فصلنامه مهندسی معدن
پیاپی 44 (پاییز 1398)

Methane is one of the gases emitted from coal seams, which is released into atmosphere through ventilation operation. The emission of this gas causes environmental problems and losing of energy resources. According to previous studies, several billion cubic meters of methane annually are emitted from coal mines in the world. Currently, methane drainage operation is done in many coal mines in the world and the drainages methane was collected by special methods and transported by pipelines to the surface. This gas was used for power generation, sales, town gas and industrial use. Tabas mechanized coal mine extract by longwall mining. In this mine coal seam gas content was varied from 10 ton per cubic meters in depth of 100 m to 20 ton per cubic meters in depth of 600 m. This condition caused that methane drainage operation is done in this mine. The aim of this paper is feasibility study of methane drainage in Tabas coal mine for power generation. Therefore, by considering the investment and operating costs as well as the income from the sale of gas to the power plant, the results has been shown that methane drainage for power generation is economic. However, methane drainage operation in Tabas coal mine, in addition to its energy efficiency and methane production as a fuel, reduce greenhouse gas emission and outburst potential 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.

Rock excavation using drill and blast method is commonly used in mining, quarrying and tunneling world-wide. Although, drill and blast method has witnessed significant technological advancements, it has inherent disadvantage of deteriorating surrounding rock mass due to 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 the underestimation of 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 varied damage Factor D. The results obtained by the proposed solution indicate that, the effects of the alteration of rock mass properties in the damaged zone may be considerable.

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 and mineral industries are one of the main sources of environmental concerns for human societies. The process of mining and mineral processing of coal due to its nature and mechanism, has a high potential of creating different types of environmental pollutions. In this study, the Alborz Sharghi coal washing plant in Iran and its effects on the environment have been investigated using different environmental impact assessment methods (EIA). Initially, a list of plant activities and the environmental components have been prepared and after that three EIA methods include modified Folchi, AHP and RIAM matrix, have been used. Average results of expert suggestions show that factory waste water, toxic gases ans leaching from tailings with the score 8,7.9 and 7.9 respectively are the most hurmful activities of the company. The variety of the results of different EIA methods has always been one of the most significant challenges of the Environmental impact assessment. In current study, the results of various EIA methods have been coupled with each other by using some integration strategies like Borda, Copeland, Kemeny and Kohler. A novel strategy called DRS (Direct Ranking Strategy) have been developed in current research. The use of coupled environmental impact assessment (C-EIA) method to aggregate various EIA methods showed that the four components of "Groundwater", "Landscape", "Soil of the area" and "Ecology" are respectively the most critical environmental components of the Alborz sharghi coal washing plant.

The prediction and optimization of blasting cost to achieve optimal fragmentation is significant, considering the control of the adverse consequences of the blast. In this study, by collecting blasting data in six limestone mines in Iran, a model was developed to predict the cost of blast using nonlinear multivariate regression. Compared to linear regression model, this model has a higher correlation coefficient (0.913) and root mean square error (1089) and in comparison, with the linear model, the nonlinear model shows a better match with the actual cost of the blast. Based on sensitivity analysis, spacing and number of holes had the highest and the least effect on the cost model of the blast, respectively. In addition, in this study along with achieving the blast cost function, the restrictive functions of the blast include fragmentation, fly rock and back break was modeled using nonlinear multivariate regression method, and these functions as inputs in the metaheuristic algorithm of Particles Swarm Optimization (PSO) were used to optimize the cost of blast. Using this method of spacing, the number and length of holes as design parameters of blast are 3.6 meters, 462 loops, and 13 meters respectively and fragmentation fly rock and back break as blasting limitations are 44 cm, 84.5 meters and 3.6 meters and blasting cost was 6235 Rials per ton, which results in a 12.9 percent reduction in the cost of blast and optimal control of the adverse consequences of the blast.

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.