A Study on the Improvement of Scaling Factor Determination Using Artificial Neural Network

서지정보

ㆍ발행기관 : 한국방사성폐기물학회 ㆍ수록지정보 : 방사성폐기물학회지 / 2권 / 1호
ㆍ저자명 : Sang-Chul Lee, Ki-Ha Hwang, Sang-Hee Kang, Kun-Jai Lee

목차

Abstract I. Introduction II. Converntional SF Determination Method III. Artificial Neural Network IV. Application of SF determination V. Results and Discussion VI. Concluded Remarks VII. Acknowledgments VIII. References

영어 초록

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information 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 determined 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 14 and 90 . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14 and 90 in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

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