Efficient Mining Research Articles

Smart Wearable Device for Enhancing Safety and Efficiency of Coal Miners

The task of ensuring worker safety in underground coal mines has never been simple. It has always been challenging to ensure worker safety in underground coal mines. Coal miners are seriously injured or killed as a result of numerous fatal and non-fatal accidents all over the world. Accidents occur as a result of lack of monitoring of mining areas and failure to implement proper safety measures. In this review, the coal miner's smart wearable safety device is implemented. A smart wearable safety device that will monitor the miner's health and provides precautionary measures for the miner's safety has been developed with the advent of the Industrial Internet of Things (IIoT). Integrating smart wearable safety devices with various health sensors such as a pulse rate sensor, temperature sensor, blood oxygen sensor, gas sensor, and camera, and then connecting it to Node MCU, and the internet enhances the safety of coal miners. Sensors constantly transmit sensor data to the cloud, and if an unusual situation arises, it notifies the responsible person in the control room as well as the miners. Since deep underground mining is especially susceptible to toxic gases, low levels of oxygen, and hazardous gases, the MQ gas sensor family can detect them. The proposed system is primarily used to improve working conditions inside coal mines and to ensure workers' safety.

Application of text mining in analysing notes to financial statements: A Hungarian case

Company stakeholders must have reliable and accurate information about the companies falling into their sphere of interest. In Hungary, one of the key sources of information for company stakeholders is the financial statements and related explanations, which are included in the notes of the financial statements (notes). This study used text mining to analyse the Hungarian annual financial statements notes for 2017, 2019 and 2021. The selection of the notes was based on the proportions of each sector in the national economy. The research analysed 28,700 company notes annually, totalling 86,100 documents for the three years. The text mining and generation of the Term Frequency Matrix have performed 'quanteda' packages of the R statistical system, which incorporate the results of artificial intelligence research to enhance the efficiency of text mining. Based on the results, the contents of the notes to the financial statements appear to be a rather mixed picture in Hungary. Analysing the term frequency matrix for the 67 most common terms has revealed no significant difference between the years. However, considerable differences have been caused by size categories and sectors. The notes are statistically significant using Jaccard similarity analysis, considering the year, corporate size, and sector.

Experimental and numerical investigations of bending mechanical properties and fracture characteristics of cemented tailings-waste rock backfill under three-point bending

The bending mechanical properties and fracture characteristics of cemented tailings-waste rock backfill (CT-WRB) have a significant impact on efficient mine production. In this study, the effects of waste rock content (WRC) and waste rock size (WRS) on the bending mechanical properties and macroscopic failure characteristics of CT-WRB were investigated through experiments. Concurrently, PFC3D numerical simulation software was used to establish a three-point bending numerical model of CT-WRB with actual waste rock shapes to explore the crack evolution law and microscopic damage characteristics. Results showed that adding appropriate waste rock improved the initial stiffness, flexural strength, and modulus of the backfill, with optimal WRC and WRS at 40 % and 4–6 mm, respectively. Simulations indicated that under load, the displacement field of CT-WRB formed a vortex, with tensile stress in the middle and lower parts and compressive stress in an upper arch. Cracks started at the bottom and extended upward, leading to failure. Compared to OCTB (without waste rock incorporation), CT-WRB had more total cracks and tension-shear composite failure of internal particles. Waste rock altered stress distribution and crack paths, resulting in curved, bifurcated, and discontinuous main cracks. These findings provide a reference for efficiently recycling tailings, waste rock and ensuring safe mine production.

A Hybrid Approach for Thyroid Disease and Classification Using Attribute Associations Rule Mining

In recent years, thyroid diseases have become increasingly prevalent worldwide. In India, for instance, one in eight women is affected by hypothyroidism, hyperthyroidism, or thyroid cancer. Research indicates that approximately 20% of Indians are diagnosed with endemic goiter. Several factors influence thyroid function, including stress, infection, trauma, toxins, low-calorie diet, and certain medications. Preventing such diseases is crucial as treatments often involve long-term medication or surgical intervention. Thyroid disease diagnosis is pivotal in medicine, where precise classification is essential for prompt treatment. This paper proposes a novel approach to thyroid disease classification by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Association Rule Mining (ARM). The hybridization of PSO and ACO enhances exploration and exploitation capabilities, thereby improving the efficiency of association rule mining for feature selection. The proposed method aims to identify significant association rules among thyroid disease attributes, thereby facilitating accurate classification. Experimental evaluations conducted on benchmark thyroid disease datasets demonstrate the effectiveness of the proposed approach in terms of classification accuracy and computational efficiency. The results indicate that the hybrid PSO and ACO-based association rule mining approach surpasses traditional methods, underscoring its potential to enhance thyroid disease diagnosis systems.

A multi-level IIOT platform for boosting mines digitalization

This paper presents an innovative IIoT multi-level platform tailored to address the specific needs of the mining domain. The platform has been conceptualized and built in the context of the illuMINEation European project. For this purpose, mining specific use cases have been designed such as promoting underground safe areas, performing efficient mining operations or boosting predictive maintenance approaches. Then, specific requirements have been identified and, as a result, the platform has been developed. It consists of four-level layered platform: (1) edge devices layer to manage several sensors deployed in the mines; (2) edge box layer to provide in-mine operations such as filtering, streaming and processing; (3) fog layer which offers an overall perspective of each mine; and (4) cloud layer to centralize the data of all the mines and to provide powerful processing capabilities. In addition, the platform is robustly secured in terms of protecting communications confidentiality and access control and also provides a toolbox aimed at manipulating 3D complex images to obtain operable mine-domain novel user interfaces. Finally, a platform validation is proposed where three different use cases are explained to better show and demonstrate the capabilities of the platform.

Optimizing Energy Efficiency in Deep-Sea Mining: A Study on Swirling Flow Transportation of Double-Size Mineral Particles

Deep-sea minerals are regarded as the most economically viable and promising mineral resource. Vertical hydraulic lifting represents one of the most promising methods for deep-sea mining lifting systems. To mitigate the potential for clogging due to the aggregation of particles in vertical pipe transport during deep-sea mining operations, this paper employs numerical simulations utilizing the computational fluid dynamics and discrete element method (CFD-DEM) model to investigate the swirling flow transportation of mineral particles. The characteristics of the swirling flow field and the motion law of double-size particles at different swirling ratios are investigated. The findings demonstrate that, in comparison to axial transport within the pipeline, the particle movement observed in swirling flow transport exhibits an upward spiral trajectory. This phenomenon facilitates the orderly movement of particles, thereby enhancing the fluidization of particles within the pipeline. An increase in the swirling ratio (SR) has a considerable impact on the velocity within the pipe. The tangential velocity distribution undergoes a gradual transition from centrosymmetric to non-centrosymmetric as the distance from the inlet increases. An increase in the SR results in an enhanced aggregation of particles at the wall, accompanied by a notable rise in the local particle concentration. The value of SR = 0.3 represents a critical threshold. When SR exceeds this value, the distribution of particles in the cross-section reaches a relatively stable state, rendering it challenging to further alter the distribution and concentration of particles, even if the SR is augmented. Furthermore, the maximum local particle concentration in the vicinity of the wall tends to be stable. These results provide valuable insights into vertical pipe swirling flow transport for deep-sea mining.

Consortium blockchain-based tunnel data bank for traceable sharing and treatment of structural health monitoring data

The volume of structural health monitoring (SHM) data is rapidly increasing due to the explosive growth of information from constructed infrastructures. However, data storage by different monitoring organizations is often isolated, even when they serve the same construction project, resulting in “data silos”. Improving the efficiency of sharing massive monitoring data and developing a reliable, distributed system can significantly enhance data mining efficiency. In this paper, a secure and anti-tampering data bank for sharing tunnel SHM data was innovatively constructed based on blockchain technology, significantly enhancing traceable storage, regulation, and collaboration between different organizations. A smart contract was implemented to manage heterogeneous data storage, monitor incremental calculations, and make alert judgments. A series of tests assessing traceability and applicability were conducted using monitoring data from a tunnel in Hangzhou, collected through Wireless Sensor Networks. The results demonstrate that the constructed data bank effectively facilitates data exchange and sharing among multiple monitoring parties.

A Study on the Optimization of Water Jet Decontamination Performance Parameters Based on the Response Surface Method

The substrate that adheres between the teeth of the traveling track plate during the operation of a deep-sea polymetallic nodule mining vehicle affects the driving performance, so this study aimed to investigate the effect of the water jet on the cleaning and decontamination performance of the track under different conditions. An optimization design method based on response surface methodology (RSM) is proposed. Based on the Box–Behnken design, the optimization variables of jet pressure, jet target distance, and impact angle, and the target response of jet strike pressure on tracks, were selected, and the numerical simulation method was combined with the response surface method to establish the regression model of the response of each optimization variable to the jet strike pressure on tracks and to determine the optimal parameter combinations. The study findings indicate that the primary factor influencing the pressure of the jet striking the crawler is the jet pressure. The hierarchical order of influence on the pressure of the jet striking the crawler, under the interaction of the three factors, is as follows: jet pressure and impact angle, jet pressure and target distance of the jet, and target distance of the jet and impact angle. The maximum pressure of the jet striking the crawler occurs when the jet pressure is 0.983 MPa, the target distance is 0.14 m, and the impact angle is 89.5°. Overall, the proposed design serves as a systematic framework for parameter optimization in the cleaning and decontamination process, and the research method and results provide theoretical references for the optimal design of mining truck desorption efficiency, which is critical for increasing mining efficiency and lowering energy consumption.

Management of Thermal Hazards in Deep Mines in China: Applications and Prospects of Mine Cooling Technology

With the continuous development of the mining industry and advancements in deep mining technology, mine environment optimization has become key to ensuring safety and improving the efficiency of mining. The high-temperature environment, particularly in deep mines, not only poses a serious threat to miners’ health but also significantly reduces operational efficiency. These issues have been determined based on the current application status and development trends of mine cooling technology, including traditional mechanical and non-mechanical cooling technologies, as well as emerging roadway insulation materials and mine cooling clothing applications. By comparing the advantages and disadvantages of each technology, the main challenges related to the use of current mine cooling technologies are pointed out, including the low energy efficiency ratio, high cost, and difficult implementation. Finally, this paper looks forward to the future development directions of mine cooling technologies, emphasizing the importance of intelligent, energy-saving, and environment-improving comprehensive system management and, in turn, promoting the progress and application of mine environment optimization technology and supporting safe and efficient deep mining.

An Improved Apriori Algorithm Based on the Spark Platform

Apriori algorithm is one of the most classical algorithms used to mine frequent term sets, but because of the need to scan the calculation method of transaction set repeatedly, the computational efficiency of the algorithm is seriously reduced and it is difficult to parallelize processing. With the advent of the era of big data, the data scale is increasing. In order to solve this problem, this paper proposes a Apriori parallelization processing method based on vertical data format and Spark computing framework GC-Apriori algorithm. Using the vertical data format, reduce the duplication between things, improve the efficiency of data storage, and the efficiency of frequent item set mining. At the same time, the broadcast variable mechanism of Spark is used to improve the overall computing efficiency. Comparing the performance with other distributed Apriori algorithms on the same scale, the computational efficiency of GC-Apriori algorithm is improved. The results show that the algorithm effectively improves the efficiency of frequent term set mining of Apriori algorithm in distributed environment.

Unsupervised and interpretable discrimination of lithium-bearing minerals with Raman spectroscopy imaging

As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICP-OES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium-mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Influence of cationic surfactants on two-phase air–liquid displacement in porous media

The displacement efficiency of leaching agent solution in orebody pores is the key factor affecting the leaching rate of rare earth ions. To improve the displacement efficiency, cationic surfactants were added to the solution as the invading phase fluid, and microfluidic visualization experiments were carried out. Based on the experimental results, the effects of cationic surfactants on air-liquid two-phase displacement and its mechanism were analyzed. The results show that cationic surfactants can change the capillary fingering to stable displacement mode. Moreover, the closed air clusters are significantly reduced, and the displacement efficiency is greatly improved, up to 96.75 %. In addition, displacement efficiency is negatively correlated with the porosity of the porous media. Cationic surfactants primarily affect the displacement by changing the interfacial properties of the solution. The research results provide theoretical guidance for the efficient mining of ionic rare earths.

Experimental investigation on the grading optimisation and storage effect of crushed gangue for backfill

ABSTRACT Backfilling with coal gangue can effectively control gangue pollution and surface subsidence. In order to optimise the control effect of crushed gangue overlying rock, this article focuses on the regulating effect of gangue particle size grading on mechanical properties. Through research on the physical properties of gangue and natural graded gangue compaction experiments, the porosity of gangue and the process of gangue crushing are analysed. It is shown that the gangue material has good load-bearing performance. By storage analysis of the natural gradation experiments, the alarm height of the gangue was 20 m. Through compression experiments of group B, it was found that the particle deformation first increased and then decreased. By the analysis of particle strain energy density and breakage energy, it is concluded that the strain energy density of the sample from high to low is B1, B2, B6, A5, B5, B4, and the breakage energy to reach the sample state from low to high is A5, B6, B5, B4, B3, B2, B1, respectivley. The B6 group sample was found to have the best control effect on the overlying rock, thus providing suggestions for optimising the efficiency and effectiveness of backfill mining.

Ethiopian Coal Deposits and Resource Development Prospects

ABSTRACTThe growing use of local coal in Ethiopia has created challenges for cement factories due to inconsistencies in grade and quality. This paper presents the main areas rich in coal resource and potential sites for their development. Field studies have been conducted extensively in some areas to explore coal and analyze its compostion for potential use as major energy source in addressing the high energy demands for cement production across the country. Ethiopian coal beds vary in thickness from a few centimeters to 6 m and encompass a range of ranks from lignite to bituminous coal. Quality also varies widely, with ash content being a significant parameter of concern, as some studies indicate elevated levels. However, because of insufficient data, it is challenging to generalize other quality parameters accurately. Ethiopia's coal exploration dates back to the 1940s, with rough estimated deposits totaling 600 million tons nationwide. Currently, small‐scale producers satisfy 66% of the cement industries coal demand, which is the major consumers. Efficient coal mining enables the country to substitute significant amounts of imported coal, reducing large expenditures in foreign currency. Local coal production offers cost savings, job creation, streamlined transport, and reduced import reliance, thereby preserving hard‐earned foreign currency and granting the country a competitive edge.

Effect of microwave on coupled rheological and mechanical properties of cemented tailings backfill

Cement tailings backfill (CTB) is a mixture of tailings, binders and water that is widely utilized to fill underground mining areas. Once placed, the CTB needs to provide adequate ground support as soon as possible to improve mining efficiency. Microwave heating can accelerate the curing process of CTB, thereby improving the mechanical performance of CTB. However, if the CTB slurry is under the heating condition, the early hydration reaction process of the mixture can be accelerated, consuming more water in the CTB materials and thus resulting in reduced fluidity. Therefore, this study proposes an experimental scheme to incorporate microwave heating into the preparing and curing of CTB, so as to realize the improvement of the mechanical strength of CTB body under the condition of also ensuring the fluidity of fresh CTB. The results indicate that with the appropriate microwave heating time (2–4 min) and power (600 W) applied, both the flowing ability and strength in the plastic and hardened CTB can be improved. The improvement in fluidity of CTB is ascribed to the intensification of molecular thermal movement, while the enhancement in strength of CTB is due to the acceleration of binder hydration progress. The current study can also contribute to further practical application of microwave heating in the preparation process of CTB before it is placed into underground mined-out areas.

Numerical Simulation of Gas Production Behavior Using Radial Lateral Well and Horizontal Snake Well Depressurization Mining of Hydrate Reservoir in the Shenhu Sea Area of the South China Sea

Improving the production capacity of natural gas hydrates (NGHs) is crucial for their commercial development. Based on the data of the first on-site testing production of NGHs in the Shenhu Sea area, numerical methods were used to analyze the production behavior of radial lateral well (RLW) and horizontal snake well (HSW) with different completion lengths when they deployed at different layers of the Class-1 type hydrate reservoir (with a fixed pressure difference of 6 MPa and continuous production for 360 days). The results indicate that compared with the single vertical well production, RLW and HSW can effectively increase production capacity by enlarging drainage area and the productivity is directly proportional to the total completion length. The RLW and HSW deployed at the three-phase layer (TPL) have optimal mining performance within a 360-day production period. Different to the previous research findings, during a short-term production period of 360 days, regardless of the deployment layer, the overall production capacity of HSW is better than RLW’s. The total gas production of HSW-2 circles well type is about four times that of a single vertical well, reaching 1.554 × 107 ST m3. Moreover, the HSW-1 lateral well type stands out with an average Qg of 3.63 × 104 ST m3/d and a specific production index J of 16.93; it has the highest J-index among all well types, which means the best mining efficiency. It is recommended to choose the HSW-1 circle well type, if the coiled tubing drilling technique is used for on-site testing production of NGHs in the future. The research results provide insights into the potential applications of RLW and HSW in this sea area.

Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data

Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data

Research on 3D geological modeling based on boosting integration strategy

Accurate geology modeling is critical in coal mining engineering for increasing mining efficiency and safety. Traditional 3D modeling approaches rely on drill hole data to fill in unknown geographical characteristics using interpolation or machine learning methods. However, the cost of individual borehole projects is high, and borehole distribution is sparse; the filling of sparse boreholes by interpolation smoothes the geologic data to some extent, and some abnormal geologic conditions cannot be shown in the interpolated geologic data; and the classification of subsurface space by machine learning is hampered by sparse training samples and model overfitting or underfitting. As a result, this paper proposes a method to reduce the complexity and uncertainty in the process of 3D geological modeling by integrating the classification results of multiple classifiers based on the machine learning classification model and constructing a 3D geological model based on gas extraction borehole data using the Boosting integration strategy. The results reveal that the Boosting integration model outperforms a single machine learning model in geological modeling, with improved classification accuracy and resilience. A comparative investigation of different classifiers demonstrates that the Boosting integrated model outperforms a single classifier in geological modeling. The geologic profile analysis confirms the dependability and stability of the Boosting integrated model, which can provide an effective solution for 3D geologic modeling of coal mine workings and is critical for increasing mining productivity and lowering accident risks.

Energy control and block performance optimization of bench blasting

In this study, rock-like model blast tests with varying toe burdens are conducted and the evolution of blast-induced fractures is analyzed using the digital image correlation (DIC) method. In addition, post-blast fragmentations are image-processed to quantitatively investigate the blasting performance. A three-dimensional numerical model is developed and validated against the experimental results. The effects of blasthole inclination (70°, 80°, and 90°) and short delays on fragment size distribution (FSD) are numerically investigated. Field trials are conducted to verify the numerical results. The experimental results reveal that the overall fragment size increases significantly in proportion to the toe burden. The explosion energy utilization rates range from 3.56 % to 13.16 %, with a tendency to initially increase and subsequently decrease. The numerical results demonstrate that fragments are more evenly distributed with a blasthole inclination of 70°, resulting in a remarkable improvement in rock fragmentation compared to a vertical blasthole. Compared to the toe burden, the influence of short delays on concrete fragmentation is insignificant. The field test results indicate that a narrower FSD range and stable pit walls are achieved by utilizing inclined blastholes in bench blasting. This study provides theoretical guidance for optimizing bench blasting parameters, which has practical significance for improving explosive energy utilization and production efficiency in open-pit mines.

Mapping manufactured capital in mainland China with harmonized night‐time light images between 1992 and 2018

AbstractThe manufactured capital, usually denoted as material stocks from an industrial ecology perspective, has thus far received wide attention in sustainability and circularity science. Sustainable resource management should be rooted in detailed knowledge of manufactured capital accumulation in society at a high spatial resolution. Previous studies demonstrated that night‐time light (NTL) data provide a great opportunity for monitoring material stocks dynamics at a higher spatial resolution on the regional and global scale. However, the potential of historical–geographical refined material stocks has not been fully analyzed and explored because of the inconsistency of NTL images detected by the different satellites. In this study, based on a new set of material stocks data in China and harmonized NTL images (1992–2018), we map the national stocks of 13 bulk materials (including cement, gravel, wood, brick, sand, asphalt, glass, lime, plastic, rubber, copper, aluminum, and steel) at a 1 × 1 km resolution from 1992 to 2018. The results find that the total material stocks increased from 190,000 to 460,000 t/km2 between 1992 and 2018. Among the five end‐use sectors, buildings have the highest density of 430,000 t/km2, while domestic appliances have the lowest density of 140 t/km2. Four manufactured capital clusters, including the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Chengdu–Chongqing agglomerations, possess 38% of the national total stocks in 2018, revealing an unbalanced distributed pattern of manufactured capital across China. Our results provide valuable support for policymakers and business decision‐makers on efficient resource management and urban mining.