Research On Bench Plasting Parameter Optimization In Baiyunebo West Mine

In the production process of Baiyun Obo West mine, bench Blasting is a veryimportant production process. And the blasting effect has the relationship with theefficiency of the mining, loading and other follow-up processes in the production. Theblasting effect also affects the final economic benefit. The difference of the rockproperties in different blasting area makes that the original rock blasting parameters andthe blasting area partitioned do not match. Finally the blasting effect changes. And thechunk rate, high explosive consumption, the residual of bed rock and so on directlyaffect the total cost of mining and the follow-up processes such as shovel, transportationand crushing.In the actual construction, the blasting effect will change as the environmentalcondition changes, and the blasting effect has relationship with many factors such as orelithology, explosive performance, blasting parameters. And these factors remain somecertain objective laws. The important issue which is to be solved in the blasting work isto understand, describe the objective laws. Finally the mature blasting experience willform.Based on the physical and mechanical properties of the rock, and the blastability ofthe rock, the article makes the Baiyun Obo West mine divide into three step blastingareas. There are explosive area, medium difficulty blasting area, and the difficultblasting area. Making full use of artificial neural network self-learning, adaptation, self-organization and nonlinear dynamics characteristics, the article established the networkforecasting model for BP neural MATLAB language, then predict the blastingparameters. The article counts, analyses, researches the blast site experimental data ofthe Baiyun Obo West mine, then makes the reasonable optimization to the blastingparameters by using BP neural network. Finally the article makes the suitable blastingparameters for the Baiyun Obo West Mine. Finally the article obtained that in the explosive zone the distance between the row is7m; and the distance between the hole inthe same row on the same level is11m; the minimum resistance line is6.5m; theconsumption of the explosives dropped to0.548kg/m3. In the medium difficultyblasting area, the distance between the row is7m; and the distance between the hole inthe same row on the same level is10m; the minimum resistance line is6.0m; theconsumption of the explosives dropped to0.578kg/m3. In the difficult blasting area, thedistance between the row is6.5m; and the distance between the hole in the same row onthe same level is10m; the minimum resistance line is5.5m; the consumption of theexplosives dropped to0.657kg/m3. After the blasting, the chunk rate is less than2%,almost no bedrock. And this solved the problems that the chunk rate, high explosiveconsumption, the residual of bed rock and so on effectively. The blasting effect isobviously improved, and the result can meet the requirement for fine blasting.Through the analysis and research of the Baiyun Obo West mine blastingparameters, the article established the blasting prediction model that meets the actualsituation by using the BP neural network. Then the article trained the model. Upon theexamination, the model is reasonable, and meets the precision requirements. Finally themodel has a good guidance in mining production, reduces the mining cost, andimproves the economic benefit of the mine.