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%.
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi; Seyyed Ali Hashemi
Abstract
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with ...
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Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications.
Exploitation
Alireza Afradi; Arash Ebrahimabadi
Abstract
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised ...
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Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised the input parameters such as Burden (m), spacing (m), powder factor (kg/m³), and stemming (m), with fragmentation (cm) as the output parameter. The analysis of these datasets was conducted using the Ant Lion Optimizer (ALO) and Crow Search Algorithm (CSA) methodologies. To assess the predictive models' accuracy, metrics including the coefficient of determination (R²), Variance Accounted For (VAF), and Root Mean Square Error (RMSE) were employed. The application of ALO and CSA to the database yielded results indicating that for ALO, R² = 0.99, RMSE = 0.005, and VAF (%) = 99.38, while for CSA, R² = 0.98, RMSE = 0.02, and VAF (%) = 98.11. Ultimately, the findings suggest that the predictive models yield satisfactory results, with ALO demonstrating a greater level of precision.
Exploitation
Sri Chandrahas; Bhanwar Singh Choudhary; MS Venkataramayya; Yewuhalashet Fissha; Blessing Olamide Taiwo
Abstract
To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An ...
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To conducting efficient blasting operations, one needs to analyze the bench geology, structural and dimensional parameters to obtain the required optimum fragmentation with minimum amount of ground vibration. Joints presence causes difficulty during drilling and subsequent rock breakage mechanism. An idea on joints density will give an idea on deciding with column charging in-terms of decking-stemming and firing patterns. The goal of the research is to develop a hybrid algorithm model to predict joints width and joint angle. In order to achieve the task, advanced softwares, machine learning models and a field data tests were used in this study.
Exploitation
R. Shamsi; M. S. Amini; H. Dehghani; M. Bascompta; B. Jodeiri Shokri; Sh. Entezam
Abstract
This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were ...
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This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were collected from the mine. Then, using GEP, a series of intelligent equations were proposed to predict flyrock distance. The best GEP equation was selected based on some well-established statistical indices in the next stage. The coefficient of determination for training and testing datasets of the GEP equation were 0.890 and 0.798, respectively. The model obtained from the GEP method was then optimized using teaching– learning-based optimization algorithm (TLBO). Based on the results, the correlation coefficient of training and testing data increased to 91% and 89%, which increased the accuracy of the Equation. This new intelligent equation could forecast flyrock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues.
