. 人工神經(jīng)網(wǎng)絡(luò)在煤礦注漿堵水工程中的應(yīng)用

1、. 人工神經(jīng)網(wǎng)絡(luò)在煤礦注漿堵水工程中的應(yīng)用宋彥波1,2, 馬念杰1（1.中國(guó)礦業(yè)大學(xué) 北京校區(qū), 北京10083 ；2.邢臺(tái)同成礦業(yè)科技有限公司, 河北邢臺(tái) 054000）摘要：采用化學(xué)注漿的方法對(duì)煤礦井下工作面或巷道進(jìn)行注漿堵水加固是提高礦井生產(chǎn)安全性的一種行之有效的方法，但注漿設(shè)計(jì)理論研究相對(duì)滯后于實(shí)踐。本文針對(duì)羊渠河礦上官莊風(fēng)井注漿堵水實(shí)際，將人工神經(jīng)網(wǎng)絡(luò)理論引入到化學(xué)注漿的理論設(shè)計(jì)分析中，成功地對(duì)注漿工程進(jìn)行了優(yōu)化設(shè)計(jì)并對(duì)注漿量進(jìn)行了預(yù)測(cè)。關(guān)鍵詞：注漿堵水 人工神經(jīng)網(wǎng)絡(luò) 注漿參數(shù) 設(shè)計(jì)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(xué)材料對(duì)煤礦井下工作面或巷道涌水圍巖進(jìn)行化學(xué)注漿，人為改善破碎煤巖體的抗?jié)B性能，對(duì)涌水進(jìn)行封堵，減少礦井的無效排水，提高礦井的生產(chǎn)安全效益，這一技術(shù)近幾年在我國(guó)煤礦取得顯著的進(jìn)展1，但存

5、在理論研究滯后于注漿工程實(shí)踐的問題。本文針對(duì)煤礦井下注漿堵水技術(shù)現(xiàn)狀，結(jié)合現(xiàn)場(chǎng)實(shí)踐，將人工神經(jīng)網(wǎng)絡(luò)引入到裂隙圍巖化學(xué)注漿理論分析中，以期達(dá)到對(duì)現(xiàn)場(chǎng)注漿堵水工程進(jìn)行優(yōu)化設(shè)計(jì)，對(duì)注漿量進(jìn)行預(yù)計(jì)，并對(duì)注漿工程進(jìn)行指導(dǎo)。1.人工神經(jīng)網(wǎng)絡(luò)簡(jiǎn)介人工神經(jīng)網(wǎng)絡(luò)是基于生物學(xué)中神經(jīng)網(wǎng)絡(luò)基本原理而建立的，由大量的簡(jiǎn)單處理單元廣泛連接而組成的復(fù)雜網(wǎng)絡(luò)。 用可實(shí)現(xiàn)的元件或神經(jīng)計(jì)算機(jī)來模擬生物體中神經(jīng)網(wǎng)絡(luò)的某些結(jié)構(gòu)和功能，并能應(yīng)用于工程及相關(guān)領(lǐng)域，是人工智能的一個(gè)重要分支2。簡(jiǎn)單的人工智能網(wǎng)絡(luò)如圖1所示：圖1、神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)示意圖(Fig 1Artificial neural network Fissure)在圖 1中，W

6、i為關(guān)聯(lián)權(quán)，表示神經(jīng)元對(duì)第i個(gè)晶枝接受到信號(hào)的感知能力，f(z)為輸出函數(shù)或激活函數(shù)。一般將激活函數(shù)定義為： y=f(z)= sgn() (1)其中： sgn(x)=01 其他x0 閥值人工神經(jīng)網(wǎng)絡(luò)的優(yōu)化計(jì)算原理是：當(dāng)關(guān)聯(lián)權(quán)wi已知時(shí)，對(duì)給定的一組輸入值（X1,X2,Xn）T，很容易計(jì)算出相應(yīng)的輸出值。而對(duì)于給定的輸入，我們則要求盡可能使相應(yīng)的計(jì)算輸出同實(shí)際輸出值相吻合。這就要求確定參數(shù)Wi的值，這就是神經(jīng)網(wǎng)絡(luò)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(jīng)網(wǎng)絡(luò)模型有前向型神經(jīng)網(wǎng)絡(luò)（feet-forward）和反饋型神經(jīng)網(wǎng)絡(luò)（feet-back）。人工神經(jīng)網(wǎng)絡(luò)模型由網(wǎng)絡(luò)的拓?fù)浣Y(jié)

7、構(gòu)、神經(jīng)元特性函數(shù)及學(xué)習(xí)算法三個(gè)要素所決定。32.化學(xué)注漿堵水技術(shù)簡(jiǎn)介礦井注漿堵水是注漿法的一個(gè)重要應(yīng)用領(lǐng)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(yù)定地點(diǎn),如突水點(diǎn)、含水巖層儲(chǔ)水空洞等，并使?jié){液擴(kuò)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(qiáng)度和隔水性能的目的。自1864年英國(guó)在阿里因普瑞貝礦的豎井井壁內(nèi)首次壓入水泥漿成功封堵井筒淋水以來5，堵水技術(shù)在煤礦及金屬礦的應(yīng)用日益廣泛。注漿堵水在礦山的應(yīng)用主要由以下六個(gè)方面。1)井筒注漿堵水：包括井筒地面預(yù)注漿、井筒工作面預(yù)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(yù)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(gòu)筑擋水墻，預(yù)設(shè)孔口管，進(jìn)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(hù)好后封閉巷道壁后的出水點(diǎn)。3） 恢復(fù)被淹礦井或采區(qū)：當(dāng)?shù)V井或采區(qū)突水被淹后，注漿封堵突水點(diǎn)是常用的最好方法，分為靜水注漿和動(dòng)水注漿兩種。4）注漿帷幕截流：在礦區(qū)主要補(bǔ)水邊界施工一定間距的鉆孔，向孔內(nèi)注漿，形成連續(xù)的隔水帷幕，阻斷或減少地下水對(duì)礦區(qū)的影響，減少礦井涌水量，保護(hù)地下水資源。總之，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(fù)擔(dān)，節(jié)省排水用電，降低噸煤成本，利于地下水資源保護(hù)和利用，改善采掘工程的勞動(dòng)條件、提高工效和質(zhì)量，加固井巷或

9、工作面的薄弱地段，減少突水機(jī)率的明顯優(yōu)點(diǎn)。3.人工神經(jīng)網(wǎng)絡(luò)輸入模型注漿堵水過程中的注漿量與注漿壓力等參數(shù)、涌水類型、圍巖裂隙度和注漿材料等因素都密切相關(guān)，可以說注漿參數(shù)之間都是非線型關(guān)系。目前，對(duì)注漿參數(shù)的計(jì)算方法多采用經(jīng)驗(yàn)方法，存在計(jì)算結(jié)果與實(shí)際相差較大的問題。采用人工神經(jīng)網(wǎng)絡(luò)方法來確定注漿量，可為化學(xué)注漿堵水工程施工提供可參考的理論依據(jù)45。3.1原始數(shù)據(jù)錄入峰峰礦務(wù)局羊渠河煤礦上官莊風(fēng)井 是七十年代施工的一進(jìn)風(fēng)斜井，井筒傾角30。，斷面積14.2m2,井壁采用料石砌碹支護(hù)。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(diǎn)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(duì)該斜井涌水進(jìn)行過治理，先在涌水部位的砌碹段表面噴漿，然后進(jìn)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(zhǎng)時(shí)間內(nèi)又恢復(fù)到原來水平，堵水效果不明顯，并給礦井的安全生產(chǎn)和經(jīng)濟(jì)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(jī)高水材料對(duì)風(fēng)井涌水段進(jìn)行了注漿，設(shè)計(jì)注漿孔間距3m ,共布設(shè)注漿孔105個(gè)，孔深68米。為了減少注漿工程中的材料浪費(fèi)，前期先鉆35個(gè)注漿孔，以驗(yàn)證神經(jīng)網(wǎng)絡(luò)理論的正確性，驗(yàn)證后再可進(jìn)一步來預(yù)測(cè)其余注漿孔的注漿量，每個(gè)注漿孔布孔

11、參數(shù)和注漿參數(shù)如表1所示。 表1 輸入樣本原始數(shù)據(jù)(Table 1 initial input data sample)孔號(hào)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(jì)算中，以鉆孔深度、注漿壓力、鉆孔傾角、漿液擴(kuò)散半徑組成輸入向量X，以注漿量為輸出向量Y。為計(jì)算簡(jiǎn)便，將輸入輸出數(shù)據(jù)轉(zhuǎn)化為（0，1）數(shù)據(jù)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(jīng)網(wǎng)絡(luò)學(xué)習(xí)樣本(learn

13、ing Sample of fuzzy neural network )孔號(hào)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(jīng)網(wǎng)絡(luò)原理依據(jù)信息擴(kuò)散原理：設(shè)知識(shí)樣本為A=a1,a2,a3,an,記ai不再簡(jiǎn)單地歸入某一個(gè)uj所在的類，而是依ai和uj的距離可以歸入兩個(gè)不同的類。設(shè)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(kuò)散原理的指導(dǎo)下，可以推導(dǎo)出信息擴(kuò)散公式： q(x,xi)=ke 式中： k常數(shù)，取k=0.4 x信息吸收點(diǎn)，相當(dāng)于信息分配中的信息控制點(diǎn)uj; h窗寬，即控制信息的擴(kuò)散范圍，與樣本A的維數(shù)有關(guān)； 如已知樣本A的最大、最小觀測(cè)值為b、a，則可得h的計(jì)算公式：h=l(b-a)/n 式中：l常數(shù)，當(dāng)n10時(shí)，取l=1.42 為保證所有信息吸收點(diǎn)的地位相同，需對(duì)信息分布結(jié)果進(jìn)行歸一化處理： q（x,xi）=q(x,xi)/ 式中：m樣點(diǎn)總數(shù)。3.3原始輸入數(shù)據(jù)處理對(duì)注漿孔深度H、注漿壓力P、鉆孔傾角A，漿液擴(kuò)散半徑R各參數(shù)進(jìn)行離散化，各參數(shù)地的離散點(diǎn)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(jù)公式有：h=l(b-a)/n 對(duì)注漿孔深度H：h=1.42(8.0-4.2)/23=0.235 對(duì)注漿孔壓力P: h=1.42(9.5-3.2)/23=0.390 對(duì)注漿孔傾角A：h=1.42（65-52）/23=0.803 對(duì)注漿滲透半徑R：h=1.42(9.8-4.0)/23=0.333 根據(jù)式計(jì)算q(hj,Hi), q(pj,Pi), q(aj,Ai), q(rj,Ri),并進(jìn)行歸一化處理，則可得如表3所示的

18、經(jīng)模糊化處理的輸入樣本向量x。表3 經(jīng)模糊化處理后的預(yù)測(cè)輸入向量表(Table 3 predicted input vector table after fuzzy)孔號(hào)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(jīng)網(wǎng)絡(luò)對(duì)注漿量進(jìn)行預(yù)測(cè)優(yōu)化后的人工神經(jīng)網(wǎng)絡(luò)如圖3所示。圖2 優(yōu)化后的人工神經(jīng)網(wǎng)絡(luò)(Optimized artificial neural network)設(shè)樣本訓(xùn)練誤差E和循環(huán)次數(shù)t是計(jì)算運(yùn)行的兩個(gè)標(biāo)準(zhǔn)，取E=0,t=10000，學(xué)習(xí)效率=0.9,動(dòng)量項(xiàng)=0.7，隱層數(shù)c=1，隱層節(jié)點(diǎn)數(shù)n1=8。用此訓(xùn)練好的網(wǎng)絡(luò)可預(yù)測(cè)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(shù)據(jù)(Table 4 Initial borehole input vector data from No24 to No35)

25、孔號(hào)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(duì)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(duì)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(xùn)練好的網(wǎng)絡(luò)，即可得到預(yù)測(cè)輸出值。同樣可得如表5所示的其余各孔的預(yù)測(cè)輸

27、入向量。表5 模糊神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)輸入向量(Table 5 Predicted input vector in fuzzy neural network)孔號(hào)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,