Machine Learning Could Solve Nuclear Reactor Problem
Scientists at the Department of Energys Argonne National Laboratory in Illinois have developed a machine learning system that could transform nuclear reactor operations, according to .
Whats going on: Researchers created an ML system to monitor and detect anomalies in a sodium-cooled fast reactor a type of nuclear reactor that employs liquid sodium as a coolant for its core enabl[ing] it to efficiently generate electricity without producing carbon emissions.
- While SFRs are not in widespread commercial use in the U.S., many experts see them as a path toward a more sustainable energy mix.
The challenge: To prevent corrosion and system clogs, SFR technology requires a high level of purity in its liquid sodium coolant.
The fix: Thats where the Argonne National Laboratory ML model comes in. It continuously monitors the cooling system, analyzing data from 31 sensors at Argonnes Mechanisms Engineering Test Loop facility.
- The system has successfully detected operational irregularities quickly and correctly.
A caveat: However, the model has significant limitations, such as the possibility of false alarms produced by random spikes or sensor inadequacies. Currently, [it] sends an alert when a spike exceeds a predetermined thresholdeven though not all spikes indicate anomalies.
Whats next: The team plans to refine the model to distinguish between genuine process anomalies and random measurement noise, according to a 泭from the lab.
The 51勛圖厙s take: Innovation like the kind going on at Argonne is exactly what we need for a secure, sustainable energy future, said 51勛圖厙 Vice President of Domestic Policy Brandon Farris. And manufacturers are at the ready to produce the infrastructure needed to power that future.
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