The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-di...The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.展开更多
The characteristics of diffusion are essential to the transport of radionuclides through buffer/backfill materials, such as bentonite, which are commonly found in waste repositories. This study used through-diffusion ...The characteristics of diffusion are essential to the transport of radionuclides through buffer/backfill materials, such as bentonite, which are commonly found in waste repositories. This study used through-diffusion techniques to investigate the diffusion behavior of HTO and ^(99)TcO_4^- on GMZ bentonite of various densities. Diffusion rates were calculated by measuring the diffusion coefficients(De, Da), plotting breakthrough curves and interpreting experiment data. The apparent and effective diffusion coefficients of HTO ranged from(1.68 ± 0.40) 9 × 10^(-11) to(2.80 ± 0.62) 9 × 10^(-11) m^2/s and from(4.61 ±1.28) 9 × 10^(-12) to (16.2 ± 2.50) 9 × 10^(-12) m^2/s, respectively.The apparent and effective diffusion coefficients of^(99)TcO_4^-ranged from(5.26 ± 0.16) 9 × 10^-12to(7.78 ± 0.43) 9× 10^-12m^2/s and from(1.49 ± 0.002) 9 × 10^(-12) to(4.16 ±0.07) 9 × 10^(-12) m^2/s, respectively. The distribution coefficients of HTO and^(99)TcO_4^-ranged from(0.70 ± 0.12) 9× 10^(-2) to(1.36 ± 0.53) 9 × 10^(-2) mL/g and from(1.12 ±0.06) 9 × 10^(-2) to(5.79 ± 2.22) 9 × 10^(-2) mL/g, respectively.The Deand Kdvalues were shown to decrease with an increase in the bulk dry density of compacted bentonite. Our results show that HTO and ^(99)Tc could be considered nonsorbent radionuclides. The data obtained in this studyprovide a valuable reference for the safety assessment of waste repositories.展开更多
基金the Key Program of National Natural Science Foundation of China(No.12335008),the Postgraduate Research and Innovation Project of Huzhou University(No.2023KYCX62)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202352712)the Huzhou science and technology planning project(No.2021GZ60)。
文摘The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.
基金the Nuclear Backend Management Department at Taiwan Power Company for financially supporting this research
文摘The characteristics of diffusion are essential to the transport of radionuclides through buffer/backfill materials, such as bentonite, which are commonly found in waste repositories. This study used through-diffusion techniques to investigate the diffusion behavior of HTO and ^(99)TcO_4^- on GMZ bentonite of various densities. Diffusion rates were calculated by measuring the diffusion coefficients(De, Da), plotting breakthrough curves and interpreting experiment data. The apparent and effective diffusion coefficients of HTO ranged from(1.68 ± 0.40) 9 × 10^(-11) to(2.80 ± 0.62) 9 × 10^(-11) m^2/s and from(4.61 ±1.28) 9 × 10^(-12) to (16.2 ± 2.50) 9 × 10^(-12) m^2/s, respectively.The apparent and effective diffusion coefficients of^(99)TcO_4^-ranged from(5.26 ± 0.16) 9 × 10^-12to(7.78 ± 0.43) 9× 10^-12m^2/s and from(1.49 ± 0.002) 9 × 10^(-12) to(4.16 ±0.07) 9 × 10^(-12) m^2/s, respectively. The distribution coefficients of HTO and^(99)TcO_4^-ranged from(0.70 ± 0.12) 9× 10^(-2) to(1.36 ± 0.53) 9 × 10^(-2) mL/g and from(1.12 ±0.06) 9 × 10^(-2) to(5.79 ± 2.22) 9 × 10^(-2) mL/g, respectively.The Deand Kdvalues were shown to decrease with an increase in the bulk dry density of compacted bentonite. Our results show that HTO and ^(99)Tc could be considered nonsorbent radionuclides. The data obtained in this studyprovide a valuable reference for the safety assessment of waste repositories.
