The Blasting Parameters Testing And Researching In The Ma Jiata Opencast Mine Of Rock Blasting

In the process of rock stripping in the Ma Jiata opencast mine, rock blasting is a very important part and not only effected the efficiency in the production process of mining,handling,transporting, and the other follow-up process, and also effected the overall economic efficiency. In the long-term process of the mine rock stripping, due to uneven distribution of lithology, resulting in the original explosion blasting parameters and different areas of specific lithological not match, making some blasting area good effect, some blasting area very poor, the bulk rate, high explosives unit consumption, the root residue, it impacting the mining,handling,transporting and the operation of the whole production system in the great. The fundamental reason is the lithological of different blasting area has no corresponding reasonable blasting parameters.A lot of theoretical study and blasting practice of the long-term shows that although it will producing different effects of blasting because of the conditions and environmental differences in actual project, but these effects are intrinsically linked to the corresponding blasting parameters,the explosive performance,and the nature of rock, and exist a certain degree of regularity between them objectively. And understanding,described this implied law, and completed the blasting experience of the accumulation and distillation is the important subject faced by the blasting of workers.This paper based on the original bursting effects of the forecasting methods and used the artificial neural network to analysis the Ma Jiata opencast mine of the rock blasting test data, and identified the inherent laws between main factors,block distribution and blasting parameters and make full use of artificial neural network with the self-learning,self-adaptive,self-organization and non-linear dynamics of the characteristics of a language-oriented MATLAB blasting parameters reverse BP neural network forecasting model, the blasting parameters reverse forecast. This model than the original blasting prediction model for innovation and sublimation, through the ideal blasting effects of anti-pushing its corresponding blasting parameters directly, and overcome shortcomings that the original model need to adjust repeatedly blasting parameters can be to predict the ideal blasting effects.This paper also introduced the Ma Jiata opencast mine rock blasting of field test situation that based on the big distance between holes and the small resistance line technology. Through the collection of a reasonable sample data on the scene, for training blasting parameters reverse BP neural network forecasting model, inspection, the model of the accuracy fully meet the requirements. Through the field test and to research the blasting parameters reverse BP neural network forecasting model based on artificial neural network, the mine has solved the existing problems and generated significant economic benefits, and the future of the mine's production of important guiding significance.