NRC Exploring Potential Role of Artificial Intelligence in Commercial Nuclear Power Operations

Morgan Lewis - Up & Atom
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Morgan Lewis - Up & Atom

As artificial intelligence (AI) and machine learning tools become more widely adopted in various products and industries, the NRC has begun studying what roles these technologies can play in commercial nuclear power operations. On April 21, as part of its study, the NRC’s Office of Nuclear Regulatory Research requested public comments on the role of these technologies “in the various phases of nuclear power generation operational experience and plant management.” The NRC requests feedback on “the state of practice, benefits, and future trends related to [these technologies’] computational tools and techniques in predictive reliability and predictive safety assessments in the commercial nuclear power industry.” These technologies “are emerging, analytical tools, which, if used properly, show promise in their ability to improve reactor safety, yet offer economic savings.” Comments are due by May 21, 2021.

The NRC intends to use the comments to enhance its understanding of the benefits of AI and machine learning as well as the “potential pitfalls and challenges associated with their application.”

The NRC has requested comments on the following questions:

  1. What is the status of the commercial nuclear power industry development or use of AI/machine learning tools to improve aspects of nuclear plant design, operations, or maintenance or decommissioning? What tools are being used or developed? When are the tools currently under development expected to be put into use?
  2. What areas of commercial nuclear reactor operation and management will benefit the most, and the least, from the implementation of AI/machine learning? Possible examples include, but are not limited to, inspection support, incident response, power generation, cybersecurity, predictive maintenance, safety/risk assessment, system and component performance monitoring, operational/maintenance efficiency, and shutdown management.
  3. What are the potential benefits to commercial nuclear power operations of incorporating AI/machine learning in terms of (a) design or operational automation, (b) preventive maintenance trending, and (c) improved reactor operations staff productivity?
  4. What AI/machine learning methods are either currently being used or will be used in the near future in commercial nuclear plant management and operations? Examples of possible AI/machine learning methods include, but are not limited to, artificial neural networks, decision trees, random forests, support vector machines, clustering algorithms, dimensionality reduction algorithms, data mining and content analytics tools, gaussian processes, Bayesian methods, natural language processing, and image digitization.
  5. What are the advantages or disadvantages of a high-level, top-down strategic goal for developing and implementing AI/machine learning across a wide spectrum of general applications versus an ad-hoc, case-by-case targeted approach?
  6. With respect to AI/machine learning, what phase of technology adoption is the commercial nuclear power industry currently experiencing and why? The current technology adoption model characterizes phases into categories such as the innovator phase, the early adopter phase, the early majority phase, the late majority phase, and the laggard phase.
  7. What challenges are involved in balancing the costs associated with the development and application of AI/machine learning tools against plant operational and engineering benefits when integrating AI/machine learning into operational decisionmaking and workflow management?
  8. What is the general level of AI/machine learning expertise in the commercial nuclear power industry (e.g., expert, well versed/skilled, or beginner)?
  9. How will AI/machine learning affect the commercial nuclear power industry in terms of efficiency, costs, and competitive positioning in comparison to other power generation sources?
  10. Does AI/machine learning have the potential to improve the efficiency and/or effectiveness of nuclear regulatory oversight or otherwise affect regulatory costs associated with safety oversight? If so, in what ways?
  11. AI/machine learning typically necessitates the creation, transfer, and evaluation of very large amounts of data. What concerns, if any, exist regarding data security in relation to proprietary nuclear plant operating experience and design information that may be stored in remote offsite networks?

The NRC is in the early stages of its review, and the agency does not promise to use the information collected in any formal regulatory action. Morgan Lewis will continue to follow the NRC’s regulatory initiatives.

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DISCLAIMER: Because of the generality of this update, the information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

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