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

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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).

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Acknowledgements

This work was supported by the Project Approved by the Provincial Education Department of Hunan Province, China (No.19A420), the Natural science foundation of Hunan Province (Grant Nos. 2020JJ5463; 2021JJ40463).

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Correspondence to Pingping Huang.

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Zhao, T., Wu, H., Sun, J. et al. Immobilization of uranium tailings by phosphoric acid-based geopolymer with optimization of machine learning. J Radioanal Nucl Chem 331, 4047–4054 (2022). https://doi.org/10.1007/s10967-022-08454-3

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