Research on optimization of key thermal parameters of the secondary loop of PWR based on improved BP neural network

Abstract

The simulation and optimization time of traditional secondary loop thermal parameters is relatively long, and the computational cost is generally high. To solve this problem, we construct an accurate model of the full-power steady-state operation of the secondary loop of the PWR, and use meta-learning combined with Bayesian optimization to realize the adaptive optimization of BP neural network hyperparameters. The effectiveness of the method is verified by designing comparative experiments by taking thermal efficiency as the optimization goal and high-pressure heater end difference and other parameters as the optimization variables, and using the improved BP neural network to optimize the accurate model. The results show that the improved secondary loop thermal parameter prediction and optimization framework can quickly and accurately predict the thermal efficiency and optimize the thermal parameters, and has good generalization ability, which lays a foundation for improving the thermal efficiency of nuclear power plants.