| Project Detail |
Although nuclear power is one pathway to achieving a zero-carbon grid, nuclear power plants are comparatively cost-intensive in some markets. Many industries are employing AI, advanced data analytics, distributed computing, powerful physics simulation tools, and other breakthroughs to advance autonomous, efficient, and low-cost O&M in their processes. O&M is approximately 80% of a reactor’s total generating cost. The nuclear energy industry has not fully explored these innovations, necessitating new designs of effective and low-cost advanced reactor O&M procedures. Knowledge gained from innovating now can lay the groundwork for optimal O&M. GEMINA sets the stage for advanced reactors to operate with a staffing plan and fixed O&M costs more akin to those of a combined cycle natural gas plant than those of the legacy light-water reactor fleet. Project Innovation + Advantages: X-energy’s digital twin project aims to reduce the fixed O&M cost of its advanced nuclear reactor design to $2/MWh. The project will use human factors engineering, probabilistic risk assessment, hazard analysis, and security and maintenance evaluations to identify areas for optimization. X-energy will develop innovative ways to leverage advanced technologies—including automation, robotics, remote and centralized maintenance, and monitoring—to optimize staffing plans while ensuring optimal plant operation. The team will develop two virtual modeling frameworks to evaluate and validate these solutions. X-energy’s Immersive Environment Toolset is a multi-disciplinary 3D model that, when combined with virtual reality, will test maintenance concepts and techniques and optimize maintenance and security staff. The digital twin framework synthesizes information from the operating plant, past operating history, and planned future evolutions as well as assimilating data from plants across the fleet to provide a holistic understanding of the plant. This includes the health and remaining lifetime of critical plant components and identifies predictive maintenance needs of physical assets. |
