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Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Latest News
NRC Hanson's renomination clears Senate committee
Hanson
The U.S. Senate Environment and Public Works Committee voted 18–1 yesterday to advance the renomination of Christopher T. Hanson as a member of the Nuclear Regulatory Commission. Hanson has been a commissioner since 2020, and was named chair by President Biden in January 2021. The full U.S. Senate will consider Hanson’s nomination later this month.
Voices of support: “Chair Hanson is a dedicated public [servant] who has thoughtfully and . . . skillfully led the [NRC] during his tenure as its chair. Throughout his time on the[NRC], he has demonstrated his commitment to ensuring the safety and the security of our nation’s use of nuclear energy,” said EPW committee chair Tom Carper (D., Del.) before the vote.
Yukiharu Ohga, Hiroshi Seki
Nuclear Technology | Volume 101 | Number 2 | February 1993 | Pages 159-167
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34777
Articles are hosted by Taylor and Francis Online.
The combination of a neural network and knowledge processing have been used to identify abnormal events that cause a reactor to scram in a nuclear power plant. The neural network recognizes the abnormal event from the change pattern of analog data for state variables, and this result is confirmed from digital data using a knowledge base of plant status when each event occurs. The event identification method is tested using test data based on simulated results of a transient analysis program for boiling water reactors. It is confirmed that a neural network can identify an event in which it has been trained even when the plant conditions, such as fuel burnup, differ from those used in the training and when the analog data contain white noise. The network does not mistakenly identify the nontrained event as a trained one. The method is feasible for event identification, and knowledge processing improves the reliability of the identification.