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Wang, B., Luo, Y., Liu, J. -hui, Li, X., Zheng, Z. -hong, Chen, Q. -qian, et al. (2022). Ion migration in in-situ leaching (ISL) of uranium: Field trial and reactive transport modelling. Journal of Hydrology, 615, 128634.
Abstract: Acid in-situ leaching (ISL) can be used as a mining technique for in situ uranium recover from underground. Acids and oxidants as lixiviants were continuously injected into a sandstone-type uranium deposit in Bayan-Uul (China). It was conducted to facilitate the dissolution of uranium minerals to generate uranyl ions, which could then be extracted for the recovery of uranium resources by the pumping cycle. A reactive transport model based on PHAST was developed to investigate the dynamic reactive migration process of uranium. The simulated results well reproduce the fluid dynamic evolution in the injecting and pumping units, as well as the dynamic release of uranium. The simulated leaching area indicates that the uranium ore leaching area was much larger than the acidification area. In addition, the pollution plume of uranium and acid water was larger than that of the leaching area, which can be used as a reference for uranium mining schemes. Furthermore, the parameter sensitivity analysis indicates the volume fraction of uranium ore and the reaction rate were the main factors affecting uranium leaching efficiency. Without considering the blockage of pores by precipitation, the Fe2+ in the reinjection fluid had a significant negative influence on uranium leaching.
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Wang, B., Luo, Y., Qian, J. -zhong, Liu, J. -hui, Li, X., Zhang, Y. -hong, et al. (2023). Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium. Journal of Hydrology, 626, 130234.
Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.
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