Earticle

초록

영어

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

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

키워드

저자

• Sang-Chul Lee [ 이상철 | 한국과학기술원 ]
• Ki-Ha Hwang [ 황기하 | 한국과학기술원 ]
• Sang-Hee Kang [ 강상희 | 한국과학기술원 ]
• Kun-Jai Lee [ 이건재 | 한국과학기술원 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  방사성폐기물학회지 [Journal of Nuclear Fuel Cycle and Waste Technology (JNFCWT)]

• 간기

  계간

• pISSN

  1738-1894

• 수록기간

  2003~2017

• 십진분류

  KDC 559 DDC 629