Nuclear Power

15 Aug 2022

Can AI Improve the Profitability of Nuclear Energy?

15 Aug 2022  by   

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Researchers at the US Department of Energy’s (DOE) Argonne National Laboratory said they believe artificial intelligence could save the nuclear industry more than $500 million a year.

Argonne is halfway through a $1 million, three-year project to explore how this technology could change the economics of nuclear.

“Operation and maintenance costs are quite relevant for nuclear units, which currently require large site crews and extensive upkeep,” said Roberto Ponciroli, a principal nuclear engineer at Argonne. ​“We think that autonomous operation can help to improve their profitability and also benefit the deployment of advanced reactor concepts.”

The project, funded by the DOE Office of Nuclear Energy’s Nuclear Energy Enabling Technologies programme, aims to create a computer architecture that could detect problems early and recommend appropriate actions to human operators.

Specifically, algorithms could verify data by learning how hundreds of sensors in a nuclear plant function and look for anomalies. Having validated a plant’s sensors, an artificial intelligence system would then interpret signals from them and recommend specific actions.

Ponciroli compared it to a car’s dashboard alerting a driver to a tire with low air pressure.

Researchers said the job of inspecting each sensor — and also the performance of system components such as valves, pumps, heat exchangers — is currently for staff who walk the plant floor.

“In a world where decisions are made according to data, it’s important to know that you can trust your data,” he said. “Sensors, like any other component, can degrade. Knowing that your sensors are functioning is crucial.”

As part of the project, Argonne engineers have built a digital twin of an advanced nuclear reactor. Researchers said while the system is designed to serve new reactor technologies, it’s flexible enough to be applied at existing nuclear plants.

The team is validating its AI technology on the simulated reactor and has completed systems to control and diagnose its virtual parts. The rest of the project will focus on the system’s decision-making ability and what it does with the data.

The final product of Argonne’s work would be a system that stitches multiple algorithms together. For example, engineers are adapting code including Argonne’s System Analysis Module (SAM), an analysis tool for advanced reactors.


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