Optimisation of Support Parameters in Mining Terrain using Artificial Intelegent Techniques

Kashyap, Sudhir Kumar (2012) Optimisation of Support Parameters in Mining Terrain using Artificial Intelegent Techniques. PhD thesis.



This dissertation describes work in the area of Artificial Intelligence technique in underground mine support system. India has a large reserve of coal as compared to other fuel
energy sources.Exploitation of coal with full safety has been a challenging job since years.Ground control operation in underground mine is an imprecise work as we are dealing with a material produced by nature.Behaviour of soil and rock in mine during excavation can hardly be predicted with the existing knowledge.Due to this reason roof falls continue to remain the single largest killer.As many as 61% of the incidences, which is 28.5% of total fatalities are due to roof fall.Roof fall, coal bumps and massive pillar failure in coal mines represent serious ground control problem resulting reduction in coal mine safety. Mine supporting system has greater role to play in preventing roof fall accidents.Whenever falls have taken place either no support was provided or the supports were inadequate in capacity and improperly set.During extraction of pillar in galleries roof are supported with roof bolts as well as standing support like prop, cog, chock etc. depending upon their inbuilt load.Under this condition, till date we have been using empirical approaches to mine support design. Consequently, expert knowledge can have a greater role to play in avoidance of accident using accurate measurement optimization of various support parameters and analysis of data a prediction based on previous results using Artificial Intelligence techniques.In the current research mainly three techniques i.e.Artificial Neural Network, Fuzzy Logic andrule based technique and their hybridization have been used for finding the parametric values required during the prop installation in underground mines.

Item Type:Thesis (PhD)
Uncontrolled Keywords:Artificial Neural Network,ANN-Fuzzy,RBNF
Subjects:Engineering and Technology > Mechanical Engineering > Machine Design
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:4439
Deposited By:Hemanta Biswal
Deposited On:07 May 2013 17:22
Last Modified:07 May 2013 17:22
Supervisor(s):Parhi, D R and Sinha, A

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