AI Nuclear Fuel Cycle Optimization

Machine learning for nuclear fuel management and cycle optimization

The Problem

“Reduce Nuclear Fuel Costs While Ensuring Safety”

Organizations face these key challenges:

High-dimensional optimization across enrichment, burnable absorbers, loading patterns, cycle length, and outage constraints with limited ability to explore alternatives

Market volatility and long lead times (uranium, conversion, enrichment, fabrication) create forecasting and contracting risk that traditional static plans cannot manage well

Expensive and time-consuming physics/economics simulations and siloed data (core performance, procurement, inventory, regulatory limits) slow decisions and increase conservatism

Impact When Solved

Lower levelized fuel cost by optimizing enrichment/assay and reload design under safety margins (1–3% fuel-cycle savings)Increase generation and reduce replacement power exposure through improved cycle length and outage alignment (0.1–0.3 pp capacity factor gain)Faster planning cycles and better governance with auditable, constraint-aware recommendations (10–30% reduction in engineering cycle time)