Journal of Mining and Environement
Volume:15 Issue: 1, Winter 2024

Detection and mapping of the Jharia coal mine fire through the integration of satellite-based observed data with ground thermography data have been used and described in this work. This assimilation has been achieved using three types of data set viz., Landsat satellite images, topographical area map, and ground temperature survey of different fire-affected sites of Jharia Coalfields (JCF). Thermal anomaly, as observed from the satellite imagery, is one of the most important characteristics of the coal fire detection process. It has been used as a prime indicator for the fire area's extent and intensity. Ground thermographic measurement has also been conducted to further substantiate the thermal anomaly. The obtained amalgamated data is plotted on topographical maps of different sites of JCF. The study reveals that around 70% of the total coal mines of JCF are in grip of either surface fire or sub-surface fire or both surface and sub-surface fire. About 93% of fires detected in the year 1988 were shifted to new locations or in a dormant condition, whereas the remaining about 7% of fires were still burning at the same locations mostly due to the shifting of these fires from the upper coal seam to the lower coal seam or vice versa. The temperature detected by satellite data was 10 to 15 times lower than the actual fire condition measured on the ground during field observation. The study concludes that the detection of several years long-standing fire conditions historical satellite data will be the best option to delineate the fire condition.

Anthracite coal seam of Democratic People’s Republic of Korea was broken into particles to be soft due to geological tectonic actions through several stages in the Mesozoic era. Because the folds and faults have excessively developed and the shape of coal seam is very complicated, it is impossible to extract the anthracite coal by longwall mining system, and coal has been mainly mined by entry caving mining system. The aim of this work is to assess effectiveness of new combination of flying squirrel search algorithm (SSA) and artificial neural-network (ANN) for back-analysis of time-depending mechanical parameters of anthracite coal based on timber loads and displacements measured in the coal face entry. The case study deals with a coal face entry in Sinchang Coal Mine located in the Unsan County, South Pyongan Province, DPR Korea. To verify the good performance of new combination of the SSA and ANN, the comparison studies between proposed back-analysis method and other methods with the same purpose, are conducted using data measured in coal face entry. The mean absolute error (MAE) of weighted error norm of ANN-SSA is relatively smaller in comparison with other methods, which is 2.49. The new back-analysis is the good method to determine the suitable time-dependent mechanical parameters of anthracite coal surrounding the entry in very soft coal seam.

Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the effectiveness of learning algorithms can vary considerably. In this study conducted in Talcher, Odisha, India, the researchers proposed the use of Artificial Neural Networks (ANNs) to classify land use based on a dataset collected by the Sentinel-2 satellite. The study focused on the Talcher region, which was divided into five distinct land use classes: coal area, built-up area, barren area, vegetation area, and waterbody area. By applying ANNs to the mining region of Talcher, the researchers aimed to improve the accuracy of land use classification. The results obtained from the study demonstrated an overall accuracy of 79.4%. This research work highlights the importance of utilizing remote sensing images and a learning framework to address the challenges associated with pixel-based land use classification. By employing ANNs and leveraging the dataset from the Sentinel-2 satellite, the study offers valuable insights into effectively classifying different land use categories in the Talcher region of India. The findings contribute to the advancement of techniques for accurate land use analysis, with potential applications in various fields such as urban planning, environmental monitoring, and resource management.

It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity of the bit, RPM, cutting angle , and rock resistance on the penetration rate of the drilling tool. In this examination, three kinds of limestone rocks were considered. The planned assessments were carried out at three stages of the considered four input variables. The statistical analysis was realized using both RSM approach and analysis of variance (ANOVA). This analysis allowed us to develop the appropriate penetration model with a higher determination coefficient of 96.19%, which demonstrates the high correlation between the predicted and experimental data, and consequently, it can be concluded that the obtained model is highly suitable for the prediction of the penetration rate. Also from variance analysis, the results obtained show that rotational speed, RPM, and weight on the bit (WOB) parameters, as well as the nature of the rock, which is determined by the rock compressive resistance, having a significant effect on the penetration rate; however, the rake angle has little effect. Finally, the optimal parameters were determined to find the best possible penetration rate of the drilling tool.

This paper presents a comprehensive study on the stability of the deep underground closed Kolar Gold Fields mine (3.2 km deep) under varying seismic loading conditions. The study utilized the Finite Element Method (FEM)-based Midas GTS NX software tool to conduct numerical simulations of seismic loads of varying intensities under multiple conditions of water level in the mine voids. The seismic loads applied were equivalent to the intensity of maximum mining-induced seismicity experienced in the mine. The study also examined the influence of the Mysore North Fault and its effects on the surface above the mining area. A seismic hazard vulnerability map of the mining area was developed based on the results for all simulated numerical model combinations. The results inferred that for a seismic load of PGA, 0.22 g, for fault and actual water level combination, very strong shaking and moderate potential surface damage were observed at vulnerable zones with a maximum PGA of   0.196 g and Peak Ground Velocity (PGV) of 0.49 m/s. The study highlights the importance of monitoring post-mining induced seismic activities using a dedicated microseismic monitoring system with sensors placed at the most vulnerable zone locations assessed from the numerical modelling studies carried out. Remedial measures suggested include regular dewatering of mine workings based on water accumulation and backfilling of mine voids with suitable fill material. The dynamic modelling approach using Midas GTS NX was found to be a more reliable, feasible, efficient, and simple method for assessing the stability of closed mines.

The goal of this research work was to use an Artificial Neural Network (ANN) model to predict the ultimate bearing capacity of circular footing resting on recycled construction waste over loose sand. A series of plate load tests were conducted by varying the thickness of two sizes of recycled construction waste (5 mm and 10.6 mm) layer (0.4d, 0.6d, 0.8d, 1d, and 1.2d, d: diameter of footing) prepared at different relative densities (30%, 50%, and 70%) overlaying.  The ultimate bearing capacity obtained for various combinations was used to develop the ANN model. The input parameters of the ANN model were thickness of recycled construction waste layer to diameter of circular footing ratio, angle of internal friction of sand, unit weight of sand, angle of internal friction of recycled construction waste and unit weight of recycled construction waste, and the model's output parameter was ultimate bearing capacity. The FANN-SIGMOD_SYMMETRIC model with topology 3-2-1 provided a higher estimate of the ultimate bearing capacity of circular footing, according to the ANN findings. The sensitivity analysis also revealed that the unit weight of sand and angle of internal friction of sand had insignificant effects on ultimate bearing capacity. The estimated ultimate bearing capacity was most affected by the angle of internal friction of recycled construction waste. The result of multiple linear regression analysis was not as good as the ANN model at predicting the ultimate bearing capacity.

Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. Recognizing this context's unique challenges and opportunities, the study aims to uncover critical ideas and theories on how AI enhances visitor experience, promotes cultural preservation, sustainability, and stakeholder collaboration. Acknowledging the distinctive challenges and opportunities presented by inter- and intra-regional mining heritage contexts, this research work underscores the critical importance of striking a harmonious equilibrium between technological advancements and preserving historical and cultural legacies. Drawing from a cross-disciplinary approach, the study examines the profound implications of integrating AI into mining heritage sites' planning and design strategies. The study reviews 199 articles on AI-driven planning and design benefits, examining potential advantages. Ethical considerations, algorithmic biases, and the role of interdisciplinary research are also explored. The study highlights the intricate interplay between AI-enhanced engagement, responsible tourism practices, and the meaningful representation of local cultures. By shedding light on this uncharted territory, the research contributes to developing informed strategies that harness AI's potential to shape inter- and intra-regional mining heritage site planning and design, fostering responsible and impactful tourism experiences. By delving into this paradigm, it hopes to arm the researchers, policy-makers, practitioners, and other stakeholders with information and understanding that will help them forge a progressive and morally upright future, in which technology co-exists peacefully with practices for cultural preservation and sustainable tourism.

In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies are part of a lithostratigraphic group called Ras Malaab, estimated to have been formed during the Miocene period. A range of image processing techniques was employed to create the final facies map including quartz and sulphate indices, composite image band combinations, band ratios, principal component analyses, decorrelation stretching, and SAM mapping followed by supervised classification. By using band combinations, mineral indices, and principal component analyses, sulphate minerals were distinguished from their surroundings. Additionally, decorrelation stretches and band ratios were used to differentiate between primary, secondary, faulted gypsum, anhydrite, and carbonates. The SAM rapid mapping algorithm was also an effective tool to distinguish between the main facies in the studied area and to differentiate between primary massive and bedded gypsum. The results of this study were summarized by creating a facies map of the area using supervised classification, which, in addition to petrographic studies, greatly aided in understanding the distribution of the different gypsum facies.

This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer function at various numbers of hidden neurons was investigated to arrive at the optimum Artificial Neural Network (ANN) model. The pure-linear function was investigated as the output function, and the Levenberg-Marquardt approach was chosen as the optimization technique. Based on 1221 datasets, a novel ANN-based empirical correlation was developed to calculate the amounts of uranium (U). The results show a wide range of uranium content, with a determination coefficient (R2) of about 0.999, a Root Mean Square Error (RMSE) equal to 0.115%, a Mean Relative Error (MRE) of -0.05%, and a Mean Absolute Relative Error (MARE) of 0.76%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.

Narmada valley development authority proposed a scheme under which 12.6 cumecs of water from the Hathani river (Tributary of Narmada) will be lifted to irrigate the command area. At the pumping station lies near Alirajpur, Madhya Pradesh, India, there was a need to protect the slope on both side as water thrust from the upstream side may lead to failure of the slope. This paper presents the stability analysis of the slope using GEO5 software. It was observed that the terrain at the site was a mixture of soil and rocks. The unit weight of the rock and backfill soil observed was 21 kN/m3 and 18 kN/m3. Using numerous techniques factor of safety was calculated for the particular slope and it was observed that a suitable mitigation measure needs to be provided to prevent the failure of the slope. The inclusion of a gabion retaining wall increased the slope's safety factor significantly. The proposed mitigation measure was executed at the site, and the completed wall has not shown any damage till date. The analysis of the slope's stability results, as well as its construction of the gabion retaining wall recommended as a protective measure, are presented in this work.

The stratified-sedimentary rock mass, as the typical host ground of coal mine tunnels, is characterized by highly non-isotropic deformation due to the very persistent discontinuity of bedding planes. This study evaluates the effect of tunnel location relative to the host ground strata on the excavation-induced displacements around a coal mine tunnel driven along the inclined coal seam. To achieve this goal, a calibrated finite element method (FEM) numerical model based on field monitoring displacements was developed for the coal mine tunnel at a depth of 300 m. This calibrated numerical model was then utilized to investigate the effect of the horizontal location of the tunnel on the induced displacement field through sensitivity analysis. Finally, the sensitivity analysis results were compared in terms of displacement components around the tunnel. The results of this study demonstrate a reasonable level of accuracy (for practical demands) of the calibrated numerical model, with an average error of about 8% for maximum displacements at measured points. The numerical models show an asymmetric spatial distribution of displacements around the tunnel due to the anisotropy of the rock mass, especially in the case of inclined layers. The arrangement of weak-strength coal and intercalary stone layers relative to the excavation line of the tunnel plays a key role in this issue. The critical state of displacements (maximum displacement in sensitivity analysis) occurs where the intersection line of the coal-intercalary stone is tangent to the tunnel excavation line. Additionally, the excavation-induced displacement decreases as the distance between the coal-intercalary stone interface and the tunnel increases, with a distance of about 1.5 m suggested for practical applications.

In this work, two rock engineering system (RES)-based models are presented, the first model to predict the roof failure when a longwall face advances toward a pre-driven recovery room (PDRR) and the second model to select the type of recovery room method for longwall mining. For the first model, an international database of 43 case histories from the pre-driven rooms including technical parameters and type of corresponding operation outcome of each case history is considered. In this regard, a vulnerability index (VI) that refers to the risk of roof failure is calculated for each case history and the VIs are compared with the type of the corresponding outcomes. The obtained results indicate that the calculated VIs have a good adaptation with the corresponding outcomes. This approach could be used to analyze the risk of failure in PDRR, and determine the critical VI that specifies the boundary between the hazard range and the safe range that leads to an accurate operational planning. In the following, a method called multi-options RES-based model (MORESM) is adopted for the selection of recovery room methods in longwall operation. By this model, selecting the optimum option from several options in terms of many effective parameters on the system is possible. Based on the evaluations, CRR, PDRR3, and PDRR2&3 are the suitable options for the case study. This model could introduce the suitable option based on geotechnical conditions but the final decision depends on the economic policy of the managing team.

Offshore produced water (OPW), a type of wastewater rich in hazardous compounds such as polycyclic aromatic hydrocarbons (PAHs), requires effective treatment. This study presents a novel methodology utilizing TiO2 nanoparticles, ultraviolet (UV) lamps, and ozonation for the degradation of phenanthrene (PHE) from OPW. Various factors including UV lamp power (10W-50W), ozone dose (0.1 mg/L-0.5 mg/L), TiO2 concentration (0.5 g/m²-2.1 g/m²), ethanol fraction (25%-85%), pH (4.5-10.5), PHE initial concentration (5 mg/L-25 mg/L), and treatment time (15 min-45 min) were systematically investigated to understand their impact on PAH degradation in the OPW. The study employs Response Surface Methodology (RSM) for modeling and optimizing PHE removal efficiency. The results contribute to the development of a mathematical model, and through optimization, optimal conditions are proposed to maximize PHE removal efficiency. Experimental implementation of the optimized conditions in a physical model resulted in an impressive 98% PHE removal efficiency. The identified optimal conditions include UV lamp power of 40 W, ozone dose of 0.5 mg/L, TiO2 concentration of 2 g/m², ethanol fraction of 25%, pH of 5.2, initial PHE concentration of 15 mg/L, and a treatment time of 40 min. This optimized approach provides valuable insights for efficient and environmentally friendly treatment of PAHs in OPW, emphasizing on the potential for practical application in soil washing effluent treatment.

Generally, mineral processing plants generate a large quantity of waste in the form of fine particles. The flotation speed of mineral microbubbles by coarse bubbles is dramatically higher than that of individual particles. The advantage of microbubbles is due to the increase of binding efficiency of conventional bubbles with fine particles coated with microbubbles. Here, the focus is on reducing chemicals consumption and improving recovery. After preparing a representative sample, XRF, XRD, and mineralogical analyses were performed. Then 50 experiments were selected by experimental design using the response surface method (RSM), and in the form of central Composite design (CCD) by (design expert) DX 13 software. The interactions of collector consumption, frother agent, pH, particle size, and solid percentage were investigated, and 25 experiments using typical flotation and without nano-microbubbles and others with nano-microbubbles were conducted. The laboratory standard limit of the collector used in the pilot plant of the Sarcheshmeh Copper copper complex is 40 g/t (25 g/t of C7240 plus 15 g/t of Z11). Here, by consuming 20 g/t of collector in the absence of nanomicrobubbles, a recovery of 79.96% and in the presence of nanomicrobubbles, a recovery of 80.07% was obtained, that is a 50% reduction in collector consumption and a 0.11% increase in recovery was observed. Also the laboratory standard limit of frother used in the pilot plant of Sarcheshemeh Copper Complex is 30 g/t (15 g/t of MIBC plus 15 g/t of A65). Here, by using 10 g/t of frother in the absence of nanomicrobubbles, a recovery of 78.12%, and in the presence of nanomicrobubbles, a recovery of 82.05% was obtained. In other words, a decrease of 66.6% in the consumption of frother and an increase of 1.93% in recovery was observed.

This paper carried out the study on removing cyanide from aqueous solutions by modified zeolite with hexadecyltrimethylammonium bromide. After determining the properties of the prepared adsorbent by the XRD, SEM, FTIR, and BET techniques, the effect of parameters such as the initial concentration of cyanide, pH, contact time, temperature, and the ionic strength of cyanide was examined by batch tests, and the effects of bed depth and flow rate on the performance of cyanide adsorption was investigated by column process. The XRD analysis showed the presence of clinoptilolite mineral in the structure of the raw zeolite, and the surface coating of raw zeolite by surfactant was detected by the SEM method. The FT-IR results confirmed the adsorption of cationic surfactant on the surface of the modified zeolite. The Langmuir, Freundlich and Tamkin adsorption models showed an excellent ability to describe the cyanide adsorption isotherm using the studied adsorbent. The adsorption capacity of cyanide by modified zeolite was 3.97 mg/g, significantly increased compared to the maximum adsorption capacity of raw zeolite cyanide (0.54 mg/g). The pseudo-second-order model has an excellent ability to describe the adsorption kinetics of cyanide contaminants using natural and modified zeolites. Maximum cyanide uptake capacity was achieved at pH value 8. Cyanide removal decreased with increasing pH and ionic strength of the stock solution and increased with an increase in solution temperature. Column study results confirmed that the adsorption capacity increased with the increasing bed depth, and decreased with increasing flow rate. Yoon-Nelson curves are closer to the experimental curves with high R2 values.

There is no acceptable method for investigating the tool wear phenomenon in soft grounds. In this article, first, a new equipment made at Sahand University of Technology is introduced, which is used for simulation of TBM tunneling mechanism. Next, the effect of various soil grading parameters such as D10, D30, and D60 (which indicate the corresponding diameters on the soil grading diagram where 10, 30, and 60% of the grains are smaller than these values, respectively), coefficient of gradation, uniformity coefficient, sorting coefficient and effective size on the cutting tools wear. The initial studies show that in soils with fine grains greater than 10%, by increase in the values of D10, D30, D60, and effective size, the tool wear increases. However, in soils with fine grains less than 10%, by increase in the above-mentioned parameters, the soil abrasiveness reduces. Also in soils with more than 10% fine grains, by increase in the coefficient of gradation value, the soil abrasiveness reduces. But in soils with fine grains less than 10%, by increase in the value of this parameter, the tool wear increases. The results of experiments show that sorting coefficient could be a good criterion for investigating the soil abrasiveness.

Tunnel Boring Machines (TBMs) are extensively used to excavate underground spaces in civil and tunneling projects. An accurate evaluation of their penetration rate is the key factor for the TBM performance prediction. In this study, artificial intelligence methods are used to predict the TBM penetration rate in excavation operations in the Kerman tunnel and the Gavoshan water conveyance tunnels. The aim of this paper is to show the application of the Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) for the TBM penetration rate prediction. The penetration rate parameter is considered as a dependent variable, and the Rock Quality Designation (RQD), Brazilian Tensile Strength (BTS), Uniaxial Compressive Strength (UCS), Density (D), Joint Angle (JA), Joint Spacing (JS), and Poisson's Ratio are considered as independent variables. The obtained results by the several proposed methods indicated a high accuracy between the predicted and measured penetration rates, but the support vector machine yields more precise and realistic outcomes.

The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.

Geosites and their contents including minerals, fossils, etc. can strongly represent the history of a region. They greatly help our understanding of the evolution of Earth, volcanic activities, plate tectonics, and the characteristics of different environments. These are some of the vital information about 4500 million years of the Earth's life, and are our common international heritage. Geoconservation’s main purpose is the protection of geosites as major units of geoheritage, and this principle is achieved through the application of specific methods such as indexing geological phenomena, assessment, preservation, valuation, and estimating the importance of each geosite, as well as monitoring (or watching these phenomena). In this paper, geoconservation is introduced as a specialized and essential branch of geological science, which is currently under development. Therefore, geoconservation principles are presented here, and their relation to other geosciences is discussed. In addition, through scientific and cultural education related to sustainable development (in regard to the geoscience), citizens can be informed that lack of conserving natural resources would reduce geo-resources, and on the other hand, is a serious threat to geoheritage of the planet Earth. This crucial subject can be achieved by making information available and by teaching skills by which making prospective and correct decisions is possible.

Due to longwall mining, a large space without any support is created, and the in-situ stress regimes change. The change of the in-situ stress regimes affects the roof and face of the adjacent panel. In other words, the strata behavior would be different from the intact condition during the previous panel mining. In this study, two adjacent panels are simulated in the FLAC3D software to study the effect of panel extraction on its adjacent panel strata behavior during longwall mining. The available data of the Tabas Parvadeh Coal Mine panels is used for this purpose. According to the numerical modeling results, the length of the first roof’s weighting effect (FRWE) in the gob of the first and second panels is calculated, respectively, as 26 and 21 meters. In other words, the gob dimension in the second panel is reduced by about 19.2%, and the vertical displacement value is increased by about 18.5%. In addition, the chance of roof collapse and face spalling during the first-panel mining is more than the second-panel. It means that roof and face instability in the (FRWE) during the first-panel mining is confirmed, while in the second-panel extraction is just very likely.