نشریه مهندسی منابع معدنی
سال دوم شماره 3 (پاییز 1396)

The importance of caverns makes it necessary to create underground structures in the rocky environment. However, in designing these structures the depth of the overburden is mostly considered, and the dynamic mechanical behavior of rocks in dynamic loading is less taken into account. This is while the mechanical properties of rocks such as compressive strengths is also changed due to the strain rate and confining stress. In this paper, the role of rock dynamics in the framework of the safe design of caverns was considered. In the following, the effect of microstructure properties on the mechanical behavior of rock in quasi-static and dynamic loading was studied, and experiments suggested by ISRM were conducted. The rate of compressive strength increase by changing the loading type, strain rate and confining stress in two marble samples with different microstructures was investigated. The rock strength increase factor with confining stress for marbles at a depth of 85 m was also determined being 1.15 in a quasi-static loading, and 1.65 in dynamic loading, which clarifies that there is a direct relationship between rock strength increase factor and the depth of deployment when strain rate is applied.

Accurate estimation of ore deposit grade plays an important role for mine planning n .and evaluation. There can be seen problems associated with some conventional methods such as Kriging for grade estimation, this paper investigates with the effectiveness of intelligent estimators such as neural network and fuzzy logic for grade estimation in Shahrak Iron ore deposit near Bijar, Kurdestan province, Iran. For this purpose, data from 9 boreholes were used for reserve estimation process. In fuzzy logic method, the data are first clustered using Fuzzy c-means clustering (FCMC) technique followed by the grade estimation done at the center of each cluster. Based on such data and an appropriate fuzzy grade control, it is possible to estimate grade at the considered points. In neural network, first a few well data are chosen as a set of data for validation and the rest of boreholes are considered as training data for the assessment of the designed network. After training the neural network, the accuracy of estimated validation data was found to be in order of 80%. The results shows that neural networks had better performance than other method.

Producing more fragmented rock by blasting requires increased costs in drilling and blasting operations. On the other hand, it leads to a reduction of loading, haulage operation and no need for secondary blasting. In this study, the effective parameters in machinary efficiency of Sarcheshmeh Copper Mine was explored. For this purpose, the ratio parameter of length to width of the blast block (RLW) and the Loading parameters including Operation(O1), Failure(F1), Ready (R1), Movement of loading machine (M1), were introduced, measured and used. The blasing block's ratio of length to width (RLW) parameter is presented in most of the proposed models. This parameter has been effective in efficiency of loading machinary. The high correlation coefficient of a loading cycle of machine’s bucket (C1) with the independent variables shows that this variable is more affected by the fragmentaion and the dimensions of blasted blocks. The best and the worst of a loading cycle of machine’s bucket (C1) are equal to 14.38 and 51.15 seconds in Sarcheshmeh Copper minefor which (D50) corresponds to 3.02 and 10.39, cm respectively and (D80) equals to 6.69 and 24.49 cm, respectively. The Correlation coefficient of 0.8 for specific loading (S1) indicates the high influence of fragmentation caused by blasting operations on specific loading (S1).

Prioritising mines for investment is one of the major issues in mining economic. In this kind of problem, mines are ranked according to the conditions and characteristics of mines like value of mine reserve, production capacity of mines etc.. Based on this ranking, investment priority for each mines determined. In this study, priority investment for oxidized copper mines in Semnan province has been found using the TOPSIS method, a multi-criteria decision-making methods. TOPSIS method is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. In this study, 12 different criteria, and 33 copper mines in Semnan province were considered for prioritization. The decision criteria were weighed by surveying of experts in the field and then the importance of each factor have been determined using AHP method. Finally, using TOPSIS method, Chah Mosa, Kolut and Chah Farakh copper mines are ranked as first, second and third priority for investment, respectively. Also, sensitivity analysis is done to find the weight of criteria which are the most effective in prioritizing mines.

The study area is located in 90 km N-E Ahwaz and 20 km S-E Masjed Soleyman. Long-term weather statistics of the region's meteorological stations indicates that the region with a mean annual precipitation of 464.8 mm, an average annual temperature of 25.3 ° C and a relative humidity of 29% is a region with a semi-arid climate according to the Demartenne classification, Most rainfall occurs in winter. Formations exposed in this area in order of the oldest to new ones include the present-day sediments, the sandstone-Marni Aghajari formation, limestone formations, Marni Mishan, Gachsaran Formation, Asmari limestone formation, respectively. Wells are the main sources of water supply in the region. In this paper, the aquifer of Golgir Plain was studied using GIS software to prepare the criterion maps, to weigh them and combine them. Preparation of these maps was done using ArcGIS 9.2 software package and fuzzy modeling was done using MATLAB software. ArcGIS software index has been selected to implement the overlapping model. For fuzzy integration, due to its high ability work with Raster data as a matrix and also its simple programming, the MATLAB software was used as well as PCI Geomatica software is used. Finally, eight intrinsic vulnerability maps are obtained. For each vulnerability map, correlation between vulnerability index and nitrate concentration have been calculated. This correlation is determined by Spearman correlation coefficient.

The mining industry and related disciplines play an important role in economic and social components of sustainable development (SD) by generating employment and wealth. However, in regard to the environmental component there are a few criticisms. Also, by taking the growth in demand for mineral resources into account the mining industry is facing some challenges, e.g. minerals and metals recycling, conservation of environment and the social related issues. Mining industry needs to design convenient management tools for the protection of environment in order to continue ply its vital role, using clean technologies and also by training the required workforce. In fact sustainable development in mining industry helps to achieve economic and social progress in country. In developing countries, in particular Iran that benefits from the existence of diverse mineral resources, if SD principals is not considered the industry not only will face problems in its progress but also encounter several social and economic difficulties. In the present study, the main goals are focused on the mining industry and mineral resources roles in SD and the challenges of mining and mineral industry to reach a sustainable status are discussed. Furthermore, some mines that have been faced to SD challenges are investigated as case studies. Finally, a general framework for companies that pursue sustainable activity is presented.