Real time estimation of radionuclides in the receiving water of an inland nuclear power plant based on difference gated neural network

Abstract

Estimation of radionuclides in closed water is an essential licensing issue since it concerns the surroundings' safety, especially for inland sites. As the receiving water of inland nuclear power plant, closed water's flow processes are slow, unstable as well as highly nonlinear, leading to difficulty in monitoring of radionuclides. The traditional method aims to detect the photopeak of γ spectrum, which is complicated and severely time-consuming for the sampling and peak detection processes. Motivated by its time-consuming computation, a real time method for radionuclide estimation in closed water is proposed. A self-defined difference gated neural network utilizing radionuclide concentration data generated from Environmental Fluid Dynamics Code to perform estimation is mainly adopted. Simulation results show that compared to multilayer perceptron and long short-term memory models, the proposed difference gated neural network achieves the accuracy of 98.7% when estimating H3, C60o, A110mg and C58o. Accordingly, the designed method can be used for monitoring of nuclear power plant's waste water, especially in closed waters.