No abstract

To classify the chemical form of stable carbon released from unirradiated stainless steel, which is the material used to simulate irradiated stainless steel, under highly alkaline and low-oxygen conditions, type 304 and 316 stainless-steel powders were immersed in 0.005 M NaOH solution. Gas and liquid samples were analyzed to identify the chemical form of carbon released from the stainless steel. The liquid samples were divided into unfiltered and filtered samples. In the gaseous phase, hydrocarbons such as methane and ethane were not detected. In the liquid phase, carboxylic acids (formic and acetic acids) were detected. However, the sum of the carbon concentrations of the carboxylic acids was significantly lower than the total organic carbon (TOC) concentration in the unfiltered samples. In the filtered samples, the TOC concentration was closer to the sum of the carbon concentrations than that for the unfiltered samples. In addition, the concentrations of the metallic elements (particularly Fe and Cr), which are the main constituents of the stainless steels, tended to decrease upon ultrafiltration. This suggests that the sorption of carbon on metallic compounds (e.g., colloidal iron hydroxide) may have occurred.

Probabilistic safety assessment has not been performed for radioactive waste disposal owing to the difficulty of dealing with the probability distributions of the parameters included in the longterm safety assessment of radioactive waste disposal. In this study, we develop a methodology of probabilistic safety assessment in consideration of both epistemic uncertainty and aleatory uncertainty, in which the probability density function (PDF) and cumulative distribution function (CDF) of the maximum annual dose can be calculated. We also propose an approach to demonstrating dose assessment results in compliance with the stepwise target annual doses of likely and less-likely scenarios according to the occurrence probability of the scenario without classifying the probabilities of parameters involved prior to the safety assessment. For the likely scenario, we can employ the larger of the modal value of the PDF and the 50th percentile of the CDF to meet the target annual dose (10 µSv y -1 ). For the less-likely scenario, we can adopt the 95th percentile of the CDF as the assessment result for comparison with the target annual dose (300 µSv y -1 ).

A modeling calculation methodology for estimating the radionuclide composition in the wastes generated at the Fukushima Daiichi nuclear power station has been upgraded by introducing an approach using Bayesian inference. The developed stochastic method describes the credible interval of the regression curve for the log-normal distribution of the measured transport ratio, which is used to calibrate the radionuclide transport parameters included in the modeling calculation. Consequently, the method can predict the probability distribution of the radionuclide composition in the Fukushima Daiichi wastes. The notable feature of the developed method is that it can explicitly investigate the improvement in the accuracy and confidence (degree of belief) of the estimation of the waste inventory using Bayesian inference. Specifically, the developed method can update and improve the degree of belief of the estimation of the radionuclide composition by visualizing the reduction in the width of uncertainty in the radionuclide transport parameters in the modeling calculation in accordance with the accumulation of analytically measured data. Further investigation is expected to improve the credibility of waste inventory estimation through iteration between modeling calculations and analytical measurements and to reduce excessive conservativeness in the estimated waste inventory dataset.

No abstract