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Power peaking factor prediction using ANFIS method

Nuclear Engineering and Technology, 2022, 54(2), , pp.608-616 [Peer Reviewed Journal]

2021 Korean Nuclear Society ;ISSN: 1738-5733 ;EISSN: 2234-358X ;DOI: 10.1016/j.net.2021.08.011

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  • Title:
    Power peaking factor prediction using ANFIS method
  • Author: Mohd Ali, Nur Syazwani ; Hamzah, Khaidzir ; Idris, Faridah ; Basri, Nor Afifah ; Sarkawi, Muhammad Syahir ; Sazali, Muhammad Arif ; Rabir, Hairie ; Minhat, Mohamad Sabri ; Zainal, Jasman
  • Subjects: Adaptive neuro-fuzzy inference system ; Power peaking factor ; TRIGA research Reactors ; 원자력공학
  • Is Part Of: Nuclear Engineering and Technology, 2022, 54(2), , pp.608-616
  • Description: Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R2 values in the range of 96%–97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.
  • Publisher: Elsevier B.V
  • Language: English;Korean
  • Identifier: ISSN: 1738-5733
    EISSN: 2234-358X
    DOI: 10.1016/j.net.2021.08.011
  • Source: Alma/SFX Local Collection
    DOAJ Directory of Open Access Journals
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