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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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NN Asks: What did you learn from ANS’s Nuclear 101?
Mike Harkin
When ANS first announced its new Nuclear 101 certificate course, I was excited. This felt like a course tailor-made for me, a transplant into the commercial nuclear world. I enrolled for the inaugural session held in November 2024, knowing it was going to be hard (this is nuclear power, of course)—but I had been working on ramping up my knowledge base for the past year, through both my employer and at a local college.
The course was a fast-and-furious roller-coaster ride through all the key components of the nuclear power industry, in one highly challenging week. In fact, the challenges the students experienced caught even the instructors by surprise. Thankfully, the shared intellectual stretch we students all felt helped us band together to push through to the end.
We were all impressed with the quality of the instructors, who are some of the top experts in the field. We appreciated not only their knowledge base but their support whenever someone struggled to understand a concept.
Taejin Kim, Donghan Yoo, Jongin Yang, Seoryong Koo, KyungTae Lim
Nuclear Technology | Volume 210 | Number 8 | August 2024 | Pages 1304-1318
Research Article | doi.org/10.1080/00295450.2023.2295147
Articles are hosted by Taylor and Francis Online.
As small modular reactors are gaining attention as the future of nuclear energy, it has become increasingly imperative to minimize the number of required operators in order to improve economic competitiveness. Although the tasks of operators have changed and their workloads have been relatively reduced as digital technologies have been applied to advanced main control rooms (MCRs), no change in the number of operators has been made when compared to conventional MCRs in the Republic of Korea. As a solution, the introduction of natural language processing (NLP) technology to replace certain operator tasks in advanced MCRs can be a valuable means of reducing MCR staff. In this paper, we suggest a novel communication framework utilizing NLP technology to minimize the number of operators in advanced MCRs. To do this, we analyze operator tasks in advanced MCRs and select those that can be replaced by NLP technology. We then develop a prototype NLP-based system and analyze the process and characteristics of the suggested communication framework.