The continued safe and efficient operation of nuclear power plants (NPPs) is crucial for meeting the energy demands of a growing population while simultaneously reducing greenhouse gas emissions. As nuclear plants age, the management and modernization of critical assets become increasingly complex and vital. This complexity underscores the pressing need for innovative technologies that can address the unique challenges of nuclear plant asset management. Aligned with the DOE's mission to ensure America's security and prosperity by addressing its energy, environmental, and nuclear challenges, this panel aims to explore the integration of Artificial Intelligence (AI) and machine learning (ML)-driven Digital Twin (DT) into Nuclear Plant Asset Management & Modernization. The focus will be on both technological advancements and the economic implications of these innovations while considering the aspects below: 1. Predictive Maintenance & Degradation Monitoring 2. Integration of Heterogeneous Data Sources and interpretability 3. Economic Optimization: Return on Investment 4. Compliance and Regulatory Considerations 5. Collaborative Strategies with DOE This panel aims to bridge the gap between technological innovation and practical implementation, offering a comprehensive view of how AI/ML/DT can revolutionize nuclear plant asset management. By fostering dialogue between industry experts, policymakers, researchers, and other stakeholders, we hope to set the stage for a new era of efficiency, safety, and economic viability in nuclear plant management.


  • Vivek Agarwal (Idaho National Lab)
  • Richard B. Vilim (Argonne National lab)
  • Raj Iyengar (U.S. Nuclear Regulatory Commission)


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