The Optimization Research on Large-diameter Longhole Blasting Parameters of Underground Mine Based on Artificial Neural Network

被引:0
|
作者
Pan Dong [1 ]
Zhou Keping [1 ]
Li Na [1 ]
Deng Hongwei [1 ]
Li Kui [1 ]
Jiang Fuliang [1 ]
机构
[1] Cent S Univ, Sch Resources & Safety Engn, Changsha, Hunan, Peoples R China
关键词
blasting parameters; optimization calculation; blasting test; artificial neural network(ANN); EasyNN-plus;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper combines with Kafang's engineering practice of Xinshan mining area, makes crater tests, and then determines the blasting parameters under experimental conditions. Train the key stakeholders blasting parameters both at home and abroad based on the BP artificial neural network (ANN) model. On the basis that the best charge depth is 1.09m which under the experimental conditions of blasting crater test. Conduct optimizing calculation of blasting parameters by using EasyNN-plus software. Through a comprehensive analysis of optimization ways and parameter error, recommend blasting parameters under experimental conditions: charge depth L=1.09m, the best crater radius R(j)=0.77-0.79m, the best crater volume V(j)=0.5-0.6m(3), and explosive consumption 1.0-1.1kg/t.
引用
收藏
页码:419 / 422
页数:4
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