Daniar Aizhulov

Ground thermal energy storage are effective systems for building climate control, yet, their modeling is complicated by a sharp geometric disproportion, particularly the diameter of the heat exchanger is several orders of magnitude smaller than its depth. This makes full-scale 3D modeling practically unfeasible due to large computational costs. The situation is exacerbated by the multi-parametric nature of the problem. As a result, classical parametric studies and sensitivity analysis require high computational power, which hinders the process of rapid iterative design. To address this problem, this paper presents a novel integrated, full-stack software architecture that automates conjugate heat and mass transfer calculations, which traditionally require complex hydrodynamic computations. The methodology relies on an accessible application and a localized database combined with an optimized explicit finite-difference backend, utilizing vectorized numerical solvers to calculate 1D hydrodynamics and thermal processes within the borehole heat exchanger and 3D heat conduction in heterogeneous soil. The reliability of the software's calculations is validated by previously obtained results of field Thermal Response Tests. The proposed tool enabled the implementation of large-scale parametric studies and system sensitivity analysis to key physical factors. The model allows for a comprehensive assessment of the impact of the thermophysical characteristics of heterogeneous soil layers and the properties of the borehole grouting material on heat extraction efficiency. The inclusion of the influence of groundwater flows into the computational scheme potentially expands functionality, allowing for full-fledged dynamic modeling of the system under changing conditions. As a result, the developed software represents a scalable and cost-effective desktop tool for rapid, dynamic modeling of geothermal systems. By providing the capability for deep parametric optimization considering real soil properties, bentonite characteristics, and the impact of groundwater flows, the program successfully combines advanced theoretical thermodynamic principles with the needs of practical 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.

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 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.

The work presents an approach to enhance the forecasting capabilities of In-Situ Leaching processes during both the production stage and early prognosis. ISL, a crucial method for resource extraction, demands rapid on-site forecasting to guide the deployment of new technological blocks. Traditional modeling techniques, though effective, are hindered by their computational demands and network throughput requirements, particularly when dealing with substantial datasets or remote computing needs. The integration of AI technologies, specifically neural networks, offers a promising opportunity for expedited calculations by leveraging the power of forward propagation through pretrained neural models. However, a critical challenge lies in transforming conventional numerical datasets into a format suitable for neural modeling. Furthermore, the scarcity of training data during the production phase, where vital parameters are concealed underground, poses an additional challenge in training AI models for In-Situ Leaching processes. This research addresses these challenges by proposing a methodology for generating training data tailored to the most resource-intensive Computational Fluid Dynamics problems encountered during modeling. Traditional numerical modeling techniques are harnessed to construct training datasets comprising input and corresponding expected output data, with a particular focus on varying well network patterns. Subsequent efforts are directed at the conversion of the acquired data into a format compatible with neural networks. The data is normalized to align with the data ranges stipulated by the activation functions employed within the neural network architecture. This preprocessing step ensures that the neural model can effectively learn from the generated data, facilitating accurate forecasting of In-Situ Leaching processes. An advantage of proposed technique lies in provision of large, reliable datasets to train neural network to predict hydrodynamic properties based on technological regimes currently active or expected on ISL site. A major implication of this approach lies in applicability of pre-trained AI technologies to forecast future or determine current hydrodynamic regime in the stratum circumventing cost deterministic simulations currently deployed at mining sites. Hence, innovative approach outlined in this paper holds promise for optimizing forecasting, allowing for quicker and more efficient decision-making in resource extraction operations while getting around the computational barriers associated with traditional methods.

The paper introduces a mathematical model that describes the cavitation process occurring during the passage of a water steam flow in various geometric configurations of a hydrodynamic device. The flow experiences a localized constriction (convergent nozzle) followed by expansion (divergent nozzle), exemplified by a Venturi tube or a Laval nozzle. A narrow flow channel connecting the convergent and divergent sections is equipped with a narrow-section nozzle for injecting methane molecules into the high-speed steam flow. As the steam-gas mixture passes through this zone, it is irradiated with an electron beam and sprayed into a cylindrical chamber at atmospheric pressure, where the distribution of methane molecules in water vapor forms an aerosol. Key geometric parameters of the constriction and expansion zones of the hydraulic system (cavitation-jet chamber) are determined to ensure the uniform distribution of dispersed-phase particles (methane) in the dispersion medium (water vapor). Velocity and pressure distributions of the mixed steam-gas flow are calculated using a turbulent mathematical model, specifically the k-ω model, while the motion of methane particles is simulated using a particle tracing method. The uniformity of methane molecule distribution in water vapor is assessed using Ripley’s K-function. The best performance of the hydrogen-producing chamber was observed when the cavitation-inducing nozzle’s convergence angle exceeded 50 degrees. The divergence angle of the nozzle within the range of 30–40 degrees provided the best distribution in terms of uniformity of the methane particles in the chamber.

Filtration process occurring during the formation of infiltration type deposits largely affects the geometry and content of uranium deposits, which creates a challenge when conventional methods of geostatistics are applied for geomodelling purposes. Verification of these methods is practically impossible since true picture of the deposit is never known. In this work authors use a synthetic model generated through reactive transport simulation accounting for specific characteristics inherent in deposits, which formed via infiltration process. Two conventional methods were tested: inverse distance weighting method and the method of kriging. Comparison shows, that in terms of resource estimation and if intrinsic parameters of the method are chosen correctly, inverse distance method can provide a higher accuracy as compared to kriging with various number of exploratory wells used. Keywords: flow in porous media, reactive transport modelling, geostatistics, kriging, inverse distance weighting, infiltration type deposits. Процесс фильтрации, участвующий при формировании месторождений инфильтрационного типа, в значительной степени влияет на геометрию и содержание урановых месторождений, что создает проблему при применении традиционных методов геостатистики при геологическом моделировании. Верификация этих методов практически невозможна, так как нет сведений о истинной картине минерализации месторождений. В этой работе авторы используют синтетическую модель, созданную в результате моделирования реагирующего переноса с учетом специфических процессов, присущих образованным в результате процесса инфильтрации отложениям. В рамках проведенных работ были протестированы два традиционных метода: метода обратных взвешенных расстояний и метода кригинга. Сравнение показывает, что с точки зрения оценки запасов и при правильном выборе параметров, метод обратных взвешенных расстояний может обеспечить более высокую точность по сравнению с кригингом при различном количестве используемых разведочных скважин. Ключевые слова: течение в пористых средах, реагирующий массоперенос, геостатистика, кригинг, метод обратных взвешенных расстояний, месторождения инфильтрационного типа. Инфильтрациялық типті кен орындарының қалыптасуына қатысатын фильтрация процессі уран кен орындарының геометриясы мен мөлшеріне аса әсер етеді, соның нәтижесінде геологиялық модельдеу барысында дәстүрлі әдістерді қолдану кезінде қиындықтар туады. Айтылған әдістерді верификациялау іс-жүзінде мүмкін емес, өйткені кен орнының нақты минерализация бейнесі туралы мәліметтер жоқ. Бұл жұмыста авторлар, инфильтрация процессі нәтижесінде қалыптасқан түзілімге тән, ерекше процесстерді ескере отырып, реакцияланатын тасымалды модельдеу нәтижесінде жасалған синтетикалық модельді қолданады. Жасалған жұмыстар аясында екі дәстүрлі әдіс тестіленді: кері өлшемді қашықтық және кригинг. Салыстыру нәтижесі, қолданылатын барлау ұңғымаларының әртүрлі сандары үшін, қорларды бағалау көзқарасынан және параметрлерді дұрыс таңдаған жағдайда кері өлшемді қашықтық әдісі кригингпен салыстырғанда жоғары дәлділікті қамтамасыз ететінін көрсетті. Түйін сөздер: кеуекті ортадағы ағым, реакцияланатын массатасымалдау, геостатистика, кригинг, кері өлшемді қашықтық әдісі, инфильтрациялық типті кен орындар.

Mineral mining using the in-situ leaching method is based on injecting of leaching solution into the ore formation through injection wells, leaching out the mineral and extracting the pregnant solution (dissolved mineral) through the production wells. During the stage of the field designing, the main task is to determine the optimal drilling-in pattern, which implies the choice of the optimal location of the injection and production wells. This article is considered the different modes of the distance between production and injection wells with a hexagonal location and determines the optimality based on the economic indicators of each mode. As a result of the study, for the problem statement, the optimal distance between the wells was determined.