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- Title
Immobilization of uranium tailings by phosphoric acid-based geopolymer with optimization of machine learning.
- Authors
Zhao, Tianji; Wu, Haoyang; Sun, Junjie; Wen, Xinhai; Zhang, Jie; Zeng, Weihao; Shen, Hao; Hu, Zhitao; Huang, Pingping
- Abstract
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/d on the 42nd day was three orders of magnitude less than the clay mixture-based geopolymer solidified bodies. The successful synthesis of geopolymer was evidenced by the X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR), the homogeneous and dense structure of solidified bodies was characterized by the scanning electron microscopy (SEM).
- Subjects
URANIUM; KAOLIN; MACHINE learning; FOURIER transform infrared spectroscopy; SCANNING electron microscopy; COMPRESSIVE strength
- Publication
Journal of Radioanalytical & Nuclear Chemistry, 2022, Vol 331, Issue 9, p4047
- ISSN
0236-5731
- Publication type
Article
- DOI
10.1007/s10967-022-08454-3