A Study on Application of Kuz-Ram model to Domestic Open-pit Limestone Mine

Abstract

Considering the applicability of Kuz-Ram model, which has been used extensively for predicting rock fragmentation size distribution by blasting, to domestic open-pit limestone mine, a total of 21 blasting tests have been executed at an open-pit limestone mine in eastern Gangwon of South Korea. A comparative analysis of field measured value and Kuz-Ram predicted value showed that there are a considerable amount of error in the predicted values regardless of application of various correction parameters for rock factor and uniformity factor; up to 56.45% in mean fragmentation size and 37.52% in uniformity index. Also the problem of applying different correction parameters has been derived even though a similar blasting pattern has been adopted for a same blasting bench. The authors therefore suggest that Kuz-Ram model needs to be modified for a proper application to domestic open-pit limestone mine.

Airflow Analysis of a Large-Opening Limestone Mine using Computational Fluid Dynamics

“…To obtain velocity distributions in a mine is difficult because the tunnel walls are very rough, and measuring the air properties is difficult. Only a few studies have provided experimental data for measuring air velocities (Arpa et al, 2008;Sasaki et al, 2008;Xu et al, 2015;Lee et al, 2018). The experimental values, as obtained by Lee et al (2018), are 0.61 m/s and 0.51 m/s for the portal entry and shaft bottom station, respectively.…”

“…Only a few studies have provided experimental data for measuring air velocities (Arpa et al, 2008;Sasaki et al, 2008;Xu et al, 2015;Lee et al, 2018). The experimental values, as obtained by Lee et al (2018), are 0.61 m/s and 0.51 m/s for the portal entry and shaft bottom station, respectively. These values on ANSYS CFX are 0.56 m/s and 0.49 m/s, respectively.…”

“…The results indicated that vortical flow increases MAA; therefore, minimizing vortical flow in the mine tunnel via the blowing duct is crucial. Lee et al (2018) performed an analysis of ventilation efficiency in an underground limestone mine with ventilation shafts where a main fan was working. The results revealed the monitoring velocity and temperature inside the portal, rampway, and ventilation shaft bottom.…”

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

“…In addition, considering traditional data sources, such as surveys and reports, unstructured data sources such as text, speech, and images are increasingly being used to gain insights into geological and geotechnical activities. To extract meaning and insights from these unstructured data sources, techniques such as semantic analytics and correlated machine learning models are being developed, often with the support of Natural Language Processing (NLP) [80], [196]- [199].…”

“…Some study shows that it has contributed to the mining hazards discrimination and classification through a good quality production data [207], [208]. In summary, machine learning (ML) can provide the following advantages in engineering applications [199], [209]- [213]:…”