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Immobilization of uranium tailings by phosphoric acid-based geopolymer with optimization of machine learning

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成果类型:
期刊论文
作者:
Zhao, Tianji;Wu, Haoyang;Sun, Junjie;Wen, Xinhai;Zhang, Jie;...
通讯作者:
Pingping Huang
作者机构:
[Zeng, Weihao; Shen, Hao; Zhao, Tianji; Huang, Pingping; Sun, Junjie; Wu, Haoyang; Zhang, Jie; Wen, Xinhai] Univ South China, Sch Resource Environm & Safety Engn, Hengyang 421001, Hunan, Peoples R China.
[Huang, Pingping] Hunan Prov Engn Technol Res Ctr Uranium Tailings, Hengyang 421001, Hunan, Peoples R China.
[Hu, Zhitao] Univ South China, Coll Mech Engn, Hengyang 421001, Hunan, Peoples R China.
通讯机构:
[Pingping Huang] S
School of Resource Environment and Safety Engineering, University of South China, Hengyang, China<&wdkj&>Hunan Province Engineering Technology Research Center of Uranium Tailings Treatment, Hengyang, China
语种:
英文
关键词:
Phosphoric acid-based geopolymer;Uranium tailings;Machine learning
期刊:
Journal of Radioanalytical and Nuclear Chemistry
ISSN:
0236-5731
年:
2022
卷:
331
期:
9
页码:
4047-4054
基金类别:
Project Approved by the Provincial Education Department of Hunan Province, China [19A420]; Natural science foundation of Hunan Province [2020JJ5463, 2021JJ40463]
机构署名:
本校为第一机构
院系归属:
机械工程学院
摘要:
To decrease the contaminant leaching and radon exhalation from uranium tailings, a phosphoric acid-based geopolymer (PAG) precursor was selected as a solidifying agent to bind coarse sands to achieve compact structures. Machine learning was applied to explore the optimal ratio of geopolymer preparation, aimed at achieving a higher compressive strength of solidified bodies. Results showed that the maximum compressive strength of 18.964 MPa appeared at the mass ratio of 2.8 for phosphoric acid/kaolin. The uranium leaching rate of 0.70 × 10−6 cm...

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