• Title/Summary/Keyword: radioactive nuclide

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Optimization of Artificial Neural Network Model in Scaling Factor Determination Method

  • Lee, Sang-Chul;Hwang, Ki-Ha;Kang, Sang-Hee;Lee, Kun-Jai
    • Proceedings of the Korean Radioactive Waste Society Conference
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    • 2004.06a
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    • pp.254-254
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    • 2004

  • 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 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.(omitted)

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A Study on Improvement of Scaling Factor Prediction Using Artificial Neural Network

  • Lee, Sang-Chul;Hwang, Ki-Ha;Kang, Sang-Hee;Lee, Kun-Jai
    • Proceedings of the Korean Radioactive Waste Society Conference
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    • 2003.11a
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    • pp.534-538
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    • 2003

  • Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed knowledge of the natures and quantities of radionuclides in waste package. Many of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the Indirect method by which the concentrations of DTM (Difficult-to-Measure) nuclide is decided using the relation of concentrations (Scaling Factor) between Key (Easy-to-Measure) nuclide and DTM nuclide with measured concentrations of Key nuclide. In general, scaling factor is determined by using of log mean average (LMA) and regression. These methods are adequate to apply most corrosion product nuclides. But in case of fission product nuclides and some corrosion product nuclides, the predicted values aren't well matched with the original values. In this study, the models using artificial neural network (ANN) for C-14 and Sr-90 are compared with those using LMA and regression. The assessment of models is executed in the two parts divided by a training part and a validation part. For all of two nuclides in the training part, the predicted values using ANN are well matched with the measured values compared with those using LMA and regression. In the validation part, the accuracy of the predicted values using ANN is better than that using LMA and is similar to or better than that using regression. It is concluded that the predicted values using ANN model are better than those using conventional model in some nuclides and ANN model can be used as the complement of LMA and regression model.

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NUCLIDE SEPARATION MODELING THROUGH REVERSE OSMOSIS MEMBRANES IN RADIOACTIVE LIQUID WASTE

  • LEE, BYUNG-SIK
    • Nuclear Engineering and Technology
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    • v.47 no.7
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    • pp.859-866
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    • 2015

  • The aim of this work is to investigate the transport mechanism of radioactive nuclides through the reverse osmosis (RO) membrane and to estimate its effectiveness for nuclide separation from radioactive liquid waste. An analytical model is developed to simulate the RO separation, and a series of experiments are set up to confirm its estimated separation behavior. The model is based on the extended Nernst-Plank equation, which handles the convective flux, diffusive flux, and electromigration flux under electroneutrality and zero electric current conditions. The distribution coefficient which arises due to ion interactions with the membrane material and the electric potential jump at the membrane interface are included as boundary conditions in solving the equation. A high Peclet approximation is adopted to simplify the calculation, but the effect of concentration polarization is included for a more accurate prediction of separation. Cobalt and cesium are specifically selected for the experiments in order to check the separation mechanism from liquid waste composed of various radioactive nuclides and nonradioactive substances, and the results are compared with the estimated cobalt and cesium rejections of the RO membrane using the model. Experimental and calculated results are shown to be in excellent agreement. The proposed model will be very useful for the prediction of separation behavior of various radioactive nuclides by the RO membrane.

  • https://doi.org/10.1016/j.net.2015.08.001 인용 KSCI

Analysis and Consideration of the Establishment of a Multiplexed Channel for Domestic RI Waste Nuclide Analysis (국내 방사성동위원소(RI) 폐기물 핵종분석 다중화채널 구축 성과 분석 및 고찰)

  • Han, Sang-Jun;Lee, Hong-Yeon;Kim, Bo-Gil;An, Eun-Mi
    • Journal of radiological science and technology
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    • v.44 no.4
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    • pp.351-358
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    • 2021

  • This research project is a program promoted to seek diversification of domestic radioactive waste analysis institutions, and seeks public development, win-win cooperation, and cooperation between the entrusted institution and the entrusted institution. Accordingly, the entrusted institution established a standard analysis procedure for establishing a quality control system for radioactivity analysis, establishing a radiation control zone, obtaining KOLAS accreditation, and performing proficiency tests, which are the performance ranges requested by the entrusted institution, and intersecting the radioactive isotope waste generated at the actual site. Verification was performed to confirm the analysis quality. In addition, facilities and equipment for radioactivity analysis were supplemented and expanded, and the basic technology foundation and technical skills were secured through securing professional technicians and education/training. It is judged that the entrusted institution will contribute to securing radiation safety through the smooth execution of treatment, disposal, and transportation through value creation and analysis of radioactive waste generated by radioactive isotope-using institutions (research institutes, hospitals, industries, etc.) by succeeding in this research project do.

  • https://doi.org/10.17946/JRST.2021.44.4.351 인용 PDF KSCI