Journal of Nuclear Fuel Cycle and Waste Technology

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed infonnation about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is detennined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14C 90Sr. In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF detemúnation methods - the LMA method and the regression method. The root mean squared eπors (RMSE) of the ANN models are compared with those of the conventional SF detenrúnation models for 14C and 90Sr in two parts divided by a training part and avalidation part. The SF detemúnation models are arranged in the order of RMSEs as the following order: ANN model

Keywords

scaling factor,artificial neural network,inventory,LMA,regression