Title :
The Optimization Research on Large-Diameter Longhole Blasting Parameters of Underground Mine Based on Artificial Neural Network
Author :
Dong, Pan ; Keping, Zhou ; Na, Li ; Hongwei, Deng ; Kui, Li ; Fuliang, Jiang
Author_Institution :
Sch. of Resources & Safety Eng., Central South Univ., Changsha, China
Abstract :
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 1.09 m 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=1.09 m, the best crater radius Rj=0.77-0.79 m, the best crater volume Vj=0.5-0.6 m3, and explosive consumption 1.0-1.1 kg/t.
Keywords :
explosions; mechanical engineering computing; mining; neural nets; BP artificial neural network; Xinshan mining area; blasting crater test; large-diameter longhole blasting parameters; underground mine; Artificial intelligence; Artificial neural networks; Computer networks; Drilling; Explosives; Intelligent networks; Mechanical factors; Ores; Testing; Tin; EasyNN-plus; artificial neural network(ANN); blasting parameters; blasting test; optimization calculation;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.109