BP neural network prediction model of floor failure depth in North China coalfield
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摘要: 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。Abstract: 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.
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图 4 15091工作面底板破坏视电阻率云图
Figure 4. Apparent resistivity map of floor failure in 15091 working face
表 1 训练集样本数据
Table 1. Sample data of training set
工作面 采深/m 倾角/(°) 采高/m 斜长/m 构造 注浆加固 采空区充填 分层开采 放顶煤开采 综合机械化开采 切顶 实测/m 告成矿21021 415 11 4.7 154 0 0 0 0 1 0 0 10.0 超化矿22121 430 17 10 101 0 0 0 0 1 0 0 15.0 超化矿22061 316 13.8 10 141 0 0 0 0 1 0 0 12.9 城郊矿21105 810 9 3.5 356 0 0 0 0 0 1 0 27.0 城郊矿2703 810 9 2.8 342 0 0 0 0 0 1 0 24.6 赵固一矿11011 710 3 3.6 175 1 1 0 1 0 0 0 23.48 赵固一矿11111 570 2 3.5 176 0 1 0 0 0 1 0 19.48 赵固一矿16001 550 6 6.4 205.5 1 1 1 0 0 1 0 28.0 陈四楼矿21301 584 10 2.7 149 1 1 0 0 0 1 0 14.0 陈四楼矿21201 440 4 2.61 175 1 1 0 0 0 1 0 12.05 陈四楼矿21110 500 25 2.62 200 1 1 0 0 0 1 0 16.2 陈四楼矿2517 765 12 2.63 196 1 1 0 0 0 1 0 34.25 朝川矿21090 452 18 3.5 195 1 0 0 0 0 1 0 24.46 朱村矿54002 210 4 1.38 102 0 0 1 0 0 1 0 12.0 朱村矿54002 210 4 1.38 102 1 0 1 0 0 1 0 5.0 古汉山15071 520 13 5.1 140 1 1 0 0 0 1 0 15.99 鹤壁三矿128 230 26 3.5 180 0 0 1 0 0 1 0 20.0 新义矿11011 700 6 4 160 1 1 0 0 0 1 0 40.0 平顶山十二矿17062 750 6 2 150 0 0 0 0 0 1 0 14.2 平顶山十一矿22080 740 20 5.7 125 1 1 0 0 1 0 0 19.7 平顶山二矿23090 473 8 1.83 164.5 1 0 0 0 0 1 0 25.5 赵固二矿11050 690 5.5 6.32 180 1 1 0 0 0 1 0 34.8 九里山矿14141 300 9.5 3.5 111 1 1 0 1 0 0 1 11.0 九里山矿15091 458 11 5.5 110 1 1 0 0 1 0 0 17.0 邯郸王凤矿1830 123 15 1.1 70 0 1 0 0 0 1 0 8.0 邯郸王凤矿1951 123 15 1.1 100 1 1 0 0 0 1 0 13.4 邯郸王凤矿1930 118 18 2.5 80 1 1 0 0 0 1 0 10.0 邯郸某矿2911 195 13 5.3 383 0 1 1 0 0 1 0 12.40 邯郸某矿1212 1 000 14 3.8 150 1 1 0 0 0 1 0 35.0 邯郸某矿6707 320 5 5.5 45 1 1 1 0 0 1 0 3.6 牛儿庄56805 452 17 1.66 130 0 1 0 0 0 1 0 16.86 邢台矿7802 259 4 3 160 1 0 0 0 0 1 0 16.40 邢台矿7607窄 320 4 5.4 60 1 1 1 0 0 1 0 9.70 邢台矿7607宽 320 4 5.4 100 1 0 1 0 0 1 0 11.70 某矿7608 400 6 2.7 80 0 0 1 0 0 1 0 10.20 邢东矿2121 1 000 12 3.7 150 1 1 0 0 0 1 0 32.50 章村矿三井39126 260 26 3.4 133 0 1 1 0 0 1 0 11.00 东庞矿9103 237 12 6.2 70 1 1 0 0 1 0 0 12.43 井陉一矿4707小1 400 9 3.5 34 0 1 0 1 0 0 0 8.00 井陉一矿4707小2 400 9 4 34 0 1 1 0 1 0 0 6.00 井陉一矿4707大 405 9 4 46 0 1 0 0 1 0 0 8.50 井陉三矿5701(1) 227 12 3.5 30 0 1 1 0 0 1 0 3.50 井陉三矿5701(2) 227 12 3.5 30 1 1 1 0 0 1 0 7.00 赵各庄1237(1) 900 26 2 200 0 1 1 0 0 1 0 27.00 赵各庄1237(2) 1 000 30 2 200 1 1 1 0 0 1 0 38.00 赵各庄12槽煤1237 1 056 26 10 200 1 1 1 0 0 1 0 35.00 东欢坨矿8煤孤岛面 436 20 3.5 90 0 0 0 0 0 1 0 15.40 峰峰二矿2701(1) 145 16 1.5 120 0 1 0 0 0 1 0 14.00 峰峰二矿2701(2) 145 15.5 1.5 120 1 1 0 0 0 1 0 18.00 峰峰三矿3707 130 15 1.4 135 1 1 1 0 0 1 0 12.00 峰峰四矿4804 110 12 1.4 100 1 1 1 0 0 0 0 10.70 羊东矿8469 700 10 1.2 160 1 1 0 0 0 1 0 25.00 辛安矿112145 636 20 4.2 170 0 0 0 0 1 0 0 20.24 九龙矿15445N 740 13.5 1.6 126 1 1 0 0 0 1 0 34.00 
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表 2 隐含层训练过程
Table 2. Implicit layer training process
隐含层节点数 训练集的均方误差 4 0.026 5 0.026 6 0.011 7 0.023 8 0.024 9 0.052 10 0.019 11 0.104 12 0.027 13 0.046
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表 3 神经网络训练结果
Table 3. Training results of neural network
误差度量项 BP神经网络 GA-BP神经网络 平均绝对误差 3.404 1.346 均方误差 18.060 2.584 均方误差根 4.251 1.608 平均绝对百分比误差/% 19.347 5.983
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表 4 样本预测值
Table 4. Sample predicted values
样本序号 实测值 GA-BP值 GA-BP误差/% 邯郸某矿1212 35.00 34.01 2.83 朱村矿54002 12.00 10.85 9.58 赵固二矿11050 34.80 33.41 3.99 辛安矿112145 20.24 20.40 0.79 邯郸王凤矿1951 13.40 13.87 3.51 朝川矿21090 24.46 27.49 12.39 开滦赵各庄12槽煤1237 35.00 37.33 6.66 东欢坨矿8煤孤岛面 15.40 16.64 8.05
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表 5 不同条件下工作面底板破坏深度的预测值
Table 5. Predicted depth of damage of the bottom plate of the working face under different conditions
工作面 分类 采深/m 倾角/(°) 采厚/m 斜长/m 构造 注浆加固 充填 分层 综放 综采 切顶 预测值/m 14141 分层开采 300 9.5 3.5 111 1 1 0 1 0 0 1 12.01 300 9.5 3.5 111 1 1 0 1 0 0 0 21.36 综放开采 300 9.5 6.9 111 1 1 0 0 1 0 1 10.97 300 9.5 6.9 111 1 1 0 0 1 0 0 17.46 15091 综放开采 458 11 5.5 110 1 1 0 0 1 0 0 16.49 458 11 6.5 110 1 1 0 0 1 0 0 16.60 458 11 7.5 110 1 1 0 0 1 0 0 18.49 458 11 8.5 110 1 1 0 0 1 0 0 22.48 458 11 10 110 1 1 0 0 1 0 0 28.66
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