MIT Scientists Challenge AI to Solve Nuclear Energy Problem in Game Form

Nuclear power now provides more carbon-free electricity in the US than solar and wind power combined

taken.This makes it a key player in the fight against climate change. However, the methods for its extraction are imperfect and outdated. The process needs to be optimized so that nuclear power can compete with coal and gas power plants in the market.

You can reduce production costs by optimizingfuel rods, deep inside a nuclear reactor. They trigger reactions, and when ideally positioned, burn less fuel and require less maintenance. After decades of trial and error, nuclear engineers have learned how to develop better layouts for expensive fuel rods to extend their life. Now artificial intelligence (AI) will help them.

Researchers at Massachusetts Institute of TechnologyInstitute (MIT) and Exelon are confident that by turning the design process into a game, the AI ​​system can be trained to generate dozens of optimal rod configurations, which can extend the life of each rod by about 5%. This saves about $ 3 million per year for a typical power plant. An artificial intelligence system can find optimal solutions faster than humans and quickly change designs in a secure simulated environment.

“This technology can be applied to anyonenuclear reactor in the world, '' explains senior study author Korish Shirvan, an assistant professor in the Department of Nuclear Science and Technology at the Massachusetts Institute of Technology. “By improving the economy of nuclear power, which supplies 20% of US electricity, we can help limit the growth in global carbon emissions and attract the best young talent to this important clean energy sector.”

In a typical reactor, the fuel rods are lined up ina grid or assembly of uranium and gadolinium oxide levels inside, like chess pieces on a board, with reactions that trigger radioactive uranium and rare earth gadolinium slowing them down. In an ideal arrangement, these competing impulses are balanced to stimulate effective responses. Engineers have tried to use traditional algorithms to improve human-designed layouts, but a standard 100-rod assembly can have an astronomical number of options to evaluate.

Researchers wondered ifDeep Reinforcement Learning, an artificial intelligence technique that has allowed superhuman proficiency in games like chess and go, to speed up the verification process. Deep reinforcement learning combines deep neural networks that excel at picking patterns in datasets with reinforcement learning, which links learning to a reward signal such as winning a game.

In a new experiment, the researchers trained theiragent to place fuel rods according to a set of restrictions, earning more points for each coup. Each constraint or rule chosen by the researchers reflects decades of expert knowledge based on the laws of physics. The agent can score points, for example, by placing low-uranium rods at the edges of the assembly to slow down reactions there.

“After you program the rules,neural networks are starting to work really well, says lead author Majdi Radaideh, a postdoc at Shirvan's lab. - They don't waste time on random processes. It was fun to watch them learn to play games, as a human does. "

Through Reinforcement Learning, AI has learnedplaying increasingly complex games as well as humans, or even better. But its capabilities remain useless in the real world. Now researchers have proven that reinforcement learning has potential.

“This study is an exciting exampleusing artificial intelligence technology for board and video games to help us solve practical problems in the world, ”concludes study co-author Joshua Joseph, Research Fellow at MIT Quest for Intelligence.

Exelon is currently testing a beta version of an artificial intelligence system in a virtual environment. According to a company representative, the system may be ready for implementation in a year or two.

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