露天煤矿抛掷爆破有效抛掷率预测

Prediction of Effective Stripping Ratio of Casting Blast in Surface Coal Mines

有效抛掷率是评价抛掷爆破效果最重要的指标之一,预测有效抛掷率可以指导露天煤矿准确制定生产计划。在分析了抛掷爆破有效抛掷率影响因素的基础上,选取抛掷爆破台阶高度、炸药单耗、底盘抵抗线、孔距、排距、煤层厚度等6个指标作为广义回归神经网络的网络输入,以有效抛掷率为网络的输出,建立了有效抛掷率预测模型,并通过MATLAB编程来实现了网络的训练和预测。实例结果表明:广义回归神经网络能够较准确地预测有效抛掷率,预测误差一般在5%左右,预测结果能够满足工程要求。

The effective stripping ratio is one of the most important indicators to evaluate the effect of casting blast, and the prediction of effective stripping ratio can guide the technical personnel to make accurate production planning. Based on analyzing the factors of effective stripping ratio, the bench height, explosive consumption, bottom burden, hole spacing, row spac- ing, thickness of coal seam were taken as the network input of generalized regression neural network （ GRNN ）, the effective stripping ratio was set as network output. The prediction model of effective stripping ratio was built, and network training and prediction were achieved through MATLAB programming. The results showed that the GRNN could predict the effective stripping ratio accurately with the forecast error normally around 5%, and the prediction results can meet the project requirements.