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SuperCritical Materials licenses technology for uranium seawater extraction
Last week, Austin, Texas–based nuclear fuel start-up SuperCritical Materials announced that it has secured a license from the Department of Energy for a patented uranium and critical materials adsorbent manufacturing process originally developed by a team led by Pacific Northwest National Laboratory.
SuperCritical aims to use this license to extract uranium from seawater at an industrial scale in the United States and abroad.
Ark O. Ifeanyi, Daniel Dos Santos, Abhinav Saxena, Jamie Coble
Nuclear Technology | Volume 210 | Number 12 | December 2024 | Pages 2387-2403
Research Article | doi.org/10.1080/00295450.2024.2323260
Articles are hosted by Taylor and Francis Online.
Control rods and elements manage the power distribution in nuclear reactors through the motion of banks of rods distributed throughout the core. These positional changes are achieved through the actuation of fine motion control rod drive (FMCRD) mechanisms. In the BWRX-300 design by GE-Hitachi, this mechanism is electrically driven by a servomotor that allows for high-precision control of power outputs. Under operational transients, such as load-following, accurate and precise operation of these servomotors is necessary over long periods of time, so they are key maintenance targets to maintain availability and operational flexibility. Swiftly and precisely identifying faults in the drive mechanisms will support predictive maintenance and reduced costs. This paper used three different types of simulated faults to test the fault detectability of principal component analysis (PCA) when considering the simulated operations of banks of control rods and their associated servomotors. These faults were stator short-circuit faults, ball screw jam faults, and ball screw wear faults. Torque and position were monitored in the simulation. The position signal was insufficient to detect mechanical faults. Torque signals for each servomotor in the bank of rods undergoing multiple position demand changes were projected to a reduced dimensional space via PCA. Q and T2 statistics were employed for anomaly detection. Using this approach, all faults were detected, and the anomalies were isolated to the faulty FMCRD mechanism.