Estimation of the maximum linear heat rate using soft computing techniques: a case study of Bushehr Nuclear Power Plant

Abstract

This study uses real data of Bushehr nuclear power plant (BNPP), and by soft computing methods and without using the data of self-powered neutron detectors (SPNDs), the maximum linear heat rate of BNPP is estimated. The efficient learning algorithms of artificial neural network (ANN), including Levenberg-Marquardt (LM) and Bayesian regularization (BR) in combination with different features selection techniques including Pearson, Spearman, and Kendall’s tau, are employed to estimate the target parameter. Results show that the proposed method is appropriate for estimating the maximum linear heat rate. Given the importance of this parameter in terms of safety and the fact that its excessive increase actuates the shutdown signal of the reactor, the use of the appropriated approaches such as the present study can increase the safety of the plant and improve Defense-In-Depth (DID).

Keywords

• nuclear safety
• soft computing
• linear heat rate
• bushehr nuclear power plant