Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks
Saber, A., El-Koliel, M., El-Rashidy, M., Taha, T. (2016). Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks. EKB Journal Management System, 10(10th International Conference on Electrical Engineering ICEENG 2016), 1-7. doi: 10.21608/iceeng.2016.30311
Amany S. Saber; Moustafa S. El-Koliel; Mohamed A. El-Rashidy; Taha E. Taha. "Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks". EKB Journal Management System, 10, 10th International Conference on Electrical Engineering ICEENG 2016, 2016, 1-7. doi: 10.21608/iceeng.2016.30311
Saber, A., El-Koliel, M., El-Rashidy, M., Taha, T. (2016). 'Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks', EKB Journal Management System, 10(10th International Conference on Electrical Engineering ICEENG 2016), pp. 1-7. doi: 10.21608/iceeng.2016.30311
Saber, A., El-Koliel, M., El-Rashidy, M., Taha, T. Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks. EKB Journal Management System, 2016; 10(10th International Conference on Electrical Engineering ICEENG 2016): 1-7. doi: 10.21608/iceeng.2016.30311

Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks

The International Conference on Electrical Engineering
Article 29, Volume 10, 10th International Conference on Electrical Engineering ICEENG 2016, April 2016, Page 1-7  XML PDF (238.85 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2016.30311
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Authors
Amany S. Saber1; Moustafa S. El-Koliel1; Mohamed A. El-Rashidy2; Taha E. Taha2
1Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt.
2Faculty of Electronic Engineering, Menoufiya University, Cairo, Egypt.
Abstract
Abstract -
The present work investigates an appropriate
algorithm based on Multilayer Perceptron Neural Network
(MPNN), Apriori association rules and Particle Swarm
Optimization (PSO) models for predicting two significant core
safety parameters; the multiplication factor Keff and the power
peaking factor Pmax of the benchmark 10 MW IAEA LEU
research reactor. It provides a comprehensive analytic method
for establishing an Artificial Neural Network (ANN) with selforganizing
architecture by finding an optimal number of
hidden layers and their neurons, a less number of effective
features of data set and the most appropriate topology for
internal connections. The performance of the proposed
algorithm is evaluated using the 2-Dimensional neutronic
diffusion code MUDICO-2D to obtain the data required for the
training of the neural networks. Simulation results
demonstrate the effectiveness and the notability of the
proposed algorithm comparing with Trainlm-LM, quasi-
Newton (Trainbfg-BFGS), and Resilient Propagation (trainrp-
RPROP) algorithms.
Keywords
Apriori Association Rules; Particle Swarm Optimization; Artificial Neural Networks; Effective Multiplication Factor; and Power Peaking Factor
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