Unconsolidated sandstone uranium deposits exploited by the in situ leaching (ISL) method, contain complex tetravalent and hexavalent uranium compounds, mostly as UO₂ and UO₃ oxides that have different dissolution rates in sulfuric acid solutions. This work investigates a reactive transport model that takes into account the dissolution of both UO₂ and UO₃ in sulfuric acid solution together with possible interactions with rock minerals during the ISL uranium extraction. Several empirical reaction rate constants were determined during lab experiments on uranium extraction assays, including dissolution rates of tetravalent and hexavalent uranium oxides, and the dissolution rate of rock components by sulfuric acid solution. Effects on the recovery of solution flow rates and ratios between tetravalent and hexavalent uranium compounds are also investigated. The experimental dissolution constants were then used in the proposed reactive transport model to be applied to a real case study in Kazakhstan for comparing the 16 months history matching of an exploitation block consisting of 18 well injectors and 4 producers. The obtained numerical results show good agreement with empirical data gathered during exploitation.

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.

In situ leaching represents an efficient and safe method for uranium mining, where a suboptimal well flow rate distribution leads to solution imbalances between wells, forming stagnant zones that increase operational costs. This study examines a real technological block from the Budenovskoye deposit, applying reactive transport modeling to optimize well flow rates and reduce operational time and reagent consumption. A reactive transport model was developed based on mass conservation and Darcy’s laws coupled with chemical kinetics describing sulfuric acid interactions with uranium minerals ($U{O}_2$ and $U{O}_3$). The model simulated a technological block with 4 production and 18 injection wells arranged in hexagonal cells over 511–542 days to achieve 90% uranium recovery. Six approaches for well flow rate redistribution were compared, based on different weighting factor calculation methods: advanced traditional, linear distance, squared distance, quadrilateral area, and two streamline-based approaches utilizing the minimum and average time of flight. The squared distance method achieved the highest efficiency, reducing operational costs by 5.7% through improved flow redistribution. The streamline-based methods performed comparably and offer potential advantages for heterogeneous conditions by automatically identifying hydraulic connections. The reactive transport modeling approach successfully demonstrated that multi-criteria optimization methods can improve ISL efficiency by 3.9%–5.7% while reducing operational costs.

The pressing issue of global warming, coupled with the increasing depletion of fossil fuels, highlights the necessity for sustainable energy solutions. In this context, hydrogen stands out as a viable option, possessing the capacity to revolutionize critical industries, including fuel cells, internal combustion engines, and gas turbines. An effective approach to enhancing numerous chemical and technological processes in liquid and steam–gas mixtures is the establishment of cavitation mixing zones for the reacting components. These zones are produced in specialized reactors that operate on the principles of hydrodynamic effects applied to the reaction medium. The study focused on the design of the cavitation-jet chamber utilizing the k–$\omega$ Turbulence Model and Particle Tracing Model. As a result, the influence of the inlet velocity on cavitation formation and the uniformity of mixing was investigated. Ripley’s K-function was used to analyze the results of particle distribution. The influence of the screw on flow turbulence and the uniformity of particles was evaluated. Analysis through the K-function indicated a decrease in uniformity at lower velocities, with noticeable turbulization of the flow occurring at high velocities, which facilitated better mixing. In contrast, without the screw, the flow exhibited a high longitudinal velocity and minimal transverse velocity, limiting particle dispersion to the radius of the nozzle and resulting in inefficient mixing. It was found that the inclusion of the screw not only enhanced particle distribution but also maintained the size of the cavitation zones, thereby improving the overall efficiency of the design.

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner.