Staff View: Characterization of ANFIS-SC method on power peaking factor

Characterization of ANFIS-SC method on power peaking factor

Prediction of power peaking factor (PPF) using ANFIS method at TRIGA research reactor has been conducted in the previous study and resulted in good predictive performances. This method could be implemented as a real-time monitoring system for various reactor types. In this paper, the ANFIS-SC traine...

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Main Authors:	Mohd. Ali, Nur Syazwani, Hamzah, Khaidzir, Idris, Faridah, Sazali, Muhammad Arif, Sarkawi, Muhammad Syahir, Basri, Nor Afifah, Jamaluddin, Khairulnadzmi, Zainal, Jasman
Format:	Article
Language:	English
Published:	Areadiscover 2022
Subjects:	T Technology (General) TP Chemical technology
Online Access:	http://eprints.utm.my/104146/1/NurSyazwaniMohdAliKhaidzirHamzahFaridahIdris2022_CharacterizationofANFISSCMethodonPower.pdf http://eprints.utm.my/104146/ https://www.jnrtmns.net/index.php/jnrt/article/view/206/198
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spelling	my.utm.1041462024-01-17T01:34:59Z http://eprints.utm.my/104146/ Characterization of ANFIS-SC method on power peaking factor Mohd. Ali, Nur Syazwani Hamzah, Khaidzir Idris, Faridah Sazali, Muhammad Arif Sarkawi, Muhammad Syahir Basri, Nor Afifah Jamaluddin, Khairulnadzmi Zainal, Jasman T Technology (General) TP Chemical technology Prediction of power peaking factor (PPF) using ANFIS method at TRIGA research reactor has been conducted in the previous study and resulted in good predictive performances. This method could be implemented as a real-time monitoring system for various reactor types. In this paper, the ANFIS-SC trained models will be characterized to investigate the generalization capability of the models against new input data. Three ANFIS-SC trained models adopted from the previous study with 0.45, 0.45, and 0.50 of the cluster radii were chosen for this characterization study. Based on the statistical analysis, the correlation coefficients of the trained models show a weak relation between predicted and actual output. The Means Absolute Error (MSE) and Root Means Square Error (RMSE) were near to zero in the range of 7.2112 x 10-7 – 9.4304 x 10-7. However, the average output of all the trained models was in the range of 1.8722 - 1.8724 while the average output of the actual PPF is 1.8728. This statistical result shows that the generalization capabilities of the ANFIS-SC method were excellent and could be improved further with a deep learning mechanism for exact prediction performances. Besides, the ANFIS-SC method also can be applied for PPF monitoring at the control room of the nuclear reactor for enhancing the reactor operation as well as for education and training. Areadiscover 2022-03-01 Article PeerReviewed application/pdf en http://eprints.utm.my/104146/1/NurSyazwaniMohdAliKhaidzirHamzahFaridahIdris2022_CharacterizationofANFISSCMethodonPower.pdf Mohd. Ali, Nur Syazwani and Hamzah, Khaidzir and Idris, Faridah and Sazali, Muhammad Arif and Sarkawi, Muhammad Syahir and Basri, Nor Afifah and Jamaluddin, Khairulnadzmi and Zainal, Jasman (2022) Characterization of ANFIS-SC method on power peaking factor. Journal Of Nuclear And Related Technologies, 19 (1). pp. 31-36. ISSN 1823-0180 https://www.jnrtmns.net/index.php/jnrt/article/view/206/198 NA
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	T Technology (General) TP Chemical technology
spellingShingle	T Technology (General) TP Chemical technology Mohd. Ali, Nur Syazwani Hamzah, Khaidzir Idris, Faridah Sazali, Muhammad Arif Sarkawi, Muhammad Syahir Basri, Nor Afifah Jamaluddin, Khairulnadzmi Zainal, Jasman Characterization of ANFIS-SC method on power peaking factor
description	Prediction of power peaking factor (PPF) using ANFIS method at TRIGA research reactor has been conducted in the previous study and resulted in good predictive performances. This method could be implemented as a real-time monitoring system for various reactor types. In this paper, the ANFIS-SC trained models will be characterized to investigate the generalization capability of the models against new input data. Three ANFIS-SC trained models adopted from the previous study with 0.45, 0.45, and 0.50 of the cluster radii were chosen for this characterization study. Based on the statistical analysis, the correlation coefficients of the trained models show a weak relation between predicted and actual output. The Means Absolute Error (MSE) and Root Means Square Error (RMSE) were near to zero in the range of 7.2112 x 10-7 – 9.4304 x 10-7. However, the average output of all the trained models was in the range of 1.8722 - 1.8724 while the average output of the actual PPF is 1.8728. This statistical result shows that the generalization capabilities of the ANFIS-SC method were excellent and could be improved further with a deep learning mechanism for exact prediction performances. Besides, the ANFIS-SC method also can be applied for PPF monitoring at the control room of the nuclear reactor for enhancing the reactor operation as well as for education and training.
format	Article
author	Mohd. Ali, Nur Syazwani Hamzah, Khaidzir Idris, Faridah Sazali, Muhammad Arif Sarkawi, Muhammad Syahir Basri, Nor Afifah Jamaluddin, Khairulnadzmi Zainal, Jasman
author_facet	Mohd. Ali, Nur Syazwani Hamzah, Khaidzir Idris, Faridah Sazali, Muhammad Arif Sarkawi, Muhammad Syahir Basri, Nor Afifah Jamaluddin, Khairulnadzmi Zainal, Jasman
author_sort	Mohd. Ali, Nur Syazwani
title	Characterization of ANFIS-SC method on power peaking factor
title_short	Characterization of ANFIS-SC method on power peaking factor
title_full	Characterization of ANFIS-SC method on power peaking factor
title_fullStr	Characterization of ANFIS-SC method on power peaking factor
title_full_unstemmed	Characterization of ANFIS-SC method on power peaking factor
title_sort	characterization of anfis-sc method on power peaking factor
publisher	Areadiscover
publishDate	2022
url	http://eprints.utm.my/104146/1/NurSyazwaniMohdAliKhaidzirHamzahFaridahIdris2022_CharacterizationofANFISSCMethodonPower.pdf http://eprints.utm.my/104146/ https://www.jnrtmns.net/index.php/jnrt/article/view/206/198
_version_	1789424385685192704
score	13.211869

Characterization of ANFIS-SC method on power peaking factor

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