华北型煤田底板破坏深度BP神经网络预测模型研究

BP neural network prediction model of floor failure depth in North China coalfield

华北型煤田开采受底板含水层严重影响,为了准确计算工作面底板破坏深度,本文结合现场实测和神经网络预测模型对其进行分析。首先采用直流电法与专门电极电缆,对九里山矿综放开采工作面15091的底板破坏深度进行观测;其次结合大量实际数据,应用遗传算法优化BP神经网络,通过优化参数构建底板破坏深度预测模型,预测模型的均方误差为0.011,平均百分比误差为5.983%,预测集预测结果误差在10%以下,模型可以预测底板破坏深度;最后以预测模型分析采厚和切顶卸压对工作面底板破坏深度的影响。研究结果表明,分层开采下,切顶卸压比未切顶卸压底板破坏深度约减少77.84%;综放开采下,切顶卸压比未切顶卸压底板破坏深度约减少59.17%;采厚对底板破坏深度的影响呈正相关。

North China coalfields are seriously affected by bottom aquifers.In order to accurately the depth of damage at the working face,this paper combined actual measurement with neural network prediction model in analysis.Firstly,DC method with special electrode cable is employed to observe the bottom plate damage depth of 15091 in the comprehensive mining face of Jiulishan mine;secondly,based on large-scale data,genetic algorithm is applied to optimize BP neural network.The prediction model of bottom plate damage depth is set up by optimizing parameters.The mean square error of the prediction model was 0.011,the average percentage error was 5.983%,and the prediction error based on prediction set was below 10%.These results indicate that the model can be used for predicting the bottom slab damage depth.Finally,the prediction model was used to analyze the effect of mining thickness and top cutting pressure relief on the depth of damage of the working face floor.Results show that under stratified mining,the depth of damage of the bottom slab is reduced by 77.84%under cut top pressure relief than uncut top pressure relief;under integrated mining,the depth of damage of the bottom slab is reduced by 59.17%under cut top pressure relief than uncut top pressure relief;and the effect of mining thickness on the depth of damage of the bottom slab is positively correlated.