Nuclear Regulatory Commission.
Request for comment.
The U.S. Nuclear Regulatory Commission (NRC) is requesting public comment on the current state of commercial nuclear power operations relative to the use of artificial intelligence (AI) and machine learning (ML) tools.
Submit comments by May 21, 2021. Comments received after this date will be considered if it is practical to do so, but the Commission is able to ensure consideration only for comments received on or before this date.
You may submit comments by any of the following methods; however, the NRC encourages electronic comment submission through the Federal Rulemaking website:
Federal Rulemaking Website: Go to https://www.regulations.gov and search for Docket ID NRC-2021-0048. Address questions about Docket IDs in Regulations.gov to Stacy Schumann; telephone: 301-415-0624; email: Stacy.Schumann@nrc.gov. For technical questions, contact the individual listed in the FOR FURTHER INFORMATION CONTACT section of this document.
Mail comments to: Office of Administration, Mail Stop: TWFN-7-A60M, U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001, ATTN: Program Management, Announcements and Editing Staff.
For additional direction on obtaining information and submitting comments, see “Obtaining Information and Submitting Comments” in the SUPPLEMENTARY INFORMATION section of this document.
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FOR FURTHER INFORMATION CONTACT:
John C. Lane, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001, telephone: 301-415-2476, email: John.Lane@nrc.gov.
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I. Obtaining Information and Submitting Comments
A. Obtaining Information
Please refer to Docket ID NRC-2021-0048 when contacting the NRC about the availability of information for this action. You may obtain publicly available information related to this action by any of the following methods:
Federal Rulemaking Website: Go to https://www.regulations.gov and search for Docket ID NRC-2021-0048.
NRC's Agencywide Documents Access and Management System (ADAMS): You may obtain publicly available documents online in the ADAMS Public Documents collection at https://www.nrc.gov/reading-rm/adams.html. To begin the search, select “Begin Web-based ADAMS Search.” For problems with ADAMS, please contact the NRC's Public Document Room (PDR) reference staff at 1-800-397-4209, at 301-415-4737, or by email to firstname.lastname@example.org. The AI/ML general solicitation request for comment is also available in ADAMS under Accession No. ML21085A611.
Attention: The PDR, where you may examine and order copies of public documents, is currently closed. You may submit your request to the PDR via email at email@example.com or call 1-800-397-4209 or 301-415-4737, between 8:00 a.m. and 4:00 p.m. (EST), Monday through Friday, except Federal holidays.
B. Submitting Comments
The NRC encourages electronic comment submission through the Federal Rulemaking website (https://www.regulations.gov). Please include Docket ID NRC-2021-0048 in your comment submission.
The NRC cautions you not to include identifying or contact information that you do not want to be publicly disclosed in your comment submission. The NRC will post all comment submissions at https://www.regulations.gov as well as enter the comment submissions into ADAMS. The NRC does not routinely edit comment submissions to remove identifying or contact information.
If you are requesting or aggregating comments from other persons for submission to the NRC, then you should inform those persons not to include identifying or contact information that they do not want to be publicly disclosed in their comment submission. Your request should state that the NRC does not routinely edit comment submissions to remove such information before making the comment submissions available to the public or entering the comment into ADAMS.
The NRC is exploring the potential for advanced computational and predictive capabilities involving AI and ML in the various phases of nuclear power generation operational experience and plant management. The NRC is soliciting comments on the state of practice, benefits, and future trends related to the advanced computational tools and techniques in predictive reliability and predictive safety assessments in the commercial nuclear power industry.
III. Specific Request for Comment
The NRC requests comments from the public, the nuclear industry and other stakeholders, as well as other interested individuals and organizations. The focus of this request is to gather information that will provide the NRC staff with a better understanding of current usage and future trends in AI and ML in the commercial nuclear power industry.
IV. Requested Information and Comments
AI and ML are emerging, analytical tools, which, if used properly, show promise in their ability to improve reactor safety, yet offer economic savings. The NRC requests comments on issues listed below in this solicitation to enhance the NRC's understanding of the short- and long-term applications of AI Start Printed Page 20745and ML in nuclear power industry operations and management, as well as potential pitfalls and challenges associated with their application.
1. What is status of the commercial nuclear power industry development or use of AI/ML 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/ML? 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/ML in terms of (a) design or operational automation, (b) preventive maintenance trending, and (c) improved reactor operations staff productivity?
4. What AI/ML methods are either currently being used or will be in the near future in commercial nuclear plant management and operations? Example of possible AI/ML 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/ML across a wide spectrum of general applications versus an ad-hoc, case-by-case targeted approach?
6. With respect to AI/ML, 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/ML tools, against plant operational and engineering benefits when integrating AI/ML into operational decision-making and workflow management?
8. What is the general level of AI/ML expertise in the commercial nuclear power industry (e.g. expert, well-versed/skilled, or beginner)?
9. How will AI/ML effect the commercial nuclear power industry in terms of efficiency, costs, and competitive positioning in comparison to other power generation sources?
10. Does AI/ML 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/ML 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?
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Dated: April 15, 2021.
For the Nuclear Regulatory Commission.
Mehdi Reisi Fard,
Chief, Performance and Reliability Branch, Division of Risk Analysis, Office of Nuclear Regulatory Research.
[FR Doc. 2021-08177 Filed 4-20-21; 8:45 am]
BILLING CODE 7590-01-P