Mining Technology Research Articles

Research on dust and rockburst control technology for coal mining and mathematical correlation

As many coal mines in China enter the deep mining stage, underground mining is starting to cause some problems, including dust disasters, rockburst, and coal and gas outburst in enclosed sites. In this study, water injection tests were conducted on coal samples using a coal rock multi-field seepage simulation experimental system under geo-stress conditions. Experimental results demonstrate that, as the cycle period increases, permeability exhibits a gradual increase. Areas with high permeability change rates are predominantly observed at the beginning and end of the cycle. The relationship between permeability and the number of cycles is initially linear during the early and middle cycles. However, beyond 30 cycles, the relationship transitions from linear to nonlinear, indicating an accelerated permeability change after a certain cycle threshold. In conclusion, this study addressed and refined the mathematical relationship between volumetric strain and permeability. The findings of this research have significant implications for the prevention of dust emissions and the mitigation of rockburst disasters in the field.

Study on the preparation of amphiphilic coal rock-cement interface enhancer and the interface strengthening mechanism

Coal rock has well-developed bedding and joint, strong brittleness, oil-wet surface, poor affinity with cement, and poor mechanical properties. Therefore, it cannot provide high-quality wellbore sealing, which further restricts the development of coal seam gas through subsequent hydraulic fracturing. In order to further improve the bonding quality of the coal rock-cement interface, the weakening mechanism of coal rock-cement bonding was studied in this paper firstly based on the properties of coal rock itself and the microscopic and macroscopic performance analysis tests. Then the interface enhancement approaches were proposed and a dual-affinity coal rock-cement interface agent GSK-II was prepared at last by surface modification of wollastonite partially, in which the modifier are KH570 and zinc stearate. The optimum preparation conditions were as follows: slurry concentration was 10 %, concentration of modifier KH570 and zinc stearate were 1 % separately, modification time was 50 min, and modification temperature was 60℃. The results showed that the coal rock-cement interface bonding strength was increased by 4.49 times after the amphiphilic interface affinity agent GSK-II was added into the drilling fluid, showing a significant interface enhancement effect. In addition, the results also indicated that the generation of solid particles accumulation zone in coal rock fractures and the hydrophobicity of coal rock surfaces were the main reasons for the deterioration of coal rock-cement interface bonding quality. The main ways to enhance the interface bonding effect are to improve the solidification and compactness of the solid particles accumulation zone, as well as to improve the hydrophilicity of coal rock surfaces. The results of the study provided theoretical guidance for improving the interfacial bonding quality of the wellbore in coalbed methane wells with developed fractures, and laid a solid foundation for the safe and effective implementation of subsequent volume fracturing and other mining technologies for coalbed methane wells, and also provided new ideas for the research and development of interface enhancers in this field in the future.

Q-learning assisted multi-objective evolutionary optimization for low-carbon scheduling of open-pit mine trucks

Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease carbon emissions, rationally scheduling shovel-truck pairs is a necessary issue. Previous studies give less consideration on carbon emissions of trucks that varies with road and driving conditions. To overcome the shortage, a constraint bi-objective optimization model is built for low-carbon scheduling problem of open-pit mine trucks, in which minimizing both idle time and carbon emissions of trucks are taken as the objectives. More especially, the limits on working time, traffic volume and the number of trucks are modeled as the constraints. Carbon emissions is formulated by multistage nonlinear function that takes road condition, load and driving state of trucks into account. As the problem-solver, Q-learning assisted multi-objective evolutionary algorithm is put forward. Four evolution states are defined by analyzing the improvement on feasibility and convergence of the population, and four problem-specific evolution operators are designed to meet different demands of the evolution. Q-learning-based selection strategy is proposed to select the most appropriate operator, with the purpose of improving the evolution efficiency. Experimental results on the real-world instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly.

Relationship between blasting operation and slope stability: a case study at Borneo Indo Bara open pit coal mine

Bench blasting is commonly used in open-pit coal mines because it effectively increases coal production and aids in overburden removal. However, uncontrolled blasting can generate significant vibrations and accelerations, which may lead to slope failure if the magnitude of permanent displacement exceeds its critical value. A prevalent method for calculating the dynamic factor of safety (FoS) of a slope due to blasting is the pseudo-static approach; however, this method tends to be overly conservative. In this study, we utilized Newmark’s sliding block method and the strength reduction factor (SRF) to assess the dynamic FoS of slopes by evaluating the permanent displacement caused by bench blasting. We based our seismic input on a complete vibration waveform obtained through signature hole analysis of a specific bench blast design. The dynamic FoS was defined as the SRF threshold that resulted in a critical slope displacement of 50 mm. The case study was conducted at Borneo Indo Bara, one of Indonesia’s largest open-pit coal mines located in South Borneo Province. We analyzed four sidewall slopes that were subjected to repeated blasting events. The results indicated that the slope’s LOM design could tolerate blasting vibrations corresponding to a peak particle acceleration (PPA) of up to 0.41 g, which yielded a dynamic FoS of 1.35. This value is significantly higher than the pseudo-static approach, which indicated a pseudo-static FoS of 1.04 for a PPA of 0.12 g. Furthermore, this study assists mining practitioners in predicting the allowable number of blast events to prevent slope failure and recommends avoiding blast events with a scaled distance of less than 8 m/kg1/2 to prevent sudden slope failures. In conclusion, the study highlights the importance of the integrated approach that combines signature hole analysis, Newmark’s sliding block method, and the SRF approach for evaluating the dynamic FoS of mine slopes subjected to bench blasting. The results of this study can offer valuable insights for mining engineers engaged in bench blasting to adopt the proposed integrated approach, which is still overlooked in Indonesia’s open pit blasting practices.

Structural parameters optimization of microwave radiation antenna in hydrate reservoir based on multiphysical coupling model

Microwave heating has emerged as a promising technology in hydrate mining, attracting significant interest. This study focus on optimizing the structural parameters of microwave radiation antenna via a multiphysical coupling model. Subsequently the model is validated through experimental results. The microwave radiation simulation model is then developed to evaluate the antenna radiation performance and to elucidate the temperature distribution mechanism within the reservoir. The optimized structure features rectangle slots with an angle of 75° and a length of 28 mm. When this optimized antenna is deployed in a one-meter radius reservoir and heated for 10 hours, it rises the average temperature from 2 °C to 7.11 °C. Moreover, the design improves the thermal uniformity within the gas hydrate reservoir, achieving a temperature standard deviation of 7.76 °C. Post-heating uniformity indicates effective microwave distribution. Overall, these results affirm that microwave heating, particularly when utilizing an optimized antenna, effectively enhances the reservoir’s sensible heat and aids in hydrate decomposition.

Evaluation of the migration and environmental effects of metal elements within cementitious gangue-fly ash backfill in underground coal mines

Cementitious gangue-fly ash backfill (CGB) is used as a green mining technology worldwide. However, under the coupled effects of geological stress and groundwater, the metal elements in the CGB tend to migrate into nearby strata, which can consequently result in pollution of the groundwater environment. In this paper, the influence of initial pH and stress damage on the migration behavior of metal elements in CGB is quantitatively studied through the multi-physical field coupling model of stress-permeability-concentration. The enhanced Nemerow index evaluation method is used to comprehensively evaluate the impact of these metal elements migration behaviors on the groundwater environment. The research results show that: (1) When the stress damage of the CGB increases from 0.76 to 0.95, the Darcy velocity at the bottom of the CGB first increases, then decreases, and finally stabilizes at 2.01 × 10−7 m/s. The longest time to reach the maximum Darcy velocity is 3 a. (2) When the damage of the CGB is 0.95, the farthest migration distances of Al, Cr, Mn, Fe, Ba, and Pb are 40.8, 34.0, 29.8, 31.9, 38.8 and 32.1 m, respectively. (3) The alkaline environment stimulates the migration of Al, Cr, Fe, Mn, and Pb, whereas Ba migrates farther under acidic conditions. The farthest migration distance of Ba is 31.6 m under pH 3. (4) The enhanced Nemerow index indicates that when stress damage increases from 0.76 to 0.95, the areas with poor water quality increase from 0 to 1.71 %, and no area is classified as very poor grade. When the initial pH changes from 3 to 11, 100 % of the region is classified as fair or above. The initial pH of the CGB has a relatively slight influence on the groundwater environment. This study provides experimental data and theoretical basis for the environmental evaluation of CGB.

Healthcare Monitoring-based Internet of Things (IOT)

Remote health care is needed when direct medical monitoring is unavailable. Modern technology provides several ways to make patient access simpler. In particular, cloud, IoT, and data mining technologies have been successful in health care and medicine. This method identifies illnesses using data collected from two groups of patients, a male group and a female group where data are based on the heart rate, systolic blood pressure, and body temperature parameters. The three classifier types use the collected data: the naïve Bayes, support vector machine (SVM), and the J48. These three classification methods were considered to measure the classification accuracy using the proposed healthcare IOT-based monitoring system. The Precision, Recall, and F-measure evaluation measurements were used to quantify the obtained accuracy results of the two groups. The obtained results showed excellent classification results of 0.96, 0.99, and 0.98 for the naïve Bayes classifier to the male group that outperformed the results of the other two classifiers for the precision, recall, and f-measure, respectively. As for the female group, similar results have been obtained. In conclusion: The use of the Internet of Things in the proposed system makes it accurate and easy to use remotely by patients.

Comminution technologies in the pharmaceutical industry: a comprehensive review with recent advances

Abstract Comminution processes play a pivotal role in diverse applications, ranging from food processing, to mining and materials engineering. The pharmaceutical industry is no exception, with an increased focus on particle engineering to overcome the growing challenges related to the complexity of new drug molecules such as poor water solubility or stability issues. Additionally, the preparation of powders for pulmonary, transdermal, topical, ophthalmic, oral or parenteral administration often requires specific particle size requirements. Thus, milling technologies offer an excellent option for controlling particle size, improving the stability, dissolution, absorption rate, and bioavailability of poorly water-soluble drugs. They also contribute to enhancing pharmaceutical forms and overall product performance. This review highlights the different types of technologies used for comminution, the respective advantages and drawbacks, as well as connected topics including feed material properties, analytical techniques, process analytical technology, process safety, new top-down technologies and key information to consider when selecting a technology. Thus, an in-depth approach of comminution in the pharmaceutical industry is presented. This compilation serves as a source of comprehensive information for those who decide to initiate research projects in this field, or to update their existing literature knowledge and understanding.

Comparative Analysis on Policy Frameworks of High-Altitude Mineral Resource Management: Implications for Sustainable Development Goals (SDGs)

As the global demand for minerals critical to clean energy and technological innovation continues to rise, the sustainable exploitation of mineral resources in high-altitude regions becomes increasingly essential for global sustainable development. Employing SWOT analysis, deep learning, and heatmap techniques, this study delves into the mineral resource policies of China, the United States, Canada, and Chile, assessing their alignment with and impact on Sustainable Development Goals (SDGs). Despite distinct policy frameworks, a shared focus on technological innovation and environmental sustainability is evident. China’s strategic resource allocation and stringent regulations drive a green, low-carbon shift, aligning with SDG 13 (Climate Action). The United States fosters SDG 9 (Industry, Innovation, and Infrastructure) through market-driven technological advancements. Canada’s collaborative approach, emphasizing indigenous rights, underpins SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production). Chile’s national governance and international collaboration mix bolsters the mining industry’s efficiency and sustainability, supporting SDG 7 (Affordable and Clean Energy). The study underscores a trend toward sustainable practices in mineral resource management and stresses the critical need for international cooperation. The study advocates for global collaboration and sharing of green mining technologies to accelerate the industry’s transition to a sustainable and responsible future and boost SDG achievements worldwide.

Evolutionary game study and empirical analysis of the adoption of green coal mining technology: A case study of ITMDB

The world is facing the challenge of energy transformation, and the integrated technology of mining, dressing, and backfilling (ITMDB) is a green mining technology that can effectively reduce resource waste and environmental pollution, thereby promoting sustainable development in the traditional energy sector. However, widespread adoption of ITMDB remains limited due to various factors. To address this, we construct an evolutionary game model between the government and coal enterprises. With system dynamics simulations, we analyze the impacts of subsidy, punishment, information sharing policy and their combinations on ITMDB adoption. Then, Empirical research was conducted to validate the simulation results and analyze the mechanism and heterogeneity effects using data from A-share listed coal companies in China. The results show that (1) A combined strategy of punishment and information sharing is most effective in encouraging ITMDB adoption. (2) The subsidy policy can have a negative effect in certain situations. (3) The optimal adoption strategy promotes technology adoption through executives green cognition (4) The optimal adoption strategy has the most significant effects on enterprises located in the central and eastern regions, areas with a lower proportion of secondary industry, and mature companies. Finally, this study provides policy recommendations for ITMDB adoption.

Empirical relationship of characteristic impedance with rock drillability and cuttability

ABSTRACT In rock and mining engineering, it is essential to predict rock cuttability and drillability for estimating drilling costs, selecting suitable drilling/cutting machine and tools, and achieving high productivity. To predict the drillability and cuttability, many destructive methods have been developed. However, they are often expensive and time-consuming. To explore a non-destructive method, this paper builds a series of empirical relationships between rock characteristic impedance and its drillability and cuttability indices by using a large quantity of measured data from various laboratory and field tests in previous publications. The relationships between rock’s UCS (uniaxial compressive strength) and its drillability and cuttability indices are also built for comparison. The results indicate that: (1) the penetration rates of both percussive drilling and rotary drilling depend on rock characteristic impedance, and they decrease with increasing impedance. (2) The specific energies in percussive drilling, rotary drilling and cutting all increase with increasing rock impedance. (3) Most of current drillability indices such as alfa, DRI, CLI and Shore hardness have a certain relationship with rock impedance. (4) Characteristic impedance has a more confident relation with drillability and cuttability than the UCS. Since determination of rock impedance does not necessarily require a destructive method, characteristic impedance has a great potential to be taken as an index to predict rock drillability and cuttability.

Multidimensional Context Clustering to Analyse Student Engagement in Online Learning Environment

Educational data mining is the application of data mining technology in an educational environment to indicate and resolve various types of issues faced in education. COVID-19 pandemic in 2020 accelerated the shift to emergency remote learning and online learning, which has continued to grow due to its flexibility and cost-effectiveness. However, several challenges exist when adopting the online learning, and which include the access to technology, technology attitude, psychological questions, teacher contact, and quality of assessment. Thus, it becomes important to focus on student engagement as a determinant of success during online learning. Student engagement is a complex construct which is comprised of four aspects which are behavioural, cognitive, emotional and social. In this study, the K-Means clustering approach is chosen to categorise students into clusters according to their active participation in the online learning process. The experiment used produces a silhouette coefficient of 0.71 clustering the datasets into three clusters. Cluster 0 are the disengaged learners who were observed to be least active across all the dimensions, while cluster 1 is composed of passive learners. The cluster 2 comprises the most engaged students with the high level of time management. These results provide information regarding the distinct engagement profiles that may be helpful when the lecturers attempt at student’s interventions.

A Review of Plume Research in the Collection Process of Deep-Sea Polymetallic Nodules

A Review of Plume Research in the Collection Process of Deep-Sea Polymetallic Nodules

Research on Strip Coal Pillar Technology under the Construction Group of Gangue Filling and Recycling

In order to reduce the various impacts of surface movement caused by coal mining on the human living environment, avoid the accumulation of coal gangue on the surface, and recover the strip coal pillar resources left under urban areas, based on the stability of the binary bearing structure composed of backfilled gangue and narrow coal pillars after strip mining and the characteristics of the equivalent mining height model, theoretical analysis and numerical simulation methods were comprehensively used to analyze the factors affecting the stability of coal pillars, the ultimate strength of coal pillars, and the safety factor of coal pillar stress under the condition of binary bearing structure. Through comparative analysis of deformation of surrounding rock after strip mining and coal pillar recovery, it was found that the mining pressure manifestation of strip coal pillar gangue filling is weak, without obvious initial and periodic pressure phenomena, and the top control is relatively easy without the risk of dynamic pressure. The research results provide useful reference and guidance for the first application of fully mechanized mining technology in urban strip coal pillar recovery in coal mines.

Experimental investigation and AutoML prediction of the resilient behaviour of coarse-grained waste rocks

Coarse-grained waste rocks have been widely used for constructing mining haul roads, but the research on their resilient behaviour is limited. In this study, a series of large-scale (300 mm diameter, 600 mm height) and conventional-scale (150 mm diameter, 300 mm height) repeated load triaxial tests were conducted to quantify the impacts of stress levels and physical properties on the resilient behaviour of coarse-grained waste rocks. The resilient modulus increased significantly with dry density at low stress levels, while it was more sensitive to the shape factor of gradation at high stress levels. An Automated Machine Learning (AutoML) model was also developed for predicting resilient modulus, and it showed higher prediction accuracy (R 2≥0.95, MAE<30 MPa) and generalizability than the proposed traditional model in this study. The advancements offer valuable insights for the utilisation of waste rocks in geotechnical and mining engineering.

Non-Pillar Coal Mining by Driving Roadway During Mining Period in High-Gas Top-Coal-Caving Working Face

To solve the problem of the inability to achieve Y-shaped ventilation in the boundary coal mining of high-gas mines and the problem of gas accumulation in the upper corner of a fully mechanized mining face, non-pillar coal mining technology is proposed by a driving roadway during the mining period. A high-gas working face requires Y-shaped ventilation to achieve upper corner gas control, but Y-shaped ventilation conditions are not available at the boundary coal body. In order to handle this challenge, studies have suggested non-pillar coal mining technology, which involves excavating roadways while mining in order to achieve non-pillar coal extraction and use recoverable wide coal pillars. During the simultaneous excavation of a working face and roadway, studies analyzed the distribution characteristics of the complicated stress environment. Following an evaluation of the impact of coal pillar width on the quality of an excavation roadway, this study’s development is in terms of an effective technique for retaining coal pillars as established. During the mining period of a working face, in the goaf of the working face, the research analyzed the distribution properties of the gas flow field, and findings from the study indicate that the width of the recovered coal pillar influences the distribution of gas. Finally, the width of the coal pillar was comprehensively determined, forming non-pillar coal mining technology by a driving roadway during the mining period. The on-site practice has shown that using a wide coal pillar with a width of 70 m to protect the roadway significantly reduces the deformation of the surrounding rock in the mining roadway, the gas concentration at the return airway is lower than the safety production standard, and by decreasing the mining succession time by 15 months, studies achieved improving the working face’s coal extraction rate by 12.6%.

Impact of emotional contagion on waste separation intention in social media settings—Evidence based on machine learning and text analysis

Emotional contagion refers to the emotional experience caused by, and matched with, others' emotions, and is a process of emotional transmission. The emergence of social media has reshaped emotional contagion and the ways in which information is disseminated. Clarifying the emotional contagion mechanism associated with waste separation discussions on social networks and its dynamic effects on residents' intentions to separate waste is important in guiding residents toward waste separation. This study used data mining technology to crawl through 906,680 texts from social media. Machine learning models were used to identify emotions, and topic variables were extracted from text data to explore the effects of emotional contagion on residents’ intentions to separate waste. The empirical analysis indicated that the information disseminators’ emotions positively affected the information receivers’ emotions, thus indicating that an emotional contagion phenomenon exists in the social network and positively promotes residents' intentions to support waste separation, but also has negative effects on residents' intentions to implement waste separation. These findings imply inconsistencies in residents' attitudes and behaviors toward waste separation. Topic popularity buffered the effect of emotional contagion: excessive publicity did not result in better publicity effects. In addition, the regional economic development level weakened the influence of emotions on residents' intentions to separate waste. This study explored the mechanisms of emotional contagion and intention transmission pertaining to environmental protection topics, and offers important insights into the government's use of social media platforms to promote waste separation behavior among the public.

Integrating lidar technology in artisanal and small-scale mining: A comparative study of iPad Pro LiDAR sensor and traditional surveying methods in Ecuador’s artisanal gold mine

Abstract This research evaluates the use of the iPad Pro 11 with LiDAR sensor (LS) for geospatial surveying in Ecuador’s artisanal and small-scale mining (ASM) sector, a key contributor to the national economy; however, it is hindered by outdated equipment and environmental challenges. It was conducted at “La Zamorana,” an underground gold mine. The study compares the efficacy of iPad LS against traditional surveying methods across five phases, including control point establishment, traditional and LiDAR surveying, data postprocessing, and an electronic survey assessing ASM’s technological readiness. The findings indicate that the iPad LS outperforms traditional methods in detail and accuracy, particularly in elevation measurements, with most discrepancies under 5 cm. The precision of the LiDAR methodology is highlighted by the closer alignment of points to control points than traditional surveying methods. In summary, the iPad Pro 11 with LS shows promise as an affordable ASM tool for geospatial surveying in ASM in Ecuador. The research emphasizes how receptive the industry is to new technology developments. However, further research is recommended to explore the technology’s effectiveness in diverse mining environments, ensuring its comprehensive applicability in ASM.

Design and research of intelligent watt hour meter fault early warning system based on data mining technology

With the rapid development of information technology and the continuous improvement of communication technology, electric energy meters are innovating and developing towards networking, informatization and intelligence. This research constructs a model design of electric energy failure early warning system based on big data mining technology under the background of big data. It analyzes and studies the intelligent electric energy meter failure early warning system. Through the analysis and comparison of the factors affected, comprehensive performance and application prospects of the intelligent electric energy meter failure early warning system under different data mining technologies, the research results show that the application of big data mining technology makes the heavy work of data collection and data analysis integrate big data mining technology, it can be popularized more widely and applied to more scenarios, thus reducing the manual workload, it makes the work efficiency more significantly improved, and can promote the reform of China’s smart grid more quickly.

Fintech adoption and sustainable deployment of natural resources: Evidence from mineral management in Brazil

This research aims to investigate the influence of FinTech implementation on responsible mineral resources management in Brazilian economy. For the study purposes it has utilized data set from 1990 to 2022 and for data analysis the autoregressive distributed lag (ARDL) approach has been used. The results show cointegration correlation between FinTech adoption, investment in eco-friendly mining, government funding for FinTech in the mining industry, environmental compliance, technological readiness, and sustainable mineral extraction. The result shows that a 1% increase in FinTech adoption and technology infrastructure readiness would improve sustainable mineral management by 0.01% and 0.03% respectively. However, a 1% increase in the government funding for FinTech in the mining industry, environmental compliance, and investments in sustainable mining technology adversely affect sustainable mineral management by 0.2%, 0.1%, and 0.1% over the long term. The finding provides insight about how variables may affect sustainable mining policies in the future. FinTech adoption and technology flexibility are expected to have a favorable impact on natural resources. This research offers important insights for using natural resources responsibly in the shift to Industry 4.0 by utilizing FinTech and technological readiness in Brazil.