Journal of Northeastern University Natural Science ›› 2014, Vol. 35 ›› Issue (11): 1641-1645.DOI: 10.12068/j.issn.1005-3026.2014.11.027

• Mechanical Engineering • Previous Articles     Next Articles

Experiment Study of Mining Technology Improvement Based on RBF Neural Network

YANG Wei1, YANG Shan1, ZHANG Qinli1, REN Shaofeng2   

  1. 1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China; 2. Southwest Energy and Mineral Resources Co., Ltd., Guiyang 550004, China.
  • Received:2013-09-23 Revised:2013-09-23 Online:2014-11-15 Published:2014-07-03
  • Contact: YANG Wei
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Abstract: The underground stoping at a metal mine was in low production efficiency, large explosives consumption and high boulder yield. To solve these problems, an orthogonal test of L9(33) on blasting parameters was proposed, based on which, the RBF neural network model, using the blasting burden spacing, borehole spacing and surroundingborehole spacing as input layer, and taking explosives consumption and boulder yield as the output layer, was established. The comprehensive expectation formula of blasting based on safety and economy is given, and it is determined that the best blasting burden spacing, borehole spacing and surroundingborehole spacing are 1 m, 1. 4m and 1m, respectively. After the mining process improvements, the stope production capacity is 4 times as before, more stopes can be filled, exposure time of stope is shorter, single stope production efficiency is increased by 75%, explosives consumption is reduced by 62%, large fragment rate is reduced by 74%.

Key words: underground mining, stope blasting, mining technology improvement, orthogonal test, RBF neural network, comprehensive expectation formula of blasting

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