Session: 08-01: New Facility Planning/ Environmental Management (EM)/ Health & Safety

Paper Number: 109887

109887 - Comparison of Intelligent Optimization Algorithms in Neural Network Model for Nuclear Accident Source Term Evaluation 

After a severe nuclear accident, the source term cannot be obtained by the forward method due to the power outage of the whole plant. But the source term needs to be estimated quickly and accurately to protect the public and the environment. The source term inversion method based on the back-propagation neural network can effectively estimate the source term, but it is prone to fall into local minimum. To solve this problem, many intelligent optimization algorithms have been proposed to optimize the weights and thresholds of neural networks. In order to compare the effectiveness of different intelligent optimization algorithms, this paper builds neural network models based on the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Particle Swarm Optimization algorithm (PSO), Simulated Annealing-Genetic Algorithm (SAGA) and Mind Evolutionary Algorithm (MEA) respectively, and generates a large amount of data for neural network training using Kincaid experimental data and CALMET-LAPMOD model. In addition, This study uses principal component analysis (PCA) to reduce data latitude and training time. The results show that the model uses the WOA has the best source term estimate ability, and the relative error is only 3%. Therefore, this study recommends using the WOA-PCA neural network in similar nuclear accident emergency or consequence evaluation scenarios.

Presenting Author: Jiayue Song North China Electric Power University

Presenting Author Biography: I am Jiayue Song, a postgraduate student, and currently studying in the School of Nuclear Science and Engineering, North China Electric Power University, focusing on the research of the neural network model in nuclear accident emergency and consequence evaluation.