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Study on Optimization of an iron ore production scale

Author: ZhangCheng
Tutor: XiongZhengMing
School: Jiangxi University of Technology
Course: Mining engineering
Keywords: Metal mines Rational Production Scale Intelligent System Data EnvelopmentAnalysis
CLC: TD861.1
Type: Master's thesis
Year: 2013
Downloads: 15
Quote: 0
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Abstract


Surface Mining has many advantages, such as the low initial construction cost, putting intoproduction quickly, good safety and efficiency, high recycling rate of resources, etc. An Anhuimine choose Surface Mining in its early stages of construction. With the increase of surface mineservice-life and mining depth gradually, surface mining will face more difficult technology,unreasonable economy and many other problems. So transition from surface mining tounderground mining is the only way, and that’s a very important factor to be determined forproduction scale after having stable production by this way.With the background of researching the optimization for rational production scale ofunderground metallic mining instead of surface mining in an Anhui Mine; the basement ofreviewing literature of rational mining production scales at home and abroad; the using ofintelligent systematic theories and methods, the thesis makes a systematic and deep research forthe rational production scale by underground metallic mining instead of surface mining. Thefollowings are the researching contents and conclusion.(1) Systematic introduction and assessment of determination methods of the rationalproduction scale by underground metallic mining transited from surface mining at home andabroad now.(2) Combined detailed production background in an Anhui Mine, the internal and externalfactors of influencing mining enterprises’ production scales is stated systematically, and apreliminary assessment is made for all kinds of factors which influence this mine.(3) The construction is based on the intelligent predication system of BP Artificial NeuralNetwork for rational mining production scale, and the systematic principle and constructure areanalyzed in detail, especially the constructure and learning algorithm which is the corecomponents of BP Neural Network subsystem.(4) The relevant production materials of35mature-production mines have been gathered.Efficient filter of these mining materials by using DEA is made to get rid of the useless oftechnologies and mines. The result is very good to learning intelligent system by using thesemining materials, and the rational production scale of mines is obtained at last.(5) Astatement of the technological measures guaranteeing rational production scale is made,and an assessment of social economy is made for this project to ensure the feasible technologyand reasonable economy in production scale which is obtained from intelligent system.

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CLC: > Industrial Technology > Mining Engineering > Mining > Metal ore mining > Ferrous metal mining > Iron
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