. 人工神经网络在煤矿注浆堵水工程中的应用

1、. 人工神经网络在煤矿注浆堵水工程中的应用宋彦波1,2, 马念杰1（1.中国矿业大学 北京校区, 北京10083 ；2.邢台同成矿业科技有限公司, 河北邢台 054000）摘要：采用化学注浆的方法对煤矿井下工作面或巷道进行注浆堵水加固是提高矿井生产安全性的一种行之有效的方法，但注浆设计理论研究相对滞后于实践。本文针对羊渠河矿上官庄风井注浆堵水实际，将人工神经网络理论引入到化学注浆的理论设计分析中，成功地对注浆工程进行了优化设计并对注浆量进行了预测。关键词：注浆堵水 人工神经网络 注浆参数 设计优化Application of artificial neural network in under

2、ground water sealing by chemical groutingSong yanbo1,2 , Ma nianjie1(1. Beijing campus, China University of mining and Technology, Beijing 100083, China; 2.Xingtai Tongcheng Mining and Technology Co., Ltd, Hebei Xingtai 054000, China)Abstract: underground water sealing by chemical grouting under coa

3、l mine for improve productive safety is the effective method. But the chemical grouting design theory is not complete and cannot predict the grouting practice accurately. The paper introduces the artificial neural network theory into the design analysis of chemical grouting in soft rock, deal with t

4、he chemical grouting method with the artificial neural network, predict the fluid volume during injection.Keywords:water sealing by grouting artificial neural network grouting parameter optimized design概述采用化学材料对煤矿井下工作面或巷道涌水围岩进行化学注浆，人为改善破碎煤岩体的抗渗性能，对涌水进行封堵，减少矿井的无效排水，提高矿井的生产安全效益，这一技术近几年在我国煤矿取得显著的进展1，但存

5、在理论研究滞后于注浆工程实践的问题。本文针对煤矿井下注浆堵水技术现状，结合现场实践，将人工神经网络引入到裂隙围岩化学注浆理论分析中，以期达到对现场注浆堵水工程进行优化设计，对注浆量进行预计，并对注浆工程进行指导。1.人工神经网络简介人工神经网络是基于生物学中神经网络基本原理而建立的，由大量的简单处理单元广泛连接而组成的复杂网络。 用可实现的元件或神经计算机来模拟生物体中神经网络的某些结构和功能，并能应用于工程及相关领域，是人工智能的一个重要分支2。简单的人工智能网络如图1所示：图1、神经网络结构示意图(Fig 1Artificial neural network Fissure)在图 1中，W

6、i为关联权，表示神经元对第i个晶枝接受到信号的感知能力，f(z)为输出函数或激活函数。一般将激活函数定义为： y=f(z)= sgn() (1)其中： sgn(x)=01 其他x0 阀值人工神经网络的优化计算原理是：当关联权wi已知时，对给定的一组输入值（X1,X2,Xn）T，很容易计算出相应的输出值。而对于给定的输入，我们则要求尽可能使相应的计算输出同实际输出值相吻合。这就要求确定参数Wi的值，这就是神经网络的主要工作，即建立模型，并确定Wi的值。目前工程中常用的人工神经网络模型有前向型神经网络（feet-forward）和反馈型神经网络（feet-back）。人工神经网络模型由网络的拓扑结

7、构、神经元特性函数及学习算法三个要素所决定。32.化学注浆堵水技术简介矿井注浆堵水是注浆法的一个重要应用领域之一。具体来说，注浆堵水系指将各种堵水材料制成的浆液压入岩层预定地点,如突水点、含水岩层储水空洞等，并使浆液扩散、凝固和硬化，从而起到堵塞空隙、隔绝水源，增大岩层整体强度和隔水性能的目的。自1864年英国在阿里因普瑞贝矿的竖井井壁内首次压入水泥浆成功封堵井筒淋水以来5，堵水技术在煤矿及金属矿的应用日益广泛。注浆堵水在矿山的应用主要由以下六个方面。1)井筒注浆堵水：包括井筒地面预注浆、井筒工作面预注浆和井筒井壁壁后注浆三种类型。2）井下巷道注浆堵水：巷道注浆包括巷道工作面预注浆与巷道壁后注

8、浆两种，前者是在含水层还未通过前，构筑挡水墙，预设孔口管，进行钻孔注浆，将浆液材料压注到岩层裂隙或空洞中，以封闭透水通道。而壁后注浆则是在巷道支护好后封闭巷道壁后的出水点。3） 恢复被淹矿井或采区：当矿井或采区突水被淹后，注浆封堵突水点是常用的最好方法，分为静水注浆和动水注浆两种。4）注浆帷幕截流：在矿区主要补水边界施工一定间距的钻孔，向孔内注浆，形成连续的隔水帷幕，阻断或减少地下水对矿区的影响，减少矿井涌水量，保护地下水资源。总之，注浆堵水是矿井水防治的重要方法之一，具有减轻矿井排水负担，节省排水用电，降低吨煤成本，利于地下水资源保护和利用，改善采掘工程的劳动条件、提高工效和质量，加固井巷或

9、工作面的薄弱地段，减少突水机率的明显优点。3.人工神经网络输入模型注浆堵水过程中的注浆量与注浆压力等参数、涌水类型、围岩裂隙度和注浆材料等因素都密切相关，可以说注浆参数之间都是非线型关系。目前，对注浆参数的计算方法多采用经验方法，存在计算结果与实际相差较大的问题。采用人工神经网络方法来确定注浆量，可为化学注浆堵水工程施工提供可参考的理论依据45。3.1原始数据录入峰峰矿务局羊渠河煤矿上官庄风井 是七十年代施工的一进风斜井，井筒倾角30。，断面积14.2m2,井壁采用料石砌碹支护。冲积层含水层、石盒子砂岩含水层的涌水通过料石缝隙涌入井筒，。涌水点主要集中在距井口30150m段，井筒总涌水量为2.

10、2m3/min，涌水沿斜井流到-110水平大巷，由该水平集中泵房排至地面。1989年羊渠河矿对该斜井涌水进行过治理，先在涌水部位的砌碹段表面喷浆，然后进行壁后注浆，使涌水量有所减少，但由于采用的是水泥浆液，而且是注浆压力较低的渗透注浆，涌水量在不长时间内又恢复到原来水平，堵水效果不明显，并给矿井的安全生产和经济效益的提高带来了非常不利的影响。为了减少矿井无效排水，采用了无机高水材料对风井涌水段进行了注浆，设计注浆孔间距3m ,共布设注浆孔105个，孔深68米。为了减少注浆工程中的材料浪费，前期先钻35个注浆孔，以验证神经网络理论的正确性，验证后再可进一步来预测其余注浆孔的注浆量，每个注浆孔布孔

11、参数和注浆参数如表1所示。 表1 输入样本原始数据(Table 1 initial input data sample)孔号钻孔深度H（m）注浆压力P(Mpa)钻孔倾角A（）半径R(m)注浆量Q(kg)1#7.55.5556.21.22#6.36.85840.63#8.26.2628.50.44#7.87.7615.61.35#5.58.7556.21.26#4.29526.50.87#7.84.3604.81.58#6.55.4544.50.79#7.56.6657.50.310#7.27.6589.20.911#6.38.1614.41.212#89.5605.71.413#7.98556

12、.80.714#6.89616.50.715#7.27.5606.60.916#7.93.2589.81.317#7.44.5575.71.118#7.78.7566.30.719#7.57.8616.70.620#6.87.4626.21.321#5.26.8546.81.422#5.77.9527.30.723#7.785625.40.6计算中，以钻孔深度、注浆压力、钻孔倾角、浆液扩散半径组成输入向量X，以注浆量为输出向量Y。为计算简便，将输入输出数据转化为（0，1）数据H/10，P/10，A/100，R/20，Q/10，则可得如表2所示的输入样本。 表2 模糊神经网络学习样本(learn

13、ing Sample of fuzzy neural network )孔号输入向量 x输出向量y1#0.750,0.550,0.550,0.3100.122#0.630,0.680,0.580,0.2000.063#0.800,0.620,0.620,0.4250.044#0.780,0.770,0.610,0.2800.135#0.550,0.870,0.550,0.3100.126#0.420,0.900,0.520,0.3250.087#0.780,0.430,0.600,0.2400.158#0.650,0.540,0.540,0.2250.079#0.750,0.660,0.650

14、,0.3750.0310#0.720,0.760,0.580,0.4600.0911#0.630,0.810,0.610,0.2200.1212#0.800,0.950,0.600,0.2850.1413#0.790,0.800,0.580,0.3400.0714#0.680,0.900,0.610,0.3250.0715#0.720,0.750,0.600,0.3300.0916#0.790,0.320,0.580,0.4900.1317#0.740,0.450,0.570,0.2850.2118#0.770,0.870,0.560,0.3150.0719#0.750,0.780,0.610

15、,0.3350.0620#0.680,0.740,0.620,0.3100.1321#0.520,0.680,0.540,0.3400.1422#0.570,0.790,0.520,0.3650.0723#0.770,0.850,0.620,0.2700.063.2模糊神经网络原理依据信息扩散原理：设知识样本为A=a1,a2,a3,an,记ai不再简单地归入某一个uj所在的类，而是依ai和uj的距离可以归入两个不同的类。设ujaiuj+1，则可定义ai归入uj，uj+1所在的模糊类的程度为： u(uj)=1-(ai-uj)/(uj+1-uj) u(uj+1)=1-(uj+1-ai)/(uj+1

16、-uj) 在信息扩散原理的指导下，可以推导出信息扩散公式： q(x,xi)=ke 式中： k常数，取k=0.4 x信息吸收点，相当于信息分配中的信息控制点uj; h窗宽，即控制信息的扩散范围，与样本A的维数有关； 如已知样本A的最大、最小观测值为b、a，则可得h的计算公式：h=l(b-a)/n 式中：l常数，当n10时，取l=1.42 为保证所有信息吸收点的地位相同，需对信息分布结果进行归一化处理： q（x,xi）=q(x,xi)/ 式中：m样点总数。3.3原始输入数据处理对注浆孔深度H、注浆压力P、钻孔倾角A，浆液扩散半径R各参数进行离散化，各参数地的离散点为： Hh1,h2,h3,h4,h

17、5=0,5,10,15,20 Pp1,p2,p3,p4,p5 Aa1,a2,a3,a4,a5=50,55,60,65,70 Rr1,r2,r3,r4,r5=2,4,6,8,10 根据公式有：h=l(b-a)/n 对注浆孔深度H：h=1.42(8.0-4.2)/23=0.235 对注浆孔压力P: h=1.42(9.5-3.2)/23=0.390 对注浆孔倾角A：h=1.42（65-52）/23=0.803 对注浆渗透半径R：h=1.42(9.8-4.0)/23=0.333 根据式计算q(hj,Hi), q(pj,Pi), q(aj,Ai), q(rj,Ri),并进行归一化处理，则可得如表3所示的

18、经模糊化处理的输入样本向量x。表3 经模糊化处理后的预测输入向量表(Table 3 predicted input vector table after fuzzy)孔号输入向量 x输出y1#0.0,0.5,0.5,0.0,0.0,0.5,0.5,0.001,0.0,0.0,0.0,1.0,0.0,0.0,0,0,0,1,0,00.122#0.0,1.0,0.0,0.0,0.0,0.0,0.121,0.870,0.009,0,0,0.2,0.98,0,0,0,1,0,0,00.063#0.0,0.0,1.0,0.0,0.0,0.009,0.87,0.121,0.0,0,0,0,0.98,0.0

19、2,0,0,0,0,1,00.044#0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.211,0.785,0.004,0.0,0.0,1,0,0,0,0,1,0,00.135#0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.212,0.788,0.0,1.0,0.0,0,0,0,0,1,0,00.126#0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.036,0.964,0.98,0.02,0.0,0,0,0,0,1,00.087#0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,

20、0.97,0.03,0.0,00.158#0.0,1.0,0.0,0.0,0.0,0.658,0.341,0.0,0.0,0.0,0.0,1.0,0.0,0,0,0,1,0,0,00.079#0.0,0.5,0.5,0.0,0.0,0.0,0.34,0.658,0.001,0.0,0.0,0.0,0,1.0,0,0,0,0,1,00.0310#0.0,1.0,0.0,0.0,0.0,0,0,0.34,0.657,0.002,0,1,0,0,0,0,0,0,0.026,0.9740.0911#0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0177,0.916,0.066,0.0,0

21、,1,0,0,0,1,0,0,0,0.1212#0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.001,0.998,0.0,0.0,1.0,0,0,0,0,1,0,00.1413#0.0,0.0,1.0,0.0,0.0,0,0.0,0.035,0.93,0.035,0,1,0,0,0,0,0,0.97,0.03,00.0714#0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.036,0.965,0.0,0.0,1.0,0,0,0,0,0,1,00.0715#0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.0,0.0,0.0,1

22、.0,0.0,0.0,0.0,0,0,1,00.0916#0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,0,0,10.1317#0.0,1.0,0.0,0,0,1,0.001,0,0,0,0,0.98,0.02,0.0,0.0,0.0,0.0,1.0,0.0,0.00.2118#0.0,0,1,0,0,0,0.0,0,0.212,0.788,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,0.0,0.00.0719#0.0,0.5,0.5,0.0,0.0,0,0,0.121,0.87,0.009,

23、0,0,1,0,0,0,0,0.996,0.004,00.0620#0,1,0,0,0,0,0.002,0.65,0.34,0.0,0.0,0.0.0,0.98,0.02,0.0,0,0.0,1.0,0,00.1321#0,1.0,0,0.0,0.0,0.0,0.121,0.8,0.0,0.0,0.0,1.0,0.0,0,0,0,0,0.97,0.03,00.1422#0,1,0,0,0,0,0,0.066,0.0916,0.018,0.98,0.02,0,0,0,0,0,0.004,0.096,00.0723#0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0

24、,0,0.98,0.02,0,0,0.0,1,0,0.00.064.用人工神经网络对注浆量进行预测优化后的人工神经网络如图3所示。图2 优化后的人工神经网络(Optimized artificial neural network)设样本训练误差E和循环次数t是计算运行的两个标准，取E=0,t=10000，学习效率=0.9,动量项=0.7，隐层数c=1，隐层节点数n1=8。用此训练好的网络可预测第24#孔到第35#孔的注浆量。 表4 第24#孔至第35#孔输入向量原始数据(Table 4 Initial borehole input vector data from No24 to No35)

25、孔号输入向量(H，P，A，R)输出Y24#7.5，2.2，5 8，6.61.125#7.8，3.5，56，9.20.826#5.2，8.5，65，6.51.227#6.8，7.6，58，5.71.428#7.4，4.5，65，9.10.929#6.3，8.9，52，6.80.830#7.7，6.6，63，8.50.631#7.8，7.6，67，6.21.232#7.1，8.6，61，7.8133#4.5，6.3，70，5.5134#5.6，7.2，63，6.90.635#7.6，8.3，66，7.81.3对24#孔有： H=7.5m, P=0.2Mpa, A=58。 ，R=6.6m。 Hh1,

26、h2,h3,h4,h5=0, 5, 10, 15, 20,h=0.235 Pp1, p2, p3, p4 ,p5 =5, 6, 7, 8, 9, p=0.39 Aa1, a2, a3, a4, a5=50, 55, 60, 65, 70,a=0.803 Rr1, r2, r3, r4, r5=2, 4, 6, 8, 10, r=0.333对24#孔来说，输入向量x为0, 1, 0, 0, 0, 0, 0, 0, 0.01, 0.899, 0, 0, 0, 0.02, 0.98, 0, 0.01, 0, 0, 0，将此向量代入训练好的网络，即可得到预测输出值。同样可得如表5所示的其余各孔的预测输

27、入向量。表5 模糊神经网络预测输入向量(Table 5 Predicted input vector in fuzzy neural network)孔号输入向量X输出Y24#0,1,0,0,0,0,0，0.001,0.01,0.899,0,0,0,0.02,0.98,0.004,0.00996,0,0,0,0.1125#0,1,0,0,0,0.982,0.018,0,0,0,0,1,0,0,0,0,0,0,0.026,0.9740.0826#0,1,0,0,0,0,0.001,0.063,0.468,0.468,0,0,0,1,0,0,0,1,0,00.1227#0,1,0,0,0,0,0.

28、044,0.394,0.482,0.08,0,0.02,0.98,0,0,0,0,1,0,00.1428#0,1,0,0,0,0.879,0.119,0.002,0,0,0,0,0,1,0,0,0,0,0.141,0.850.0929#0,1,0,0,0,0,0,0.018,0.304,0.677,0.978,0.02,0,0,0,0,0,0.975,0.026,00.0830#0,0,1,0,0,0.044,0.394,0.481,0.079,0.001,0,0,0,1,0,0,0,0,1,00.0631#0,0,1,0,0,0,0.022,0.299,0.544,0.134,0,0,0,1,0,0,0,1,0,00.1232#0,1,0,0,0,0,0,0.047,0.428,0.523,0,0,1,