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Blasting scheme selection method based on neural network optimization genetic algorithm

A neural network and genetic algorithm technology, applied in the field of blasting plan selection based on neural network optimization genetic algorithm, can solve problems such as lack of theoretical basis and poor effect.

Inactive Publication Date: 2014-05-07
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, the effect of super-explosion and flying stone research based on empirical formulas is not good. This is because the empirical formulas simply conform to the experimental data and may have no theoretical basis at all.

Method used

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  • Blasting scheme selection method based on neural network optimization genetic algorithm
  • Blasting scheme selection method based on neural network optimization genetic algorithm
  • Blasting scheme selection method based on neural network optimization genetic algorithm

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Embodiment Construction

[0026] In order to make the above objects, features and advantages of the present invention more obvious and comprehensible, the present invention will be further described in detail below in combination with relevant theories and specific implementation methods used.

[0027] An iron mine of Anshan Iron and Steel Group Co., Ltd. figure 2 As shown, it is located 12km southeast of Anshan City. The iron ore deposit is located on the northwest edge of the Qianshan Mountains. The landform is hilly. The highest point in the current mining area is located in the northeast of the mining area, with an altitude of 100.2m. Now it is mined to a level of about -280m. The ore body occurs in the metamorphic rock series of the Anshan Group. The footwall of the ore body is in contact with the gneissic granite migmatite by the F15 fault, the hanging wall is in integrated contact with the chlorite quartz schist, and the east end is in contact with the Qianshan granite by the F1 fault. The west...

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Abstract

This invention discloses a blasting scheme selection method based on neural network optimization genetic algorithm and is characterized by using blasting impact factors and blasting hazard forms as an input value and an output value of the neural network to practice, and the practiced neural network is used as a fitness function for the genetic algorithm. The blasting impact factor include : blasthole (HL), spacing ((i)S( / i)), charge deepness ((i)B(i)), blocking deepness ((i)ST( / I)), specific charge ((i)PF( / i)), and hole drilling rate ((i)SD( / i)), and the blasting hazard forms include overbreak deepness ((i)B( / i)) and a distance of flying rocks ((i)FR( / i)). The genetic algorithm (GA) is used to find the best overbreak deepness ((i)B( / i)) and the distance of flying rocks ((i)FR( / i)) so as to optimize the blasting scheme parameters. The blasting scheme parameter optimization comprises data collection, fitness function construction based on genetic algorithm of ANN, blasting scheme parameter preference based on the genetic algorithm (GA) and determination of the final result of the blasting optimization scheme according to Pareto picture. The blasting scheme selection method can be widely applicable to the blasting scheme optimization selection during an exploitation of a strip mine.

Description

Technical field [0001] The invention involves An open -air mining blasting scheme selection, Especially involved Blasting scheme selection method based on neural network optimization of genetic algorithms. Background technique [0002] The formulation of the blasting plan is an important part of mining work.The parameters in the plan are affected by many factors.The blasting schemes used in different mining areas are different, mainly to consider yield, geological conditions, physical mechanical properties of rocks, and groundwater environment. [0003] The determination of blasting parameters should meet the requirements of security, technology and economic.No one will have serious accidents, of which ultra -explosive depth (BB) and flying stone distance (FR) (FR) (hereinafter referred to as ultra -explosive and flying stone) are one of the most common and dangerous accidents.The ultra -explosion is the phenomenon of blasting depth caused by inappropriate parameter settin...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/12
Inventor 张洋王雨虹刘涛
Owner LIAONING TECHNICAL UNIVERSITY
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