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A mathematical model for column leaching of ion adsorption-type rare earth ores

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Abstract

Column leaching experiments with ion adsorption-type rare earth ores for different lixiviant concentrations and different column heights were carried out. A mathematical model of column leaching was constructed based on the experimental data. Two parameters (a and b) in the model were determined according to the following methodology: the ore column was divided into several units; each unit was treated with multiple leaching steps. The leaching process was simulated as a series of batch leaching experiments. Parameter a of the model was determined based on the selectivity coefficient of the balanced batch leaching experiment. Further, the influences of ammonium sulfate concentration, rare earth grade, column height, permeability coefficient, and hydrodynamic dispersion coefficient on the extraction were analyzed. Relationships between parameter b, the ammonium sulfate concentration, and the physical and mechanical properties of the ore column, were examined using dimensional analysis. It was determined that the optimal ammonium sulfate concentration for different column heights (2.5, 5.0, 7.5, and 10.0 cm) using the mathematical model were 5.9, 6.2, 7.3, and 7.7 g/L, respectively. The mathematical model can be used to estimate the breakthrough curve, leaching rate, and leaching period of rare earth ores, to achieve optimal extraction.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Nos. 51664015, 41602311, and 51774156), and the Jiangxi University of Science and Technology Qingjiang Youth Elite Support Program (No. JXUSTQJBJ2016007).

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Correspondence to Guan-shi Wang.

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Cite this article

Long, P., Wang, G., Zhang, S. et al. A mathematical model for column leaching of ion adsorption-type rare earth ores. Int J Miner Metall Mater (2020) doi:10.1007/s12613-019-1883-9

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Keywords

  • ion adsorption-type rare earth ores
  • extraction
  • ore leaching period
  • concentration optimization