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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.
J. C. Mailen
Nuclear Technology | Volume 30 | Number 3 | September 1976 | Pages 325-332
Technical Paper | Uranium Resource / Chemical Processing | doi.org/10.13182/NT76-A31647
Articles are hosted by Taylor and Francis Online.
Correlations have been developed that use chemical equilibrium considerations to predict the operation of bubble-cap columns using hyperazeo-tropic nitric acid (Iodox system) to treat air streams containing I2 or CH3I. The error in predicting decontamination factors (DFs) by the developed equations is on the same order as the uncertainty in the DFs determined in experimental tests. Methyl iodide is trapped less efficiently than molecular iodine; this effect is explained by the lower distribution of methyl iodide to concentrated nitric acid from air. The presence of NO2 in the gas stream was calculated to cause a reduction in the DF for the first few stages, but with little effect on later stages.