F. Khorram; O. Asghari; H. Memarian; A. Hoseein Morshedy; X. M. Emery
Abstract
The key input parameters for mine planning and all subsequent mining activities is based on the block models. The block size should take into account for the geological heterogeneity and the grade variability across the deposit. Providing grade models of smaller blocks is more complex and costly than ...
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The key input parameters for mine planning and all subsequent mining activities is based on the block models. The block size should take into account for the geological heterogeneity and the grade variability across the deposit. Providing grade models of smaller blocks is more complex and costly than larger blocks, but larger sizes cannot represent areas with high spatial variability accurately. Hence, a unique block size is not an optimal solution for modeling a mine site. This paper presented a novel algorithm to create an adaptive block model with locally varying block sizes aiming to control dilution and ore loss in Sungun porphyry copper deposit of Iran with a complex geometry characterized by multiple dikes. Three grade block models with different block sizes and simulated by direct block simulation are the inputs of algorithm. The output is a merged block model, assigning the smaller blocks to the complex zones, such as ore-waste boundaries, and larger blocks to the continuous and homogeneous zones of the ore body. The presented algorithm is capable to provide an accurate spatial distribution model with a fewer number of blocks in comparison to the traditional block modeling concepts.
Gh. Khandouzi; H. Memarian; M. H. Khosravi
Abstract
The dynamic fracture characteristics of rock specimens play an important role in analyzing the fracture issues such as blasting, hydraulic fracturing, and design of supports. Several experimental methods have been developed for determining the dynamic fracture properties of the rock samples. However, ...
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The dynamic fracture characteristics of rock specimens play an important role in analyzing the fracture issues such as blasting, hydraulic fracturing, and design of supports. Several experimental methods have been developed for determining the dynamic fracture properties of the rock samples. However, many used setups have been manufactured for metal specimens, and are not suitable and efficient for rocks. In this work, a new technique is developed to measure the dynamic fracture toughness of rock samples and fracture energy by modifying the drop weight test machine. The idea of wave transmission bar from the Hopkinson pressure bar test is applied to drop weight test. The intact samples of limestone are tested using the modified machine, and the results obtained are analyzed. The results indicate that the dynamic fracture toughness and dynamic fracture energy have a direct linear relationship with the loading rate. The dynamic fracture toughness and dynamic fracture energy of limestone core specimens under the loading rates of 0.12-0.56kN/µS are measured between 9.6-18.51MPa√m and 1249.73-4646.08J/m2, respectively. In order to verify the experimental results, a series of numerical simulation are conducted in the ABAQUS software. Comparison of the results show a good agreement where the difference between the numerical and experimental outputs is less than 4%. It can be concluded that the new technique on modifying the drop weight test can be applicable for measurement of the dynamic behavior of rock samples. However, more tests on different rock types are recommended for confirmation of the application of the developed technique for a wider range of rocks.
Rock Mechanics
R. Shafiei Ganjeh; H. Memarian; M. H. Khosravi; M. Mojarab
Abstract
Dynamic slope stability in open-pit mines still remains a challenging task in the computational mining design. Earthquake and blasting are two significant sources of dynamic loads that can cause many damages to open-pit mines in active seismic areas and during exploitation cycles. In this work, the effects ...
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Dynamic slope stability in open-pit mines still remains a challenging task in the computational mining design. Earthquake and blasting are two significant sources of dynamic loads that can cause many damages to open-pit mines in active seismic areas and during exploitation cycles. In this work, the effects of earthquake and blasting on the stability of the NW slope of Chadormalu mine are compared by a numerical modeling method. The dynamic results show that the maximum displacement under earthquake and blasting loads within the slope are 844 mm and 146 mm, respectively. According to the shear strain results, both the earthquake and blasting waveforms are destructive, while the earthquake waveforms cause more damages to the slope. Moreover, the deterministic and probabilistic seismic hazard analyses are carried out to assess the seismicity of the mine area. The experimental results indicate that the maximum values for the vertical and horizontal accelerations are 0.55 g and 0.75 g, respectively. The maximum calculated acceleration is then scaled to the selected earthquake accelerograms. In order to show the effective impact of the established scale, the model is executed using the original accelerograms. The results obtained show that the established scale prevents overestimation and underestimation of the displacement and strain. Therefore, applying scaled accelerograms in a dynamic slope stability analysis in mine slopes leads to more reliable and robust results. The overall results show that a strong earthquake causes plenty of damages to the slope, and consequently, interrupts the mining cycle. Hence, the seismic study and dynamic slope stability should be considered as a part of the computational mining design.
Rock Mechanics
M. T. Hamzaban; H. Memarian; J. Rostami
Abstract
Rock abrasivity is an essential factor for selecting cutting tools, estimating tool wear and life, and ultimately, matching various mechanized excavation systems with a given geologic condition. It also assists engineers to determine economic limits of different cutting tools and machines used in civil ...
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Rock abrasivity is an essential factor for selecting cutting tools, estimating tool wear and life, and ultimately, matching various mechanized excavation systems with a given geologic condition. It also assists engineers to determine economic limits of different cutting tools and machines used in civil and mining projects. The Cerchar abrasion test is a simple and most widely used method for rock abrasivity assessments. However, it has some shortcomings to describe the steel-rock interaction during the cutting process. In this work, two new parameters are used to describe the pin-rock interaction in the Cerchar abrasion test and to evaluate the efficiency of the rock scratching process. A set of 41 different rock samples are tested by a newly developed testing device. The device provides a more precise control of the testing operational parameters, and measures the applied frictional force on the pin and its horizontal and vertical displacements on the sample surface. The results obtained are used to calculate the Modified Cerchar Abrasion Index (MCAI) and the Scratch Energy Index (SEi), as two newly developed parameters. The accuracy of the calculated parameters is discussed. Our investigations show that MCAI has closer correlations with rock mechanical parameters than CAI, and therefore, has a higher potential to estimate the rock cutting tool wear in tunneling applications. Also SEi shows sensible correlations with sample hardness and mechanical properties. The results obtained show that SEi can be used to compare the efficiency of various pin hardnesses to create scratches on various rock samples, and could be used as a determinative parameter in selecting the cutting tool hardness.
Amir Mollajan; Hossein Memarian; Behzad Tokhmechi
Abstract
Detection of Oil-Water Contacts (OWCs) is one of the primary tasks before evaluation of reservoir’s hydrocarbon in place, determining net pay zones and suitable depths for perforation operation. This paper introduces Bayesian decision making tool as an effective technique in OWC detecting using ...
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Detection of Oil-Water Contacts (OWCs) is one of the primary tasks before evaluation of reservoir’s hydrocarbon in place, determining net pay zones and suitable depths for perforation operation. This paper introduces Bayesian decision making tool as an effective technique in OWC detecting using wire line logs. To compare strengths of the suggested method in detecting OWC with conventional one, the same database was used. Proposed method was applied to wire line logs in three wells of a carbonate reservoir in an oil field of the southwestern Iran and its results have been evaluated by well testing results. Results indicate that the usage of Bayesian method in detecting OWC is more accurate than conventional method and may improve the results about 5% on average. In addition, using this method, any variation of water saturation (Sw) log and reservoir fluid types may be detectable.
F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh
Abstract
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...
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In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock samples in all images. Neural network used as an intelligent tool for ore grade estimation and the features of every image were combined with weighted average method. In order to feature dimensional decrease, principal component analysis method was used. Six principal components, which were extracted from the feature vectors, captured 90.661% of the total feature variance. Components were used as the input to neural network and four grade attributes of limestone (CaCO3, Al2O3, Fe2O3 and MgCO3) were used as the output. The root of mean squared error between the observed values and the model estimated values for the test data set are 6.378, 4.847, 0.1513 and 0.0284, the R2 values are 0.7852, 0.8663, 0.7591and 0.8094 for the mentioned chemical composition respectively. The magnitude of R2 indicates the correlation between actual and estimated data. Therefore, it can be inferred that the model can successfully estimate the limestone chemical compositions percentage.
