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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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From operator to entrepreneur: David Garcia applies outage management lessons
David Garcia
If ComEd’s Zion plant in northern Illinois hadn’t closed in 1998, David Garcia might still be there, where he got his start in nuclear power as an operator at age 24.
But in his ninth year working there, Zion closed, and Garcia moved on to a series of new roles—including at Wisconsin’s Point Beach plant, the corporate offices of Minnesota’s Xcel Energy, and on the supplier side at PaR Nuclear—into an on-the-job education that he augmented with degrees in business and divinity that he sought later in life.
Garcia started his own company—Waymaker Resource Group—in 2014. Recently, Waymaker has been supporting Holtec’s restart project at the Palisades plant with staffing and analysis. Palisades sits almost exactly due east of the fully decommissioned Zion site on the other side of Lake Michigan and is poised to operate again after what amounts to an extended outage of more than three years. Holtec also plans to build more reactors at the same site.
For Garcia, the takeaway is clear: “This industry is not going away. Nuclear power and the adjacent industries that support nuclear power—and clean energy, period—are going to be needed for decades upon decades.”
In July, Garcia talked with Nuclear News staff writer Susan Gallier about his career and what he has learned about running successful outages and other projects.
Changan Ren, He Li, Jichong Lei, Jie Liu, Wei Li, Kekun Gao, Guocai Huang, Xiaohua Yang, Tao Yu
Nuclear Technology | Volume 209 | Number 9 | September 2023 | Pages 1365-1372
Research Article | doi.org/10.1080/00295450.2023.2199098
Articles are hosted by Taylor and Francis Online.
With the advancement of artificial intelligence technology, intelligent diagnostic technology has been gradually implemented across various industries. This study proposes the use of convolutional neural networks–long short-term memory (CNNs-LSTM) for diagnosing faults in CPR1000 nuclear power plants (NPPs). To automatically extract data related to different types and levels of faults in the PCTRAN program, the study utilizes a self-developed AutoPCTRAN software and selects several key nuclear parameters as feature quantities. The study uses random sampling to create the training, validation, and test sets in an 8:1:1 ratio and identifies acceptable parameters to build the CNN-LSTM model. Test results show that the CNN-LSTM–based model for diagnosing CPR1000 NPP faults achieves a problem recognition rate of 99.6%, which validates the efficacy of the CNN-LSTM–based nuclear power fault diagnosis model.