The application of In Situ Leaching (ISL) has significantly boosted uranium production in countries like Kazakhstan. Given that hydrodynamic and chemical processes occur underground, mining enterprises worldwide have developed models of reactive transport. However, modeling these complex processes demands considerable computational resources. This issue is particularly significant in the context of numerical analyses of mining processes or when modeling production scenarios in uranium mining by the ISL technique, given that a substantial portion of computational resources is allocated to solving the hydraulic head equation. This work aims to explore the applicability of PINNs to accelerate hydrodynamic simulations of the ISL process. The solution of the Poisson equation is accelerated by generating an initial approximation for the iterative method through the application of the Inverse Distance Weighting (IDW) interpolation and PINNs. The impact of various factors, including the computational grid and the spacing between wells, on both the accuracy and efficiency of initial approximation and the overall solution of the elliptic equation are explored. Employing the hydraulic head distribution obtained through PINNs as the initial approximation led to a significant reduction in computation time and a decrease in the number of iterations by a factor of 2.8 to 7.10.

This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models.

The rollfront type deposits are crescent shaped accumulation of mineralization including uranium, selenium, molybdenum in reduced permeable sandstones. It generally forms within a geochemical barrier between mostly reduced and predominantly oxidized environments. Redox reactions between oxidant and reductant creates favorable conditions for uranium precipitation, while constant flow of oxidant continuously dissolves uranium minerals thereby creating a reactive transport. Several previous works had either focused on the characteristics of the rollfront type deposits, or on the description of chemical and geological processes involved in their genesis. Based on these previous works, authors aimed to mimic laboratory experiments numerically by reactive flow and numerical simulation. Data from one particular experiment was used to determine reaction rates between reactants to produce a model of reactive transport and chemical processes involved in the formation of rollfront type deposits. The resulting model was used to identify the causes of crescent like formations and to determine main mechanisms influencing rollfront evolution. A better understanding and simulation of the mechanism involved in the formation of rollfront type deposits and their properties would contribute to decreased exploration and production costs of commodities trapped within such accumulations. The results of this work can be used to model other deposits formed through infiltration and subsequent precipitation of various minerals at the redox interface.