Online autonomous calibration of digital twins using machine learning with application to nuclear power plants
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DOI: 10.1016/j.apenergy.2022.119995
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- Nian, Victor & Mignacca, Benito & Locatelli, Giorgio, 2022. "Policies toward net-zero: Benchmarking the economic competitiveness of nuclear against wind and solar energy," Applied Energy, Elsevier, vol. 320(C).
- Stewart, W.R. & Velez-Lopez, E. & Wiser, R. & Shirvan, K., 2021. "Economic solution for low carbon process heat: A horizontal, compact high temperature gas reactor," Applied Energy, Elsevier, vol. 304(C).
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Cited by:
- Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
- Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
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Keywords
Nuclear power plant; Digital twin; Online calibration; K-means cluster; Artificial neural networks;All these keywords.
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