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Novel method for analyzing transport parameters in through-diffusion tests
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Novel method for analyzing transport parameters in through-diffusion tests

Bo-Tsen Wang, Chuan-Pin Lee, Ming-Chee Wu, Tsuey-Lin Tsai, Shih-Chin Tsai 和 Kuo-Chin Hsu
Journal of environmental radioactivity, 卷.196, 頁碼.125-132
01/2019
PMID: 30448765
Web of Science ID: WOS:000454381200014

摘要

Analytical solution Diffusion coefficients Iterative process Parameter inversion Through-diffusion test
Buffer materials such as bentonite are vital for absorbing radionuclide leakage and retarding migration from radioactive waste canisters. The diffusion coefficient and the retardation factor are the predominant properties controlling the diffusion-reaction process in a buffer material. Diffusion experiments combined with Crank's graphical method are a well-established process for determining asymptotic diffusion coefficients. However, the inaccuracy of the diffusion coefficient that results from the subjective judgement of the late-time linear part of the cumulative concentration data in Crank's graphical method will deteriorate the estimate of the retardation factor. A novel parameter identification process based on an iterative and analytical method (PIPIAM) is proposed here to obtain the diffusion coefficients and porosity of bentonite using concentration data. The results of PIPIAM and the graphical method are compared through an error analysis of concentration. The results show that PIPIAM outperforms the graphical method in terms of the error reduction of the concentration and the uncertainty decrease of the estimated parameters. The proposed method is thus a good alternative for acquiring transport parameters for use in safety assessments of nuclear waste repositories. •PIPIAM is proposed to acquire key transport parameters of MX-80 bentonite in through-diffusion tests.•Non-reactive and reactive tracer experiments data were analyzed for diffusion coefficient, porosity and retardation factor.•PIPIAM is superior to the conventional graphical method in terms of estimation error reduction and uncertainty decrease.

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