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, Pages 1-7 PDF (238.85 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iceeng.2016.30311 | ||
| 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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