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2026 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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What’s the most difficult question you’ve been asked as a maintenance instructor?
Blye Widmar
"Where are the prints?!"
This was the final question in an onslaught of verbal feedback, comments, and critiques I received from my students back in 2019. I had two years of instructor experience and was teaching a class that had been meticulously rehearsed in preparation for an accreditation visit. I knew the training material well and transferred that knowledge effectively enough for all the students to pass the class. As we wrapped up, I asked the students how they felt about my first big system-level class, and they did not hold back.
“Why was the exam from memory when we don’t work from memory in the plant?” “Why didn’t we refer to the vendor documents?” “Why didn’t we practice more on the mock-up?” And so on.
Hyeonmin Kim, Seo-Ryong Koo, Geon-Pil Choi, Jung Taek Kim (KAERI)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 563-572
There are five operating modes of Nuclear Power Plants (NPPs): refueling, startup, low power, normal power, and shutdown. In these operating modes, the startup and the shutdown operating modes of NPPs are completely manually operated. From the Operational Performance Information System (OPIS) for NPPs, which is the overall database for controlling safety performance, human error under startup and shutdown was found to be 9% during last 20 years in South Korea. For reducing the operator’s load from the startup and shutdown operations of existing NPPs, it is necessary to develop an operator support system based on Artificial Intelligence (AI). Recently, AI technology has facilitated a breakthrough by accumulating data, advanced algorithms, and growing computing power. Among these factors, the key technology of the breakthrough is deep learning that leads current AI technology. In many technical fields, the development of automation and autonomous systems has been studied by using deep learning. Therefore, in this study, an automation system for the startup and shutdown in NPPs develop using deep learning. The automation system is based on an expert system due to characteristics of the startup and shutdown operating modes, and a variety of operating controls depending on each operator are simulated by deep learning. A feasibility study is conducted by using the Compact Nuclear Simulator (CNS) that is a simulator based on Westinghouse 3-loop NPPs. The target scenario for the feasibility study is bubble creation in a pressurizer under startup. In addition, a selected deep-learning algorithm is a Recurrent Neural Network (RNN), which is a robust method for time series analysis.