. 人工神經網絡在煤礦注漿堵水工程中的應用

1、. 人工神經網絡在煤礦注漿堵水工程中的應用宋彥波1,2, 馬念杰1（1.中國礦業(yè)大學 北京校區(qū), 北京10083 ；2.邢臺同成礦業(yè)科技有限公司, 河北邢臺 054000）摘要：采用化學注漿的方法對煤礦井下工作面或巷道進行注漿堵水加固是提高礦井生產安全性的一種行之有效的方法，但注漿設計理論研究相對滯后于實踐。本文針對羊渠河礦上官莊風井注漿堵水實際，將人工神經網絡理論引入到化學注漿的理論設計分析中，成功地對注漿工程進行了優(yōu)化設計并對注漿量進行了預測。關鍵詞：注漿堵水 人工神經網絡 注漿參數 設計優(yōu)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概述采用化學材料對煤礦井下工作面或巷道涌水圍巖進行化學注漿，人為改善破碎煤巖體的抗?jié)B性能，對涌水進行封堵，減少礦井的無效排水，提高礦井的生產安全效益，這一技術近幾年在我國煤礦取得顯著的進展1，但存

5、在理論研究滯后于注漿工程實踐的問題。本文針對煤礦井下注漿堵水技術現狀，結合現場實踐，將人工神經網絡引入到裂隙圍巖化學注漿理論分析中，以期達到對現場注漿堵水工程進行優(yōu)化設計，對注漿量進行預計，并對注漿工程進行指導。1.人工神經網絡簡介人工神經網絡是基于生物學中神經網絡基本原理而建立的，由大量的簡單處理單元廣泛連接而組成的復雜網絡。 用可實現的元件或神經計算機來模擬生物體中神經網絡的某些結構和功能，并能應用于工程及相關領域，是人工智能的一個重要分支2。簡單的人工智能網絡如圖1所示：圖1、神經網絡結構示意圖(Fig 1Artificial neural network Fissure)在圖 1中，W

6、i為關聯權，表示神經元對第i個晶枝接受到信號的感知能力，f(z)為輸出函數或激活函數。一般將激活函數定義為： y=f(z)= sgn() (1)其中： sgn(x)=01 其他x0 閥值人工神經網絡的優(yōu)化計算原理是：當關聯權wi已知時，對給定的一組輸入值（X1,X2,Xn）T，很容易計算出相應的輸出值。而對于給定的輸入，我們則要求盡可能使相應的計算輸出同實際輸出值相吻合。這就要求確定參數Wi的值，這就是神經網絡的主要工作，即建立模型，并確定Wi的值。目前工程中常用的人工神經網絡模型有前向型神經網絡（feet-forward）和反饋型神經網絡（feet-back）。人工神經網絡模型由網絡的拓撲結

7、構、神經元特性函數及學習算法三個要素所決定。32.化學注漿堵水技術簡介礦井注漿堵水是注漿法的一個重要應用領域之一。具體來說，注漿堵水系指將各種堵水材料制成的漿液壓入巖層預定地點,如突水點、含水巖層儲水空洞等，并使?jié){液擴散、凝固和硬化，從而起到堵塞空隙、隔絕水源，增大巖層整體強度和隔水性能的目的。自1864年英國在阿里因普瑞貝礦的豎井井壁內首次壓入水泥漿成功封堵井筒淋水以來5，堵水技術在煤礦及金屬礦的應用日益廣泛。注漿堵水在礦山的應用主要由以下六個方面。1)井筒注漿堵水：包括井筒地面預注漿、井筒工作面預注漿和井筒井壁壁后注漿三種類型。2）井下巷道注漿堵水：巷道注漿包括巷道工作面預注漿與巷道壁后注

8、漿兩種，前者是在含水層還未通過前，構筑擋水墻，預設孔口管，進行鉆孔注漿，將漿液材料壓注到巖層裂隙或空洞中，以封閉透水通道。而壁后注漿則是在巷道支護好后封閉巷道壁后的出水點。3） 恢復被淹礦井或采區(qū)：當礦井或采區(qū)突水被淹后，注漿封堵突水點是常用的最好方法，分為靜水注漿和動水注漿兩種。4）注漿帷幕截流：在礦區(qū)主要補水邊界施工一定間距的鉆孔，向孔內注漿，形成連續(xù)的隔水帷幕，阻斷或減少地下水對礦區(qū)的影響，減少礦井涌水量，保護地下水資源?？傊?，注漿堵水是礦井水防治的重要方法之一，具有減輕礦井排水負擔，節(jié)省排水用電，降低噸煤成本，利于地下水資源保護和利用，改善采掘工程的勞動條件、提高工效和質量，加固井巷或

9、工作面的薄弱地段，減少突水機率的明顯優(yōu)點。3.人工神經網絡輸入模型注漿堵水過程中的注漿量與注漿壓力等參數、涌水類型、圍巖裂隙度和注漿材料等因素都密切相關，可以說注漿參數之間都是非線型關系。目前，對注漿參數的計算方法多采用經驗方法，存在計算結果與實際相差較大的問題。采用人工神經網絡方法來確定注漿量，可為化學注漿堵水工程施工提供可參考的理論依據45。3.1原始數據錄入峰峰礦務局羊渠河煤礦上官莊風井 是七十年代施工的一進風斜井，井筒傾角30。，斷面積14.2m2,井壁采用料石砌碹支護。沖積層含水層、石盒子砂巖含水層的涌水通過料石縫隙涌入井筒，。涌水點主要集中在距井口30150m段，井筒總涌水量為2.

10、2m3/min，涌水沿斜井流到-110水平大巷，由該水平集中泵房排至地面。1989年羊渠河礦對該斜井涌水進行過治理，先在涌水部位的砌碹段表面噴漿，然后進行壁后注漿，使涌水量有所減少，但由于采用的是水泥漿液，而且是注漿壓力較低的滲透注漿，涌水量在不長時間內又恢復到原來水平，堵水效果不明顯，并給礦井的安全生產和經濟效益的提高帶來了非常不利的影響。為了減少礦井無效排水，采用了無機高水材料對風井涌水段進行了注漿，設計注漿孔間距3m ,共布設注漿孔105個，孔深68米。為了減少注漿工程中的材料浪費，前期先鉆35個注漿孔，以驗證神經網絡理論的正確性，驗證后再可進一步來預測其余注漿孔的注漿量，每個注漿孔布孔

11、參數和注漿參數如表1所示。 表1 輸入樣本原始數據(Table 1 initial input data sample)孔號鉆孔深度H（m）注漿壓力P(Mpa)鉆孔傾角A（）半徑R(m)注漿量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計算中，以鉆孔深度、注漿壓力、鉆孔傾角、漿液擴散半徑組成輸入向量X，以注漿量為輸出向量Y。為計算簡便，將輸入輸出數據轉化為（0，1）數據H/10，P/10，A/100，R/20，Q/10，則可得如表2所示的輸入樣本。 表2 模糊神經網絡學習樣本(learn

13、ing Sample of fuzzy neural network )孔號輸入向量 x輸出向量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模糊神經網絡原理依據信息擴散原理：設知識樣本為A=a1,a2,a3,an,記ai不再簡單地歸入某一個uj所在的類，而是依ai和uj的距離可以歸入兩個不同的類。設ujaiuj+1，則可定義ai歸入uj，uj+1所在的模糊類的程度為： u(uj)=1-(ai-uj)/(uj+1-uj) u(uj+1)=1-(uj+1-ai)/(uj+1

16、-uj) 在信息擴散原理的指導下，可以推導出信息擴散公式： q(x,xi)=ke 式中： k常數，取k=0.4 x信息吸收點，相當于信息分配中的信息控制點uj; h窗寬，即控制信息的擴散范圍，與樣本A的維數有關； 如已知樣本A的最大、最小觀測值為b、a，則可得h的計算公式：h=l(b-a)/n 式中：l常數，當n10時，取l=1.42 為保證所有信息吸收點的地位相同，需對信息分布結果進行歸一化處理： q（x,xi）=q(x,xi)/ 式中：m樣點總數。3.3原始輸入數據處理對注漿孔深度H、注漿壓力P、鉆孔傾角A，漿液擴散半徑R各參數進行離散化，各參數地的離散點為： 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 根據公式有：h=l(b-a)/n 對注漿孔深度H：h=1.42(8.0-4.2)/23=0.235 對注漿孔壓力P: h=1.42(9.5-3.2)/23=0.390 對注漿孔傾角A：h=1.42（65-52）/23=0.803 對注漿滲透半徑R：h=1.42(9.8-4.0)/23=0.333 根據式計算q(hj,Hi), q(pj,Pi), q(aj,Ai), q(rj,Ri),并進行歸一化處理，則可得如表3所示的

18、經模糊化處理的輸入樣本向量x。表3 經模糊化處理后的預測輸入向量表(Table 3 predicted input vector table after fuzzy)孔號輸入向量 x輸出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.用人工神經網絡對注漿量進行預測優(yōu)化后的人工神經網絡如圖3所示。圖2 優(yōu)化后的人工神經網絡(Optimized artificial neural network)設樣本訓練誤差E和循環(huán)次數t是計算運行的兩個標準，取E=0,t=10000，學習效率=0.9,動量項=0.7，隱層數c=1，隱層節(jié)點數n1=8。用此訓練好的網絡可預測第24#孔到第35#孔的注漿量。 表4 第24#孔至第35#孔輸入向量原始數據(Table 4 Initial borehole input vector data from No24 to No35)

25、孔號輸入向量(H，P，A，R)輸出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對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對24#孔來說，輸入向量x為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，將此向量代入訓練好的網絡，即可得到預測輸出值。同樣可得如表5所示的其余各孔的預測輸

27、入向量。表5 模糊神經網絡預測輸入向量(Table 5 Predicted input vector in fuzzy neural network)孔號輸入向量X輸出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,