Geochemical investigation and statistical analysis on rare earth elements in Lakehsiyah deposit, Bafq district

Rahimi

et al. 2021

Preprint

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In the United States, an unexpected and severe increase in coal miners’ lung diseases in the late 1990s prompted researchers to investigate the causes of the disease resurgence. This study aims to scrutinize the effects of various mining parameters, including coal rank, mine size, mining method, coal seam height, and geographical location on the prevalence of CWP in surface and underground coal mines. A comprehensive dataset was created using the U.S. Mine Safety and Health Administration (MSHA) Employment and Accident/Injury databases. The information was merged based on the mine ID by utilizing SQL data management software. A total number of 123,643 mine-year observations were included in the statistical analysis. Generalized Estimating Equation (GEE) model was used to conduct a statistical analysis on a total of 29,707, and 32,643 mine-year observations for underground and surface coal mines, respectively. The results of the econometrics approach revealed that coal workers in underground coal mines are at a greater risk of CWP comparing to those of surface coal operations. Furthermore, underground coal mines in the Appalachia and Interior regions are at a higher risk of CWP prevalence than the Western region. Surface coal mines in the Appalachian coal region are more susceptible to CWP than miners in the Western region. The analysis also indicated that coal workers working in smaller mines are more vulnerable to CWP than those in large mine sizes. Furthermore, coal workers in thin-seam underground mine operations are more likely to develop CWP.

Section: Descriptive Statisticsmentioning

confidence: 99%

An Analysis of Contributing Mining Factors in Coal Workers’ Pneumoconiosis Prevalence in the United States Coal Mines, 1986-2018

Rahimi

et al. 2021

Preprint

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American Journal of Engineering and Applied Sciences

“…Mining is the most fundamental industry in the supply chain of resources for manufacturing, technology development and construction. Modern life depends on the exploration and extraction of minerals, such as metals, non-metals, aggregates, coal and strategic elements like REEs (Rahimi et al, 2016;Shekarian et al, 2017). The population growth exacerbates the demand for resource extraction (Futurist, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Even coal remains the primary source of energy up to 2050 (Cliff et al, 2019; National Academies of Sciences, Engineering and Medicine, 2018). The mineral extraction, however, is challenging due to the uncertainty in ore reserve estimation, unknown nature of in situ rock, supplying energy for the operation, commodity price volatility, dangerous working conditions for health and safety of miners and environmental concerns (Rahimi et al, 2016;Fairhurst, 2017;Carvalho, 2017;Saxena, 2017). Recent advances in technology have been facilitated the mining industry to overcome these challenges with remote sensing, three-dimensional ore reserve simulation, remote automated machinery, highly efficient excavators and real-time data monitoring (Saxena, 2017;National Research Council, 1992;Sabins, 1999;Humphreys, 2001).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

Molaei

Rahimi

Siavoshi

et al. 2020

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The integration of computer-based technologies interacting with industrial machines or home appliances through an interconnected network, for teleoperation, workflow control, switching to autonomous mode, or collecting data automatically using a variety of sensors, is known as Internet of Things (IoT). When applied inside an industrial context, it is possible to immediately benefit from the analytics obtained, contributing to process optimization, machine health, the safety of workers and asset management. IoT can assist real-time platforms in remotely monitoring and operating a complex production system with minimal intervention of humans. Hence it can be beneficial for hazardous industries, such as mining, by increasing the safety of personnel and equipment while reducing operation costs. An ideal smart automated mine could potentially be achievable by gradually taking advantage of IoT. Currently, different sensors are used in mine-related activities, such as geophones in exploration and blast control, piezometers in dewatering and toxic gas detectors in working frontlines. However, a fully integrated automated system is challenging in practice due to infrastructural limitations in communication, data management and storage. Moreover, the tendency of mining companies to continue with traditional methods instead of relying on untested novel techniques decelerates this progress. In this study, the adaptability of the mining industry to IoT systems and its current development is reviewed. Significant challenges of this progress are investigated and recommendations to develop a comprehensive model suited for different mining sections such as exploration, operation and safety considering flexible technologies such as Wireless Sensor Networks and the introduction of Global Data Management.

American Journal of Engineering and Applied Sciences

“…(Joseph, 1961). Moreover, there are some elements like trace and rare earth elements into the natural bitumen and oil sands, which makes them more valuable as a by-product (Tsoy, 2015;Rahimi et al, 2016;Shekarian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

New Approach in Application of the AHP–Fuzzy TOPSIS Method in Mineral Potential Mapping of the Natural Bitumen (Gilsonite): A Case Study from the Gilan-e-Gharb Block, the Kermanshah, West of Iran

Rahimi¹,

Shekarian²,

Farahani³

et al. 2020

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The Gilan-e-Gharb block is known as a prospective area for hydrocarbon resources in the form of oil, gas in deep potential and natural bitumen (Gilsonite) on the surface. Natural bitumen is not clearly detectable by geochemical or geophysical methods. Hereupon, identifying high potential areas for further exploration, attempted with the help of AHP-Fuzzy and TOPSIS methods. The comprehensive database of geological and geostructural records, satellite imagery analysis by remote sensing and mine indexes counted as the inputs for this method. First, the lithological unit as the main mineralization hosts determined with respect to the dominant geological structures as a factor of controlling natural bitumen placement (fold, fracture and faults) in the Gilan-e-Gharb block. The Gachsaran, Asmari, Pabdeh and Gurpi Formations identified as the most important lithological units for mineralization. Placement and distribution of natural bitumen mineralization in the form of mine indexes are added to the geology database. Finally, we assigned appropriate weights to applied information layers using Analytical Hierarchy Processing (AHP) based on knowledgeable information and field studies to synthesize exploratory data. Then, we used the FTOPSIS method to define the Positive Ideal Solution (FPIS, A+) that allows maximizing the beneficial characteristics and minimizing the impediment characteristics and the Negative Ideal Solution (FNIS, A-) that minimizes the beneficial characteristics and maximizes the impediment characteristics. This method, as a new approach in the exploration of minerals with a shortage of data, is applicable to other mineral deposits.