Journal
ANNALS OF NUCLEAR ENERGY
Volume 142, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2020.107395
Keywords
Compacted bentonite; Thermal conductivity; Regression analysis; Machine learning method
Categories
Funding
- Hanbat National University
Ask authors/readers for more resources
Compacted bentonite is one of the widely used buffer materials for the disposal of high-level radioactive waste (HLW). Since the buffer is located between a disposal canister and near-field rock, it prevents the release of radionuclides, and protects the canister from external impact and the penetration of ground-water. To dissipate decay heat from the canister, the buffer should have high thermal conductivity, so one of the most important properties for HLW repository is the thermal conductivity of compacted bentonite. In this study, predictive models of thermal conductivity for the compacted bentonite has been designed and analyzed using machine learning methods including linear regression, decision tree, support vector machine, ensemble, Gaussian process regression (GPR), artificial neural network, and deep belief network. Most of the methods showed better performance in comparison with the previously proposed regression model, while the GPR with exponential kernel and the ensemble with XGBoost showed the best performance. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available