Intelligent prediction method for roof gas drainage roadway layout
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摘要:
顶板瓦斯抽采巷因具有大流量和连续抽采的优点，被广泛用于高瓦斯或突出矿井回采工作面瓦斯治理。如何确定合理的顶板巷布置位置，以高效抽采采空区卸压瓦斯，是保障工作面瓦斯治理效果的关键。为此，在深入分析顶板瓦斯抽采巷布置原则及其布置位置影响因素的基础上，提出了一种基于GA–BP神经网络模型的顶板瓦斯抽采巷布置位置智能预测方法；采用灰色关联分析法确定了GA–BP神经网络模型的预测指标，并设计开发了顶板瓦斯抽采巷布置位置智能预测系统。研究结果表明：①工作面的采厚、埋深、覆岩结构、煤层倾角、倾向长度等5个物理指标是顶板瓦斯抽采巷布置位置的主控因素，且其权重值排序由大到小依次为采厚、埋深、覆岩结构、煤层倾角、倾向长度。②随着遗传代数的增加，GA–BP神经网络适应度不断减小，且当遗传代数为60时其适应度变化基本稳定，表明GA–BP神经网络初始权重和偏置效果较好。③在当前训练样本数据集的前提下，基于GA–BP神经网络模型的顶板瓦斯抽采巷布置位置的预测结果与实际工况值的相对误差仅为0.43%～11.27%，在可接受的范围内。该研究可为顶板瓦斯抽采巷精准设计提供一定的参考。
Abstract:The roof gas drainage roadway, with its advantages of large flow and continuous extraction, is widely used in the gas control of high gas or outburst mine working faces. How to determine the reasonable arrangement position of the roof roadway to efficiently extract the pressure-relief gas in the goaf is key to ensuring the effect of gas control on the working face. Therefore, through a deep analysis of the arrangement principles of the roof gas drainage roadway and the main controlling factors of its arrangement position, an intelligent prediction method for the arrangement position of the roof gas drainage roadway based on the GA–BP neural network model is proposed. The prediction indicators of the GA–BP neural network model were determined using the grey correlation analysis method, and an intelligent prediction system for the arrangement position of the roof gas drainage roadway was designed and developed. The research results show: ① The mining thickness, burial depth, overlying rock structure, coal seam dip angle, and dip length of the working face are the main controlling factors for the arrangement position of the roof gas drainage roadway, and their weight values are ranked as: mining thickness > burial depth > overlying rock structure > coal seam dip angle > dip length; ② With the increase of genetic generations, the fitness of the GA–BP neural network continuously decreases, and when the genetic generation is 60, its fitness change is basically stable, indicating that the initial weight and bias of the GA–BP neural network are good; ③ Under the premise of the current training sample data set, the relative error of the prediction result of the arrangement position of the roof gas drainage roadway based on the GA–BP neural network model and the actual working condition value is only 0.43%～11.27%, which is within an acceptable range. This research can provide a certain reference for the precise design of the arrangement of the roof gas drainage roadway.
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图 6 顶板瓦斯抽采巷预测系统运行原理
Figure 6. Operating principle of roof gas extraction roadway prediction system
表 1 覆岩采动裂隙空间形态的影响因素
Table 1 Factors influencing spatial morphology of mining-induced fractures
影响因素 影响规律 采厚 覆岩导气裂隙带高度和“O”形圈裂隙区宽度随工作面采厚的增加而增大，且“O”形圈宽度变化整体近似呈对数分布[12-13] 倾向长度 在一定的覆岩结构条件下，工作面倾向长度越大，覆岩“O”形圈裂隙区宽度越大[11,15] 覆岩结构 在其他条件相同时，覆岩岩性越坚硬，覆岩的采动裂隙发育越显著[12,14,19] 埋深 随着工作面埋深的增加，顶板岩层的地应力增大，工作面覆岩裂隙越发育[6] 煤层倾角 当煤层倾角小于45°时，覆岩裂隙带高度随着煤层倾角增大而增大；当煤层倾角为45°～60°时，
覆岩裂隙带高度随倾角的增大而减小[23]推进速度 较慢的推进速度增加覆岩的变形量和裂隙分布，加剧覆岩的破坏程度；而较快的推进速度则可以减小
覆岩变形量和裂隙发育，有效抑制覆岩裂隙发育[20]
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表 2 寺家庄15303工作面覆岩结构
Table 2 Overlying strata structure of No. 15303 working face in Sijia Village
岩层岩性 厚度/m 距15号煤累厚/m 81号煤 0.90 89.61 砂质泥岩 1.00 88.61 82号煤 0.44 88.17 粉砂岩 4.75 83.42 砂质泥岩 1.39 82.03 粉砂岩 5.80 76.23 泥岩 1.40 74.83 84号煤 1.20 73.63 泥岩 2.30 71.33 9号煤 1.30 70.03 砂质泥岩 1.60 68.43 9号下煤 0.60 67.83 粉砂岩 1.50 66.33 中砂岩 3.73 62.60 细砂岩 1.00 61.60 粗砂岩 6.05 55.55 石灰岩 1.50 54.05 砂质泥岩 0.30 53.75 11号煤 0.30 53.45 砂纸泥岩 2.90 50.55 12号煤 0.90 49.65 粉砂岩 3.90 45.75 细砂岩 3.80 41.95 中砂岩 3.00 38.95 泥岩 0.93 38.02 石灰岩 4.00 34.02 13号煤 0.60 33.42 细砂岩 7.15 26.27 砂质泥岩 3.30 22.97 泥岩 2.00 20.97 石灰岩 4.70 16.27 14号煤 0.23 16.04 泥岩 1.00 15.04 粉砂岩 3.20 11.84 14号下煤 0.26 11.58 砂质泥岩 2.20 9.38 中砂岩 3.56 5.82 细砂岩 3.82 2.00 砂质泥岩 2.00 0 15号煤 5.13 — 砂纸泥岩 4.00 0 细砂岩 6.00 4.00
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表 3 顶板巷布置位置训练集
Table 3 Roof gas drainage roadway location training set
序号 工作面 采厚/m 覆岩结构 倾向长度/m 倾角/(°) 埋深/m 日推进距离/m 垂直层位/m 伸入工作面
水平距离/m抽采效果 1 五阳煤矿7603[29,30] 6.10 0.55 180 8.5 480 5.6 35 40 抽采量22 m3/min 2 赵庄矿1310[31] 4.88 0.33 219 10 510 3.6 30 20 抽采浓度29% 3 赵庄矿1307[32,33] 4.50 0.21 220 3 568 4.2 47 34 抽采量30 m3/min 4 漳村煤矿2601[34] 6.00 0.56 225 5 550 4.8 22 21 抽采量8 m3/min 5 寺家庄矿15106[35] 5.40 0.32 286.2 4 480 6.4 30 40 抽采量29 m3/min 6 新大地15201[3] 5.20 0.32 180 10 406 3.0 46 54 抽采率92% 7 李村煤矿1303[36] 5.10 0.40 280 5 482 5.2 45 35 抽采浓度18% 8 阳泉三矿K8206[10] 6.80 0.55 252.2 5 547 3.4 60 50 抽采率87% 9 阳煤三矿K8108[37] 6.35 0.33 190.3 7 580 3.0 70 50 抽采量59 m3/min 10 阳煤三矿K8110[38] 6.42 0.48 190.3 5 527 3.5 80 63 抽采量49 m3/min 11 阳煤一矿S8101[39] 6.02 0.28 240 3 434.5 4.2 67 51 抽采率66% 12 石港煤矿15101[40] 7.02 0.18 152 9 470 2.5 54 60 抽采率70% 13 开元9404[10] 4.23 0.59 180 5 365 3.0 54 28 抽采率77% 14 阳泉一矿S8310[41] 6.51 0.74 220 6 602 5.6 70 50 抽采量28 m3/min 15 开元9801[42] 5.58 0.32 180 8 440 3.6 43 40 抽采浓度45% 16 五阳煤矿7607[43] 6.06 0.52 227 5 450 3.6 35 41 抽采率55% 17 白羊岭煤矿15118[44] 4.60 0.26 240 9 390 3.2 52 45 抽采浓度21% 18 阳煤五矿83206[45] 6.40 0.61 191.5 8 520 2.9 57 45 抽采率90% 19 漳村煤矿2603[46] 5.85 0.46 240 5 538 4.8 20 15 抽采量8 m3/min 20 马兰矿18305高瓦斯工作面[47] 4.20 0.71 228 3 468 6.0 40 25 抽采量16 m3/min
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表 4 影响指标权重
Table 4 Influence weight of indicators
影响指标 垂直层位关联度 水平位置关联度 垂直层位影响权重 水平位置影响权重 总权重 综合排名 采厚 0.652 0.744 0.177 0.189 0.366 1 覆岩结构 0.624 0.624 0.170 0.159 0.329 3 倾向长度 0.598 0.647 0.162 0.164 0.326 5 倾角 0.584 0.666 0.159 0.169 0.328 4 埋深 0.651 0.668 0.177 0.170 0.347 2 推进速度 0.572 0.585 0.155 0.149 0.304 6
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表 5 GA–BP神经网络参数取值
Table 5 Parameter setting of GA–BP neural network
遗传算法参数 取值 神经网络参数 取值 种群大小 60 输入层节点数 5 遗传代数 70 输出层节点数 2 交叉概率 0.7 隐藏层层数 1 变异概率 0.2 隐藏层节点数 8
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表 6 模型预测结果
Table 6 Model prediction results
工作面 采厚/m 硬岩岩性
比例系数倾向长度/m 倾角/(°) 埋深/m 垂直层位/m 垂直层位
相对误差/%伸入工作面水平距离/m 伸入工作面
水平距离/%预测 实际 绝对误差 预测 实际 绝对误差 寺家庄15110 5.67 0.35 180.0 5 327 48.6 53 4.4 0.83 46 48 2 4.17 阳煤K8205 6.90 0.62 260.8 8 580 48.8 55 6.2 11.27 47.2 47 0.2 0.43 阳煤五矿8204 6.50 0.58 225.0 8 623 56.7 60 3.3 0.55 41.2 40 0.2 0.50
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