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

• Published in: Journal of radioanalytical and nuclear chemistry Vol. 331; no. 9; pp. 4047 - 4054
• Main Authors: Zhao, Tianji, Wu, Haoyang, Sun, Junjie, Wen, Xinhai, Zhang, Jie, Zeng, Weihao, Shen, Hao, Hu, Zhitao, Huang, Pingping
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2022; Springer; Springer Nature B.V
• Subjects: Chemistry; Chemistry and Materials Science; Compressive strength; Contaminants; Diagnostic Radiology; Exhalation; Fourier transforms; Geopolymers; Hadrons; Heavy Ions; Inorganic Chemistry; Kaolin; Leaching; Machine learning; Nuclear Chemistry; Nuclear Physics; Optimization; Phosphates; Phosphoric acid; Physical Chemistry; Tailings; Uranium; Phosphoric acid-based geopolymer; Uranium tailings; Machine learning
• ISSN: 0236-5731; 1588-2780
• DOI: 10.1007/s10967-022-08454-3

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).