A control rod worth prediction using Adaptive Neuro-Fuzzy Inference System for Pre-Calibration Method at TRIGA PUSPATI Reactor
Teh Zhi Hui
1
,
Nur Syazwani Mohd Ali
2
,
Nur Syazwani Mohd Ali
2
,
Mohd Sabri Minhat
3
,
JASMAN ZAINAL
2
,
Muhammad Arif Sazali
2
,
Muhammad Syahir Sarkawi
2
,
Khairulnadzmi Jamaluddin
2
,
Nor Afifah Basri
2
,
Mohsin Mohd Sies
2
,
Mohsin Mohd Sies
2
,
Nahrul Khair Alang Md Rashid
2
Publication type: Journal Article
Publication date: 2024-06-01
scimago Q1
wos Q1
SJR: 0.897
CiteScore: 4.7
Impact factor: 2.3
ISSN: 03064549, 18732100
Nuclear Energy and Engineering
Abstract
One of the control rod calibration methods in research reactors is the doubling time. However, this method reduces the operation time and limits the number of research activities. A pre-calibration method is proposed in this study by utilizing an ANFIS method. Two data inputs based on the annual rod worth, and the worth drop of the Shim and Transient rods were collected to predict the Safety and Regulating rod's worth. The results showed that ANFIS can predict the Safety rod worth with the lowest MAE and RMSE errors of 0.0156 and 0.0204 while 0.0616 of the percent error. For the Regulating rod's worth, the predicted and actual output data were less accurate due to the composition of air in the Transient rod. Nevertheless, the utilization of ANFIS as the pre-calibration method for control rod calibration at research reactors could be implemented and optimized in future studies.
Found
Nothing found, try to update filter.
Found
Nothing found, try to update filter.
Top-30
Journals
|
1
|
|
|
IEEE Access
1 publication, 50%
|
|
|
Flow Measurement and Instrumentation
1 publication, 50%
|
|
|
1
|
Publishers
|
1
|
|
|
Institute of Electrical and Electronics Engineers (IEEE)
1 publication, 50%
|
|
|
Elsevier
1 publication, 50%
|
|
|
1
|
- We do not take into account publications without a DOI.
- Statistics recalculated weekly.
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
2
Total citations:
2
Citations from 2024:
2
(100%)
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Zhi Hui T. et al. A control rod worth prediction using Adaptive Neuro-Fuzzy Inference System for Pre-Calibration Method at TRIGA PUSPATI Reactor // Annals of Nuclear Energy. 2024. Vol. 200. p. 110410.
GOST all authors (up to 50)
Copy
Zhi Hui T., Syazwani Mohd Ali N., Mohd Ali N. S., Minhat M. S., ZAINAL J., Sazali M. A., Syahir Sarkawi M., Jamaluddin K., Basri N. A., Mohd Sies M., Sies M. M., Khair Alang Md Rashid N. A control rod worth prediction using Adaptive Neuro-Fuzzy Inference System for Pre-Calibration Method at TRIGA PUSPATI Reactor // Annals of Nuclear Energy. 2024. Vol. 200. p. 110410.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1016/j.anucene.2024.110410
UR - https://linkinghub.elsevier.com/retrieve/pii/S0306454924000720
TI - A control rod worth prediction using Adaptive Neuro-Fuzzy Inference System for Pre-Calibration Method at TRIGA PUSPATI Reactor
T2 - Annals of Nuclear Energy
AU - Zhi Hui, Teh
AU - Syazwani Mohd Ali, Nur
AU - Mohd Ali, Nur Syazwani
AU - Minhat, Mohd Sabri
AU - ZAINAL, JASMAN
AU - Sazali, Muhammad Arif
AU - Syahir Sarkawi, Muhammad
AU - Jamaluddin, Khairulnadzmi
AU - Basri, Nor Afifah
AU - Mohd Sies, Mohsin
AU - Sies, Mohsin Mohd
AU - Khair Alang Md Rashid, Nahrul
PY - 2024
DA - 2024/06/01
PB - Elsevier
SP - 110410
VL - 200
SN - 0306-4549
SN - 1873-2100
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2024_Zhi Hui,
author = {Teh Zhi Hui and Nur Syazwani Mohd Ali and Nur Syazwani Mohd Ali and Mohd Sabri Minhat and JASMAN ZAINAL and Muhammad Arif Sazali and Muhammad Syahir Sarkawi and Khairulnadzmi Jamaluddin and Nor Afifah Basri and Mohsin Mohd Sies and Mohsin Mohd Sies and Nahrul Khair Alang Md Rashid},
title = {A control rod worth prediction using Adaptive Neuro-Fuzzy Inference System for Pre-Calibration Method at TRIGA PUSPATI Reactor},
journal = {Annals of Nuclear Energy},
year = {2024},
volume = {200},
publisher = {Elsevier},
month = {jun},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0306454924000720},
pages = {110410},
doi = {10.1016/j.anucene.2024.110410}
}