IRANIAN JOURNAL OF MINING ENGINEERING (IRJME)
Year:2019 | Volume:14 | Issue:44 |Papers Count:11

Methane is one of the gases emitted from coal seams and released into atmosphere through ventilation operation. The emission of this gas causes environmental problems and loss of energy resources. According to the previous studies, several billion cubic meters of methane are annually emitted from coal mines in the world. Currently, methane drainage is in operation in many coal mines around the world; and the drained methane is collected using special methods, and transported to the surface by pipelines. The gas is then used for power generation, sales, and industrial use. Tabas Mine is the only mechanized coal mine in Iran, extracted by longwall mining. The gas content of coal seams in this mine varies from 10 tonnes per cubic meters (in depth of 100 m) to 20 tonnes per cubic meters in depth of 600 m, which has caused problems in the excavation process as the depth increases, making the methane drainage operation necessary in this mine. The aim of this paper is the feasibility study of methane drainage in Tabas Coal Mine for power generation. Considering investment and operating costs, as well as the gas sales income, results showed that the methane drainage for power generation is feasible. Finally, methane drainage operation in Tabas Coal Mine is energy efficient, due to the production of methane as a fuel, which reduces greenhouse gas emission and outburst risks in this mine.

Heavy metal pollution is one of the main environmental problems associated with mineral, industrial and agricultural activities in the world. Due to the long life of these toxic metals and the degree of solubility in acidic and even non acidic conditions, they can have destructive effects on water, soil and humans life in the long time. The lead-zinc mine of Anguran is one of the largest world-class sulfide-carbonate reserves, which, due to mining, has produced a significant amount of mineral wastes, which can be the source of heavy metals to the water and soil of the downstream areas. Therefore, the use of quick and cost-effective methods to classify the risk of contamination of this type of waste can be a useful tool for monitoring, recovery and reconstruction programs in the future. The purpose of this research is to use multivariate statistical techniques, such as discriminant analysis (DA) method and utilize of artificial intelligence technique in order to classify and predict the potential of pollution in mining wastes. To achieve the goals, the samples taken from different surficial parts of the waste dump were analyzed by using the ICP-MS method to determine the concentration of heavy metals. These metals includes: Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb and Zn. The environmental risk assessment indices (ERAI) and potential load index (PLI) were then modelled. At the end, by using the DA and neural network (NN) methods, potential risk of contamination on the damp surface was classified in three low, medium and high levels with the accuracy of 91. 49 and 93. 6 percent respectively. The results also showed that these techniques can be used as effective tools for classifying new waste dumps and designing of new constructing dumps based on their contamination level.

Drill and blast excavation method is commonly used in mining, quarrying, and tunneling, around the world. Despite the fact that drill and blast method has witnessed significant technological advancements, it has inherent disadvantage of deteriorating surrounding rock mass, due to the development of a blast induced damage zone, with reduced strength and stiffness parameters. Traditional tunnel analysis adopts a single value of blast damage factor D for the Hoek-Brown (HB) criterion for the entire rock mass, leading to an underestimation of the tunnel stability. However, the blast damage zone with finite thickness is significant in tunnel design and stability analysis. In this paper, the behavior of tunnels under different damage conditions is examined. In this regard, a fully analytical solution is used. The solution is presented for tunnels excavated in elastic– brittle– plastic rock masses with Mohr-Coulomb failure criterion. The damaged zone is assumed to have cylindrical shape with a varied damage factor D. Results obtained by the proposed solution indicate that the alteration of rock mass properties may considerably affect the damaged zone.

Estimation Face pressure and Thrust force of TBM are one of the basic requirements in mechanized boring. In case of incorrect estimation, there is a possibility of face instability, shrinkage of shield and machine stuck, which leads to costs increasing and prolonged completion time of project. The Kani Sib tunnel consist of two sections of rock and alluvium. Due to the presence of underground water, low mechanics rock parameters and high overburden on alluvial section, this section is one of the most dangerous parts of the tunnel. For that reason, in this paper, estimation of face pressure and trust strength in mechanized excavation of the alluvial section has been studied. At first, in order to do this, required face pressure for maintenance was calculated by analytical method and verified by numerical method. After checking the accuracy of selected pressure, the pressure on the shield and thrust force, which includes force applied from face, shield fraction force, disc cutter force and tension force of the shield, was determined.

More than 21 Mt of coal wastes is being produced by Anjir Tangeh coal washing plant during 22 years. Wastes have been deposited in the area without considering of environmental regulations in such an unspoiled area. Moreover, the pervious researches revealed that the pile has a potential of acid mine drainage generation. Therefore, it is essential to present a model for investigating risk of acid mine drainage generation in this pile. This paper presents a probabilistic method to prediction of acid mine drainage is being generated within Anjir Tangeh coal wastes pile. After building a database, pyrite remaining fraction and pH were considered as output data while depth of the wastes, bicarbonate concentration and oxygen fraction were input parameters. Then, the best probability distribution functions were presented on each of input parameters applying Monte Carlo simulation. Also, the best linear statistical relationships between input data and output data were presented. Afterward, the best probability distribution functions of input parameters were inserted to the relationships to find the probability distribution functions of output data. Eventually, the results revealed that the remaining pyrite fraction and pH values were lower than 0. 894 and 4. 6 at a 5% probability level, respectively.

Mining industries have always been a major source of environmental concern for human societies. Coal extraction and processing has a high potential for creating the various types of environmental pollutions, due to its nature. In this research, the Alborz Sharghi coal washing plant in Iran and its effects on the surrounding environment have been investigated using different environmental impact assessment methods. First of all, a list of plant activities and the environmental impacts of these activities was prepared, then the environmental impacts of the plant was measured by modified Folchi matrix, analytical hierarchy process, and rapid impact assessment matrix. Experts opinions showed that “ wastewater from the plant” , “ toxic pollutants” and “ tailings discharge” with scores of 8, 7. 9, and 7. 9, respectively, were the most important impacting factors of the plant. The differences between the results of various EIA methods have always been a challenge in environmental impact assessment. In the present study, it has been attempted to combine the results of different EIA methods using various integration strategies such as Borda, Copeland, Kemeny, Kohler, and the Direct Ranking Strategy (DRS). Using the method of Coupled Environmental Impact Assessment (C-EIA) to combine the different methods, showed that four components of "groundwater", "area landscape", "soil of the area" and "ecology" with about 0. 12 score are the most critical environmental components of the plant, respectively.

In this research, a model is presented to predict the blasting cost by collecting blasting data from six limestone mines in Iran using a nonlinear multivariate regression. This model has a higher correlation coefficient (0. 913) and lower root mean square error (1089) than the linear multivariate regression model, thus indicated a better compliance with the actual blasting costs. In this study, in addition to achieving the blasting cost function using a nonlinear multivariate regression method, a model was proposed for blasting limiting functions including fragmentation, fly rock, and back break. These functions were used as constraints to the particle swarm optimization (PSO) metaheuristic algorithm in order to optimize the blasting cost. Results showed that the spacing, borehole number, and length of holes were 3. 6 m, 462 loops, and 13 m as blasting design parameters, respectively. The mean grading, fly rock, and back break 44 cm, 84. 5 m and 3. 6 m, respectively, were the blasting constraints, and also the blasting cost was calculated to be 6235 Rials per ton. These results showed a 12. 9% reduction in blasting costs, and an optimal control of the adverse consequences of blasting in comparison to the conventional blasting patterns. Keywords: Blasting cost, limestone mines, nonlinear multivariate regression, particle swarm optimization algorithm.

Determining the transition limit from the open pit mining to an underground mining method is one of the key decisions where there is the possibility to mine using both open pit and underground mining methods. Transition level is a depth where open pit mining is the best choice above this depth and underground mining is prioritized below it regarding the technical and economic aspects. In the early-stage mine planning studies, including the determination of transition limit, simplicity, practicality, and quickness of decision making are of the requirements, while achieving the acceptable answers is regarded. In this research, a practical approach based on combining Lerchs and Grossman algorithm and the Equilibrium Stripping Ratio (ESR) method is proposed to determine the transition limit. For this purpose, firstly nested and developing pits are generated with the objective of maximizing the net present value (NPV), then the Incremental Stripping Ratios (ISR) of each of these optimal developing pits are calculated. According to the alternative underground mining method(s), the ESR is calculated and finally, the transition limit will be determined comparing the ISR of these developing pits and the calculated ESR. In the present research, this approach is applied to determine the transition level in an iron ore deposit. To verify the determined transition limit, calculating the cumulative net present value shows that mining by underground method below the determined transition level can increase the NPV by 23% compared to continuing open pit mining.