Vol 51, No 01 (2024)


DRAGLINE PERFORMANCE CARE STUDY IN INDIAN COAL MINES

Dasari Appaji#1, K Charan Reddy#2, G Yogendra#2, P saikiran#2,G Srinivasa Rao#2

Abstract

:Draglines have been used solely in the coal mining sector as strippers or coal extractors for several decades now. Given its intrinsic advantages over competitors, this equipment can be used endlessly to produce great production at minimal cost. As a result of aggressive coal production ambitions (up to 10 MT/year), India's vast surface mining operations, such as Jayanth and Bina, are boosting demand for technologies capable of rapidly removing massive amounts of overburden. As a result, shovel mining has been replaced by dragline mining as the primary method for overburden excavation in surface coal mining. Coal India Limited (CIL) recently introduced standardized draglines in two different diameters for their mines: 10/70 and 24/96. The dragline runs seven days a week and is critical to the operation of most mines. In many coal mines, the dragline is the only primary extraction apparatus, and the mine's production is completely dependent on its good operation. As a result of these considerations, dragline design is the only area that necessitates a greater emphasis on developing components with greater dependability and predictability; this allows for the scheduling of component replacements and restorations during periods with minimal negative effects on the overall mining operation. Before installing draglines in mines, numerous factors must be considered to guarantee that the proper diameter is chosen. The operation and mechanisms of the heavy earth moving machine dragline (HEMM) have been detailed.

Keywords:

:Dragline, Minimum, Mines, Installation, Machinery.


Full Text:

PDF


References


1. Acarog1u, 0., Ozdemir, L., & Asbury, B. (2008). A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnelling and Underground Space Technology, 23(5), 600--608. doi:l0.1016/j.tust.2007.11.003 2. Baafi, E. V, Mirabediny, H., &Whitchurch, K. (1995). A Simulation Model for Selecting Suitable Digging Method for a Dragline Operation. APCOM XXV, 9-14. 3. Coal production in the united states -an historical overview (pp. 1-19). Retrieved from http://www.eia.gov/cneaf/coal/page/coalyroduction_review.pdf British Petroleum (BP). (2012). 4. Drag1ine dynamic modelling for efficient excavation. International Journal of Mining, Reclamation and Environment, 2 3( 1 ), 4-20. doi:10.1080/17480930802091166 Dessureault, S. (2007). 5. Drezner, Z., Turel, 0., &Zerom, D. (2010). A Modified Kolmogorov-Smirnov Test for Normality. Communications in Statistics-Simulation and Computation, 39(4), 693-704. doi: 10.1080/03610911003615816 Drives & Controls Services. (2003). 6. Exxon Mobil. (2013). The outlook for energy: A view to 2040. Gibbons, J.D., & Chakraborti, S. (2010). Nonparametric Statistical Inference (Fifth.). 7. Graham, J. W. (2009). Missing data analysis: making it work in the real world. Annual review of psychology, 60,549-76. doi:10.1146/annurev.psych.58.110405.085530 8. Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Prentice Hall. 9. Kizil, M. (2010). improving Dragline Productivity using a Diggability index as an indicator. Society for Mining, Metallurgy & Exploration, Inc. (SME), 134-141. 10. Little, R. J. A. (1992). Regression With Missing X's: A Review. Journal~~ American Statistical 11. Shapiro, S S, Wilk, M. B., & Chen, H. J. (1968). A Comparative Study of Yarious Tests for Normality, 63(324), 1343-1372. 12. Zhu, Y. Q., & Yin, Z. D. (2008). A new evaluation system for energy saving based on energy efficiency and loss ratio. International Conference on Sustainable Energy Technologies (ICSET), Singapore: IEEE, 121-124.

Refbacks

  • There are currently no refbacks.