Molten Salt Reactor, which employs molten salt mixture as fuel, has many advantages in reactor size and operation compared to conventional nuclear reactor. In developing Molten Salt Reactor, the behavior of fission product in operation should be preliminary evaluated for the correct design of reactor and its associated system including off-gas treatment. In this study, for 100 Mw 46 KCl- 54 UCl3 based Molten Salt Reactor with operating life time of 20 year, the fission product behavior was estimated by thermodynamic modeling employing FactSage 8.2. Total inventory of all fission product were firstly calculated using OpenMC code allowing depletion during neutronic calculation. Then, among all inventory, 46 element species from Uranium to Holmium were chosen and given to the input for equilibrium module of Factsage with its mass. In phase equilibrium calculation, for the correct description of solution phase, KCl-UCl3 solution database based on modified quasichemical model in the quadruplet approximation (ANL/CFCT-21/04) was employed and the coexisting solid phase was assumed to pure state. With the assumption of no oxygen and moisture ingress into reactor system, equilibrium calculation showed that 1% of solid phase and of gas phase were newly formed and, in gas phase, major species were identified : ZrCl4 (47%), Xe (33%), UCl4 (14%), Kr (5%), Ar (1%) and others. This result reveals that off-gas treatment of system should account for the appropriate treatment of ZrCl4 and UCl4 besides treatment of noble gas such as Xe and Kr.

Nuclear facilities present the important task related to the migration and retention of radioactive contaminants such as cesium (Cs), strontium (Sr), and cobalt (Co) for unexpected events in various environmental conditions. The distribution coefficient (Kd) is important factor for understanding these contaminants mobility, influenced by environmental variables. This study focusses the prediction of Kd values for radionuclides within solid phase groups through the application of machine-learning models trained on experimental data and open source data from Japan atomic energy agency. Three machine-learning models, such as the convolutional neural network, artificial neural network, and random forest, were trained for prediction model of the distribution coefficient (Kd). Fourteen input variables drawn from the database and experimental data, including parameters such as initial concentration, solid-phase characteristics, and solution conditions, served as the basis for model training. To enhance model performance, these variables underwent preprocessing steps involving normalization and log transformation. The performances of the models were evaluated using the coefficient of determination. These results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. These models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and longterm risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

Radioactive contaminants, such as 137Cs, are a significant concern for long-term storage of nuclear waste. Migration and retention of these contaminants in various environmental media can pose a risk to the surrounding environment. The distribution coefficient (Kd) is a critical parameter for assessing the behavior of these contaminants and can introduce significant errors in predicting migration and remediation options. Accurate prediction of Kd values is essential to assess the behavior of radioactive contaminants and to ensure environmental safety. In this study, we present machine learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict Kd values for Cs in soils. We used three different machine learning models, namely the random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN), to predict Kd values. The models were trained on 14 input variables from the JAEA-SDB, including factors such as Cs concentration, solid phase properties, and solution conditions which are preprocessed by normalization and log transformation. We evaluated the performance of our models using the coefficient of determination (R2) value. The RF, ANN, and CNN models achieved R2 values of over 0.97, 0.86, and 0.88, respectively. Additionally, we analyzed the variable importance of RF using out-of-bag (OOB) and CNN with an attention module. Our results showed that the initial radionuclide concentration and properties of solid phase were important variables for Kd prediction. Our machine learning models provide accurate predictions of Kd values for different soil conditions. The Kd values predicted by our models can be used to assess the behavior of radioactive contaminants in various environmental media. This can help in predicting the potential migration and retention of contaminants in soils and the selection of appropriate site remediation options. Our study provides a reliable and efficient method for predicting Kd values that can be used in environmental risk assessment and waste management.

The Ag0-containing sorbents synthesized by Na, Al, and Si alkoxides have higher maximum iodine capture capacity and textural properties than zeolite-based Ag0-containing sorbents. However, these sorbents were prepared in the form of granules via a step for cutting cylindrical alcogels. Since asmade sorbents decreased packing density, they must be additionally crushed and then classified into an appropriate size for increasing packing density. The bead formation in the step of sol-gelation could bring about the simplification of sorbent preparation process and an improvement of packing density. In the Na, Al, and Si alkoxides as starting materials, sol solution was hydrophilic and lower density than vegetable oil, which transformed sol droplets to sol-gel beads. Thus, in these precursors, sol droplets, which must be sprayed by single nozzle placed at bottom side of oil column, can rise up through oil column. Acetic acid (HOAc) was used as the catalyst for the hydrolysis of Na alkoxide (TEOS) and gelation of the Na+AlSi-OH alcosol. For obtaining sol-gel beads, experiments were performed by the flowrate change of sol solution and HOAc at different nozzle sizes using soybean oil column of 1 m in length. At a sol/HOAc flowrate ratio of 3.85, some Na+AlSi-OH alcogel beads were obtained. After the Ag/Na ion-exchange, Ag content in Ag+AlSi-OH hydrogel was low due to reaction between Na+ and HOAc during sol-gelation and aging step. The Ag+AlSi-OH hydrogel with high Ag content could be prepared by Na addition. After the solvent exchange and drying at ambient pressure, the bead sorbents had higher Ag0 content and larger pore size than granular sorbents. However, further experiments are needed to increase yield rate in bead sorbent.

In KAERI, the nuclide management technology is currently being developed for the reduction of disposal area required for spent fuel management. Among the all fission products of interest, Cs, I, Kr, Tc are considered to be significantly removed by following mid-temperature and high-temperature treatment, however, a difficulty of spent-fuel thermal treatment experiment limits the development of such thermal treatment. In this study, we applied our previously developed two-stage diffusion release model coupled to UO2 oxidation model to the development of optima thermal treatment scenario. Since the formation of cesium pertechnetate should be avoided and the fission release behavior is considerably affected by the extent of oxygen, we obtained oxygen-content dependent model parameters for two-stage fission release model and applied the model to the evaluation of fission release behavior to different oxygen content and thermal treatment procedure. It was found that the developed fission release model closely describes the experimental behavior of fission product of interest, implying a validity of model prediction and the thermal treatment condition reducing the chemical reaction between cesium and technetium could be developed.

Sow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using the SGE model. The phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The direct effect on ADG had a significantly negative genetic relationship with STAY, whereas the social effect on ADG had a neutral genetic relationship. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive, unlike the direct effects. The genetic correlation of the total effect on ADG with that of STAY was negative but non-significant, owing to the social effect. These results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity owing to the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.