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Division Spotlight
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.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has since been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that Unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. local time on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
Marzio Marseguerra, Enrico Zio, Fabio Marcucci
Nuclear Technology | Volume 154 | Number 2 | May 2006 | Pages 224-236
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT06-A3730
Articles are hosted by Taylor and Francis Online.
The control and operation of complex power-generating systems, such as nuclear power plants, rely on the measurements of several sensors that monitor the process and the system state. On the basis of the sensor measurements, the system is operated for maximum economic efficiency and safety. Out-of-calibration sensors can lead to misinterpretation of the system state and problems with control and operation of the process, with possible economic losses, equipment damage, and safety consequences. To avoid such occurrences, periodic sensor calibrations are scheduled to ensure that sensors are operating correctly. These calibrations are performed manually and involve all sensors, independent of the actual need for calibration of each sensor. Continuous sensor calibration monitoring would then be most desirable both to ensure correct process control and system operation and to reduce maintenance costs associated with performing unnecessary manual sensor calibrations. This latter issue is of great relevance in nuclear power plants due to the large number of sensors employed, which are tested for calibration at each refueling outage. In this paper, the artificial neural network-based sensor calibration monitoring system is proposed to provide continuous sensor status information and virtual estimates for faulty sensors. In particular, we illustrate the design of an autoassociative artificial neural network for sensor fault detection and validation. The efficiency of the proposed method is verified through its application to eight critical transient signals coming from a U-tube steam generator of a pressurized water reactor modeled by means of a validated simulation code.