Journal of Mining and Environement
Volume:15 Issue: 2, Spring 2024

Due to the critical nature of seismic risk in metro tunnels, the seismic response of underground tunnels is a highly delicate topic. The seismic response of a sub-surface structure depends more on the properties of the surrounding ground and the induced earth deformation during an earthquake than on the structure's inertial properties. This paper examines the seismic response of a typical section of the underground tunnel of Delhi Metro Rail Corporation (DMRC) between Rajiv Square and Patel Square in New Delhi's Connaught Place. Three-dimensional elasto-plastic analysis of Delhi metro underground tunnels under the seismic loading has been performed by finite element method using the Plaxis 3D software. Additionally, the influence of various boundary conditions on the dynamic response of metro tunnels has been examined. A comparison of the three-dimensional analysis with the two-dimensional plane-strain analysis has also been made. Horizontal displacements were experienced maximum compared to the longitudinal and vertical displacements in the soil-tunnel system. In dynamic analysis, the absorbent boundary is much more effective in controlling the displacements and the induced acceleration than the elementary boundary or the free-field boundary.

Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can be used to predict groundwater quality and leachate characteristics to manage this issue efficiently. This study proposes a machine learning-based model for the prediction of groundwater quality and leachate characteristics using the effective water quality index (EWQI). The leachate dataset used in this study was obtained from a landfill site, and the groundwater quality dataset was collected from literature review. The mean values of TDS, Ca, Mg, NO3-, and PO4- exceeded the prescribed limit for drinking water purposes. The proposed model utilizes a machine learning architecture based on a convolutional neural network (CNN) to extract relevant features from the input data. The extracted features are then fed into a fully connected network to estimate the EWQI of the input samples. The model, trained and tested on leachate and groundwater quality datasets, achieves a high accuracy and computational efficiency, aiding in predicting groundwater quality and leachate characteristics for waste management.

Nigeria is abundantly blessed with solid mineral resources such as copper, gold, and tantalite, which are essential for the economic growth of the country. The extraction of these mineral resources comes with the generation of huge amount of waste. This study examines the possibility of utilizing some mine wastes from Jos, Nigeria, in embankment construction by subjecting them to relevant laboratory geotechnical experiments. The results indicates that the overburden materials contain clay-sized fraction ranging 5-20%, while the sand fraction ranged 42-82%, which is an indication of the predominance of sand size particles. On the other hand, the clay-sized particles in the tailings range 5-21%, while the sand fractions range 65-80%. The overburden materials recorded liquid limit values ranging 26-48% and plasticity index ranging 6.3-21%, while the liquid limit and plasticity index of the tailings range 23-32.8% and 6.2-11.6%, respectively. The maximum dry density (MDD) and optimum moisture content (OMC) of the overburden materials vary 1.84-1.98 mg/m3 and 1.4-17.2%, respectively, with an average of 1.89 mg/cm3 and 16%. On the other hand, the tailings recorded MDD ranging 1.88-2.06 mg/m3 with their OMC ranging 14.4-16% with an average 14.86%. The soaked California bearing ratio (CBR) of the overburden materials range 27-32%, while that of tailings ranges 25-32%. The geotechnical evaluation of the overburden materials and tailings reveals that most of the materials are suitable for embankment construction. However, the high linear shrinkage of some wastes renders them unsuitable.

This study examines the revitalization of mining ghost towns (MGTs) through heritage tourism, focusing on sustainability and heritage preservation. The study highlights the transformative potential of heritage tourism in revitalizing these towns, highlighting the economic resilience achieved through diversified local economies and responsible tourism practices. Cultural preservation ensures the endurance of unique identities and cultural legacies, sparking community pride and cultural exchange. Sustainability measures extend beyond heritage preservation, promoting environmental stewardship and long-term ecological well-being. Community engagement, educational initiatives, and responsible tourism practices are crucial in sustaining the heritage of these towns. The implications extend beyond individual communities, offering a model for responsible and sustainable tourism practices with global relevance. The significance of revitalizing MGTs through heritage tourism lies in preserving history, empowering communities, and creating vibrant, sustainable destinations for generations.

Despite a decline in mining accidents and improvements in safety performance, the proportion of accidents in mines remains high in developing countries. Although underground mining is one of the most hazardous occupations, surface mining also carries multiple risks that receive comparatively less attention. In developing countries like Pakistan, research is focused mainly on fatal and serious accidents, often overlooking minor and near-miss accidents. This study assesses the risks of fatalities and injuries faced by occupational groups engaged in surface mining. For this purpose, an analytical hierarchy process is used to analyze fatalities data and Fuzzy TOPSIS for injuries data. It can be concluded that all occupational groups are exposed to fatalities and injuries risks due to various hazards. However, some activities are more prone to fatalities while others are to injuries. Laborers are most frequently involved in such accidents. Common risks such as falling rocks and slippage from the top affect all occupational groups equally. Incidents involving slippages from the tops result in more fatalities, whereas machinery-related risks lead to more injuries than fatalities. Hazards causing minor injuries are frequently overlooked in terms of prevention and control efforts until they lead to serious injuries/fatalities. It is suggested that every accident, regardless of severity, be reported and thoroughly analyzed regularly to minimize the recurrence of incidents. The essential measures for creating a safer mining environment include implementing appropriate mechanization, providing regular training to workers, enforcing the use of personal protective equipment, and strict adherence to mining laws.

The present work aims to assess the pressure-settlement behaviour of sand beds under a square footing reinforced with coir geotextile using the PLAXIS 3D software. The angle of internal friction of sand was varied from 28° to 38°. The effect of length of coir geotextile (1B, 2B, 3B, 4B, and 5B; B is width of footing) and position of coir geotextile (0.2B, 0.4B, 0.6B, 0.8B, and 1B) to ultimate bearing capacity of sand were examined. A remarkable improvement in ultimate bearing capacity of sand beds was obtained with provision of coir geotextiles.  It was observed that the bearing capacity of sand increases by placing coir geotextiles up to a depth of 0.4B from base of footing, thereafter it starts decreasing. The optimum length of coir geotextile was found as 4B-5B. An insignificant improvement in the bearing capacity ratio of sand reinforced with coir geotextile was observed at higher values of angle of internal friction.

The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. In this study, the efficacy of granite fragmentation was assessed using the WipFrag software. The relationship between particle size distribution, blast design, blast efficiency, and uniformity index were analyzed using the WipFrag result. The optimum blast design was also identified and recommended for mine production. The result revealed that large burden distances result in bigger X50, X80, and Xmax fragmentation sizes. A burden distance of 2 m and a 2 m spacing were identified as the optimum burden and spacing. The finding revealed that blast mean size and 80% passing mesh size have a positive correlation. The result from this study indicated that the uniformity index has a positive correlation with blast efficiency and a negative correlation with maximum blast fragmentation size. The prediction accuracy of the developed models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). The error analysis revealed that the ANN model is suitable for predicting quarry-generated profit.

Potentially harmful elements enter into the environment through mining and agricultural activities, causing water and stream sediment pollution.  Ecological risk analysis helps to determine sediment pollution, to recommend remediation measures for human health safety and the survival of aquatic species. The sediments were analysed for acidity and redox potential using a pH-meter and spectrophotometer, respectively. Nickel, cadmium, arsenic, chromium, lead, zinc, and iron were measured using atomic absorption spectrophotometer. The mean value of Cd exceeded the threshold effect limit guideline indicating its adverse effect to water dwelling organisms. Anthropogenic metal input identified cadmium, lead, arsenic, zinc and chromium contamination in locations 3, 6, and 7. Modified risk assessment code, toxic response index and comprehensive ecological risk values exhibited considerable to high ecological risks in locations 3, 6, and 7. The highest comprehensive ecological risk value recorded 653.2 in location 3, showing high ecological risk to water dwelling organisms. Durbin Watson ecological risk value (2.34) is between a critical value of 1.5 < d < 2.5 showing auto correlation of the data. Potentially harmful elements obtained Durbin Watson value of 2.77, which exceeded the range showing lack of auto correlation. Strong correlation of arsenic, lead and zinc showed their affinity and common source of enrichment. Principal component analysis indicated that the sources of the elements were mostly geological weathering, sewage disposal, industrial wastes and agricultural fertilizers. The study integrated recent ecological risk indices with multivariate and regression statistics. This is helpful in interpreting related environmental problems by scientists in other parts of the world.

Among all methods for ground improvement, stone columns have become more popular recently, owing to their simple construction and plentiful availability of raw materials. However, in relatively softer soils, ordinary stone columns (OSCs) experience significant bulging owing to the minimal confinement offered by the surrounding soil. This necessitates the introduction of reinforcements in the stone column, to enhance their strength in such circumstances. The subject of this investigation was the assessment of the behavior of horizontally reinforced stone columns (HRSCs), introduced in layered soil, under the raft foundation. The soil material included was idealised using an isotropic linearly elastic fully plastic model with a Mohr-Coulomb failure criterion. There are a total of six separate factors required by the Mohr-Coulomb criterion. These include cohesion (c), the soil's dry unit weight (γd), the Poisson ratio (μ), the angle of internal friction (φ), the angle of dilatancy (ψ), and the Young's modulus of elasticity (E). At the very beginning, the load-settlement response of unreinforced soil was evaluated followed by a comparative study between square and triangular arrangements of stone columns, at different spacings, under the raft, to arrive at the configuration that encounters minimal settlements and lateral deformations. Furthermore, circular discs of suitable geogrid material were introduced along the length of the stone column. The elastic behaviour of geogrids is governed by two properties: tensile modulus and yield strength. The load-settlement behavior and lateral deformations of the resulting reinforced stone columns, with OSCs were compared. Furthermore, the spacing between the circular discs of geogrids was kept at D/2, D, 2D, and 3D, where D is the diameter of the stone column. According to the findings of an investigation conducted using FEM software, the performance of a granular pile group that is laid out in the shape of a triangle encounters much less lateral deformation and settlement than the square arrangement. The results also show that the performance of HRSCs was way better than those of OSCs, under the same in-situ soil conditions.

The aim is to use the Concentration-Volume (C-V) fractal model to identify high-quality parts of coal seams based on sulfur and ash concentrations. In the K1 and K7 coal seams in the North Kochakali coal deposit, 5 and 6 different populations of ash and sulfur content were obtained based on the results. According to this model, sulfur and ash concentrations below 1.81% and 33.1% for the K7 seam, and below 4.46% and 37.1% for the K1 seam, respective base on Russian standard for ash and high sulfur content of North Kochakali coals were considered as appropriate values. In order to identify the high-quality parts of K1 and K7 coal seams, plans at different depths were used based on the C-V fractal model. Plans at different depths suggests that the southern part of the K1 seam and the northern part of the K7 seam have the highest-quality based on sulfur and ash concentrations, which should be considered in the extraction operation. The logratio matrix was used to compare the results of the C-V fractal model with the geological data of pyrite veins and coal ash. This matrix indicates that sulfur content above 3.8% for the K7 seam and above 4.41% for the K1 seam have good and very good correlation with pyritic veins of geological data, respectively. There are good overall accuracy (OA) values in the correlation between parts of the seam with ash concentration above 37.1% and 45.7% for the K1 and K7 seams, respectively, and the coal ash obtained from the geological data.

Tabas coal possesses favorable plastometric properties that make it suitable for use in metallurgical industries as coking coal. However, its high sulfur content, which stands at approximately 2%, poses a significant environmental pollution risk. Additionally, reducing ash content to below 10% is a critical objective of this study to prevent a decline in coal's thermal efficiency in the metallurgical industries. This research work investigates the removal of sulfur and ash from Tabas coal samples using the biological methods including bioflotation and bioleaching. Initially, a combination of mesophilic bacteria including Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, and Leptosprillium ferrooxidans were employed in the bioflotation method to detain pyrite sulfur in the Tabas coal samples. The highest reduction percentages of pyrite sulfur and ash were equal to 62% and 54.18%, respectively. In the next stage, bioleaching experiments were conducted, the effect of the test time, percentage of bacteria by volume, percentage of coal solids, and absence of bacteria on the amount of sulfur and ash removal was investigated. The test time emerged as the most critical factor. The best sulfur removal was achieved using bioleaching, with a maximum removal of 72.43%, observed for the PE coal sample. Bioflotation also achieved significant sulfur removal, with a maximum removal of 61% observed for the same sample. On the other hand, the best ash removal was achieved using bioflotation, with a maximum removal of 68.98% observed for the PE coal sample, and a maximum removal of 69.34% observed for the B4B2 coal sample using bioleaching. Finally, this research work conducted a comparison of biological methods to determine the amount of sulfur and ash reduction achieved. The results showed that both bioleaching and bioflotation were effective for coal desulfurization and ash removal, with bioleaching performing slightly better for sulfur removal and bioflotation performing slightly better for ash removal.

Mineral prospectivity mapping (MPM) is a multi-staged process aiming at delimiting exploration targets. Experts’ knowledge is an indispensable component of MPM, and might be required (i) while translating signature features of ore-forming processes into a suite of maps, namely evidence layers, (ii) while assigning weights to evidence layers, and (iii) while interpreting maps of mineral prospectivity. The latter is important as MPM integrates weighted evidence layers into a continuous map of mineral prospectivity. Although high values in prospectivity maps pertain to prospective zones, maps of mineral prospectivity are devoid of interpretation. One, therefore, should adopt a classification scheme to categorize or prioritize exploration targets from a map of mineral prospectivity. In addition to previous frameworks applied for interpreting maps of mineral prospectivity, this paper introduces an optimization-based framework, the Gray Wolf Optimizer (GWO) algorithm, for addressing this problem. In addition to GWO, we also used percentile maps of 85, 90, and 95% for interpreting the results of our prospectivity model. These methods were applied to a fuzzy-based map of mineral prospectivity derived for the Alut area, NW Iran. Overall, the map derived by the GWO has involved more Au occurrences, 66% of explored Au occurrences by GWO versus 33% by percentile maps; also introduces more targets as high-potential zones of Au mineralization that may be neglected by traditional methods like percentile maps.

Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved by delivering a suitable cooling lubricant into the drilling environment. The present study examines the mechanical characteristics of the drilled rock, the physical qualities of the cooling lubricant employed, and the drilling rig operational parameters related to the drilling-specific energy (DSE). To this end, seven rock samples (granite, marble, and travertine) were drilled using water and five other fluids as the cooling lubricants. A total of 492 drilling experiments were conducted with a custom-designed and built laboratory-scale drilling rig on cuboid rock specimens. The univariate linear regression analysis of experimental results revealed a significant drop in DSE after using cooling lubricants instead of conventional cooling fluid (i.e. water). Under constant conditions in terms of mechanical properties of the rock, using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of water led to 34% and 43% DSE reductions in the granite samples, 48% and 54% in the marble samples, and 41% and 50% in the travertine samples, respectively. These variations in specific energy suggest that the drilling efficiency and performance can be augmented using properly selected cooling lubricants.

Evaporites are sediments that chemically precipitate due to the evaporation of an aqueous solution. Most evaporite formations, in addition to evaporite minerals, include detrital rocks such as mudstone, marl, and siltstone. Principal Component Analysis (PCA), Directed Principal Component Analysis (DPCA), and Band Ratio methods were applied to Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data for mapping the Gachsaran evaporite formation and distinguishing its lithological units in the Masjed Soleiman oil field, located in southwestern Iran. This oil field was the first recognized oil field in the Middle East. Colour composites of PCs 4, 5, and 2, as RGB images, effectively discriminated this formation from other sedimentary formations. The gypsum spectrum, resampled to the 9 band centres of ASTER, exhibited reflectance in bands 4 and 8 and absorption in bands 6 and 9. As a result, these bands were selected for DPCA application. PC4 effectively highlighted gypsum outcrops as bright pixels, while the band ratio 2/1 accentuated ferric iron, appearing as bright pixels, which correlated with the red marls. The results of this study demonstrate that ASTER image processing is a cost- and time-effective method that can be utilized for mapping evaporite formations and distinguishing their lithological units.

The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a 2617 square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, 64.17% and 83.5% of overall accuracy, and 0.602 and 0.807 of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of 2.25 out of 4, signifying a 56% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area.

The Zafarghand area (as a porphyry Cu deposit) is located in the northeast of Isfahan and southeast of Ardestan, which is a part of the Iran-Central structural zone, and more precisely, it is located in the Urmia-Dokhtar volcanic belt. In the porphyry Cu deposits exploration, identifying and determining the alteration zones is of special importance. The aim of the present study is to identify and highlight the alteration zones of Zafarghand area, with the help of the U-statistic method in the processing of ASTER sensor satellite images. Accordingly, considering the raster nature and digital form of satellite images, the digital number values of each pixel from the image matrices were considered as samples in a systematic network. Finally, the U spatial statistic algorithm was implemented as a moving window algorithm for determining anomaly samples in the set of digital number (DN) values of ASTER satellite image pixels. The results of this technique show that the application of the U-statistic method, considering its structural nature and neighboring samples in decision-making, has been successful and has proven to be very effective in determining the alteration zones in the Zafarghand area.

The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, operational efficiency, and sustainability. Multicriteria decision-making methods (MCDM) have been considered valuable decision-support tools in the mining industry. This article comprehensively examines MCDM methods and their applications in the mining industry. This article discusses the basic principles and concepts of MCDM methods, including the ability to prioritize and weigh conflicting, multiple criteria and support decision-makers in evaluating diverse options. According to the results, 1579 MCDM articles in mining have been published from the beginning to April 15, 2023, and a scientometric analysis was done on these articles. In another part of this article, 19 MCDM methods, among the most important MCDM methods in this field, have been examined. The process of doing work in 17 cases of the reviewed methods is presented visually. Overall, this paper is a valuable resource for researchers, mining industry professionals, policymakers, and decision-makers that can lead to a deeper understanding of the application of MCDM methods in mining. By facilitating informed decision-making processes, MCDM methods can potentially increase operational efficiency, resource optimization, and sustainable development in various mining sectors, ultimately contributing to mining projects' long-term success and sustainability.