Prediction of blasting-induced fragmentation in Meydook copper mine using empirical, statistical, and mutual information models

Ghaeini, N.; Mousakhani, Mojtaba; Amnieh, Hassan Bakhshandeh; Jafari, Arian

doi:10.1007/s12517-017-3189-4

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Ghaeini et al and Ghaeini Hesarouieh et al used the Mutual Information (MI) method to predict the blasting fragmentation of the Meydook Mine and compared it with the Kuz-Ram empirical model and statistical model. The results show that the MI model has higher accuracy than the Kuz-Ram and statistical models [16,17]. Based on meta-heuristics and machine learning algorithms, Xie et al predicted the rock size distribution in mine blasting using various novel soft computing models [18].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Pan

Zhang

et al. 2022

Minerals

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For San-Xin gold and copper mine, deep blasting large block rate is high resulting in difficulty in transporting the ore out; secondary blasting not only increases blasting costs but is more likely to cause the top and bottom plate of the underground to become loose causing safety hazards. Based on the research background of Sanxin gold and copper mine, deep hole blasting parameters were determined by single-hole, variable-hole pitch, and oblique hole blasting tests, further using the inversion method to determine the optimal deep hole blasting parameters. Meanwhile, the PSO-BP neural network method was used to predict the block rate in deep hole blasting. The results of the study showed that the optimal minimum resistance line was 1.24–1.44 m, which was lower than 1.6–1.8 m in the original blasting design, which was one of the reasons for the higher blasting block rate. In addition, the PSO-BP deep hole blasting fragmentation prediction model predicts the block rate of the optimized blasting parameters and predicted a block rate of 6.83% after the optimization of hole network parameters. Its prediction accuracy is high, and the blasting parameter optimization can effectively reduce the block rate. It can reasonably reduce the rate of large pieces produced by blasting, improve blasting efficiency, and save blasting costs for enterprises. The result has wide applicability and can provide solutions for underground mines that also have problems with blasting large block rate.

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Section: Introductionmentioning

confidence: 99%

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Pan

Zhang

et al. 2022

Minerals

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“…Blasting, as an economical and e cient method for breaking rock and ores, has been widely utilized in mining, civil works, and water conservancy and hydropower projects. Blast fragmentation of muck pile (BFMP) is central to the blast results, directly a ecting the e ciency of the subsequent loading and transportation process and second delineation of ores [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Statistical Estimation of Blast Fragmentation by Applying 3D Laser Scanning to Muck Pile

Liu

Fu-qiang

Wang

et al. 2022

Shock and Vibration

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Run-of-mine fragmentation is an important aspect of mine productivity optimization, as it affects all mine-to-mill processes. The current blasting fragmentation calculation methods do not consider the 3D geometric information. Therefore, their calculation results are imprecise. 3D laser scanning is a technique for extracting the 3D geometric information of an object by constructing a 3D point cloud model, with which extra information on the geometrical characteristics of an object could be captured than with the technique of 2D image processing. In this paper, 3D laser scanning technology was utilized for the calculation of the rock blocks on the surface of a muck pile, and the information about the surface blocks was utilized as the samples for the statistical estimation of blasting fragmentation of muck pile (BFMP). Monte Carlo simulation was utilized as the statistical estimation method for the BFMP. In the lab experiment, results from 2D image processing technique and from 3D laser scanning technique combined with statistical estimate were compared with the physical measurements utilizing a water tank, which show that results with 3D laser scanning are more similar to the physical measurement. Finally, the applicability of 3D laser scanning technology combined with statistical methods to the calculation of blast fragmentation was estimated through field tests in Biesikuduke and Santanghu mine, two open-pit coal mines in Xinjiang Province of western China. Results show that the accuracy of the statistical estimation results of BFMP has a particle size deviation of 1–3 cm.

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State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Zhou,

Zhang,

Qiu

2024

Artif Intell Rev

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The technological difficulties related with blasting operations have become increasingly significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the threats posed by environmental effect of blasting (EEB). To address these challenges, numerous scholars have conducted extensive research employing various assessment techniques with the aim of mitigating risks and preventing the emergence of unfavorable EEB. The occurrence of EEB is prevalent during the excavation of hard rock, and it presents significant hazards to personnel safety, equipment integrity, and operational continuity. Therefore, conducting a systematic review of EEB is of utmost importance as it enables a comprehensive understanding of the contributing factors. Such an understanding plays a vital role in advancing EEB prediction and prevention methods. The careful selection of an appropriate EEB assessment method is a crucial aspect of blasting operations. However, there is a lack of comprehensive discussions on the applications of machine learning (ML) and optimization algorithms (OA) in addressing various EEB. Only a limited number of papers have briefly touched upon this topic. Therefore, the primary objective of this paper is to bridge this gap by conducting an analysis of global trends using Cit-eSpace and VOSviewer software from the year 2000 onwards. It comprehensively explores EEB classification and definition, encompassing air overpressure (AOp), ground vibration, dust, backbreak, flyrock, and rock fragmentation. Furthermore, the paper provides a compendium of the most recent ML and OA prediction techniques used to addresses EEB. Finally, the paper concludes by proposing future directions for exploring innovative approaches that combine data-driven ML techniques with knowledge-based or physicsbased methods. Such integration has the potential to mitigate hazards during blasting operations and reduce the likelihood of unfavorable EEB occurrences.

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Prediction of blasting-induced fragmentation in Meydook copper mine using empirical, statistical, and mutual information models

Cited by 8 publications

References 11 publications

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Statistical Estimation of Blast Fragmentation by Applying 3D Laser Scanning to Muck Pile

State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

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