Illustrative Examples
Disclaimer: These illustrative examples demonstrate potential outcomes based on industry capabilities. The following examples represent typical results that can be achieved through advanced AI-driven nuclear operations and radiation protection techniques. Contact us to discuss how these capabilities could apply to your specific needs.
Outage Dose Reduction
Industry capability example: Potential for commercial BWR facilities to reduce collective dose by 42% (from 12.5 to 7.25 person-rem) and outage duration by 3 days during planned refueling outages using real‑time dosimetry systems, digital twin simulation, and AI‑optimized outage planning tools. Pre-outage modeling can identify high-dose activities and enable job redesign, with potential savings of $2.1M from reduced outage days and avoided dose costs. These represent typical industry results achievable with advanced AI-driven solutions.
Decommissioning Success
Potential outcome: Legacy research reactor decommissioning and site remediation projects can achieve cost reductions of 23% (potential $4.8M savings) while maintaining full regulatory compliance and exemplary worker safety. Predictive analytics platforms have the capability to optimize waste characterization workflows, potentially reducing characterization time by 40%. AI-assisted waste sorting can minimize disposal volumes, with potential additional savings of $1.2M in waste disposal fees. These results represent industry benchmarks for 18-month decommissioning projects.
AI‑Driven Predictive Maintenance
Industry capability: AI platforms have the potential to predict critical equipment failures at research reactor facilities, potentially avoiding unexpected shutdowns with savings of up to $3.7M in lost productivity and emergency repairs. By integrating digital twin models with machine learning on sensor data, these systems can provide actionable insights an average of 22 days in advance of potential failures. Capabilities include identifying bearing degradation, pump seal wear, and cooling system anomalies before they reach critical thresholds. These metrics represent typical performance achievable with advanced predictive maintenance systems.