Journal of Mining and Environement
Volume:15 Issue: 4, Autumn 2024

The rapid development of road networks needs huge construction materials. Mining and industrial wastes can be used as sustainable road construction materials and will be alternatives to fulfill the huge demand in road construction. Zinc tailing is one such mining waste and has the potential for road construction. This material was collected from Zawar mines (Rajasthan), and characterization was carried out for embankment/subgrade applications. A physical model test was conducted in the laboratory to examine the stress-settlement behaviour. To improve the modulus value of tailing, it was reinforced with geogrid in two different laying patterns, viz. layer/loop and stress-settlement behavior was studied. Different parameters were studied: reinforcement depth, layer of reinforcement, number of loops, and depth of loop of reinforcement. The experimental result was validated with the numerical finite element method (SoilWorks). Tailing comprises fine-grained silt-size particles (61%) with no swelling behavior and non-plastic nature. It has values of MDD and OMC as 1.86 g/cm3 and 11%, respectively. It has a higher value of CBR (12%) and internal friction angle (34.6o) with cohesionless nature. The variation of settlement with stress is linear for reinforced and unreinforced tailing fill. As the depth of reinforcement increases, settlement increases in both layer and loop reinforcement. The settlement trajectory obtained from a numerical method closely resembles that of a laboratory physical model, particularly when the applied stress is up to 600 kPa. The modulus of elasticity of tailing was significantly improved with the introduction of geogrid reinforcement either in layer or loop.

The convergence of Mining Heritage Tourism (MHT) and Artificial Intelligence (AI) presents a transformative paradigm, reshaping heritage preservation, visitor engagement, and sustainable growth. This paper investigates the dynamic synergy between these realms, probing how AI-driven technologies can augment the authenticity, accessibility, and educational significance of mining heritage sites. Focusing on the profound impact of AI on MHT, this study centers its examination on the Barr Conglomerate located in the culturally rich Pali District, India. Employing a mixed-methods approach involving survey data analysis and neural network modelling, the research work explores AI applications that enhance visitor experiences, interpret historical narratives, optimize resource allocation, and mitigate the adverse effects of over-tourism. The study meticulously navigates a vast landscape of AI technologies, spanning machine learning, natural language processing, and augmented reality, show-casing their potential to enrich encounters with mining heritage. While AI promises to revolutionize heritage management, the paper emphasizes the critical importance of ethical considerations and cultural sensitivities. Balancing innovation with preservation, the study advocates for an inclusive approach that honors diverse cultural values and encourages community engagement. Through this exploration, the paper delves into the practical implementation of AI, unveiling best practices lessons learned and illuminating challenges and opportunities. Ultimately, this research work envisions a future where AI empowers mining heritage to transcend temporal boundaries, cultivating immersive experiences resonating with authenticity, global understanding, and sustainable stewardship.

Landslides affecting life and property losses has become a serious threat in various countries worldwide which highlights the importance of slope stability and mitigation. The methods and tools employed for slope stability analysis, ranging from traditional limit equilibrium methods to worldly-wise numerical modelling techniques. It focuses on the importance of accurate and reliable data collection, including geotechnical investigations, in developing precise slope stability assessments. Further, it also addresses challenges associated with predicting and mitigating slope failures, particularly in dynamic and complex environments. Mitigation strategies for unstable slopes were systematically reviewed of different researchers, encompassing both traditional and innovative measures. Traditional methods, such as retaining walls and drainage systems, the mitigation strategies were explored, emphasizing both preventive measures and remedial interventions. These include the implementation of engineering solutions such as slope structures, and Matrix Laboratory (MATLAB) techniques along with the comprehensive analysis of four prominent slope stability assessment tools: Rock Mass Rating (RMR), Slope Mass Rating (SMR), and the Limit Equilibrium Method (LEM). The comparative analysis of these tools highlights their respective strengths, limitations, and areas of application, providing researchers, authors, and practitioners with valuable insights to make informed choices based on project-specific requirements. To ensure the safety and sustainability of civil infrastructure, a thorough understanding of geological, geotechnical, and environmental factors in combination with cutting-edge technologies is required. Furthermore, it highlights the important role that slope stability assessment and mitigation play a major role in civil engineering for infrastructure development and mitigation strategies.

The study examined the influence of cohesion, friction angle, and tunnel diameter on stability within engineering and geotechnical frameworks, while considering the consequences of nearby excavations on the overall stability assessment. The results show that a higher angle of internal friction leads to a decrease in soil stability number and weighting coefficient. Tunnel diameter significantly affects face support pressure, with larger diameters requiring stronger support due to increased stress. Higher friction angles help stabilize tunnel faces and mitigate diameter-related pressure effects. Stress redistribution around the tunnel is significant within 2 meters from the center, transitioning to elastic behavior elsewhere. A safety factor of 1.3 ensures tensile failure prevention in single and twin tunnels. Balanced stress distribution between tunnels with a slight difference is observed under isotropic in-situ stress. Numerical modeling enhances stress estimations and reveals changes during tunnel excavation, weakening the rock mass. Ground reaction curve analysis with support measures shows reduced tunnel convergence after implementation, suggesting support strategies like extended bolts using updated rock mass rating. The study improves tunnel design and stability assessment by comprehensively understanding stress redistribution and support strategies.

Landslides affecting life and property losses has become a serious threat in various countries worldwide which highlights the importance of slope stability and mitigation. The methods and tools employed for slope stability analysis, ranging from traditional limit equilibrium methods to worldly-wise numerical modeling techniques. It focuses on the importance of accurate and reliable data collection, including geotechnical investigations, in developing precise slope stability assessments. Further, it also addresses challenges associated with predicting and mitigating slope failures, particularly in dynamic and complex environments. Mitigation strategies for unstable slopes were systematically reviewed of different researchers, encompassing both traditional and innovative measures. Traditional methods, such as retaining walls and drainage systems, the mitigation strategies were explored, emphasizing both preventive measures and remedial interventions. These include the implementation of engineering solutions such as slope structures, and Matrix Laboratory (MATLAB) techniques along with the comprehensive analysis of four prominent slope stability assessment tools: Rock Mass Rating (RMR), Slope Mass Rating (SMR), and the Limit Equilibrium Method (LEM). The comparative analysis of these tools highlights their respective strengths, limitations, and areas of application, providing researchers, authors, and practitioners with valuable insights to make informed choices based on project-specific requirements. To ensure the safety and sustainability of civil infrastructure, a thorough understanding of geological, geotechnical, and environmental factors in combination with cutting-edge technologies is required. Furthermore, it highlights the important role that slope stability assessment and mitigation play a major role in civil engineering for infrastructure development and mitigation strategies.

The utilisation of potash reserves has various environmental consequences, such as the generation of substantial volumes of solid waste containing high levels of sodium chloride. The accumulation of environmental harm gives rise to an unfavourable environmental scenario in potash production areas, which requires the investigation of waste management solutions. The predominant approach to reducing surface waste involves backfilling mined areas. In other countries, salt dump reclamation is utilised alongside backfilling. The distinctive characteristic of salt dump reclamation lies in the water-solubility and phytotoxicity of the dump rock. This research aims to evaluate the morphometric and biochemical parameters (using phytotesting) of vegetation throughout the process of salt dump reclamation using different variants. A model reclamation was carried out in a laboratory setting, where three different variants were subjected to experimentation. A reduction in the thickness of the protective clay barrier resulted in a decline in morphometric aspects of the experimental crops as well as the woody vegetation. Reducing the thickness of the protective clay barrier leads to an elevation in the redox activity of the examined crops, thus pointing towards potential environmental toxicity. Superior morphometric and biochemical parameters were noted in vegetation possessing a substantial protective covering, hinting at the feasibility of utilising insulating layers for salt dump reclamation. Phytotesting serves as an indicative approach to assessing soil toxicity and as a parameter for determining soil resilience against pollution. The findings hold potential for application in further research within the field of biological reclamation in areas with dump sites.

The installation gallery/set-up room of a longwall panel is driven for installation of the longwall face machineries to start the extraction of coal from the longwall panel. The width of the installation gallery is 8 to 9 m. This gallery needs to be stabilized till the face machineries to be deployed from the driving of the room as it required to stand more than 8 to 10 months and develop the high stress concentration, roof-to-floor convergence and yield zone in the roof and sides. Hence, in this study, a deep longwall mine of India is considered to analyze the behavior of set-up room. For this, a total of twelve 3D numerical models are developed and analyzed considering Mohr’s-Coulomb failure criterion. Three panels located at 417, 462, 528 m having three different widths (8, 10 and 12 m) of set-up rooms are examined. The width of the set-up room is taken based on the length of the shield support. The results in terms of vertical stress distribution, vertical displacement, roof-to-floor convergence, plastic strain and yield zone distribution are presented.

In the past, assessing water quality has typically involved labor-intensive and costly processes such as laboratory analysis and manual sampling, which do not provide real-time data. In addition to tasting bad, drinking acidic water on a regular basis can result in acid reflux and recurrent heartburn while high total dissolved solids water can cause kidney stones, especially when the hard water content is more than 500ppm. With growing concerns about water quality, there is a need for continuous monitoring of pH and TDS levels in surface and groundwater sources. To address this, a cutting-edge wireless sensor system leveraging on Internet of Things (IoT) technology has been developed. This system incorporates top-notch pH and TDS sensors known for their accuracy, durability, and environmental compatibility. Integrated with microcontrollers featuring wireless communication capabilities, these sensors enable seamless data transmission to a central server through IoT protocols like cellular networks. The collected data is processed and calibrated to ensure reliability and precision. The IoT platform connected to the central server manages device connectivity, data storage, and analysis, making real-time data accessible via user-friendly web or mobile applications with interactive graphs and dashboards. Power-saving features are implemented to optimize battery life in remote and off-grid locations, and weather-resistant enclosures protect the sensor nodes from harsh environmental conditions. By deploying this wireless-based sensor system, users can gain valuable real-time insights into water quality in surface and groundwater monitoring locations.

The aim of this study is to thoroughly analyze the relaxation zone developing around sublevel stopes in underground mines and identify the main parameters controlling its extent. A numerical approach based on the finite element method, combined with the Hoek-Brown failure criterion, was implemented to simulate various geometric configurations, geological conditions, and in-situ stress states. A total of 425 simulations were carried out by varying depth, horizontal-to-vertical stress ratio (k), rock mass quality (RMR), foliation orientation and spacing, as well as the height, width, and inclination of the sublevels. The results enabled the development of robust predictive models using regression analysis techniques and artificial neural networks (ANNs) to estimate the extent of the relaxation zone as a function of the different input parameters. It was demonstrated that depth and the k ratio significantly influence the extent of the relaxation zone. Additionally, a decrease in rock mass quality leads to a substantial increase in this zone. Structural characteristics, such as foliation orientation and spacing, also play a decisive role. Finally, the geometric parameters of the excavations, notably the height, width, and inclination of the sublevels, directly impact stress redistribution and the extent of the relaxation zone. The overall ANN model, taking into account all these key parameters, exhibited high accuracy with a correlation coefficient of 0.97. These predictive models offer valuable tools for optimizing the design of underground mining operations, improving operational safety, and increasing productivity.

The primary objective of this research was to apply machine learning techniques to predict the production of an open pit mine in Peru. Four advanced techniques were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (RB). The methodology included the collection of 90 datasets over a three-month period, encompassing variables such as operational delays, operating hours, equipment utilization, the number of dump trucks used, and daily production. The data were allocated 70% for training and 30% for testing. The models were evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), and the Coefficient of Determination (R2). The results indicated that the Bayesian Regression model was the most effective in predicting production in the open pit mine. The RMSE, MAPE, VAF, and R2 for the models were 3686.60, 3581.82, 4576.61, and 3352.87; 12.65, 11.09, 15.31, and 11.90; 36.82, 40.72, 1.85, and 47.32; 0.37, 0.41, 0.41, and 0.47 for RF, XGBoost, KNN, and RB, respectively. This research highlights the efficacy of machine learning techniques in predicting mine production and recommends adjusting each model's parameters to further enhance outcomes, significantly contributing to strategic and operational management in the mining industry.

Innovation in mineral exploration occurs either in the construction of new ore deposit models or the development of new techniques used to locate the ore deposits. Band ratio is the image processing technique developed for mineral exploration. The present study presents a new approach used to evaluate the band ratio technique for discrimination and prediction of the Iron-Titanium mineralization exposed in the Khamal area, Western Saudi Arabia using the ensemble Random Forest model (RF) and SPOT-5 satellite data. SPOT-5 band ratio images are prepared and used as the explanatory variables. The target variable is prepared in which (70%) of the target locations are used for training and the rest are for validation. A confusion matrix and the precision-recall curves are constructed to evaluate the RF model performance and the Receiver Operating Characteristics curves (ROC) are used to rank the band ratio images. Results revealed that the 3/1, 2/1 & 3/2 band ratio images show excellent discrimination with AUC values of 0.986, 0.980 & 0.919 respectively. The present study successfully selects the 3/1 band ratio image as the best classifier and presents a new Fe-Ti mineralization image map. The present study proved the usefulness of the Random Forest classifier for the prediction of the Fe-Ti mineralization with an accuracy of 97%.

One of the most critical designs in open-pit mining is the ultimate pit limit (UPL). The UPL is frequently computed initially through profit-maximizing algorithms like the Lerchs-Grossman (LG). Then, in order to optimize net present value (NPV), production planning is executed for the blocks that fall within the designated pit limit. This paper presents a mathematical model of the UPL with NPV maximization, enabling simultaneous determination of the UPL and long-term production planning. Model behavior is nonlinear. Thus, in order to achieve model linearization, the model has been partitioned into two linear sub-problems. The procedure facilitates the model solution and the strategy by decreasing the number of decision variables. Naturally, the model is NP-Hard. As a result, in order to address the issue, the Dynamic Pit Tracker (DPT) heuristic algorithm was devised, accepting economic block models as input. A comparison is made between the economic values and positional weights of blocks throughout the steps in order to identify the most appropriate block. The outcomes of the mathematical model, LG, and Latorre-Golosinski (LAGO) algorithms were assessed in relation to the DPT on a two-dimensional block model. Comparative analysis revealed that the UPLs generated by these algorithms are consistent in this instance. Utilizing the new algorithm to determine UPL for a 3D block model revealed that the final pit profit matched LG UPL by 97.95%.

Drift-and-fill mining is a variation of cut-and-fill mining method. Drift-and-fill mining method refers to the excavation of several parallel drifts in ore. Excavation of a new drift could start when its adjacent drifts are backfilled or not excavated. The amount of ore material and its grade depends on the excavation sequence of drifts. As the number of drifts increases, one will need a model to optimize the drift excavation and backfilling sequence. This paper introduces a mathematical model to determine the optimal drift-and-fill sequence while the safety constraints, excavation, and backfilling capacities and their dependencies are satisfied. The model seeks to minimize the deviations from some predefined goals, and it handles the long-term and short-term constraints in separate and integrated scenarios. An application of the model is presented based on the data available from a lead/zinc underground mining project. There are 91 drifts in the selected level. Based on the monthly planning horizon, the integrated model leads to the slightest deviations in both the mining rate and average grade, and the deviation from the predetermined annual goals is negligible. For the case where long-term and short-term plans are determined separately, the deviation is approximately 10%.

Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and subsequent operational phases. Groundwater inflows, often perceived as rare geological hazards, can induce instability in the surrounding rock formations, leading to severe consequences such as injuries, fatalities, and substantial financial expenditures. The primary objective of this research is to explore the application of machine learning techniques to identify the most accurate method of forecasting tunnel water seepage. The prediction of water loss into the tunnel during the forecasting phase employed a tree equation based on gene expression programming (GEP). These results were compared with those obtained from a hybrid model comprising particle swarm optimization (PSO) and artificial neural networks (ANN). The Whale Optimization Algorithm (WOA) was selected and developed during the optimization phase. Upon contrasting the aforementioned methods, the Whale Optimization Algorithm demonstrated superior performance, precisely forecasting the volume of water lost into the tunnel with a correlation coefficient of 0.99. This underscores the effectiveness of advanced optimization techniques in enhancing the accuracy of groundwater inflow predictions and mitigating potential risks associated with tunneling activities.

The main and economic mineral of antimony is stibnite or antimony sulfide, and the research and processes in the world are based on it, and oxide minerals are not considered among the economic and important reserves of antimony due to the difficulty of processing and the lack of optimal efficiency of the flotation method. On the other hand, taking into account that a large part of the antimony reserve of Sefidabeh is made up of low-grade oxidized ore; this research on the method of economic extraction and the possibility of recovering this type of reserve will be important due to the strategic nature of antimony metal. According to the experiments conducted in this research, the effective parameters for flotation include: pH, collector concentration, activator concentration, depressant concentration, activator type, and humic acid concentration. DX7 software was used for statistical modeling of experiments. Based on the above parameters, the design of the experiment was carried out using a partial factorial method and finally the number of 16 experiments was determined for the effect of the above factors on the grade and weight recovery of the sample. Antimony ore flotation with a grade of 4.32% was carried out in a two-stage method. In this method, in the first stage, flotation of antimony sulfur (stibnite, Sb2S3) was performed at a specific pH by adding the activator of copper sulfate or lead nitrate and the depressant together, potassium amyl xanthate collector and MIBC. In the second stage of flotation, the tailings of the first stage of flotation for antimony oxides were treated with a sodium oleate collector (with determined concentrations) at a specific pH by adding copper sulfate or lead nitrate activator, sodium oleate collector and humic acid and MIBC frother agent. The interaction between pH and activator concentration (BD) has a direct effect on the amount of concentrated antimony, with an increase in pH from 6 to 8 antimony when using an activator concentration of 300 g/t, and a decrease when using an activator concentration of 500 g/t. Flotation was done. In the best conditions, with two-stage flotation of antimony, 68.99% recovery and 13.32 grade were obtained.

The stability analysis of chain pillars is crucial, especially as coal extraction rates increase, making it essential to reduce the size of these pillars. Therefore, a new method for estimating the load on chain pillars holds significant importance. This research introduces a novel solution for estimating side abutment load and analyzing the stability of chain pillars using the dynamic mode of the Coulmann Graphical (CG) method. The solution is implemented using Visual Studio software and is named Coulmann Chain Pillar Stability Analysis (CCPSA). The CG method is widely recognized in civil engineering as a highly efficient technique for determining soil side abutment pressure in both static and dynamic conditions. This method involves calculating the top-rupture wedge of chain pillars using the CG method. The CCPSA software functions share significant similarities with those of the Analysis Longwall Pillar Stability (ALPS) method. However, the main point of departure between the proposed method and the ALPS empirical method lies in their respective approaches to calculating side abutment load on chain pillars and evaluating subsidence conditions. The effectiveness of this method has been validated using a database of chain pillars from various mines worldwide and has been compared with the ALPS method. The results of the comparison demonstrate that the CCPSA is highly effective in evaluating chain pillar stability. This underscores the potential of the CG method and CCPSA software in providing valuable insights for assessing and ensuring the stability of chain pillars in mining operations.

The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential layers include airborne magnetic data, airborne radiometric data (potassium and thorium), lineament density map, cu geochemistry signature, and multi-variate geochemistry signature (PC1). High magnetic anomalies, lineament structures, and alteration zones (K/Th) were derived from airborne geophysics data. Geochemistry signatures (Cu and PC1) were derived from stream sediment data. The principal Component Analysis (PCA) as an unsupervised machine learning method and five evidential layers were used to produce a porphyry prospectivity model. As a result of this combination, mineral prospectivity model was produced. Then a plot of cumulative percent of the studied area versus pca prospectivity value was used to discrete high potential areas. Then to evaluate the ability of this MPM, the location of known cu indications was used. The results confirm an acceptable outcome for porphyry prospectivity modeling. Based on this model high-potential areas are located in south southwestern and eastern parts of the studied area.