A sequential column experiment was conducted for uranium removal of excessively high or highly U-contaminated soils, simultaneously. Two pilot-scale acryl columns with a 24 cm ID and 48 cm length were uniformly packed with each U-contaminated soil (both < 2 mm, 119, and 22.4 Bq/g as initial U-238 activities). A column packed with soil contained very high U constant located first then sequentially located second columns with relatively lower U-contaminated soil. Thus the effluents which passed very high U-contaminated soil and having extremely high dissolved U concentration was directly inflowed the second columns. Both columns initially and respectively flushed with demi water (or condensing water of air conditioner generated from radiation controlled area) to saturate and displace the air from the pore space. Elution was carried out with alkaline and acidic solutions, respectively, and sequentially. The uranium removal efficiencies were found and a comparison was made with the pilot soil flushing experiments. During this study, a new approach to reducing acidic flushing waste which is considered the biggest defect of soil washing/flushing was established, and optimal factors were calculated to demonstrate industrial-scale uranium decontamination of soil with high uranium content.
3D modeling is a technology for representing real objects in a virtual 3D space or modeling and reproducing the physical environment. 2D drawings for viewing the existing building structure have limitations that make it difficult to understand the structure. By implementing this 3D modeling, specific visualization became possible. 3D technology is being applied in a wide range of preevaluation work required for nuclear decommissioning. In Slovakia, 3D modeling was applied to determine the optimal cutting strategy for the primary circuit before dismantling the VVER type Bohunice V1. In Japan, the Decommissioning Engineering Support System (DEXUS) program has been developed that incorporates VRDose, a decommissioning engineering support system based on 3D CAD models. Through this, the cutting length of the work object and the quantity of containers for packaging waste are calculated, exposure dose calculations in various dismantling scenarios, and cost estimation are performed. Korea also used 3D technology to evaluate the decommissioning waste volume for Kori Unit 1 and to evaluate the optimal scenario of the decommissioning process procedure for the research reactor Unit 1. 3D technology is currently being used in various pre-decommissioning evaluations for VVER, PWR, and research reactors. Overseas, a program that matches various decommissioning pre-evaluation tasks with cost estimation has also been developed. However, most 3D technologies are mainly used as a support system for dose evaluation and amount of decommissioning waste calculation. In this study, 3D modeling was performed on the PHWR structure, and physical and radiological information about the structure was provided. The information on the structure can present the unit cost for the work object by confirming the standard of the applied unit cost factor (UCF). The UCF presents the unit cost for repeated decommissioning operations. The decommissioning cost of the work object can be obtained by multiplying the UCF by the number of repetitions of the work. If the results of this study are combined with the process evaluation and waste quantity estimation performed in previous studies, it is judged that it will be helpful in developing an integrated NPP decommissioning program that requires preliminary evaluation of various tasks. In addition, it is judged that a clear cost estimation of the object to be evaluated will be possible by matching the 3D work object with the UCF.
The saturation rates of the spent fuel (SF) wet storage at the Kori Nuclear Power Plant (NPP), Hanbit, and Hanul are 83.3%, 74.2%, and 80.8% as of the fourth quarter of 2021. The storages of Kori NPP and Hanbit NPP are expected to be saturated in 2031, and Hanul is expected to be saturated in 2032. Therefore, the construction of an interim storage facility to store the SF temporarily stored in the NPP was planned, and preparations for the safe transport of the SF are required. In this paper, radiological preliminary assessment using NRC-RADTRAN in the process of sea transport of SF from the wet storage or ISFSI of the Hanbit NPP to the optional interim storage facility was performed. Since domestic SF transport vessels are not currently in operation, the specifications of the UK Pacific Grebe vessel which can carry up to 20 casks were used. The transport cask used the specifications of KORAD-21, a transport container developed in Korea. Because it can carry more SF assemblies than the existing KN-18. In addition, a land transport safety test was conducted in 2020 and a sea transport test is planned. The sea transport route was entered by referring to the transport route of domestic low and intermediate level waste. The accidents rate was calculated using statistics on maritime accidents from 2017 to 2021. The probability accidents along the transportation route were evaluated as 3.152E -10. When transporting to an interim storage facility, the SF expected to be the main transport target was selected as WH 17X17, combustion 45,000 MWD/MTU, and concentration of 4.5%. The source term was calculated and entered according to this data and the release fraction was entered with reference to the DOE report. In the case of normal transport without accident, the individual dose of the crew member and public residents were estimated to be 0.0525% and 0.000492% of the annual limit of 1 mSv/yr for the general public. Under the accident conditions, the annual individual doses of residents were 0.0011%, 0.0023%, 0.0034%, and 0.0046% of the annual limit of 1 mSv/yr when carrying 5, 10, 15, and 20 casks. Currently, the site of the interim storage facility has not been precisely determined, but a preliminary radiation assessment through sea transport resulted in a significantly lower than the limit. Combined scenario sea transport followed by land transport will be carried out in the next stage of study.
The aim of this study was to investigate the effect of isolated lactic acid bacteria (LAB) on the quality of high moisture rye silage. Rye forage (Secale cereale L.) was harvested at the heading stage (27.3% of dry matter (DM)) and cut into approximately 3-5 cm lengths. Then, the forage divided into 4 treatments with different inoculants: 1) No additives (CON); 2) Lactobacillus brevis strain 100D8 at a 1.2 x 105 colony-forming unit (cfu)/g of fresh forage (LBR); 3) Leuconostoc holzapfelii strain 5H4 at a 1.0 x 105 cfu/g of fresh forage (LHO); and 4) Mixture of LBR and LHO (1:1 ratio) applied at a 1.0 x 105 cfu/g of fresh forage (MIX). About 3 kg of forage from each treatment was ensiled into a 20 L mini-bucket silo in quadruplicate for 100 days. After silo opening, silage was collected for analyses of chemical compositions, in vitro nutrient digestibilities, fermentation characteristics, and microbial enumerations. The CON silage had the highest concentrations of neutral detergent fiber and acid detergent fiber (p = 0.006; p = 0.008) and a lowest in vitro DM digestibility (p < 0.001). The pH was highest in CON silage, while lowest in LBR and MIX silages (p < 0.001). The concentrations of ammonia-N, lactate, and acetate were highest in LBR silage (p = 0.008; p < 0.001; p < 0.001). Propionate and butyrate concentrations were highest in CON silage (p = 0.004; p < 0.001). The LAB and yeast counts were higher in CON and LHO silages compare to LBR and MIX silages (p < 0.001). However, the mold did not detect in all treatments. Therefore, this study could conclude that L. brevis 100D8 and Leu. holzapfelii strain 5H4 can improve the digestibility and anti-fungal activity of high moisture rye silage.
Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.