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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
DOE on track to deliver high-burnup SNF to Idaho by 2027
The Department of Energy said it anticipated delivering a research cask of high-burnup spent nuclear fuel from Dominion Energy’s North Anna nuclear power plant in Virginia to Idaho National Laboratory by fall 2027. The planned shipment is part of the High Burnup Dry Storage Research Project being conducted by the DOE with the Electric Power Research Institute.
As preparations continue, the DOE said it is working closely with federal agencies as well as tribal and state governments along potential transportation routes to ensure safety, transparency, and readiness every step of the way.
Watch the DOE’s latest video outlining the project here.
Pedro Mena, R. A. Borrelli, Leslie Kerby
Nuclear Technology | Volume 208 | Number 2 | February 2022 | Pages 232-245
Technical Paper | doi.org/10.1080/00295450.2021.1905470
Articles are hosted by Taylor and Francis Online.
Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics.