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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NERS publishes report on machine learning and microreactors
The University of Michigan’s Department of Nuclear Engineering and Radiological Sciences (NERS) has published a summary of a study on nuclear microreactors and machine learning (ML) that was conducted by researchers from NERS and Idaho National Laboratory. The full paper, “Nuclear Microreactor Transient and Load-Following Control with Deep Reinforcement Learning,” was featured in the July issue of Energy Conversion and Management: X.
Robert E. Uhrig
Nuclear Technology | Volume 88 | Number 2 | November 1989 | Pages 157-165
Technical Paper | NSF Workshop on the Research Needs of the Next Generation Nuclear Power Technology / Fission Reactor | doi.org/10.13182/NT89-A34322
Articles are hosted by Taylor and Francis Online.
Application of automation using digital systems to control and manage nuclear power plants, along with the introduction of some of the techniques of artificial intelligence, can offer the public assurance that nuclear power plants are being controlled and managed safely and efficiently. Automation could dramatically reduce the minor glitches that detract from the truly fine performance of these plants. Demands for increased safety margins, lower environmental impacts, improved performance, and greater investment protection will inevitably lead to automation of most nuclear power plant functions. Technologies that should be supported to ensure the proper development of this generation of nuclear power plants include fault-tolerant digital control and safety systems, automated software production, human factors, sensor-reading validation, artificial intelligence, expert systems, neural networks, alternate computer technologies, and robotics.