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Tachi, Yukio; Suyama, Tadahiro; Ochs, M.*; Ganter, C.*
JAEA-Data/Code 2010-031, 168 Pages, 2011/03
JAEA-Data-Code-2010-031.pdf:6.48MB
JAEA has developed sorption database (SDB) compiling Kd data in buffer material and rock, which is key parameter for performance assessment (PA) of the geological disposal. The present report focuses on developing and updating of the JAEA-SDB as basis of integrated approach for PA-related Kd setting. This includes an overview of database structure, contents and functions including additional data evaluation function focusing on multi-parameter dependence, operating method, PA-related applications of the web-based SDB. Kd data and their QA results are updated by focusing our recent activities on the Kd setting and mechanistic model development. As a result, total Kd values in the SDB are about 28,540, and QA/classified Kd data are about 39 % for all Kd data. The updated JAEA-SDB is expected to make it possible to obtain quick overview of the available data, and to have suitable access to the respective data for PA-related Kd setting in effective, traceable and transparent manner.
Ochs, M.*; Tachi, Yukio; Ganter, C.*; Suyama, Tadahiro; Yui, Mikazu
JAEA-Research 2010-052, 59 Pages, 2011/02
JAEA-Research-2010-052.pdf:1.1MB
For reliable parameter setting in performance assessment of geological disposal, JAEA has developed the integrated sorption and diffusion (ISD) model/database in bentonite systems. The main goal of the mechanistic model/database development is to provide a tool for a consistent explanation, prediction, and uncertainty assessment. This report focuses on developing the thermodynamic sorption model (TSM) including selection of model design and surface species, and on the quantification and handling of model uncertainties, based on illustrating by example of Ni sorption on montmorillonite/bentonite. Two fundamentally different approaches are presented and compared for representing TSM uncertainties: (1) TSM parameter uncertainties based on optimization routines and statistical procedure, (2) overall error estimated by direct comparison of modeled and experimental Kd values. The overall error in Kd is viewed as the best representation of model uncertainty in ISD model/database development.
Suyama, Tadahiro; Ganter, C.*; Kunze, S.*; Tachi, Yukio; Ochs, M.*
JAEA-Data/Code 2010-026, 72 Pages, 2011/02
JAEA-Data-Code-2010-026.pdf:1.05MB
Sorption of radionuclides in bentonites and rocks is one of the key processes in the safe geological disposal of radioactive waste. JAEA has developed sorption database (JAEA-SDB) which includes extensive compilation of sorption Kd data. JAEA has been continuing to update the SDB in view of potential future data needs, focusing on assuring the quality level and practical applications to Kd-setting for the geological environment. In this report, the QA/classification of selected entries in the JAEA-SDB, focusing on key radionuclides (Th, Np, Am, Se and Cs) sorption on tuff existing widely in geological environment, was done following the approach/guideline defined in our previous report. As a result, the reliability of 560 Kd values was evaluated and classified. This classification scheme is expected to make it possible to obtain quick overview of the available data from the SDB, and to have suitable access to the respective data for Kd-setting in PA.
Ochs, M.*; Tachi, Yukio; Ganter, C.*; Suyama, Tadahiro; Yui, Mikazu
no journal, ,
The thermodynamic sorption models (TSMs) are being considered as powerful tools for quantifying the sorption of radionuclides for a given set of geochemical conditions, and especially for assessing the influence of variable geochemical conditions on the extent of sorption, for the performance assessment of disposal of radioactive waste. The aim of the present study is to address various aspects of TSM development that are important for lending credibility in the context of applications related to radioactive waste disposal. Specifically, the following points are addressed to use the example of Ni sorption to smectite: (1) evaluation of the experimental database and different modeling options in cases where different datasets are available, (2) representation of model uncertainties, (3) testing of the resulting TSM against additional data and evaluation of the applicability to more complex systems.