Solution Mining Research Articles

A Portfolio Management Method for Process Mining-Enabled Business Process Improvement Projects

AbstractProcess mining has received tremendous attention from research and industry, establishing itself as a highly sought-after technology. Despite the technological maturity of process mining solutions, which has been achieved through extensive investments in research and development, organizations still face the challenge of elusive value when systematically adopting process mining. The authors attribute this dilemma to a lack of support for scaling and managing process mining project portfolios. To address this practical need and research gap, the authors propose a method for managing portfolios of so-called process mining value cases, which are defined as process mining-enabled business process improvement projects, towards an evolutionary roadmap (mapper). The method is designed to support organizations identify portfolios of process mining projects that generate value by improving business processes. The method was developed through a combination of design science research and situational method engineering and comprises five activities that each outline techniques, roles, and tools: strategize, identify, select, implement, and monitor. The method has been instantiated as a software prototype and iteratively evaluated for applicability and real-world fidelity by involving an expert panel of academics and practitioners. The usefulness of the artifact was substantiated through a real-world case study in a naturalistic setting.

In Situ Uranium Extraction through the Synthesis of the Uranyl Peroxide Studtite Using a Nonthermal Plasma.

Extraction of uranium from water is an essential step in in situ leach (ISL) mining and environmental decontamination. This is often done by precipitating uranium in solution as the uranyl peroxide studtite, [(UO2)(O2)(H2O)2](H2O)2, by adding hydrogen peroxide, which is energy-intensive to produce and hazardous to transport. Here, we present a method for synthesizing studtite, by generating reactive oxygen species in solution using a nonthermal plasma. Precipitation of studtite is observed within 5 min of the onset of plasma treatment as confirmed by X-ray diffraction and Raman spectral analysis. The faradaic efficiency of studtite formation is analyzed to estimate the values of hydrogen peroxide yield, 1.23 molecules per incident ion, and the rate constant of the studtite-forming reaction, 4.44 × 107 M-1 s-1. This work is a proof of concept and identifies significant parameters for the future development of a larger scale, higher throughput system.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Acceleration of Numerical Modeling of Uranium In Situ Leaching: Application of IDW Interpolation and Neural Networks for Solving the Hydraulic Head Equation

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.

Artificial Neural Networks for Mineral Production Forecasting in the In Situ Leaching Process: Uranium Case Study

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.

Developing Data Mining Prediction System for Health Center Medicine Inventory using Naïve Bayes Classifier Algorithm

Public health centers mostly use conventional methods in managing drug supply, usage, and demand data, without a system that can predict the number of drug requests. This research aims to develop a data mining solution by implementing a prediction system using the Naïve Bayes Classifier algorithm to predict drug supplies from the Koni Health Center, Jambi, to the Health Office Pharmacy Installation. The method applied in this research is a quantitative approach through the experimental method. The research data includes inventory, usage, and remaining stock of various types of drugs from 2017 to 2021 which are divided into four quarters. The results of this study show that the classification system using the Naïve Bayes Classifier method is able to classify data quickly and efficiently according to drug supply. The system test results show an accuracy of 73.91%, recall of 85.71%, and precision of 54.54%. These findings can help Puskesmas in optimizing drug inventory management, reducing errors in inventory estimates, and increasing accuracy in meeting patient drug requests.

Comparison of Analgesia Methods Through a Web Platform in Patients Undergoing Thoracic Surgery: Pilot Design, Implementation, and Validation Study.

Pain management is a vital and essential part of postoperative pectus excavatum (PE) care. Given the lack of an international consensus on guidelines for postoperative handling and evaluation, further research is necessary to compare the efficacy of existing pain management methods regarding pain relief, side effects, and long-term outcomes. In this context, the use of eHealth solutions for data mining can enhance data collection efficiency, reduce errors, and improve patient engagement. However, these digital health care frameworks are currently underused in the context of pain management for PE. This research is part of the broader Cryoanalgesia for Pain Management After Pectus Excavatum Repair (COPPER) study conducted by Giannina Gaslini Children's Hospital to address postoperative pain and recovery in PE patients treated with either standard thoracic epidural analgesia or cryoanalgesia, which is considered its innovative alternative approach. Specifically, this work is aimed at introducing a valuable tool for a comprehensive and quantitative comparison of the 2 analgesia strategies. The tool is a web and mobile app designed to facilitate data collection, management, and analysis of clinical data for pain assessment. The adopted approach involves a careful design based on clinician input, resulting in an intuitive app structure with 3 main screens. Digital surveys are borrowed from paper surveys, including medical history and preoperative, postoperative, and follow-up evaluations. XTENS 2.0 was used to manage the data, and Ionic facilitated cross-platform app development, ensuring secure and adaptable data handling. Preliminary analysis on a pilot cohort of 72 patients (36 treated with standard therapy and 36 treated with cryoanalgesia) indicated successful patient enrollment and balanced representation across treatment groups and genders. Notably, hospital stay was significantly shorter with cryoanalgesia than with standard therapy (Mann-Whitney-Wilcoxon 2-sided test with Bonferroni correction; P<.001; U statistic=287.5), validating its treatment efficacy. This work is a step toward modernizing health care through digital transformation and patient-centered models. The app shows promise in streamlined data collection and patient engagement, although improvements in multilingual support, data validation, and incentivization of questionnaire completion are warranted. Overall, this study highlights the potential of digital health solutions in revolutionizing health care practices, fostering patient involvement, and improving care quality.

Comprehensive Study on Hydrogeological Conditions and Suitability Evaluation of In Situ Leaching for Sandstone-Hosted Uranium Deposit in Erlian Basin

As a strategic mineral and energy resource, the enrichment and metallogenic mechanism of sandstone-hosted uranium deposits are highly dependent on hydrogeological conditions. However, the relationship between sandstone uranium mineralization and hydrogeological conditions has not received sufficient attention yet. The pumping test, hydrogeological parameters and hydrochemical characteristics were employed to analyze the change characteristics of hydrogeological conditions and evaluate the suitability of in situ leaching (ISL). The results showed that the study area in the Inner Mongolia Autonomous Region could be divided into two groundwater subsystems, namely Quanzha-Engeriyin and Luhai-Zhendai. The latter with relatively high water richness is confined and a main ore-bearing aquifer, which consists of four orebodies. The well discharge (Q) and hydraulic conductivity (K) of Orebody II ranged from 98.40 to 867.36 m3/d and 0.25 to 5.64 m/d, respectively, indicating the aquifer is suitable for the migration, enrichment and mineralization of uranium due to relatively high permeability and fast flow rate. The water storage of Orebodies III-IV gradually deteriorated from east to west in a stepped pattern. And the highest values of Q and K in Orebodies III-IV decreased from 1200 m3/d to 120 m3/d and 1.75 m/d to 0.035 m/d, respectively, suggesting these were conducive to a reduction in and accumulation of uranium under poor hydrodynamic conditions. Additionally, the study area would be defined as three grades, including favorable, relatively favorable and unfavorable areas of ISL according to a comprehensive evaluation. This study provided a scientific basis for evaluating the possibility of in situ leaching for sandstone-hosted uranium deposit.

Naturally fractured reservoir characterisation in heterogeneous sandstones: insight for uranium in situ recovery (Imouraren, Niger)

Abstract. This study delves into the characterisation of a heterogenous reservoir, the Tchirezrine II sandstone unit in northern Niger. The characterisation is crucial for potential uranium in situ recovery (ISR) in a naturally fractured and faulted context. Employing a multifaceted approach, including well log data, optical borehole imagery, and hydrogeological tests, alongside satellite-based lineament analysis, this study provides a comprehensive understanding of the structures and their impact on fluid flow. Lineament analysis reveals scale-dependent patterns, consistent with spatially homogeneous joint networks restricted to mechanical units, as well as nearly scale-invariant patterns, better corresponding to spatially heterogeneous fault networks. Various deformation structures are detected from borehole imagery, including extensional fractures, cataclastic deformation bands, and brecciated–cataclastic fault cores. Based on well log data, the Tchirezrine II reservoir displays heterogeneous porosity and permeability related to its fluvial context. These data differ from the traditional porosity–permeability relationship obtained in a sandstone reservoir matrix but are instead consistent with Nelson's classification, emphasising the impact of deformation structures on such petrophysical properties. Hydrological tests have been implemented into a zone of E–W-trending deformation structures, revealing a strong permeability anisotropy. This strong E–W anisotropy is consistent with the presence of the observed E–W structures, i.e. with a draining behaviour of extensional open fractures and a sealing behaviour of both cataclastic bands and fault rocks. Considering implications for ISR mining, this study allows the discussion of the interplay between fractures, faults, and fluid flow properties. It suggests that a well pattern perpendicular to the main permeability orientation can attenuate channelled flow, thus improving the contact of the leach solution with the mineralised matrix. These results provide an integrated approach and a multi-scale characterisation of naturally fractured reservoir (NFR) properties in sandstone, offering a basis for the optimisation of NFR production such as ISR development.

TRAA: a two-risk archive algorithm for expensive many-objective optimization

Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have gained widespread attention in dealing with them. As the objective dimension increases, the error of predicting solutions based on surrogate models accumulates. Existing algorithms do not have strong selection pressure in the candidate solution obtaining and adaptive sampling stages. These make the effectiveness and area of application of the algorithms unsatisfactory. Therefore, this paper proposes a two-risk archive algorithm, which contains a strategy for mining high-risk and low-risk archives and a four-state adaptive sampling criterion. In the candidate solution mining stage, two types of Kriging models are trained, then conservative optimization models and non-conservative optimization models are constructed for model searching, followed by archive selection to obtain more reliable two-risk archives. In the adaptive sampling stage, in order to improve the performance of the algorithms, the proposed criterion considers environmental assessment, demand assessment, and sampling, where the sampling approach involves the improvement of the comprehensive performance in reliable environments, convergence and diversity in controversial environments, and surrogate model uncertainty. Experimental results on numerous benchmark problems show that the proposed algorithm is far superior to seven state-of-the-art algorithms in terms of comprehensive performance.

Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques

Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios.In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directionsbetween 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predictedby the RFR model using SAR interferometry along two perpendicular profiles confirmedthe model's precision.

CONCENTRATION OF LUNAR PLAGIOCLASE FOR SOLAR CELLS FABRICATION. AN ISRU CONCEPTUAL ARCHITECTURE

Solar panels are required on the Moon to provide power for human activities, especially mining and civil operations. To provide enough power and maintain human settlements working, a technical solution known as the Tall Lunar Tower (TLT) claims to be able to capture sunlight 93% of the time through solar panel structures and provide 50 kW per tower. A typical photovoltaic panel is made of 76% glass, 10% polymer, 8% aluminum, 5% silicon, and 1% other metals. Delivering these materials from Earth is expensive and risky. Fortunately, lunar regolith contains large amounts of silicon and aluminum oxides and silicates, thus, it would be feasible to use the resources in situ for metal production, hence, we just need to transport polymers, wire, and minor components from Earth. This article presents an ISRU (in-situ resource utilization) architecture to provide plagioclase concentrate, the economic lunar ore for aluminum and silicon extraction. The document details engineering aspects and technological solutions for lunar mining, including excavation, transport, and beneficiation operations; based on a hypothetical construction and deployment of TLT at the South Pole. Processing techniques such as screening and magnetic separation are discussed to evaluate their advantages and drawbacks to obtain an expected plagioclase concentration of 70% grade with 18% recovery. Finally, an outline of recommendations for industrial manufacture is discussed, considering the sequential lunar metals extraction and the quality required.

Experimental investigation of dynamic characteristics of leaching tubing for solution mining of salt cavern carbon and energy storage

Salt caverns are extensively utilized for storing various substances such as fossil energy, hydrogen, compressed air, nuclear waste, and industrial solid waste. In China, when the salt cavern is leached through single-well water solution mining with oil as a cushion, engineering challenges arise with the leaching tubing, leading to issues like damage and instability. These problems significantly hinder the progress of cavern construction and the control of cavern shape. The primary cause of this is the flow-induced vibration instability of leaching tubing within a confined space, which results in severe bending or damage to the tubing. This study presents a model experimental investigation on the dynamic characteristics of leaching tubing using a self-developed liquid-solid coupling physical model experiment apparatus. The experiment utilizes a silicone-rubber pipe (SRP) and a polycarbonate pipe (PCP) to examine the effects of various factors on the dynamic stability of cantilevered pipes conveying fluid. These factors include external space constraint, flexural rigidity, medium outside the pipe, overhanging length, and end conditions. The experiments reveal four dynamic response phenomena: water hammer, static buckling, chaotic motion, and flutter instability. The study further demonstrates that the length of the external space constraint has a direct impact on the flutter critical flow velocity of the cantilevered pipe conveying fluid. Additionally, the flutter critical flow velocity is influenced by the end conditions and different external media.

Pore structure characterization and permeability prediction of uranium-bearing sandstone based on digital core

The permeability of the ore-bearing layer is an important indicator affecting the in-situ leaching (ISL) of uranium-bearing sandstone, which is related to various factors such as pore shape, distribution, and size. In order to study the effect of pore structure on seepage in low-permeability uranium-bearing sandstone, CT scanning tests were conducted to create a 3D digital core based on scanning images and to calculate the fractal dimension using the box counting dimension method, which integrated fractal theory to define the core samples' pore structure. The permeability prediction was realized based on the porosity-permeability model and the fractal theory model. Results indicated that this type of sandstone is obviously characterized by pore connectivity, large differences in distribution, and strong microscopic inhomogeneity. The pores are dominated by micro- and nano-pores, as well as small pores, accounting for 90 %; macropores are few in number, but the diameters of their single pores are large. The distribution of pore structure in this type of sandstone exhibits a good fractal characteristic; the three-dimensional fractal dimensionality is 2.044–2.310. The porosity-permeability model was established, and permeability prediction was realized by combining the fractal theory to provide theoretical support for determining the values of well field parameters in ISL.

Analysis of tire/gripper slip forces during the handling of large off-the-road tires

Handling large off-the-road tires poses significant safety risks in surface mines, often leading to fatal accidents. This study aims to scientifically address the industry-wide knowledge gap regarding the impacts of various operational and tire parameters on tire handler gripper and tire traction performance. The specific research objectives include (i) quantifying the maximum lift force under various pad-to-tire contact conditions, (ii) identifying critical parameters influencing the point of first slip, and (iii) offering suggestions for improving tire handling operations. To achieve these goals, a portable tire handling test rig weighing 7 t was designed, constructed, and commissioned. Subsequently, in July 2021, functional performance and initial tire slip tests were conducted at the service center of Bridgestone Mining Solutions Australia in Oxley, Brisbane. Overall, 44 lifting tests were conducted on new, semi-worn, and worn Bridgestone 42/90R57 tires using a Cascade 10 t H-Series tire handler. Further, a design of an experiment matrix was formulated to test the influence of various parameters on the slip force, which is defined as the point of the first slip. The investigated parameters included tire orientation (vertical or horizontal), tread surface (dry or wet), tire wear (new, semi-worn, or fully worn), and tire handling pressure (unpressurized standalone and 0, 5, 10, 15, 20, and 30 psi for the tire and wheel set). The results revealed that to mitigate the hazard of sudden pressurized air release, tires must be preferably lifted at the lowest pressure adequate for ensuring handling performance, preventing bead-seat displacement during lifting. Furthermore, the risk of bead-seat displacement was found to be higher when lifting tires from an initial horizontal position owing to the action of shear forces on the bead seat. Considering these factors, an optimal pressure of 10 psi was recommended for lifting semi-worn 42/90R57 tires vertically under both wet and dry conditions, contingent upon the use of a concave gripper design typical of the Cascade H-Series tire handlers.

Workplace performance measurement: digitalization of work observation and analysis

AbstractProcess improvement initiatives require access to frequently updated and good quality data. This is an extremely difficult task in the area of production processes, where the lack of a process digital footprint is a very big challenge. To solve this problem, the authors of this article designed, implemented, and verified the results of a new work measurement method. The Workplace Performance Measurement (WPM) method is focused not only on the measurement of task duration and frequency, but also on searching for potential anomalies and their reasons. The WPM method collects a wide range of workspace parameters, including workers' activities, workers' physiological parameters, and tool usage. An application of Process Mining and Machine Learning solutions has allowed us to not only significantly increase the quality of analysis (compared to analog work sampling methods), but also to implement an automated controlling solution. The genuine value of the WPM is attested to by the achieved results, like increased efficiency of production processes, better visibility of process flow, or delivery of input data to MES solutions. MES systems require good quality, frequently updated information, and this is the role played by the WPM, which can provide this type of data for Master Data as well as for Production Orders. The presented authorial WPM method reduces the gap in available scholarship and practical solutions, enabling the collection of reliable data on the actual flow of business processes without their disruption, relevant for i.a. advanced systems using AI.

Reference architecture design and evaluation for digitalization of underground mining

Digitalization of underground mining operations requires interaction and interoperability between multiple stakeholders and their technical systems. A reference architecture (RA) facilitating design of multi-stakeholder digital solutions for underground mining would be valuable design knowledge for mining industry digitalization activities and would facilitate the design of concrete architectures to the underground mining context. The main contribution of this work is RA design, which is missing in the current research literature. The RA has empirical foundation on the implementation architectures of two prototypes, which were demonstrated and evaluated in an underground mining environment. The prototypes were evaluated based on several design science research evaluation criteria (feasibility, efficiency, robustness, operability) for fulfilling architectural concerns of related stakeholders. Digital Twin-based modeling, MQTT-based messaging, high accuracy location monitoring of vehicles with Lidars, reliable communication with 5G SA network, and utilization of safety zones and safety messaging for enabling emergency stops were identified as important aspects for RA design. Digital Twin (DT)-based data models focusing on the modeling of mining site, moving vehicles, communication data, and related equipment for the underground mining context may be considered as an additional contribution of our study.

Evaluation of Slow-Releasing Energy Material (SREMA) Injection for In-Situ Rock Breaking Under Uniaxial and Triaxial Loading: An Experimental Study

Permeability enhancement of impervious rock formations is crucial for the exploitation of ore deposits, which are uneconomic using conventional mining methods. These deposits have the potential to be mined with in-situ recovery (ISR) mining methods, provided the deposit’s permeability is enhanced artificially. Slow-releasing energy material (SREMA) has been identified as a promising preconditioning technique to enhance rock permeability for ISR. However, the fracturing potential of SREMA in the presence of in-situ stresses remains under-explored in laboratory experimental research studies and needs to be assessed in detail prior to field trials. A unique confining cell was developed in this study to represent realistic confining stresses underground, and the impacts of different stress conditions on the fracturing potential of SREMA were investigated. In addition, the effects of SREMA expansion in pre-existing joints were also assessed using laboratory fracturing experiments conducted by injecting SREMA into sandstone specimens with artificially created horizontal persistent joints, which is the first attempt to the best of the author’s knowledge. Under uniaxial stress conditions (10 MPa), multiple radial cracks emerged from the borehole at the centre of the specimen, while vertical splitting of the specimen was observed due to the outward expansion of SREMA inside the joint. In comparison, under triaxial confinement (5 MPa and 10 MPa), a decrease in the damage caused to the rock mass due to the expansive pressure generated by SREMA was observed, and this was overcome by introducing multiple injection holes and stress relief holes into the rock mass.

The behavior of pyrite during in-situ leaching of uranium by CO2 + O2: A case study of the Qianjiadian uranium deposit in the Songliao Basin, northeastern China

Oxidative weathering of pyrite, a widespread iron sulfide mineral, is crucial for the uranium mineralization in sandstone-hosted uranium deposits and the ore utilization leaching under manual intervention. To understand the behavior of pyrite under CO2 + O2 leaching conditions in sandstone-hosted uranium deposits, samples of uranium ore from the mineralized zone of the Qianjiadian IV uranium deposit located in the lower section of the Yaojia Formation were collected for this study. The stirring leaching experiment was carried out through the processes of the pyrite-bearing uranium ore samples thinned before and after the reaction, followed by an optical microscopic study and the TESCAN Integrated Mineral Analyzer analysis, and finally, the thermodynamic simulation by making use of PHREEQC3.0.The results revealed the following: (1) The dissolution particles of pyrite become smaller, which may increase the porosity of the ore layer and the exposure ratio of uranium minerals. And more soluble pyrite with a high free surface will be more competitive with uranium minerals for the consumption of O2. (2) The ion (Fe2+) produced by the dissolution of pyrite will be oxidized to Fe3+ by excess O2 (4 mol), which provides an oxidant for the oxidation of uranium minerals, and the generated SO42- will combine with the Ca2+ released from calcite to form a permanent precipitate of gypsum, which may block pores and cover the uranium ore. (3) Pyrite and calcite will consume O2 and CO2 in the injection system, thus jointly inhibiting the leaching of uranium minerals. After the injection of excessive CO2 and O2, the concentration of Ca2+, SO42-, Fe3+, UO22+, and HCO3– in the system will continue to accumulate, resulting in the secondary precipitation of calcite, co-precipitation of gypsum, iron minerals, colloids, and hexavalent uranium minerals, resulting in plugging pores and seriously affecting the effective leaching of uranium.This study contributes to the understanding of the behavior of pyrite during uranium recovery through CO2 + O2 leaching. It also contributes to the understanding of quantitative uranium mineralization in sandstone-hosted uranium deposits and process parameters in in-situ leaching (ISL) of uranium.

Sociotechnical perspectives of digital technologies in sustainable mining

This paper adopts an interpretive case study approach to understand the role of digital technologies in addressing seemingly contradictory sustainability goals in mining. The sociotechnical model of information systems was used as a framework to guide the analysis of twenty-five in-depth interviews with globally dispersed digital technology experts working collaboratively at an industry-leading hi-tech mining solutions company. The sociotechnical-led thematic analysis findings highlight the trade-offs experts face in balancing narrow technological imperatives and economic outcomes with broader sustainability goals. The analysis moves beyond the technological and economic to a harmonious perspective of social, human, environmental, and technological interactions. A visual thematic map is presented to aid practitioners in designing and optimally implementing digital technologies to simultaneously address the United Nations Sustainable Development Goals while prioritising business sustainability. We conclude by drawing from the proposed sociotechnical perspectives approach for digital sustainability to provide scholars with possible pathways for future responsible information systems research.