INTERNATIONAL JOURNAL OF MINING AND GEO-ENGINEERING
Year:2017 | Volume:51 | Issue:1|Papers Count:12

The paper describes an artificial neural network (ANN) model to predict the height of destressed zone (HDZ). This zones are usually considered to be equal to the combined height of caved and fractured zones above the mined panel in longwall mining. The suitable datasets were collected from the literatures to be used for modeling. The data were used to construct a multilayer perceptron (MLP) network to approximate the unknown nonlinear relationship between the input parameters and HDZ. The proposed MLP model predicted the values in enough agreements with the measured ones by a satisfactory correlation of R2=0.989. To approve the capability of proposed ANN model, the obtained results are compared to that of the conventional regression analysis (CRA) method. The calculated performance evaluation indices show the higher level of accuracy of the proposed ANN model compared to CRA. For further evaluation, the ANN model results were compared with the results of available models and the reported in-situ measurements in literatures. Comparative results present a logical agreement between ANN model and available methods. The results remark that the proposed ANN model is a suitable tool in HDZ estimation. At the end of modeling, the parametric study showed that the most effective parameter is the unit weight. The elastic modulus, on the other hand, is the least effective parameter on HDZ in this study.

Combining two methods of computational fluid dynamics (CFD) and design of experiments (DOE) was proposed in modeling to simultaneously benefit from the advantages of both modeling methods. The presented method was validated using a coal hydraulic classifier in an industrial scale. The effects of operating parameters, including feed flow rate, solid content and baffle length, were evaluated based on the classifier overflow velocity and cut-size as the process responses. The evaluation sequence was as follows: the variation levels of parameters was first evaluated using industrial measurement, and then a suitable experimental design was carried out and the DOE matrix was translated to CFD input. Afterwards, the overflow velocity values were predicted by CFD, and the cut-size values were determined using industrial and CFD results. The overflow velocity and cut-size values were statistically analyzed to develop the prediction models for DOE responses; and finally, the main interaction effects were interpreted with respect to DOE and CFD results. Statistical effect plots along with CFD fluid flow patterns showed the effects of type and magnitude of operating parameters on the classifier performance, and visualized the mechanism by which those effects occurred. The suggested modeling method seems to be a useful approach for better understanding the real operational phenomena within the fluid-base separation devices. Furthermore, the individual interaction effects can also be identified and used for interpretation of responses in nonlinear processes.

One of the key outcomes of blasting in mines is rock fragmentation that profoundly affects downstream expenses. In fact, size prediction of rock fragmentation is the first step towards the optimization of blasting design parameters. This paper attempts to present a model to predict rock fragmentation using Mutual Information (MI) in Meydook copper mine. Ten parameters are considered to influence fragmentation. On the other hand, Rock Engineering System (RES) is employed in order to compare different models. To validate the results, six blasting scenarios were selected and the results were compared. The coefficient of correlation (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used to assess the performance of presented models. The R2, RMSE and MAE values for 30 blasting cycles were calculated to be 0.81, 10.7, and 9.02 for MI model, and 0.75, 11.87, and 9.61 for RES, implying the better capability of MI model to predict fragmentation.

The proximate analysis is the most common form of coal evaluation that reveals the quality of a coal sample. It examines four factors including moisture, ash, volatile matter (VM), and fixed carbon (FC) within the coal sample. Every factor is determined through a distinctive experimental procedure under ASTM specified conditions. These determinations are time consuming and require various laboratory equipment. The calorific value is one of the most important properties of a solid fuel and its experimental determination requires special instrumentation and highly trained operator. This paper develops mathematical and ANFIS models for estimation of two factors of proximate analysis based on the other two factors. Furthermore, the estimation of calorific value of coal samples based on proximate analysis factors is performed using multivariable regression, the Minitab 16 software package, as well as ANFIS and MATLAB software package. The results indicate that ANFIS is a more powerful tool for estimation of proximate analysis factors and calorific value than multivariable regression method. The following equation estimates the calorific value of coal samples with high precision: Calorific value (btu/lb)=12204 - 170 Moisture+46.8 FC - 127 Ash.

Rapid development of engineering activities expands through a variety of rock engineering processes such as drilling, blasting, mining and mineral processing. Rock dynamic fracture mechanics methods are required to characterize the rock behavior in these activities. Dynamic fracture toughness is an important parameter for analysis of engineering structures under dynamic loading. Several experimental methods are used to determine the dynamic fracture properties of materials. Among them, the Hopkinson pressure bar and the drop weight have been frequently used to analyze the rocks properties. On the other hand, numerical simulations have been proved to be useful in dynamic fracture studies. Among various numerical techniques, the powerful extended finite element method (XFEM) enriches the finite element approximation with appropriate functions extracted from the fracture mechanics solution around a crack-tip. The main advantage of XFEM is its capability in modeling different states on a fixed mesh, which can be generated without considering the existence of discontinuities. In this paper, first, the design of a drop weight test setup was presented, and afterwards, the experimental tests on igneous (basalt) and calcareous (limestone) rocks of single-edge-cracked bend specimen were discussed. Then, each experimental test is modeled with the XFEM code. Finally, the obtained experimental and numerical results were compared. The results indicate that the experimentally predicted dynamic fracture toughness has less than 8 percent difference with the dynamic fracture toughness calculated from extended finite element method.

In an Open-Pit Production Scheduling (OPPS) problem, the goal is to determine the mining sequence of an orebody as a block model. In this paper, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA) is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D) copper orebody model. The orebody is featured as two-dimensional (2D) array of blocks. Likewise, counterpart 2D GA array was used to represent the solution space of an OPPS problem. Thereupon, the fitness function is defined according to the OPPS problem objective function to assess the solution domain. Also, new normalization method was used to handle the block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficient in solving OPPS problem.

Ground vibration is one of the undesirable outcomes of blasting operations. Different methods have been proposed to predict and control ground vibration that is caused by blasting. These methods can be classified through laboratory studies, fieldwork and numerical modeling. Among these methods, numerical modeling is the one which takes into account the basic principles of mechanics and provides step by step time-domain solutions to save time and budget. In order to use numerical analysis in predicting the results of blasting operations, the accuracy of the output must be verified through field test. In this study, the ground vibration caused by blasting in a field operation in Miduk Copper Mine was recorded using 3-component seismometers of the Vibracord seismograph and analyzed by Vibration-Meter software. Propagation of the waves caused by blasting in the mine slope was modeled using discrete element logic in the UDEC numerical software and was compared to that of the field test. Having tested the accuracy of the results, the effect of primer location and the direction of detonation propagation in the blast hole on the rate of ground vibration caused by blasting was investigated. The results show that by changing primer location from the bottom of the hole to its top, the rate of ground vibration caused by blasting increases.

Determining the maximum stable undercut span is an important parameter in undercut slopes design. The maximum stable undercut span is a function of slope geometry, the strength parameters of the slope material, condition of discontinuities, underground water condition, etc. However, the desired production capacity and therefore the size of excavating equipment will sometimes ask for a wider undercut span. The influence of arching phenomenon in geo-material on the stability of undercut slopes is investigated earlier. It is believed that due to the arching effect, some load transfer from the undercut area into stationary remaining side toes leads to a more stable slope. However, the transferred load may result in ploughing failure of side toes. One technique for preventing the ploughing failure is application of counterweight balance on side toes. In this study, the influence of counterweight size on the stability of the undercut slopes was investigated through a series of numerical model tests using FLAC3D software. It was concluded that there was a meaningful relationship between the counterweight balance size and the maximum stable undercut span where increasing a counterweight size resulted in a wider stable span. Finally, the numerical results were compared with pre-conducted physical modeling test and a nonlinear relationship was proposed between the counterweight size and the maximum stable undercut span.

The critical parameters in investigating the performance of designed support system of tunnels are the structural forces i.e. peak values of axial and shear forces, and moments. In this research, a complete database was firstly prepared using finite element method. Using finite element models, we modeled the segmental tunnel lining that was composed of 5+1 concrete segments in one ring. Then, an artificial neural network (ANN) model of multi-layer perceptron was developed to estimate the lining structural forces. To do this, the number of neurons and their arrangement were optimized based on the obtained minimum values from the root mean square error (RMSE). To prove the efficiency of the developed ANN model, we calculated the coefficient of efficiency (CE), determination coefficient (R2), variance account for (VAF), and RMSE values. The results demonstrated a promising precision and high efficiency of the presented ANN method for estimating the structural forces of tunnel lining composed of concrete segments instead of alternative costly and tedious solutions. Finally, the sensitivity analysis showed that among the input variables, the tunnel cover is the most influencing variable on the lining structural forces. However, other input variables, i.e. lateral earth pressure and key segment position were the second important variables affecting the induced stresses on tunnel lining.

Due to the uncertainties in input geometrical properties of fractures, there is no unique solution for assessing the stability of slopes in jointed rock masses. Therefore, the necessity of applying probabilistic analysis is inevitable on these cases. In this study, a probabilistic analysis procedure along with relevant algorithms were developed using Discrete Fracture Network-Distinct Element Method (DFN-DEM) approach. In the right abutment of Karun 4 dam and downstream, five joint sets and one major joint were identified. According to the geometrical properties of fractures in the Karun river valley, instability situations seemed applicable on this abutment. In order to evaluate the stability of a rock slope, different combinations of joint set geometrical parameters were selected, and a series of numerical DEM simulations were performed on generated and validated DFN models in DFN-DEM approach to measure minimum required support patterns in dry and saturated conditions. Results indicate that the distribution of required bolt length was well fitted with a lognormal distribution in both circumstances. In dry conditions, the calculated mean value was 1125.3 m, and more than 80 percent of models needed only 1614.99 m of bolts which was equivalent to a bolt pattern of 2 m spacing and 12 m length. However, as for the slopes with saturated condition, the calculated mean value was 1821.8 m, and more than 80 percent of models needed only 2653.49 m of bolts which was equivalent to a bolt pattern of 15 m length and 1.5 m spacing. Comparing the obtained results with that of numerical and empirical methods show that the investigation of a slope stability with different DFN realizations which were conducted in different block patterns was more efficient than the empirical methods.

This work presents an investigation on the chalcopyrite leaching under different conditions, and studies the surficial characteristics of chalcopyrite using Scanning Electron Microscope (SEM) to determine the composition of the passivation layer on the surface of chalcopyrite. The study of chalcopyrite dissolution was carried out in H2SO4 solution systems at pH of 1.2, with a chalcopyrite concentrate 5% in the redox potential of 460 mV at 90°C. The tests were meant to study the leaching of chalcopyrite in ferric sulfate solution to extract copper by adding pyrite, silver and silver coated pyrite. Using these approaches, the achieved recoveries were different. The results showed that in presence of pyrite, an elemental sulfur layer formed around chalcopyrite particles which hindered the complete dissolution of copper in chalcopyrite. No commercial process has been so far developed using silver as a catalyst to recover copper from chalcopyrite due to precipitation of argentojarosite which forms during the leaching process and limits the availability of silver ion as a catalyst. But, silver and pyrite assemblage to form silver coated pyrite caused an increase in dissolution. However, in presence of the pyrite coated silver, the leaching rate was very fast, and complete copper extraction was achieved within 10 hours.

Traditional methods of chromite exploration are mostly based on geophysical techniques and drilling operations which are expensive and time-consuming. Furthermore, they suffer from several shortcomings such as lack of sufficient geophysical density contrast. In order to overcome these drawbacks, the current research was carried out to introduce a novel, automatic and opto-geometric image analysis (OGIA) technique for extracting the structural properties of chromite minerals using polished thin sections prepared from outcrops. Several images were taken from polished sections through a reflected-light microscope equipped with a digital camera. The images were processed in filtering and segmentation steps to extract the worthwhile information of chromite minerals. The directional density of chromite minerals, as a textural property, was studied in different inclinations, and the main trend of chromite growth was identified. Microscopic inclination of chromite veins can be generalized for exploring the macroscopic layers of chromite buried under either the surface quaternary alluvium or the overburden rocks. The performance of the OGIA methodology was applied on a real case study, where several exploratory boreholes were drilled. The results show that the microscopic investigation outlines through image analysis are in good agreement with that of obtained from interpretation of boreholes. The OGIA method represents a reliable map for absence or existence of chromite ore deposits in different horizontal surfaces. Directing the exploration investigations toward more susceptible zones (potentials) and preventing from wasting time and budget are the major contributions of the OGIA methodology. It helps to make optimal managerial and economical decisions.