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A Discrete Event Simulation and Data-Based Framework for Equipment Performance Evaluation in Underground Coal Mining
Date
2021-01-01
Author
Yılmaz, Enver
Erkayaoğlu, Mustafa
Metadata
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© 2021, Society for Mining, Metallurgy & Exploration Inc.Coal is still a part of energy generation worldwide and has a significant role in the sustainable development of countries. The amount of shallow coal deposits is getting scarce, making efficient underground mining operations crucial for both local and global economies. In underground mining operations, longwall mining is a commonly utilized method to extract deep coal deposits. One of the leading engineering goals in longwall mining is increasing the efficiency of the system by investigating the effects of operational factors on equipment. This paper investigates the shearer performance for a longwall top coal caving operation by discrete event simulation that includes modeling the double-drum shearer, belt conveyors, stage loader, and armored face conveyors (AFC) to evaluate system performance by considering the amount of daily coal production of the shearer. As a result, the most influential factor on mine production is determined and a data-based framework is developed. This study suggests a framework for data collection, data analysis, and the interpretation of the operational data to evaluate the primary production unit’s performance. The models developed for the face coal extraction operations in a longwall top coal caving (LTCC) mine have a bi-directional cutting system and can be implemented in other operations readily.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85109262529&origin=inward
https://hdl.handle.net/11511/91375
Journal
Mining, Metallurgy and Exploration
DOI
https://doi.org/10.1007/s42461-021-00455-2
Collections
Department of Mining Engineering, Article
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E. Yılmaz and M. Erkayaoğlu, “A Discrete Event Simulation and Data-Based Framework for Equipment Performance Evaluation in Underground Coal Mining,”
Mining, Metallurgy and Exploration
, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85109262529&origin=inward.