Dolerite structures such as dykes and sills are the main target for groundwater exploration in Karoo Supergroup area which is the main stratigraphy unit in South Africa. Morgenzon Farm is one of the sites in Karoo Supergroup, including a dolerite dyke, which is interested here. Magnetization/susceptibility and resistivity of dolerite dyke are significantly larger than those of encompassing sedimentary materials. Therefore, low induction number electromagnetic (EM-LIN) and magnetometry approaches may be useful to its detection. EM-LIN is composed of three techniques: EM38, EM31 and EM34, with the latter being manipulated. Since both EM34 and magnetometry inverse problems are linear, regularized weighted minimum length solution algorithm is utilized for their inverse modeling, but with one main discrepancy: model weighting function for magnetometry method is attained from multiplication of depth weighting and compactness constraints, while model weighting function is only equal to depth weighting for EM34 approach. Recovered susceptibility and conductivity sections derived respectively from magnetic and EM34 data sets show high consistency. Inverted models represent a dolerite dyke in the middle of the profile with depth range of 4 to 15m.
The slope stability analysis is recognized as one of the most significant issues in rock mechanics engineering. It plays a fundamental role in the design of various rock and soil structures, including mining slopes, roads, and tunnels. To date, various methods have been proposed to address the issue of stability, including limit equilibrium methods, numerical methods, and artificial intelligence techniques. In the present study, the stability analysis of mine wall slopes has been conducted using a neuro-fuzzy integrated approach (ANFIS). For this purpose, utilizing data from the Choghart iron mine, two neuro-fuzzy networks were developed to analyze the safety and stability of circular failures under static loading conditions. In the circular failure model, six parameters were identified as the most significant inputs, with the safety factor (SF) and stability (S) state as outputs, under two different scenarios for analysis. The results obtained indicate that the stability and safety analysis networks possess low error and high correlation, such that the average error for the safety factor and stability was 0.05 and 0.013, respectively, demonstrating the network's high generalization capability. Additionally, the artificial intelligence outputs of test data identified the southern wall of the mine as the most critical section, calculating the safety factor and stability of this area to be 0.81 and 0.66, respectively.
The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. Muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the principal component analysis. Finally, the best models for predicting throw and drop are determined. Analyzing the performance of the proposed models indicates their superiority in predicting muckpile shape. As a result, the Mean Squared Error of throw is 0.53 for train data and 1.24 for test data. While for the drop, the errors are 0.45 and 0.58 for the training and testing data. Furthermore, sensitivity analysis shows that specific-charge effects drop and throw more.
Loss of copper minerals in the tailings of the rougher and scavenger circuits poses a significant challenge in copper processing plants, diminishing the circuit's efficiency. Part I of this paper identified the causes of copper mineral loss in the scavenger circuit tailings of the Sungun copper concentration plant, situated in northwestern Iran. Changes in feed composition, particularly the ratio of copper oxide to sulfide minerals, along with alterations in the mineralogical properties of the input feed to the scavenger circuit, emerged as pivotal factors contributing to the loss of copper minerals into the tailings. In line with these findings, the objective of the present paper (part II) is to optimize the scavenger circuit by proposing a solution to mitigate the loss of copper minerals to the tailings. Samples were collected from the feed, concentrate, and final tailings, as well as from each cell of the scavenger circuit, followed by comminution and flotation tests on each sample. The results indicate that redirecting the scavenger circuit tailings to the input of the rougher cells, owing to their higher copper grade compared to the tailings of the rougher circuit, can enhance the circuit's recovery by more than 4%. Additionally, employing a combination of sulfide and oxide collectors, along with sulfidation to float the copper oxide minerals in the scavenger circuit, resulted in an overall recovery increase exceeding 11%. Furthermore, adjusting the size of the air bubbles to capture fine copper mineral particles from the scavenger circuit cells proved to be an effective strategy for boosting recovery. Moreover, modifying the grinding circuit to liberate the minerals present in the scavenger circuit feed, predominantly the concentrate of the scavenger circuit itself, led to a recovery increase of approximately 5%. Considering the mineralogical characteristics of the scavenger circuit feed, derived from the tailings of the cleaner cells, implementing changes in the operating conditions of the cleaner circuit—such as employing hybrid bubbles (Nano and coarse bubbles) and utilizing sulfide and oxide collectors—significantly impacted the recovery of fine copper mineral particles and copper oxide minerals to the cleaner concentrate, thereby enhancing the scavenger circuit's performance.
For the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed fragility curve, as the value increases value decreases. A fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment.
Gravity inversion methods play a fundamental role in subsurface exploration, facilitating the characterization of geological structures and economic deposits. In this study, we conduct a comparative analysis of two widely used regularization methods, Tikhonov (L2) and Sparse (L1) regularization, within the framework of gravity inversion. To assess their performance, we constructed two distinct synthetic models by implementing tensor meshes, considering station spacing to discretize the subsurface environment precisely. Both methods have proven ability to recover density distributions while minimizing the inherent non-uniqueness and ill-posed nature of gravity inversion problems. Tikhonov regularization yields stable results, presenting smooth model parameters even with limited prior information and noisy data. Conversely, sparse regularization, utilizing sparsity-promoting penalties, excels in capturing sharp geological features and identifying anomalous regions, such as mineralized zones. Applying these methodologies to real gravity data from the Safu manganese deposit in northwest Iran, we assess their efficacy in recovering the geometry of dense ore deposits. Sparse regularization demonstrates superior performance, yielding lower misfit values and sharper boundaries during individual inversions. This underscores its capacity to provide a more accurate representation of the depth and edges of anomalous targets in this specific case. However, both methods represent the same top depth of the target in the real case study, but the lower depth and density distribution were not the same in the XZ cross-sections. Inversion results imply the presence of a near-surface deposit characterized by a high-density contrast and linear distribution, attributed to the high grade of manganese mineralization.
The properties of metallic minerals and metallic minerals-electrolyte interface have always been a concern in the induced polarization (IP) geophysical method due to their effects on the IP response. Electrochemical reactions, if carried out, affect the interface characteristics. Hence, the occurrence of the reactions and their effects on the IP signal have been modeled through recent research, but they are not well-known yet. Identifying these matters can help to create more realistic physical and petrophysical models, for a better explanation of IP effects. So, in the present study, 11 metallic mineral samples and the laboratory method named bipolar electrochemistry, introduced for the first time to the IP research field, have been used to show the performance of electrochemical reactions at the interface and the effect of various metallic minerals on them. The results showed that if the applied external electric potential is high enough, electrochemical reactions are carried out at the metallic minerals-electrolyte interface. In this study, these reactions were electrolysis of water and were carried out in all minerals (except sphalerite). However, the potential required to initiate the reactions was different for different minerals. The lack of water electrolysis reaction on the surface of sphalerite can probably be attributed to its non-conductivity. On the other hand, the external potential responsible for the interface reactions was linearly linked to the potential difference between the two sample’s extremities. Considering the different potentials required to start the reactions in various samples-electrolyte interfaces, and the absence of these reactions in the case of sphalerite samples, it can be concluded that the samples’ compounds affect the reactions and their commencing potentials. So, we believe that by studying these reactions, some properties of the metallic minerals can be achieved. Identifying the minerals’ properties and the reactions that can occur at their surfaces is essential for a detailed understanding of the factors affecting the IP phenomenon. To do this, we found bipolar electrochemistry as an appropriate way.
The productivity of the quarry during the wet season heavily depends on how well the personnel adjusts to the mine's environmental conditions and management plans. The improvement of granite production through workers' impact identification and mining advancement decision-making in Ondo State, Nigeria, has been considered in this study. The rate of granite production and the factors influencing workers’ efficiency were assessed using a well-structured survey and descriptive-analytic technique. To improve the production rate, the Multiple Criteria Decision-Making (MCDM) technique was used to select the most productive pit depending on a number of key labor impact factors. Health and safety in employment, the energy crisis, market conditions and level of competition, on-site accidents, natural disasters, and language barriers were some of the factors identified as external influencer factors affecting mine labor efficiency in granite quarrying. Finally, using the criteria's significance through the inter-criterion (CRITIC) approach, the mine workers’ influence on production was estimated and utilized for the best pit selection. The result of the MCDM revealed that the five pits (Pit 1, Pit 2, Pit 3, Pit 4, and Pit 5) had the following decision performance scores: 0.659, 0.617, 0.5, 0.5, and 0.5, respectively. This made Pit 1 the best production pit to be considered during the rainy season. The optimal solution was validated with the 2021 production report. The report shows that production from Pit 1 had the highest revenue of $16,000 Per annum, the lowest dewatering cost, and the highest production rate compared to the other four pits.
The objective of this study was to investigate the impact of various operational parameters, including aeration rate, feed flow rate, collector and frother dosage, on the efficiency of coal flotation. A pilot-scale column flotation process was utilized. CCD method and ANOVA were used to develop the process model from the input-output data set of a pilot scale column flotation process and to test the weight recovery models and concentrate ash percentage. By optimizing the parameters, such as using a fuel oil collector dosage of 900 g/t, MIBC frother dosage of 340 g/t, and an aeration rate of 10594.6 ml/min, a column flotation operation achieved a recovery of 50.06% and an ash concentrate of 11.6%. The results showed that the aeration rate had the most significant influence on the ash content of concentrate and recovery, compared to the collector and frother dosages.
Designing an ash pond for High Concentrated Slurry Disposal (HCSD) in hilly terrains poses a unique set of challenges. In such regions, the availability of barren land adjacent to the project sites is limited, making it difficult to locate suitable sites for ash pond construction. Furthermore, the hilly terrain and complex topography demand careful planning and design to ensure the long-term stability and safety of the ash pond and associated infrastructure. This paper discusses the design considerations for the construction of an ash pond dykes in a hilly region of Vietnam for HCSD disposal. The case study highlights detailed geotechnical design investigations such as soil type, slope stability and groundwater conditions. In addition, geotechnical instrumentation to monitor the performance of ash pond dykes and the subsoil supporting it during and after construction is also discussed. The paper also examines the environmental and hydrological design aspects of the ash pond, including development of drainage and channelization concept along the hilly terrain to manage storm water and prevent the mixing of rain water with ash slurry, which could otherwise create an environmental hazard.
Mass movements of land, such as landslides, pose significant threats to human safety and infrastructure. This study focuses on advancing the understanding of landslide dynamics through the application of geophysical surveys, specifically Electrical Resistivity Tomography (ERT) and Seismic Refraction Tomography (SRT). Unstructured meshing, as a pivotal technique in geophysics simulation studies, provides flexibility in discretizing complex geological structures. This method allows for refined mesh elements where needed, optimizing computational resources. In the field of geophysics, unstructured meshing is particularly advantageous for accurately representing subsurface heterogeneities. This study employs pyGIMLi, a Geophysical Inversion and Modeling Python library. This Python programming library, though devoid of a GUI, offers a comprehensive suite of tools for geophysical data analysis and inversion. This library incorporates unstructured meshing capabilities. This feature enhances the accuracy of simulations, enabling researchers to model intricate geological formations with more precision. Using this library empowers users to seamlessly generate, manipulate, and analyze unstructured meshes, facilitating robust simulations and detailed investigations of subsurface properties in geophysics. In this study, we present a novel approach to simulate a 3-layered landslide using ERT and SRT, coupled with inverse modeling through utilizing the unstructured meshing of the inversion area. The synthetic model produced has a depth of study extending to 65 meters. The SRT model reveals a dense coverage in layer 2, providing crucial information about the subsurface characteristics. The utilization of ERT and SRT in tandem allows for a comprehensive understanding of the landslide structure, offering insights into detecting the slip surface of the landslide. The study's innovative methodology provides a robust framework for the analysis of complex geological scenarios. The results obtained from this simulation contribute to the broader knowledge of landslide dynamics and offer a valuable tool for assessing and mitigating landslide risks in similar geological settings.
Bituminous sand is an important geomaterial due to its numerous engineering applications and great economic potential. The occurrence is diverse in nature and therefore, it is crucial to study the engineering behaviour and understand the inherent variability in the engineering parameters needed in exploitation, design, and construction in and/or on them. This study presents laboratory investigations into the engineering behaviour and the inherent variability in bituminous sand considering index, physical, fabric and geochemistry, mineralogy, oedometer, and strength characteristics on the samples in the intact and reconstituted states. The findings can be summarised as follows: (1) The gradings are characterised by different particle sizes and hence, the bituminous sands are well-graded, (2) The fabrics are heterogeneous and isotropic, composed mainly of bituminous materials-coated particles aggregations into clusters with inter-and intra-particle/cluster voids, (3) Silica and quartz dominate the composition and mineralogy with other elements and minerals in lesser proportions, (4) Bituminous sands have convergent behaviour and the compressibility is dependent on fines content, (5) Intact behaviour shows the presence of significant structure, (6) The inherent variability is high in some engineering grading descriptors, clay mineral, yield stress and strength, and low in the fines content, quartz mineral, compressibility and in situ specific volume, and (7) Depending on the properties needed by the practitioners, interpolation and extrapolation from one point/location to another could be made in these geomaterials..
The paper presents the results of a comprehensive investigation of the applicability of various intelligence methods for optimal prediction of rock mass caveability in block caving by using effective geomechanical parameters. However, due to the complexity of the prediction of rock mass cavability, artificial intelligence-based methods, including classification and regression tree (CART), support vector machines (SVM), and Artificial neural network (ANN), have been selected. For validating and comparing the results, common MVR was used. Because of the dependency of the modeling generality and accuracy on the number of data, we attempted to obtain an adequate database from the result of numerical modeling. The distinct element method (DEM) used to study the rock mass cavability. The results indicated that ANN is the most accurate modeling technique with a determination coefficient of 0.987 as compared with other aforesaid methods. Finally, the sensitivity analysis showed that joint spacing, friction angle, joint set number, and undercut depth are the most prevailing parameters of rock mass cavability. However, the joint dip has shown the minimum effect on rock mass cavability in block caving mining method.
Euler deconvolution system is a well-known approach to estimate the depth of underground sources in potential field geophysics. Over-determined Euler linear equations are usually solved independently and separately for the gravity and magnetic data, and each result is an estimate for the depth of the potential sources. This technique is widely utilized to analyze individually the depth variations of magnetic and gravity sources. However, depth estimation of each of the mentioned potential fields may return specific and exclusive results regarding the complex nature of the subsurface structures, and the gravity and magnetic separate depth estimation solutions may be discordant in many aspects. In cases of low resolution for gravity and magnetic data sets, this study indicates that independently solved Euler depth estimation systems cannot yield reliable and accurate solutions of potential field sources. Combining the gravity and magnetic data and simultaneously solving the Euler equations for gravity and magnetic potential fields, this research presents a novel approach called the joint Euler method with a proper capability to return more accurate and improved depth estimations for boundary and body of potential field sources. The presented method was solved and examined over homogeneous and non-homogeneous synthetic scenarios with reduced resolution, and the depth solutions were also compared with the separate approach. After obtaining the desired results from the synthetic models, the joint Euler technique was applied to the gravity and magnetic data of the Kifl oil trap located in Iraq. The results were quite promising rather than the separate depth estimations, proving the sufficiency and applicability of the proposed potential field method in terms of interpretational aspects.
Induced polarization (IP) tomography measurements as a near-surface geophysical method can provide information about the degree of chargeability of subsurface materials, and are commonly used in mineral exploration, engineering studies (e.g., sediment/bedrock interface identification, crushed zones and faults detection, and landslide and soil properties imaging.), as well as in environmental investigations (contaminant plums identification and landfill characterization). The purpose of these measurements is to obtain the distribution of polarizability characteristics inside an object, generally below the surface, at the boundary of the object, or outside the area in question. The result of such measurements can be mathematically modeled for the specific polarizability properties by the solution of Poisson’s equation restricted by appropriate boundary conditions. In this paper, we focus on the importance of simulating induced-polarization responses and retrieving chargeability distributions in geo-materials to enhance the characterization of subsurface structures. We present the methods for forward modeling and nonlinear inversion of induced-polarization measurements. To this end, in the first step, Poisson’s equation for a two-dimensional ground with arbitrary distribution of conductivity is solved using the finite difference numerical method and in the next step, based on the existing relations between conductivity and chargeability (Siegel’s formulation), the apparent induced polarization response is calculated. Finally, we solve the nonlinear chargeability inversion problem following a nonlinear apparent resistivity inversion. This is achieved by imposing physical constraints to prevent the estimation of unrealistic model parameters, using a Newton-based optimization method. To evaluate the efficiency of the proposed methodology, we utilized the proposed algorithm to two simulated examples and a real data set. Our numerical results show that the algorithm reliably represents the main features and structure of the Earth’s subsurface in terms of the resistivity and chargeability models. All the algorithms presented in this paper have written in the MATLAB programming language.
Long-term production planning for open-pit mines is recognised as one of the vital decision-making issues in open-pit mining operations. In addition, the ore grade is one of the most significant sources of uncertainty in a mining operation, as it classified run-of-mine material into ore and waste. In the classical approach, the destination of mining blocks is determined by comparing the estimated grade with a pre-determined cut-off grade. However, the uncertainty of material grade dramatically affects production planning. In this paper, a novel model was developed based on the idea of simulating the grade to incorporate the risk of grade uncertainty. In the proposed model, the economic consequences of the assigned destination are calculated using the profit and loss functions and they are integrated with the production scheduling. The proposed production planning was implemented in an iron ore mine, and the results were discussed for classical, loss, and profit models. Results show that the net present value increases by 3.64% by implementing the profit function. In contrast, the loss function method reduces the net present value by 2.23% compared to the classic model. This happens because the amount of ore class is increased by 7.46% using the profit function method and decreased by 2.49% using the loss function method. Additionally, the coefficient of variation, as an index of uncertainty, was investigated. The results show that the loss function approach attempts to extract more reliable blocks in the early years and postpone the high-uncertain blocks to the later years of the production.
Loss of copper minerals to tailings of the rougher and scavenger circuits is one of the challenges in copper concentration plants, which reduces the efficiency of the circuit. The goal of this research is to utilize mineralogy-based techniques to identify the cause of the loss of copper minerals to tailings of the scavenger circuit. Process mineralogy studies have been performed on the scavenger of the flotation circuit - the Sungun copper concentration plant (located in northwestern of Iran). First, the feed, final concentrate, and tailing, as well as the concentrate and tailing flows of each of the scavenger circuit cells were sampled at different time intervals. Then, chemical composition analysis, laser particle size distribution analysis, and optical microscopy studies were performed on them. According to the results, the presence and changes in the abundance of copper oxide minerals, which make up about 45% of copper minerals, is one of the main reasons for the copper loss to tailings. Also, fine-grained particles of copper sulfide minerals (d80~45 µm), and the interlocking of copper sulfide minerals, especially chalcopyrite, with pyrite and gangue minerals (silicate), are among the most important causes of increased copper grade in the scavenger circuit tailings. In addition, the malfunction of the scavenger circuit cells due to the simultaneous presence of sulfide and oxide minerals and their fineness is another cause of the lost to tailings.
In this study, a novel numerical approach is proposed to characterize the dissolution of rock minerals and wormhole propagation in carbonate rocks using the Darcy scale model. Accordingly, only the spatial variables of the governing partial differential equations are discretized, while the time variable remains continuous. Consequently, the partial differential equations are turned into ordinary ones, which are then numerically solved by high-order Runge-Kutta methods. The proposed approach is verified against the analytical solution in a 1D core model. Afterwards, it will be utilized to investigate the effect of multiple transport and reaction phenomena on the matrix acidizing in 2D carbonate formations. Also, the staggered grid technique is employed to accurately predict the wormhole patterns during several injection regimes. Compared to the previous studies, the proposed numerical approach is less complicated and straightforward. Furthermore, the computational cost is more affordable.
The mechanical behavior of silty sands is one of the crucial topics in the field of soil mechanics. However, many studies have been conducted to determine the main features of the silty sand mixtures; there are some non-clarified mechanisms yet. Most of the previously applied studies have been focused on the behavior of silty sands under conventional paths such as consolidated drained and consolidated undrained stress-strain paths. Recently done investigations have shown the assumption that the mentioned conventional paths are not the critical ones for all situations. Therefore, considering partially drained paths not only can help to understand the mechanical behavior of silty sands better but also is necessary to ensure the safety of the projects. In this paper, 14 triaxial shear tests are applied to assess the effects of the partially drained paths on the main features of the shearing mechanism of silty sands. As the water inlet is the most critical path between the partially drained tests, this research is done by considering only this type of partial drainage and ignoring other non-crucial partially drained strain paths. Achieved results indicate that partial drainage can affect the behavior of samples with a little fine content (up to 5%) significantly, while for samples with more fine content, these effects are not considerable. In other words, samples, which not exhibit a fully static liquefaction (completely softening behavior), will be considerably affected by partial drainage. Effects of water inlet during shearing on the asymptotic stress ratios, excess pore water generation, and experienced stress paths are investigated, as well.
The effect of removing suction on energy consumption, displacement, and separation of particles with different sizes and densities in the jig was investigated by CFD-DEM coupling. Water velocity functions were categorized into four modes: normal sinusoidal, partial removal of suction, complete removal of suction, and optimal. Particles rise to a certain height in the normal sinusoidal mode Particles rise to a certain height in the normal sinusoidal mode, while their jump height increases in the case of partial or complete removal of suction. The jump was controlled by fluid and added (Hutch) water velocities. Increasing the maximum jump height of particles leads to a decrease in separation, an increase in operational costs, and heightened particle mixing and energy consumption. In both modes of partial or complete suction removal, the fluid velocity should be reduced. The extent of velocity reduction depends on the power required to move the particles. Simulation of coarse particles (7 and 8 mm) revealed that in complete removal of suction, the velocity should be decreased to less than half of the normal sinusoidal mode, and the hutch water velocity should be equivalent to the velocity amplitude. The energy consumption for the optimal mode was significantly lower than that of the other modes.
