JOURNAL OF ANALYTICAL AND NUMERICAL METHODS IN MINING ENGINEERING
Year:2020 | Volume:10 | Issue:24|Papers Count:11

Estimating the costs of mining projects is among the most important and main stages in justifying the economic plan of a mine. Suboleski presented a model based on annual production capacity in underground mines and the size of the mine in order to estimate machinery and costs, which was published in the SME, Mining Engineering Handbook. The second edition of this model in the year 1992 was based on the financial conditions of that time and could be applied outside of Iran and especially in North America. The given relations of this model cannot be used to estimate the costs in Iran. In this Study, the relations are modified and updated to present an estimation suitable with the local conditions of Iran using official statistics and data and taking the special status of Iran into account. In order to do this, the percentage of each of the effective scopes of work in each of the relations with the influential coefficients were achieved, then the effect of the changes in currency value and the inflation status from the base year to the target year are considered in order to update each of the relations. Finally, a table of Suboleski’ s relations due to the ratio of 2. 39 to update the US dollar-time relationship and another table for the specific relations of Iran with regard to the adjustment coefficients of each sector were presented (for each separate section due to the specific modification of that section calculations are provided with). Using tables and daily Tonnage that mined, the users can get an estimation with at least 25% precision of operating cost and capital cost of the mine.

Summary: One of the main problem of mine planning is long-term production scheduling, which is very important in the theoretical research of open-pit mining and determines the distribution of cash flow throughout the life of the mine. In fact, the purpose is to maximize the net present value of the future profits generated. Among the uncertainties, grade uncertainty will play a major role in the accuracy of long-term production scheduling. In this paper, a hybrid model is presented by Lagrangian relaxation method and bat algorithm to solve the problem of long-term production of open-pit mines, in which the uncertainty of the grade is also considered. The new approaches proposed are based on optimizing Lagrange coefficients and comparing them with the traditional method. The bat algorithm is used to update the Lagrange coefficients. The results of the case study show that the hybrid strategy can provide an acceptable solution compared to the traditional approximation method, so that over a given period the net present value using the proposed hybrid method is 6. 69% higher than the traditional one. Introduction: In recent years, a new approach to cheaper computational algorithms, such as meta-heuristic techniques, has attracted more attention from researchers to solve production scheduling problems. Although these techniques do not guarantee optimization as a final solution for production, they can provide suitable solutions for production at lower computational cost. Methodology and Approaches: In this paper, an optimal hybrid model by Lagrangian relaxation method and bat algorithm is presented to solve the problem of long-term production of open-pit mines, where the uncertainty of the grade is also considered. The new approach proposed is based on optimizing Lagrange coefficients and comparing it with the traditional method. The results of the proposed approach are also compared with the combined approach based on Lagrangian relaxation method and genetic algorithm. The bat algorithm is used to update the Lagrange coefficients. Results and Conclusions: The results of a case study show that the Lagrangian relaxation method can provide a suitable solution to the main problem and the combined strategy can produce a more effective solution than the traditional approximation method. It was also found that the proposed method has advantages, such as stable convergence property and prevention of early convergence. Over a given period, the net present value using the LR-BA hybrid method is 6. 69% higher than the traditional method and also 5. 58% higher than the LR-GA method.

Summary: Recent development in Extended Finite Element Method (XFEM) opened new avenues thorough crack propagation problems. However, it ability to predict crack path in micro scale medium of a real porous rock is questionable. In this work, we compare two strategies and introduce one as the best strategy to use the XFEM for such a purpose in Abaqus Software. We demonstrate our claim by comparing numerical results with analytical solution and experimental test. Introduction: Crack growth has always been one of the major challenges in the rock mechanics. Although pores, joints and fractures are the most critical structures controlling cracks initiation and propagation, their spatial distribution and geometrical effects are not still well-understood. In this study, we aim to numerically model the crack growth in a real porous rock. Methodology and Approaches: We use the extended finite element method (XFEM), which has recently attracted more attentions due to its ability to estimate the discontinuous deformation field by using special shape functions. Since direct use of the XFEM does not lead to an accurate result, two different strategies are considered for applying the XFEM on a porous model to simulate the crack propagation. Results and Conclusions: Our results showed that applying several different partitions and enrich them individually lead more logical results than to allocate reduced elastic modulus to porosity. We used this strategy to evaluate the XFEM both analytically and experimentally as a possible numerical solution. Thus, two simple models were constructed, both numerically and experimentally (Granite): i. a sample with one void and one crack, and ii. a sample with two voids and one crack between them. Analytical solutions for the stress intensity factor revealed that the XFEM modeling can compute this parameter with an error less than 5%. On the other hand, experimental results showed that the XFEM with partitioning strategy can predict the correct crack growth path comparative to the experimental results. Accordingly, digital images of Berea sandstone were used as a real reservoir rock and, then, this method was implemented to simulate multi-crack propagation through the exact medium of rock.

Anisotropy of a deposit is due to its directional variations of grade or structure. Locally varying anisotropy (LVA) is the specific case of anisotropy in some structurally-controlled deposits. Recently, there has been presented a graph-based solution to implement an LVA field of the study area into estimations. The field then is used in an MDS-based distance matrix calculation which is the core of LVA-variography and-kriging (OKLVA). In this research, using ant colony application in geochemical anomaly detection and LVA field of the study area, an algorithm (ACLVA) has been developed to smartly direct the ants into the more continuous paths and ants, meanwhile act as moving average agents over their routes. Ordinary kriging (OK), OKLVA and ACLVA were applied on borehole samples of Miduk copper deposit as the case study and estimations were validated with blasthole samples. The estimations were improved with ACLVA.

Almost all of the proposed models of Discrete Fracture Networks (DFN) embedded within rock masses are discontinuities with zero tension strength. While, potential discontinuities and weak surfaces such as rock bridges, veinlet and schistose surfaces are candidate for breakage under stress and have also significant effect on rock mass strength. Simultaneously with geometrical parameters, this geomechanical heterogeneous nature of fractures are crucial for understanding rock mass behavior and characteristics. This paper focuses on the probabilistic effect of potential discontinuities property on the rock mass strength using Potential Discrete Fracture Networks (PDFN)-bonded block models (BBM) framework. By means of this approach, potential fractures such as weak surfaces such as rock bridges, veinlet and schistose surfaces (surfaces with tension and shear strength) are considered, which have significant effect on rock mass strength. Maximum obtained rock mass strength differences for 4 fracture intensities: 1. 2, 3. 6, 6 and 8. 4 m-1 are 12. 9, 7. 4, 15. 1 and 10. 2 MPa respectively. Results indicates performance and importance of the proposed model for assessment of realistic rock mass strength.

Improvement of the performance of TBM machine in tunneling has always been in the spotlight of research and development. The type of cutting tools, which is selected based on the mechanical properties of the formations along the tunnel route, could affect the efficiency and production rate in tunneling. In Tehran Metro Line 6, the consumption of disc cutter increased with decreasing the efficiency of disc cutters, along with increasing the value of thrust and torque. Hence, some of the disc cutters of the TBM were replaced with ripper tools in order to improve the TBM performance. In this paper, the effect of this replacement on the performance of the TBM is examined by statistical analysis of the field measurements. The value of operational parameters-thrust, torque and penetration rate of the TBM-in different sections before and after the replacement is investigated. The statistical analysis shows that the mean value of both thrust and torque as well as their standard deviations after the replacement have considerably decreased in comparison with the figures for before the replacement, while the mean value of penetration rate has been approximately identical in both before and after the replacement of cutting tools. The results show that this replacement of disc cutters with ripper tools in alluvial conditions of Tehran has improved the performance of TBM machine.

The inversion of gravity data is one of the most important steps in interpretation these data. The purpose of this work is to estimate the distribution of the unknown sub surface model by the measured data on the ground. The main problem in inversion of the data obtained from the operation of gravity survey is the non-uniqueness response due to the inversion of geophysical data. Linear inversion of gravity data is a underdetermined and bad-condition. It is important to determine the optimal regularization parameter for inversion of gravity data. One of these methods is the Generalized Cross Validation (GCV). In this research, the quadratic method has been used as an optimization method. This method has three algorithms, a trust region, an internal point and an active set method. These three algorithms will be checked for the duration of the inversion implementation as well as the stability of each of these algorithms against noise. In order to validate the proposed method, used the gravity data from two synthetic models and gravity data from Mobrun copper mine in the state of Quebec, Canada.

The main problem of traditional methods of environmental impact assessment is that in most of the existing algorithms and methods, such as Leopold matrix, Folchi method and RIAM matrix, the main attention is to the destructive effects of the proposed plan and the advantages of the plan are less noticeable. This has led to a permanent challenge between environmental organizations and industrial stakeholders. DEA is a new approach to assessing the industrial units also considers the positive economic and social impacts of the project and provides a comprehensive assessment of the industrial unit. With this approach, the environmental impacts of an industrial unit have be considered as inputs and its positive economic and social impacts considered as the outputs of the models. Therefore, the problem of impact assessment change into a DEA model. In the present study, the Alborz Sharghi Coal washing plant in northern Iran has been considered as a case study, and 19 plant activities and 11 environmental components have been used to evaluate the effects of the plant. To solve the problem, two commonly used DEA approaches, called BCC and CCR, have been used. The results showed that the components of "ecology" and "area landscape" as the most critical components should be considered seriously. Also, drawing of the "potential improvement" diagram in the DEA method is an effective tool for determining the high risk potential activity of the factory and using in factory development plan. Moreover, using the BCC model with maximize-output approach showed that some of the plant activities such as “ Input feed dump” , ” Wastewater from the plant " and " Water consumption of the plant " had the most differences with optimal mode and in future development plans these components should be considered. Finally, it can be concluded that, assessing the environmental impact of the coal washing plant with BCC maximize-output approach, is closer to the concept of sustainable development.

In this paper, to predict the size distribution of rock fragmentation (D80), the blasting data and rock mass characteristics of Chogart, Chadormalu and Sechahum mines were used. Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry. To quantity the shape of in-situ blocks, the fractal geometry is suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural network were used. The results shown that the nonlinear regression and neural network were ability to predict the size distribution of rock fragmentation.

Summary: This paper reflects the role of different constitutive models on the deformations induced by tunneling in urban area. These constitutive models were applied in finite element analysis of tunnel-induced subsidence for case study of Amirkabir Tunnel in Tehran. The results of this paper indicate that numerical simulation of tunneling induced settlement with hardening soil small strain stiffness model is much more accurate than other constitutive models. Introduction: Accurate prediction of tunneling induced settlement is one of the most important challenges encountered in urban underground projects. Generally, such predictions are usually obtained by application of numerical simulation, where the accuracy of the results depends on several factors. The constitutive models play an indicative role on the accuracy of numerical simulation of tunneling induced settlement. This issue was studied by comparing the effect of different constitutive models on the development of ground deformations around tunnel and also the tunneling induced settlement for a case study. Methodology and Approaches: Finite element analysis of tunneling induced deformations using PLAXIS software was performed for three different elasto-plastic constitutive models including Mohr-Coulomb, hardening soil, and small strain hardening. The input data of numerical simulation were captured from different in-suite and laboratory tests on the host ground of Amirkabir tunnel as case study. Tunnel construction was modeled based on the as-built condition of excavation stages of T4 section of Amirkabir tunnel. Finally, numerical results were compared and verified with monitoring results and field measurements. Results and Conclusions: Results show that the Mohr-Coulomb model shows lower prediction of vertical displacements comparing to two other implemented models. Furthermore the Mohr-Coulomb model shows an unrealistic uplift of tunnel floor after all of the excavation stages. Results illustrated that using hardening soil models, with sophisticated features including non-linearity pre-failure and high stiffness under small strain, considerably improves the prediction of displacements. It is observed that using hardening soil small strain stiffness model the accuracy of predictions comparing to the field measurements increase obviously. A fully comparison between the results from Mohr-Couloumb and Hardening Soil cases yields some important differences which are presented in this paper.

Summary: The complete knowledge of the cutting process and the performance of sawing machine can help increase the efficiency and quality of the manufactured product. During the field studies, 12 rock samples from two kinds of ornamental stone were collected and some major mechanical properties including uniaxial compressive strength (UCS), Mohs hardness (Mh), Schimazek’ s F-abrasiveness factors (SF-a) and young modulus (YM) were measured. Also, Fuzzy C-means has been used as a soft computing technique in this study. All different studied rock samples are classified into 3, 4 and 5 classes. Then, the results of classification were compared with consumed energy values. Finally, it can be concluded that FCM could be a simple but efficient tool in evaluation of the sawability of ornamental stone as one of the most important factors to estimation and prediction of production cost and productivity of processing plant. Introduction: The rock sawability and prediction of consumed energy is one of the most important factors to estimation and prediction of production cost and productivity of processing plant. In this study, for laboratory tests, some rock blocks were collected from the studied quarries. Then, by using the fuzzy C-means clustering approached and considering laboratory results of rock samples, all of rocks have been classified in 3, 4 and 5 separate clusters. The results clearly showed that FCM algorithm used as a reliable and efficient tool for classifying the ornamental stone. Methodology and Approaches: Nowadays, with the increasing growth of uncertain problems, the use of soft computing techniques with a high ability in solving these kinds of problems has increased significantly. Assessment of the sawability of ornamental stone is a most important factor in identification and prediction of production cost and productivity of processing plant. Given the unreliability of all experimental labatory, one of the most important tasks in the sawability of ornamental stone’ classification is using a method with the highest possible accuracy. According to the importance of the issue, in this study, Fuzzy C-means algorithm is used for the classification. Four important physical and mechanical characteristics of 12 rock samples are considered for the classification of sawability of ornamental stone from two kinds of ornamental stone, including hard and soft rock groups using Fuzzy C-means Optimization, including uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor and Young's modulus. Finally, in this study, the results of classification are compared with consumed energy. Results and Conclusions: In this study employing Fuzzy C-means as soft computing technique and some major mechanical properties such as uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor and Young's modulus to evaluate the sawability of ornamental stone, 3 to 5 classes are considered. Generally, according to this research, the following remarks can be concluded: 1-The 12 rock samples from two kinds of ornamental stone, including hard and soft rock groups, are evaluated and tested. 2-A comparison was made between 3 different models (3 classes from 3 to 5 classes) with consumed energy of sawing machine. The results show that all 12 rock samples are classified as very suitable. 3-In comparison to all classes, it can be concluded that FCM is a reliable technique for clustering the sawability of ornamental stone with highly acceptable degrees of robustness