A low- and intermediate-level radioactive waste repository contains different types of radionuclides and organic complexing agents. Their chemical interaction in the repository can result in the formation of radionuclide-ligand complexes, leading to their high transport behaviors in the engineered and natural rock barriers. Furthermore, the release of radionuclides from the repository can pose a significant risk to both human health and the environment. This study explores the impact of different experimental conditions on the transport behaviors of 99Tc, 137Cs, and 238U through three types of barrier samples: concrete, sedimentary rock, and granite. To assess the transport behavior of the samples, the geochemical characteristics were determined using X-ray diffraction (XRD), X-ray fluorescence (XRF), Fouriertransform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and Brunauer-Emmett-Teller (BET) analysis. The adsorption distribution coefficient (Kd) was used as an indicator of transport behavior, and it was determined in batch systems under different conditions such as solution pH (ranging from 7 to 13), temperature (ranging from 10 to 40°C), and with the presence of organic complexing agents such as ethylenediaminetetraacetic acid (EDTA), nitrilotriacetic acid (NTA), and isosaccharinic acid (ISA). A support vector machine (SVM) was used to develop a prediction model for the Kd values. It was found that, regardless of the experimental parameters, 99Tc may migrate easily due to its anionic property. Conversely, 137Cs showed low transport behaviors under all tested conditions. The transport behaviors of 238U were impacted by the order of EDTA > NTA> ISA, particularly with the concrete sample. The SVM models can also be used to predict the Kd values of the radionuclides in the event of an accidental release from the repository.

Korea Atomic Energy Research Institute (KAERI) is planning to disposal of the radioactive contaminated cement waste form to the final disposal facility. The final disposal facility require evaluation of immersion, compressive strength, and radionuclide inventory of radioactive wastes to meet the acceptance criteria for safe disposal. According to the LILW acceptance criteria of the Nuclear Safety and Security Commission ok Korea (NSSC), the disposal limit radioactivity of 129I (3.70×101 Bq/g) is lower than other radionuclides. 129I emits low energy as its disposal limit is low, so it is difficult to analyze in the presence of 137Cs and 60Co which emit high energy. Therefore, it is essential to an accurately separate and analyze iodine in radioactive waste. In this study, we focused on the determination of 129I in cement waste form containing 137Cs, 60Co. We added 1 g of 129I(11.084 Bg), 137Cs(1,300 Bq) and 60Co(402 Bq) to cement waste form, respectively. The separation of 129I in cement waste form was carried out using an acid leaching method. And, we confirmed the specific activity of 137Cs and 60Co at each separation step. It was observed that an acid leaching method showed the remove efficiency 137Cs(99.97%) and 60Co(99.94%), respectively. In addition, 129I was also analyzed at approximately 96.44% in simulated contaminated cement waste form. In conclusion, through this experiment, it was confirmed that 129I could be successfully separated and analyzed by using the acid leaching method in cement waste form containing excessive 137Cs and 60Co.

According to the Nuclear Safety and Security Commission (NSSC) Notice No. 2021-26 “Delivery Regulations for the Low- and Intermediate Level Radioactive Waste (LILW)”, the activity of 3H, 14C, 55Fe, 58Co, 60Co, 59Ni, 63Ni, 90Sr, 94Nb, 99Tc, 129I, 137Cs, 144Ce, and gross alpha must be identified. Currently, the scaling factor of the dry active waste (DAW) for LILW is applied as an indirect evaluation method in Korea. The analyses are used the destructive methods and 55Fe, 60Co, 59Ni, 63Ni, 90Sr, 94Nb, 99Tc, and 137Cs, which are classified as nonvolatile nuclides, are separated through sequential separation and then measured by gamma detector, liquid scintillation counter (LSC), alpha/beta total counter (Gas Proportional Counter, GPC), and ICP-MS. We will introduce how to apply the existing nuclide separation method and improve the measurement method to supplement it.

In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

Concrete is one of the largest wastes, by volume, generated during the decommissioning of nuclear facilities, which significantly influences the projected costs for the disposal of decommissioning wastes. Concrete consists of aggregates and a cement binder. In radioactive concrete, the radioisotopes are mainly associated with the cement component. If the radioactive isotope can be separated from the concrete to below the clearance criteria, the volume of radioactive concrete waste could be reduced effectively. We were studied to separate the radioactive materials from the concrete by using the thermomechanical and chemical treatment processes, sequentially. From the study, separated aggregate could be treated to achieve the clearance level. However, these processes generate a large volume of secondary acidic radioactive wastewater, which might be a critical problem to reduce the volume of radioactive concrete waste. In this research, separating the 137Cs and 90Sr from dissolved concrete wastewater to below the discharge criteria by precipitation method, it would be released to the environment under industrial waste guidelines. The experiments were conducted to using a simulated radioactive wastewater, formed by the dissolution of concrete within HCl, which was spiking the 137Cs and 90Sr, respectively. In addition, we applied the chemical precipitation methods with wastewater, using ferrocyanide for 137Cs and BaSO4 coprecipitation for 90Sr. As a result, targeted radionuclides could be removed to the discharge level (137Cs: 0.05 Bq·ml−1, 90Sr: 0.02 Bq·ml−1) by precipitation method. Therefore, it could reduce the secondary wastewater effectively by precipitation method and enhance the additional volume reduction for radioactive concrete waste.

TGS(Tomographic Gamma Scan)분석 기술은 방사성폐기물 드럼을 10×10×16개의 단위부피로 분할하여 분할단위 마다 밀 도 및 방사능 농도를 각각 측정하기 때문에 기존 기술에 비해 높은 분석정확도를 갖는 장점에 비하여 낮은 정밀도를 갖는 단 점이 있다. 이를 보완하기 위해 하나의 에너지를 구별하는 전흡수피크(Full Energy Peak)의 범위(ROI : Region of Interest) 를 넓게 설정하여 정밀도를 최적화한다. 하지만 전흡수피크의 범위 증가는 인접한 에너지를 방출하는 핵종간 상호간섭이 발 생할 확률이 높아진다. 본 연구에서는 TGS분석에서 기준 핵종인 137Cs(661.66 keV 반감기 30.5 년) 정량분석에 간섭을 일으 키는 원인을 규명하였으며 그 원인으로 인접한 110mAg(657.75 keV 반감기 249.76 일)임을 확인하였다. 이러한 간섭을 제거 할 수 있는 방안으로 최적화된 ROI를 결정할 수 있는 새로운 교정기술을 개발하였으며 본 교정기술을 적용 후 정확도 검사 에서 기준핵종 137Cs을 정확히 판정함을 확인하였다.

This study was evaluated the applicability of the membrane filtration process (Micro Filtration (MF), nanofiltration membranes (NF), reverse osmosis (RO)) on the major radioactive substances, iodine (I-) and cesium (Cs+) using membranes produced in Korea and domestic raw water. Iodine (I-) or cesium (Cs+) in the microfiltration membrane (MF) process could not be expected removal efficiency by eliminating marginally at the combined state with colloidal and turbidity material. At the domestic raw water (lake water, turbidity 1.2 NTU, DOC 1.3 mg/L) conditions, nanofiltration membrane (NF) and reverse osmosis (RO) showed a high removal rate of about 88 ~ 99% for iodine (I-) and cesium (Cs+) and likely to be an alternative process for the removal of radioactive material.

국내 경수로원전 1차 냉각재와 중저준위 방사성폐기물 내 핵종방사능비에 대한 유관성을 검토하고자 특수하게 제작된 RCS sampling kit를 이용하여 원전 정상운전기간 동안 핵종을 포집하였다. 시료채취는 경수로형 전 원자력 발전소를 대상으로 2004년과 2005년에 걸쳐 시료를 채취하였고, 방사화학적 방법인 시료 전처리 및 핵종분리를 통하여 핵종 방사능을 분석하였다. RCS sampling kit 내 필터와 수지에서 분석된 핵종 방사능비는 각각 2.32-2와 7.3E-1을 보였으며, 동일주기 내 발생된 중 저준위 방사성폐기물인 농축폐액, 폐수지, 잡고체시료 내 핵종 방사능비는 각각 6.3E-1, 6.7E-1 및 5.7E-2로 시료유형 에 따라 1차 냉각재와 유사성을 갖는 것으로 확인하였다.

[ ]의 토양침적으로 인한 농작물 오염 평가를 위한 동적격실모델이 제시되었다 토양침투(percolation), 쟁기질에 의한 토양혼합(soil mixing), 뿌리흡수(plant uptake), 용출(leaching to a deep soil), 토양고착(fixation to a clay mineral)이 모델에서 고려된 의 주요 이동경로이며 의 토양이동에 대한 토양특성(pH, 점토함량, 유기물함량, 이온교환성 K 농도)의 영향을 반영하기 위하여 Absalom 모델을 적용하였다. 모델의 검증을 위해 다른 토양특성을 가진 17종류의 논토양에서 2년 연속 벼를 재배하면서 수행한 모의침적실험으로부터 구한 벼에 대한 전이계수를 모델에 의한 예측치와 비교하였다. 측정된 벼의 전이계수는 pH와 점토함량 변화에는 뚜렷한 경향을 보여주지 않았으나, 유기물함량의 증가 또는 이온교환성 K 농도의 감소에 따라 다소 증가하는 경향을 보여주었다. 측정된 전이계수는 모델에 의한 예측치와 대체적으로 유사한 값을 가졌다.