 416-429
Technical Paper | doi.org/10.13182/NT15-6
Articles are hosted by Taylor and Francis Online.
This paper introduces VEGAS, a lightweight and fast-executing fuel cycle simulator primarily intended to augment higher-fidelity fuel cycle simulators. VEGAS can act as a preconditioner as well as a platform for implementing endogenous decision making within these simulators. In preconditioner mode, VEGAS offers an iterative method for accelerating the convergence of fuel cycle optimization problems while ensuring that material balance and other constraints are met. The methodology utilized by VEGAS, particularly its mass balance and reactor deployment algorithms, is presented here. Unique to the VEGAS simulator is a rollback feature that undoes reactor build decisions if material balance constraints are violated. Included in the VEGAS code is an economics package, also documented here, that calculates the evolving levelized cost of electricity. Benchmark comparisons against the VISION (Verifiable Fuel Cycle Simulation Model) simulator are also presented, along with an example of VEGAS’s preconditioning capability in which the objective is to achieve a target system transuranics inventory while installing as little reprocessing capacity as possible.