Solution Mining Research Articles

PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports

Over the last few decades the pharmaceutical industry has generated a vast corpus of knowledge on the safety and efficacy of drugs. Much of this information is contained in toxicology reports, which summarise the results of animal studies designed to analyse the effects of the tested compound, including unintended pharmacological and toxic effects, known as treatment-related findings. Despite the potential of this knowledge, the fact that most of this relevant information is only available as unstructured text with variable degrees of digitisation has hampered its systematic access, use and exploitation. Text mining technologies have the ability to automatically extract, analyse and aggregate such information, providing valuable new insights into the drug discovery and development process. In the context of the eTRANSAFE project, we present PretoxTM (Preclinical Toxicology Text Mining), the first system specifically designed to detect, extract, organise and visualise treatment-related findings from toxicology reports. The PretoxTM tool comprises three main components: PretoxTM Corpus, PretoxTM Pipeline and PretoxTM Web App. The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated by toxicology experts. This corpus was used to develop, train and validate the PretoxTM Pipeline, which extracts treatment-related findings from preclinical study reports. The extracted information is then presented for expert visualisation and validation in the PretoxTM Web App.Scientific ContributionWhile text mining solutions have been widely used in the clinical domain to identify adverse drug reactions from various sources, no similar systems exist for identifying adverse events in animal models during preclinical testing. PretoxTM fills this gap by efficiently extracting treatment-related findings from preclinical toxicology reports. This provides a valuable resource for toxicology research, enhancing the efficiency of safety evaluations, saving time, and leading to more effective decision-making in the drug development process.

Preparation and characterization study of composite dust suppressant based on xanthan gum

To address the critical issue of particulate matter emitted from mining operations, which poses a significant risk to human health, production safety, and the environment, a novel dust suppressant based on xanthan gum (XTG) as a coalescing agent was introduced in this study. The formulation innovatively combines anionic surfactants SDS (Sodium dodecyl sulfate) and SDBS (Sodium dodecyl benzene sulfonate) as well as nonionic surfactants OP-10, Tween-20 (Polyoxyethylene anhydrous sorbitan monolaurate) and Tween-80 (Polyoxyethylene anhydrous sorbitan monooleate) with 0.05% XTG to enhance the wettability of the solution. Considering the economy and practicality, 0.05% OP-10 + 0.05% XTG (M-OX) was finally selected as the optimal dust suppressant formulation. Through a series of experiments, including surface tension, contact angle, sedimentation, pH value, water retention, and wind erosion resistance tests, we proved that M-OX, with a pH value of 8.03, has the least impact on the environment, has better water retention, and has a significant dust suppression effect. The curing layer formed by M-OX can adapt to complex weather, maintains its integrity after rain and wind erosion, and exhibits excellent rain and wind erosion resistance. This study provides an environmentally friendly and effective dust suppression solution for surface coal mines with significant social and economic benefits.

Assessment of Peak Particle Velocity of Blast Vibration using Hybrid Soft Computing Approaches

Abstract Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) and the CatBoost algorithm, with hyperparameters globally optimized using the improved hippocampus optimization algorithm (IHO). Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.

Influence of Low‐Grade Solid Potash Mineral Composition on Potassium Extraction and Lithium Distribution During Solution Mining in Mahai Salt Lake, Qaidam Basin

ABSTRACTAs high‐grade potash resources become increasingly scarce and lithium is recognised as a valuable co‐product, the low‐grade solid potash deposits in the Mahai Salt Lake, Qaidam Basin, have become increasingly significant targets for potash resource development. However, the inefficient release of potassium from these low‐grade ores constrains their economic feasibility for industrial use. This study addresses this challenge by employing x‐ray diffraction (XRD), scanning electron microscopy coupled with energy dispersive spectroscopy (SEM‐EDS), and observational borehole monitoring data to systematically analyse the mineralogical characteristics of these ores and their influence on potassium ion release during solid–liquid conversion. The results identify carnallite, sylvite, and polyhalite as the primary potash minerals, with a significant spatial variations across the study area that directly impact potassium ion release and migration. Sylvite and carnallite in shallow strata are more reactive with solvents, facilitating rapid potassium ion release, whereas polyhalite, hosted within the intercrystalline clastic materials of halite, demonstrates limited pore connectivity, resulting in a slower release rate. Monitoring borehole data indicate that variations in solvent injection volumes cause significant fluctuations in water levels and potassium ion concentrations, thereby affecting potash dissolution rates. As solution mining progresses, high‐lithium areas gradually expand, with lithium concentrations in brine reaching up to 120.4 mg/L and generally ranging from 20 to 70 mg/L. The spatial correlation between potassium and lithium distribution suggests that lithium can be effectively recovered as a co‐product. Adjustments in solvent injection rates and concentrations, based on mineral distribution and monitoring data, are essential for optimising the dissolution efficiency of both potassium and lithium during production.

Towards the Implementation of Heuristics Miner in .Net Framework

Process mining is an interdisciplinary field that bridges the gap between data mining and business process analysis. It revolves around the core concept of leveraging recorded event logs to automate the generation, validation, and refinement of process model. A majority of currently accessible process mining solutions are distributed in form of black box systems - software with no openly accessible source code - or as libraries for scripting languages like Python or R. In this sense, it is necessary to design and build an understandable foundation of a process mining library with the intention of presenting the possibility of incorporating process mining into many programs written in the language or aiding other programmers interested in the potential of such technologies. Therefore, this paper describes and demonstrates the usability of an advanced algorithm for process mining in the .NET platform, namely, the Heuristic miner for process modelling.

Efficient removal of uranium and sulfate in acid contaminated groundwater by flow electrode capacitive deionization

In-situ leaching (ISL) causes non-negligible groundwater pollution. It is urgent to remediate the groundwater after ISL activities. In this study, we evaluated the effectiveness of flow electrode capacitive deionization (FCDI) to treat a simulated groundwater, the uranium (U) and SO42− concentration of which are comparable to groundwater in acid in-situ leaching (AISL) uranium mine for the first time. Moreover, the removal mechanism of U and SO42− were investigated in-depth. It is found that the operational mode, applied voltage and initial SO42− concentration significantly affect the removal of U and SO42− by FCDI. The removal efficiency of U and SO42− were above 98 % at 75 min under optimal condition, although U in groundwater mainly existed in the form of uncharged UO2(SO4), followed by UO22+ and UO2(SO4)22−. UO22+ and UO2(SO4)22− in groundwater migrated into the two poles and were quickly absorbed by flow electrode, which promoted the dissociation of UO2(SO4) or complexation of UO2(SO4) with SO42−. In addition, the anion exchange membrane can absorb UO2(SO4) through complexation. These resulted in the efficient removal of U(VI). FCDI can reduce the U and SO42− concentration of the contaminated water (CU = 10 mg L−1, CSO42− = 5 g L−1) to a value lower than the Chinese emission limit (U: 300 μg L−1; SO42−: 250 mg L−1) even after 18 cycles with each cycle operated for 120 min, which informed that FCDI system using activated carbon is of great potential for acidic contaminated water treatment.

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.

An Integrated Quality Management System (QMS) Combined with AI/ML Guidelines for the Design and Development of Sustainable Products and Services for IT and Engineering R&amp;D Companies Providing Solutions to Global Mining Organizations

As per Nature1 , AI may act as an enabler on 134 targets (79%) across all Sustainable Development Goals (SDGs), generally through technological improvement and by putting the right processes in place to ensure compliance with applicable regulations, certifications, and standards. As the sustainability and green tech market2 is expected to grow at 21% CAGR and will reach $73 billion by 2023, it's the best time for IT and Engineering R&amp;D (ERD) companies to strengthen their internal processes to ensure that mining customers are served in a better way. The mining solutions are driven by tightening regulations, shifting customer preferences, and higher compliance requirements. Mining companies must act on many fronts, changing supply networks, manufacturing processes, and business models. Companies are also rethinking how their products are designed, engineered, and used, looking for ways to meet performance and quality requirements while using fewer resources across the full life cycle of everything they make/implement/use. India is a leading software exporter, and many companies are supporting global mining customers across the world. It’s now the right time for IT and ERD companies to ensure that mining customers are supported in this fast-changing business and regulatory environment. For sustainable product development/services, companies need to put a strong internal Quality Management System (QMS), ensuring better compliance and usage of AI/ML and tools and related standards/certifications. So, the researcher is proposing a conceptual integrated QMS to support sustainable product development/services which can be used by IT and ERD companies developing mining solutions. The proposed system will provide a high-level view of the required standards, procedures, guidelines, templates etc., which will be compliant with the global sustainability standards/certifications requirements.

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.