Exploitation
Ali Nemati vardni; Masoud Monjezi; Hasel Amini Khoshalan; Jafar Hamidi Khademi; Mojtaba Rezakhah
Abstract
Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt ...
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Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt hammer rebound hardness. To achieve this, a dataset comprising the drilling operations of 85 blastholes from the Sungun copper mine in Iran was prepared and analyzed using statistical and intelligent methods. Multivariate regression analysis and artificial neural networks developed in Python, utilizing optimization algorithms such as gradient descent, stochastic gradient descent, and adaptive moment estimation, were applied to predict the penetration rate of drill bits in this study. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) served as performance indicators to evaluate the methods employed. Among these, the adaptive moment estimation (Adam)-based model exhibited superior performance compared to alternative models, achieving values of R² = 0.96, MAE = 4.55, and RMSE = 4.30. Furthermore, the sensitivity analysis revealed that mining rock mass rating is the most influential factor on the rate of penetration, while thrust pressure has the least impact.
Rock Mechanics
Sajjad Khalili; Masoud Monjezi; Hasel Amini Khoshalan; Amir Saghat foroush
Abstract
Determining the appropriate blasting pattern is important to prevent any damage to the tunnel perimeter in conventional tunneling by blasting operation in hard rocks. In this research work, the LS-DYNA software and numerical finite element method (FEM) are used for simulation of the blasting process ...
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Determining the appropriate blasting pattern is important to prevent any damage to the tunnel perimeter in conventional tunneling by blasting operation in hard rocks. In this research work, the LS-DYNA software and numerical finite element method (FEM) are used for simulation of the blasting process in the Miyaneh-Ardabil railway tunnel. For this aim, the strong explosive model and nonlinear kinematic plastic material model are considered. Furthermore, the parameters required for the Johnson-Holmquist behavioral model are based on the Johnson-Holmquist-Ceramic material model relationships and are determined for the andesitic rock mass around studied tunnel. The model geometry is designed using AUTOCAD software and Hyper-mesh software is applied for meshing simulation. After introducing elements properties and material behavioral models and applying control and output parameters in LS-PrePost software, the modeling process is performed by LS-DYNA software. Different patterns of blastholes including 66, 23, and 19 holes, with diameters of 40 and 51 mm, and depths of 3 to 3.8 m are investigated by three-dimensional FEM. The borehole pressure caused by the ammonium nitrate-fuel oil (ANFO) detonation is considered based on the Jones-Wilkins-Lee (JWL) equation of state in the LS-DYNA software. The outer boundaries of the model are considered non-reflective to prevent the wave’s return. The results showed that LS-DYNA software can efficiently simulate the blasting process. Moreover, the post-failure rate of the blasting is reduced by more than 30% using the main charge with less explosive power and reducing the distance and diameter of contour holes.
J. Shakeri; H. Amini Khoshalan; H. Dehghani; M. Bascompta; K. Onyelowe
Abstract
In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis ...
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In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.
