Prediction Model of Longwall Powered Support Capacity Using Field Monitored Data of a Longwall Panel and Uncertainty-Based Neural Network

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• 作者：Abhiram Kumar Verma ; Kaushal Kishore…
• 关键词：Longwall mining ; Powered support ; Leg pressure ; Bayesian neural network
• 刊名：Geotechnical and Geological Engineering
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：34
• 期：6
• 页码：2033-2052
• 全文大小：2,820 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geotechnical Engineering
  Hydrogeology
  Terrestrial Pollution
  Waste Management and Waste Technology
  Civil Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-1529
• 卷排序：34

文摘

Failure of longwall projects in India is attributed to many factors, one among them is the selection of under-rated capacity of powered support. It is evident with the catastrophic failures at longwall project of Kothadih in 1997, Charcha west in 1989 and Dhemomen in 1998 or increase in the existing capacity of the powered support at Balrampur and Rajendra longwall projects of SECL and PVK and GDK-10A mines of SCCL just after their installation. The mining or geotechnical engineers, researchers, mine management, planners and the government agencies are striving hard since the inception of mechanized longwall technology in Indian subcontinent. Therefore, proper understanding of pressure on longwall support is very much essential for accurate selection of power support for longwall panel. In this paper, Bayesian neural network (BNN) model is developed for estimation of leg pressure and associated uncertainty. The 396 data sets from seven longwall panels are collected which includes depth of working, height of extraction, main roof thickness, face retreat distance, overhung length behind powered support and leg pressure of hydraulic powered support. The leg pressure developed in a hydraulic powered support is a manifestation of the various geo-mining parameters and therefore functional relationship exists between them. The results from BNN are compared with the Levenberg–Marquardt neural network and linear regression and found that BNN applied for prediction of leg pressure perform better than the other two approaches. The developed model can be used to predict the leg pressure and hence powered support capacity for longwall projects.