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.

Abstract: 81Kr and 36Cl can both be used to date groundwater beyond the dating range of 14C. 81Kr usually provides reliable groundwater ages because it has uniform initial distribution and negligible subsurface generation, while 36Cl is commonly influenced by subsurface sources or “dead” chloride dissolution. Therefore, the combined use of 81Kr and 36Cl could provide clues on the evolution history of groundwater. In the present study, we performed 36Cl and 81Kr dating of geothermal water in Weihe Basin of China and interpreted the possible cause of disagreement. Two distinct water masses were identified with distinctive isotopic signals: groundwater with significant δ18O shifts (up to −2.0‰), dissolved dead Cl and ages < 1.0 Ma (Cluster A), and older water with little δ18O shifts, negligible dissolved Cl and ages >1.0 Ma (Cluster B). The results confirm the eastward flow path of Cluster B to the Ancient Sanmen Lake with an increasing trend of Cl concentration and age. Modern recharge from the mountains flows to the basin center with intense interaction between water and carbonate under respective reservoir temperatures (100 ∼ 130 °C). These waters flow through the saline stratum emerging from the spillover of the Ancient Sanmen Lake, resulting in higher dead Cl dissolution. A significant linear relationship is observed with the older end-member of ∼1.3Ma under the topographically-driven faster circulation effect. 81Kr ages seem to support the hypothesis that the birth of the modern Yellow River was at about 1.0–1.3 Ma. We inferred the drainage reorganization from the Ancient Sanmen Lake to the modern Yellow River since the Mid-Pleistocene Transition induced the change in groundwater dynamics as well as its chemical evolution. The excavation of the Ancient Sanmen Lake and the accentuated incision of the Weihe River induced groundwater gradient, and therefore the recharge from precipitation from both slopes of the Qinling Mountains in the south and the Beishan Mountains in the north. Our results highlight the effects of dead Cl on 36Cl dating and demonstrate the significant impact of catchment reorganization on groundwater dynamics and its chemistry.

Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.