Mohammad Rezaei; Milad Ghasemi
Abstract
Resource estimation and determining the grade distribution is one of the most important stages in planning and designing the open-pit and underground mines. In this work, a new mythology is used for resource estimation of the Angouran underground mine based on the optimized integration of the indicator ...
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Resource estimation and determining the grade distribution is one of the most important stages in planning and designing the open-pit and underground mines. In this work, a new mythology is used for resource estimation of the Angouran underground mine based on the optimized integration of the indicator kriging (IK), simple kriging (SK), and inverse distance weighted (IDW) methods. For this purpose, waste blocks are first removed from the block model using the IK method. Then the amount of mineral resource is estimated using the SK and IDW methods. Indeed, variograms are developed to estimate the grade of zinc minerals in the three used methods. Variograms analysis in three directions prove that the studied resource is anisotropic. Also the validation results confirm that the correlation coefficients between the measured and estimated zinc values by the SK and IDW methods equal to 0.76 and 0.75, respectively. Knowing this satisfactory result, a 3D model of the resource is prepared using the IK method, in which the ore and waste sections of the Angouran underground mine are separated definitely. According to the above methodology, the calculated resource of the Angouran underground mine using the SK method is achieved 1373962.5 tons with an average grade of 30.11%, whereas the estimated amount of this resource is attained 1349325 tons with an average grade of 31.88% using the IDW approach. The verification results show that the suggested methodology based on the optimized integration of the IK, SK, and IDW methods can be successfully applied for resource modeling and grade estimating of the Angouran underground mine.
Mohammad Rezaei; Navid Nyazyan
Abstract
Rock drilling is one of the most important processes in the mining operations, which involves high costs. Deep knowledge of the drilling conditions and rock mass properties can help the optimum selection of drilling system, precise determination of type and number of drilling equipment, and accurate ...
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Rock drilling is one of the most important processes in the mining operations, which involves high costs. Deep knowledge of the drilling conditions and rock mass properties can help the optimum selection of drilling system, precise determination of type and number of drilling equipment, and accurate prediction of drilling rate. The above process leads to enhance the drilling efficiency and mining productivity. In this work, relationships between the rock the physico-mechanical properties and horizontal drilling rate (HDR) are investigated. For this purpose, HDR is firstly measured during the drilling process at the Malawi marble quarry mine, Islamabad-e-Gharb, Iran. Then core samples are prepared from the representative minor rock blocks to conduct the laboratory tests and evaluate the influence of rock properties on HDR. The experimental results prove that natural density (ρn), dry density (ρd), slake durability index (Id), Schmidt hammer rebound (SHR), compression wave velocity (Vp), point load index (PLI), uniaxial compressive strength (UCS), and modulus of elasticity (E) have inverse relationships with HDR. Conversely, HDR has a direct relationship with porosity (n), water content (Wa), Los Angeles abrasion (LAA), and Poisson ratio (ν). Generally, it is proved that HDR is more associated with the rock's physical properties than the mechanical characteristics. Moreover, sensitivity analysis confirm that n and ρd are the most and least effective variables on HDR. Furthermore, new optimum empirical equations with acceptable accuracy are proposed to predict HDR based on the statistical modeling. Finally, experimental verification analysis confirm the superiority of this study compared to the prior similar studies.
Rock Mechanics
M. Rezaei; M. Asadizadeh
Abstract
Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which ...
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Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especially during the drilling operation in cracked, fractured, and weak rocks. Therefore, some attempts have recently been made to develop the indirect methods, i.e. intelligent predictive models for rock UCS estimation, which require no core preparation and laboratory equipment. This work focuses on the application of new combinations of intelligent techniques including adoptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA), and particle swarm optimization (PSO) in order to predict rock UCS. These models were constructed based on the collected laboratory datasets upon 93 core specimens ranging from weak to very strong rock types. The proposed hybrid model results were compared with each other, and the real data and multiple regression (MR) results. These comparisons were made using coefficient of correlation, mean of square error, mean of absolute error, and variance account for indices. The comparison results proved that the ANFIS-GA combination had a relatively higher accuracy than the ANFIS-PSO combination, and both had a higher capability than the MR model. Furthermore, the ANFIS-GA and ANFIS-PSO model results were completely in accordance with the UCS laboratory test, and they were more accurate than the previous single/hybrid intelligent models. Lastly, a parametric study of the suggested models showed that the density and Schmidt hammer rebound had the highest influence, and porosity had the lowest influence on the output (UCS).
Rock Mechanics
M. Rezaei
Abstract
Estimation of the height of caved and fractured zones above a longwall panel along with the stability conditions of the goaf area are very crucial to determine the abutment stresses, ground subsidence, and face support as well as designing the surrounding gates and intervening pillars. In this work, ...
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Estimation of the height of caved and fractured zones above a longwall panel along with the stability conditions of the goaf area are very crucial to determine the abutment stresses, ground subsidence, and face support as well as designing the surrounding gates and intervening pillars. In this work, the height of caving-fracturing zone above the mined panel is considered as the height of destressed zone (HDZ). The long-term estimation of this height plays a key role in the accurate determination of maximum ground surface subsidence and the amount of transferred loads towards the neighbouring solid sections. This paper presents a new stability analysis model of caved material system in the goaf area. For this aim, a theoretical energy-based model of HDZ determination in long-term condition is developed. Then the stability condition of the caved material system is investigated using the principle of minimum potential energy. On the basis of the actual data gathered from the literature, the unstable time period of the caved material system is also calculated. Moreover, the effects of time- and temperature-related parameters and constant coefficients as well as their inherent relations with HDZ are evaluated. Furthermore, sensitivity analysis shows that the two temperature-related constants material constant and time are the most effective variables in HDZ, and the slope of material hardening is the least effective one. The estimated HDZ and the stability time of the caved materials can be successfully applied to determine the induced stress and the maximum surface subsidence, respectively, due to longwall mining.
