Artificial neural network for predicting nuclear power plant dynamic behaviors
A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually...
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| Published in: | Nuclear engineering and technology Vol. 53; no. 10; pp. 3275 - 3285 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.10.2021
Elsevier |
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| ISSN: | 1738-5733 |
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| Abstract | A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies. |
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| AbstractList | A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies. |
| Author | Nagasaki, S. El-Dakhakhni, W. El-Sefy, M. Yosri, A. Wiebe, L. |
| Author_xml | – sequence: 1 givenname: M. surname: El-Sefy fullname: El-Sefy, M. email: elsefym@mcmaster.ca organization: Department of Civil Engineering, NSERC-CREATE Program on Canadian Nuclear Energy Infrastructure Resilience Under Systemic Risk, McMaster University, Hamilton, ON, L8S 4L7, Canada – sequence: 2 givenname: A. surname: Yosri fullname: Yosri, A. email: ahmeda69@mcmaster.ca organization: Department of Civil Engineering, Institute for Multi-hazard Systemic Risk Studies (INTERFACE), McMaster University, Hamilton, ON, L8S 4L7, Canada – sequence: 3 givenname: W. surname: El-Dakhakhni fullname: El-Dakhakhni, W. email: eldak@mcmaster.ca organization: Department of Civil Engineering, and Director, NSERC-CaNRisk-CREATE Program and teh INTERFACE Institute, McMaster University, Hamilton, ON, L8S 4L7, Canada – sequence: 4 givenname: S. surname: Nagasaki fullname: Nagasaki, S. email: nagasas@mcmaster.ca organization: Department of Engineering Physics, McMaster University, Hamilton, ON, L8S 4L7, Canada – sequence: 5 givenname: L. surname: Wiebe fullname: Wiebe, L. email: wiebel@mcmaster.ca organization: Department of Civil Engineering, NSERC-CREATE Program on Canadian Nuclear Energy Infrastructure Resilience Under Systemic Risk, McMaster University, Hamilton, ON, L8S 4L7, Canada |
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| Keywords | Data-driven models Artificial neural network Nuclear power plant Artificial intelligence Back-propagation algorithm |
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