According to NSSC Notice No. 2021-10, safety analysis needs to be introduced in the decommissioning plan. Public and occupational dose analyses should be conducted, specifically for unexpected radiological accidents. Herein, based on the risk matrix and analytic hierarchy process, the method of selecting accident scenarios during the decommissioning of nuclear power plants has been proposed. During decommissioning, the generated spent resin exhibits relatively higher activity than other generated wastes. When accidents occur, the release fraction varies depending on the conditioning method of radioactive waste and type of radioactive nuclides or accidents. Occupational dose analyses for 2 (fire and drop) among 11 accident scenarios have been performed. The radiation doses of the additional exposures caused by the fire and drop accidents are 1.67 and 4.77 mSv, respectively.

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.

The organic complexing agents such as ethylenediaminetetraacetic acid (EDTA), nitrilotriacetic acid (NTA), and isosaccharinic acid (ISA) can enhance the radionuclides’ solubility and have the potential to induce the acceleration of radionuclides’ mobility to a far-field from the radioactive waste repository. Hence, it is essential to evaluate the effect of organic complexing agents on radionuclide solubility through experimental analysis under similar conditions to those at the radioactive waste disposal site. In this study, five radionuclides (cesium, cobalt, strontium, iodine, and uranium) and three organic complexing agents (EDTA, NTA, and ISA) were selected as model substances. To simulate environmental conditions, the groundwater was collected near the repository and applied for solubility experiments. The solubility experiments were carried out under various ranges of pHs (7, 9, 11, and 13), temperatures (10°C, 20°C, and 40°C), and concentrations of organic complexing agents (0, 10-5, 10-4, 10-3, and 10-2 M). Experimental results showed that the presence of organic complexing agents significantly increased the solubility of the radionuclides. Cobalt and strontium had high solubility enhancement factors, even at low concentrations of organic complexing agents. We also developed a support vector machine (SVM) model using some of the experimental data and validated it using the rest of the solubility data. The root mean square error (RMSE) in the training and validation sets was 0.012 and 0.016, respectively. The SVM model allowed us to estimate the solubility value under untested conditions (e.g., pH 12, temperature 30°C, ISA 5×10-4 M). Therefore, our experimental solubility data and the SVM model can be used to predict radionuclide solubility and solubility enhancement by organic complexing agents under various conditions.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

High level radioactive waste disposal repository is faced thermos-hydro-mechanical-radioactive condition. Factors according to these complex conditions are measured using multiple sensors installed in the disposal repository to check integrity of the structure. Wires of the sensors can be potential pathways of groundwater and nuclide flow and these pathways accelerates bentonite saturation. Therefore, it is worth to developing wireless sensors buried in the bentonite buffer which can communicate without wires. In start of the study, widely-utilized wireless communication methods including WiFi and LoRa are tested using compacted bentonite blocks to estimate the performance of them. Compacted bentonite blocks are prepaired using di-press method with metal molds and the dry density of them are about 1.6 g/cm3. All wireless communication methods are well communicated through the bentonite blocks over 50 cm. The further experimental tests will be conducted with different dry density and water contents. The results of these experimental tests give a possibility of wireless communications in compacted bentonite buffer and will be utilized for the design of wireless sensor systems for the repository monitoring.