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

摘要：

Thls paper combines with Kafangs 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-plns 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 Rj=0.77-0.79m, the best crater volume Vj=0.5-0.6m3, and explosive consumption 1.0-1.1kg/t.

关键词： blasting parameters optimization calculation blasting test artificial neural network(ANN) EasyNN-plus

作者: Pan Dong Zhou Keping Li Na Deng Hongwei Li Kui Jiang Fuliang

作者单位: School of Resources and Safety Engineering Central South University Changsha, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 419-422

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）