Solution Mining Research Articles

Parameter optimization of solution mining under nitrogen for the construction of a gas storage salt cavern

Using nitrogen as the blanket for the construction of a gas storage salt cavern by the solution mining under gas (SMUG) method has many advantages, such as improved safety, enhanced environmental protection and better economy. A critical issue for its field application is how to accurately predict and optimize the cavern leaching parameters when using SMUG. We propose a mathematical model for predicting leaching parameters based on the “U-tube” principle and the pressure equilibrium of liquid columns in the wellbore. We derive expressions of leaching parameters, including water injection pressure, nitrogen injection pressure, volume of nitrogen blanket, and depth of Gas-Brine (GB) interface. A calculation program is developed based on the derived equations using C# computer language. We take the JT1 cavern in Jintan district, Jiangsu province, China, as an example, and simulate the water injection pressure, nitrogen injection pressure, volume of nitrogen blanket, and depth of GB interface under different leaching rates and different de-brining pressures. The optimized leaching parameters were used in the leaching of the JT1 cavern for field tests. Compared with field monitoring data, the proposed mathematical model has high accuracy and strong reliability with an overall error of less than 3.7%, which satisfies the requirements of field cavern leaching. This study provides guidance for predicting and optimizing leaching parameters for gas storage salt cavern leaching with a nitrogen blanket.

The μTOSCA toolchain: Mining, analyzing, and refactoring microservice‐based architectures

SummaryExploiting microservices to architect enterprise applications is becoming commonplace. This makes it crucial to provide some support for designing and analyzing microservice‐based applications, for example, for understanding whether a microservice‐based application adheres to the main design principles of microservices and for choosing how to refactor it when this is not the case. To provide such support, in this article we present the TOSCA toolchain. More precisely, we first introduce the TOSCA model to represent the architecture of microservice‐based applications with the OASIS standard TOSCA. We then describe a technique to automatically mine the architecture of a microservice‐based application and represent it with TOSCA, given the Kubernetes deployment of the application. We also present a methodology to analyze the TOSCA representation of a microservice‐based architecture to systematically identify the architectural smells potentially affecting the corresponding application and to resolve them. Finally, we present two prototype tools, Miner and Freshener, implementing our mining solution and the support for identifying and resolving architectural smells in microservice‐based applications, respectively. We then assess —by discussing some case studies— how effectively Miner, Freshener, and the TOSCA toolchain can support researchers and practitioners working with microservices.

Corrosion Behavior of Rockbolts in Simulated Underground Mining Solutions Using Macro-scale and Micro-scale Electrochemical Measurements

This paper focused on the corrosion of rockbolts with different S contents and yield strengths in simulated underground mining solutions using macro-electrochemical and micro-electrochemical measurements. The effect of Cl- ion on the pitting behavior was also investigated. It was found that the increase in Cl- concentration decreased the corrosion potential and corrosion current of anode polarization. The MnS inclusion was found to be the preferred site for corrosion. Based on in-situ observation and SEM characterization, it was found that the corrosion process started with the formation of corrosion trenches around the MnS, which accelerated the dissolution of the surrounding matrix.

Mining heritage and community identity in the social licence of proposed renewed mining

Mining legacies are manifest in multiple ways. One of these relates to heritage values of former mining places which may contribute to regional identity and potentially tourism. Changes in mining methods and technologies make it economically feasible to re-open some mines that previously closed due to market factors, ore grades or difficulties in accessing residual mineral deposits. In this paper, we consider the factors affecting the social licence for proposed renewed mining activity in a post-mining town (Kapunda, South Australia). We examined how residents of Kapunda view the mining sector in general and how it fits with town identity. Focus groups and interviews explored the extent to which a potential renewed mining industry would align to community values, considering the possible application of copper in-situ recovery (ISR) for an historic deposit in the town. The research found that participants were open to the prospect of a new copper ISR operation, provided it was well managed, environmentally responsible, and able to align with tourism. The paper concludes that an important dimension of social licence for re-opening mines is whether new mining could occur in a way that doesn't undermine the heritage values associated with a former mining legacy.

Reactive transport numerical modeling of CO<sub>2</sub>+O<sub>2</sub> <italic>in-situ</italic> leaching in sandstone-type uranium ore

The CO2+O2 in situ leaching (ISL) technique represents the third generation of in situ uranium leach mining in China. The key point of CO2+O2 ISL is the interaction between the complex hydrodynamic behavior and geochemical reactions. To deal with hydrodynamic evolution, leaching solutions-rock interaction, and reactive kinetics of uranium minerals during ISL uranium mining, a CO2+O2 ISL reactive transport numerical simulation implemented in TOUGHREACT is proposed herein in Qianjiadian uranium deposit. In this study, we developed a chemical thermodynamic database suitable for sandstone-hosted uranium deposits and a method for modeling the kinetic uranium dissolution under the control of oxygen (O2(aq)) and bicarbonate (HCO3–). We conduct a sensitivity analysis to assess the effect of the uncertainties associated with the modeling parameters on the simulation results. The reactive transport simulation successfully reproduced the behavior of dynamic uranium recovery. The sensitivity analysis showed that the initial volume of uranium (uranium grade) and injecting oxygen concentration were very sensitive to changes in the leaching concentration of uranium. This study provides technical support for accurate simulation and dynamic regulation of the CO2+O2 leaching process at a large mining area.

Finders Keepers

Current space law is unclear as to whether private entities may claim possession of resources extracted from their endeavors in outer space. The lack of certainty prevents private entities from entirely investing in infrastructure and capabilities to access new deposits of resources due to the depletion of minerals and resources on Earth. The establishment of a new space regime devoid of non-appropriation principles found in international law is necessary to motivate private entities to invest the capital in extracting and transporting space resources back to Earth. This Comment seeks to understand how the current framework of space law impacts the property rights of private entities and their claim to resources in space. The 1967 Outer Space Treaty prohibited the claiming of property by sovereign nations. However, the concept of private entities now having the capability to extract resources from outer space has reignited the issue of property rights in outer space. With resources becoming scarcer or priced out of the market, the solution of mining these resources from celestial bodies has caused a new space race. Past multilateral agreements have dealt with similar discoveries such as the polymetallic nodules on the ocean floor; however, these agreements led to disputes as to ownership and the rights to extract said resources. With little to no support from the industrialized nations, the structure of any new regime must ensure access for the benefit of humankind. The benefit of allowing these private entities the right to claim mined resources must be weighed against potential drawbacks in order to create a framework that balances the interest of the free market with that of the common heritage principle. In determining that a suitable framework fails to guide a new space regime, this Comment proposes that a new governing body comprising a rotation of space-faring and nonspacefaring nations act as a regulatory body for the interest of all of humankind.

Regional groundwater flow and karst evolution\u2013theoretical approach and example from Switzerland

In regional scale aquifers in the Rhine Valley and Tabular Jura east of Basel (Switzerland), the groundwater circulation was investigated using regional-scale geological and hydraulic 3D models. The main aquifers in the area comprise the Quaternary aquifer of unconsolidated gravel deposits along the River Rhine and its tributaries, as well as the regional scale karst aquifer within the Upper Muschelkalk. Land subsidence, a process likely associated with salt solution mining, indicates further subordinate groundwater bearing segments and complex groundwater interactions along fault zones. In the aquifer systems we investigated, regional-scale groundwater circulation was simulated and visualized in relation to the geological settings. Lithostratigraphic units and fault structures were parameterized and analyzed, including the sensitivity of hydraulic properties and boundaries. Scenario calculations were used to investigate the sensitivity that the aquifer systems had to hydraulic parameter changes during Quaternary aggradation and degradation in the main valley. Those calculations were also done for base-level changes in the Rivers Rhine and Birs. For this purpose, this study considered probable historic base-levels before river regulation occurred, and before river dams and power plants were constructed. We also focused on scenarios considering increased groundwater recharge rates, e.g. due to exceptional long-lasting precipitation, or heavy rainfall events in the catchment area. Our results indicate that increased groundwater recharge rates in the catchment areas during such events (or periods) are associated with orders of magnitude increases of regional inflow into the Upper Muschelkalk karst aquifer. Furthermore, the groundwater fluctuations and groundwater saturated regions within the karst aquifer shift to places where high densities of sinkholes are documented. When the surface water base-levels adapt to probable historic levels, it leads to increased hydraulic gradients (i.e. local lowering of the groundwater level by up to 7 m). Those increased gradients are associated with increased groundwater flow within some aquifer regions that are particularly prone to karst development.

Uncertainty quantification for uranium production in mining exploitation by In Situ Recovery

Uranium In Situ Recovery (ISR) is based on the direct leaching of the uranium ore in the deposit by a mining solution. Fluid flow and geochemical reaction in the reservoir are difficult to predict due to geological, petrophysical and geochemical uncertainties. The reactive transport simulation code used to model ISR is very sensitive to the spatial distribution of physical and chemical properties of the deposit. Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of geological properties. The direct propagation of geological uncertainties by multiple ISR mining simulations is intractable in an industrial context. The CPU time needed to perform one ISR numerical simulation is too heavy. This work presents a way to propagate geological uncertainties into uranium production uncertainties at a reduced computational cost, thanks to a scenario reduction method. A subset of geostatistical simulations is built to approximate the variability of a larger set. The selection is obtained using a proxy of reactive transport simulation. The main contribution of this work is the development of the proxy, which is based on an artificial mineral exploitation that has common properties with uraninite. It allows the discrimination of geostatistical realizations in terms of potential uranium production. Then, the ISR simulation carried out with the selected geostatistical realizations gives a good approximation of the uranium production variability over the whole set of geostatistical simulations. This approximation is then used to quantify the uncertainties on the uranium production. The proposed approach is assessed on real case studies.

Extraction of descriptive driving patterns from driving data using unsupervised algorithms

Understanding drivers’ behavioral characteristics is critical for the design of decision-making modules in autonomous vehicles (AVs) and advanced driver assistance systems (ADASs). Current relevant studies are mainly based on supervised learning methods which involve extensive human efforts in model development. This paper proposed a framework for automatic descriptive driving pattern extraction from driving sequence data using unsupervised algorithms. Based on the Bayesian multivariate linear regression model, two unsupervised algorithms were utilized to segment driving sequences into fragments. Three extended latent Dirichlet allocation models were applied to cluster the fragments into multiple descriptive driving patterns. The collected driving data from a naturalistic driving experiment was applied to examine the effectiveness of our proposed framework. Results show that the unsupervised segmentation algorithms could help effectively detect the switch characteristics between two continuous driving maneuvers along time, and the clustered patterns could effectively describe the characteristics of each driving maneuver. The proposed unsupervised framework provides an effective and efficient data mining solution to facilitating deep and comprehensive understanding on drivers’ behavioral characteristics, which will benefit the development of AVs and ADASs.

Predicting Student Academic Performance by Means of Associative Classification

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments.

Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases

The existing data mining solutions to identify risk factors associated with diseases are burdened with quite a few shortcomings. They usually use crisp partitions for numerical features and also do not use patient-specific profiles. These shortcomings create limitations for solving real problems. Discretizing a numerical feature through crisp partitions can also generate substantial partitioning errors, particularly for features whose values are closer to crisp boundaries. Since the normal range of each numerical feature varies according to the age, gender, and medical conditions of the patients, then ignoring these differences can undermine the accuracy of the extracted itemsets and rules. This paper presents a profile-based fuzzy association rule mining (PB-FARM) approach for the assessment of risk factors highly correlated with diseases. The proposed approach has three phases. Phase I involves creating profiles for patients based on their age, gender, and medical conditions, to determine a normal range of each numerical feature. Then fuzzy partitioning is done for all features (namely, numerical and categorical), and consequently, a structure, called FirstScan, is created. In Phase II, the FirstScan structure is utilized to mine for large fuzzy k-itemsets. Ultimately, in Phase III, the given k-itemsets are employed to generate fuzzy rules for associations between risk factors and diseases. To evaluate the performance of the proposed method the Z-Alizadeh Sani coronary artery disease (CAD) dataset, containing 303 records and 54 features, was used. The results show a positive correlation between typical chest pain and old age with the incidence of CAD. The comparisons made in this study showed that, firstly, the proposed algorithm has a higher partitioning accuracy than other methods, and secondly, it has a reasonably short execution time.

Efficient privacy-preserving frequent itemset query over semantically secure encrypted cloud database

In recent years, more users tend to use data mining as a service (DMaaS) provided by cloud service providers. However, while enjoying the convenient pay-per-use mode and powerful capacity of cloud computing, users are also threatened by the potential risk of privacy leakage. In this paper, we aim to efficiently perform privacy-preserving DMaaS, and focus on frequent itemset mining over encrypted database in outsourced cloud environment. Existing work apply different encryption methods to design various privacy-preserving mining solutions. Nevertheless, these approaches either cannot provide sufficient security requirements, or introduce heavy computation costs. Some of them also need users staying on-line to execute computations, which are not practical in real-world applications. In this paper, we propose a novel efficient privacy-preserving frequent itemset query (PPFIQ) scheme using two homomorphic encryptions and ciphertext packing technique. The proposed scheme protects transaction database with semantic security, preserves mining privacy and resists frequency analysis attacks. Meanwhile, efficiency is guaranteed by inherent parallel computations for packed plaintexts and users could stay off-line during the mining process. We provide formal security analysis and evaluate the performance of our scheme with extensive experiments. The experiment results demonstrate that the proposed scheme can be efficiently implemented on large databases.

Benchmarking data mining approaches for traveler segmentation

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.

A systematic mapping study on agent mining

ABSTRACT Over the past two decades, many studies have been published in diverse fields of application combining agent abilities (knowledge processing, communication, learning, mobility, etc.) and data mining approaches (clustering, decision trees, ontologies, etc.). We performed a systematic mapping study to quantitatively analyse these contributions about agent mining. We determined that most of the publications were in the field of data mining using agent systems to collect or mine the data. Some used data mining solutions to improve agent behaviour, and very few publications integrated both agent and mining approaches. In the latter case, most were published in the last few years, which highlights the advances in research integrating agents and mining approaches.

RETRACTED ARTICLE: KM-LA: knowledge-based mining for linear analysis of inconsistent medical data for healthcare applications

Healthcare data analysis is a prominent field of research supporting information technologies in the medical industry. Handling large volumes of data and mining them for application-related services requires time-efficient and less complex processing. With the implication of machine learning in computing processes, the analysis systems and mining performance are improved. In this manuscript, knowledge-based mining with a linear analysis (KM-LA) model is presented. This analysis model relies on a knowledge base and definitive learning in handling big medical data for health application-centric services. This proposal aims to provide a definite linear solution for medical data mining through less complex analysis for simpler healthcare services. The analysis model is proposed to reduce the inconsistency in handling extensive medical data without causing service failures. The linear analysis model’s performance is verified using suitable experiments to verify service latency, analysis time, computation complexity, and inconsistency.

Research on performance of mine dehumidification and cooling system based on heat pump

A heat pump-based mine solution dehumidification and cooling system is used in deep mines, which can not only reduce the temperature and humidity of the working face, but also recycle the low-grade heat energy and improve the utilization rate of energy.Under the condition of long distance air supply, within the range of 10m from the heading face, the equivalent temperature of the air can fall to 24.7 °C, the cooling range is 5.6 °C, and the relative humidity decreases by 23.79%. Compared with the original cooling system, the COP value of the system can reach 3.43 under the same working conditions, 19% higher than the original cooling equipment.

Rock Salt Production by Solution Mining Method

Tuz, tüm canlılar için önemli bir maden kaynağı olmasının yanında tıp, kimya, ilaç, deri, tarım vb. birçok endüstride kullanılmaktadır. Tuz üretiminde kullanılan madencilik metotları; buharlaştırma, yeraltı madenciliği ve çözelti madenciliği olarak üç çeşittir. Çözelti madenciliği ile tuz üretim teknolojisinin kullanımı dünyada son yıllarda teknolojinin gelişimine bağlı olarak artmıştır. Madencilik sektöründe son derece güvenli, teknolojik ve çevre dostu bir yöntem olan çözelti madenciliği yöntemi ile çözücüsü genelde su olan tuz, soda vb. minerallerin işletilmesi oldukça ekonomik olmaktadır. Genellikle jeolojik ve epirojenik hareketlerle belli bir yönden bağlantısı kesilen bir iç deniz çukurunun dolmasıyla yüzlerce metre aşağıda oluşan halit domundan tuz üretimi, boru hattından pompalar yardımıyla tatlı su gönderip tuzun çözdürülmesini sağlayarak tuzlu su eriyiği elde edilmesiyle sağlanmaktadır. Bu çalışmada, tuz madeninde uygulanan çözelti madenciliği ile ilgili bilgilere ilaveten yeraltında oluşan tuz domlarından çözelti madenciliği yöntemiyle tuzun nasıl kazanılacağına dair açıklamalar, şekiller ve kaynaklara yer verilmiştir.

Uncovering Two Decades of Open Innovation Benefits: A Qualitative Meta-Analysis

Research on open innovation (OI) benefits has received growing interest from researchers and practitioners in the past two decades. But it lacks valuable frameworks that categorize these benefits. This paper aims to explore OI benefits since 2003 and to come up with a comprehensive framework categorizing them. To do so, the authors conducted a qualitative meta-analysis of 708 articles from the Scopus platform by mobilizing IRAMUTEQ 0.7 Alpha2 and NVivo12 solutions for text mining and qualitative content analyzes. The findings highlight six categories of the following interrelated OI benefits: (1) Performance, (2) innovation process, (3) market, (4) product and service, (5) knowledge, and (6) customer. This classification offers researchers constructs for potential models of OI impacts and brings innovation managers potential indicators for the dissemination and absorption of OI practices by organizations.

Multi-Temporal Satellite Interferometry for Fast-Motion Detection: An Application to Salt Solution Mining

Underground mining is one of the human activities with the highest impact in terms of induced ground motion. The excavation of the mining levels creates pillars, rooms and cavities that can evolve in chimney collapses and sinkholes. This is a major threat where the mining activity is carried out in an urban context. Thus, there is a clear need for tools and instruments able to precisely quantify mining-induced deformation. Topographic measurements certainly offer very high spatial accuracy and temporal repeatability, but they lack in spatial distribution of measurement points. In the past decades, Multi-Temporal Satellite Interferometry (MTInSAR) has become one of the most reliable techniques for monitoring ground motion, including mining-induced deformation. Although with well-known limitations when high deformation rates and frequently changing land surfaces are involved, MTInSAR has been exploited to evaluate the surface motion in several mining area worldwide. In this paper, a detailed scale MTInSAR approach was designed to characterize ground deformation in the salt solution mining area of Saline di Volterra (Tuscany Region, central Italy). This mining activity has a relevant environmental impact, depleting the water resource and inducing ground motion; sinkholes are a common consequence. The MTInSAR processing approach is based on the direct integration of interferograms derived from Sentinel-1 images and on the phase splitting between low (LF) and high (HF) frequency components. Phase unwrapping is performed for the LF and HF components on a set of points selected through a “triplets closure” method. The final deformation map is derived by combining again the components to avoid error accumulation and by applying a classical atmospheric phase filtering to remove the remaining low frequency signal. The results obtained reveal the presence of several subsidence bowls, sometimes corresponding to sinkholes formed in the recent past. Very high deformation rates, up to −250 mm/yr, and time series with clear trend changes are registered. In addition, the spatial and temporal distribution of velocities and time series is analyzed, with a focus on the correlation with sinkhole occurrence.

The optimum interwell distance analysis of two-well-horizontal salt cavern construction

ABSTRACT Large-scale energy is often stored in underground salt caverns. Because China has abundant thinly bedded rocksalt, two-well-horizontal (TWH) caverns are considered as alternatives for large-scale energy storage, which possess higher construction rate and larger volume than the vertical caverns. To-date, the analysis of the development of shape and the control methodology with different interwell distances of TWH caverns are not fully understood. In order to eliminate these drawbacks, TWH-cavern construction has been simulated in the current work. A platform was established based on TWHSMC V2.0 simulation code to obtain the theoretical basis for TWH-cavern leaching construction for a more user-friendly visual analysis of the cavern development. Setting cavern construction efficiency as the objective functions for optimization, a series of theoretical simulations were conducted to analyze how the interwell distance affects the TWH-cavern construction. The results obtained illustrate that the interwell distance correlates with a rapid decrease in the leaching time until a tendency to stabilization is observed. The optimum interwell distance is 115 m at a flow rate of 300–500 cubic meters per hour. The interwell distance also influences the longitudinal span of the cavern. When the interwell distance is 115 m, the cavern has the smallest span in the longitudinal span. Therefore, the potential crossing of the interlayer can be minimized, which is good for the safety of cavern. Abbreviations: UGS: Underground gas storage; SPR: strategic petroleum reserves; CAES: Compressed air energy storage; SWV: Single-well-vertical; TWH: Two-well-horizontal; SIMPLE: Semi-Implicit Method for Pressure-Linked Equations; TWHSMC: Two-well-horizontal solution mining cavern; 2D: Two dimensional; VOF: Volume of Fluid; 3D: Three dimensional; API: American Petroleum Institute