Exploitation
Abbas Khajouei Sirjani; Ruqyah Heydari; Ramin Rafiee; Mohammad Amiri Hosseini
Abstract
In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and ...
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In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and disruption in drilling and charging operations in subsequent stages. The objective of this research is to predict and optimize back-break by combining statistical models with the Firefly Algorithm (FA). For this purpose, a database comprising data from 28 blasts in the waste rock section of Gol-e-Gohar Iron Ore Mine No. 1 was compiled. After data collection, the input parameters, including blast hole length, burden, spacing, Stemming, charge per delay, and Number of holes in the last row, were identified and utilized in the modeling process. To predict back-break, modeling was performed using multiple regression analysis. Among the developed models, the Polynomial statistical model with non-integer coefficients model with an adjusted coefficient of determination 0.885 was identified as the best-performing model and was subsequently used as the objective function in the Firefly Algorithm. The optimization process was then carried out using this algorithm. According to the findings of this research, the implementation of the current operational patterns in the mine along with the optimized proposed patterns resulted in a reduction of 4 meters in the average back-break, decreasing it from 7.5 meters in the waste rock section. The results demonstrate that the Firefly Algorithm is a highly effective and reliable tool for model optimization and a more accurate reduction of back-breaks. This approach has the potential to significantly enhance the efficiency of mining operations and reduce operational costs.
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
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The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
M. Ghaedi Ghalini; M. Bahaaddini; M. Amiri Hossaini
Abstract
Estimation of the in-situ block size is known as a key parameter in the characterization of the mechanical properties of rock masses. As the in-situ block size cannot be measured directly, several simplified methods have been developed, where the intrinsic variability of the geometrical features of discontinuities ...
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Estimation of the in-situ block size is known as a key parameter in the characterization of the mechanical properties of rock masses. As the in-situ block size cannot be measured directly, several simplified methods have been developed, where the intrinsic variability of the geometrical features of discontinuities are commonly neglected. This work aims to estimate the in-situ block size distribution (IBSD) using the combined photogrammetry and discrete fracture network (DFN) approaches. To this end, four blasting benches in the Golgohar iron mine No. 1, Sirjan, Iran, are considered as the case studies of this research work. The slope faces are surveyed using the photogrammetry method. Then 3D images are prepared from the generated digital terrain models, and the geometrical characteristics of discontinuities are surveyed. The measured geometrical parameters are statistically analysed, and the joint intensity, the statistical distribution of the orientation, and the fracture trace length are determined. The DFN models are generated, and IBSD for each slope face is determined using the multi-dimensional spacing method. In order to evaluate the validity of the generated DFN models, the geological strength index (GSI) as well as the stereographic distribution of discontinuities in the DFN models are compared against the field measurements. A good agreement has been found between the results of the DFN models and the filed measurements. The results of this work show that the combined photogrammetry and DFN techniques provide a robust, safe, and time-efficient methodology for the estimation of IBSD.
