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.
Exploration
Mojtaba Bazargani Golshan; Mehran Arian; Peyman Afzal; Lili Daneshvar Saein; Mohsen Aleali
Abstract
The aim is to use the Concentration-Volume (C-V) fractal model to identify high-quality parts of coal seams based on sulfur and ash concentrations. In the K1 and K7 coal seams in the North Kochakali coal deposit, 5 and 6 different populations of ash and sulfur content were obtained based on the results. ...
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The aim is to use the Concentration-Volume (C-V) fractal model to identify high-quality parts of coal seams based on sulfur and ash concentrations. In the K1 and K7 coal seams in the North Kochakali coal deposit, 5 and 6 different populations of ash and sulfur content were obtained based on the results. According to this model, sulfur and ash concentrations below 1.81% and 33.1% for the K7 seam, and below 4.46% and 37.1% for the K1 seam, respective base on Russian standard for ash and high sulfur content of North Kochakali coals were considered as appropriate values. In order to identify the high-quality parts of K1 and K7 coal seams, plans at different depths were used based on the C-V fractal model. Plans at different depths suggests that the southern part of the K1 seam and the northern part of the K7 seam have the highest-quality based on sulfur and ash concentrations, which should be considered in the extraction operation. The logratio matrix was used to compare the results of the C-V fractal model with the geological data of pyrite veins and coal ash. This matrix indicates that sulfur content above 3.8% for the K7 seam and above 4.41% for the K1 seam have good and very good correlation with pyritic veins of geological data, respectively. There are good overall accuracy (OA) values in the correlation between parts of the seam with ash concentration above 37.1% and 45.7% for the K1 and K7 seams, respectively, and the coal ash obtained from the geological data.
