郭家河煤矿2.4Mta新井设计【含CAD图纸+文档】
收藏
资源目录
压缩包内文档预览：
编号：22944584
类型：共享资源
大小：5.73MB
格式：ZIP
上传时间：2019-11-04
上传人：机****料
认证信息
个人认证
高**（实名认证）
河南
IP属地：河南
50
积分
- 关 键 词：
-
含CAD图纸+文档
郭家河
煤矿
2.4
Mta
设计
CAD
图纸
文档
- 资源描述：
-
郭家河煤矿2.4Mta新井设计【含CAD图纸+文档】,含CAD图纸+文档,郭家河,煤矿,2.4,Mta,设计,CAD,图纸,文档
- 内容简介：
-
翻译部分英文原文Practical Neural Network Applications in the Mining IndustryL. Miller-Tait, R. PakalnisDepartment of Mining and Mineral Process Engineering,University of British Columbia,Vancouver, B.C., CanadaABSTRACTThe mining industry relies heavily upon empirical analysis for design and prediction. Neural networks arecomputer programs that use parallel processing, similar to the human brain, to analyze data for trends andcorrelation. Two practical neural network applications in the mining industry would be rockburst predictionand stope dilution estimates. This paper summarizes neural network data analysis results for a 1995Goldcorp/Canmet study on rockbursting and a 1986 UBC/Canmet study on open stope dilution at theRuttan Mine.1 INTRODUCTIONMany aspects of mine design are based upon empirical data. Neural Networks analyze data and predictions based on previous results. Neural networks haveadvantages over conventional empirical designapproaches.These advantages include: Neural networks can easily use multiple inputs to analyze data. By using multiple hidden layers and nodes neural networks investigate the combined influence of inputs. Neural networks can be easily retrained as new data becomes available making them a more dynamicand flexible empirical estimation approach. Neural network software is inexpensive and easy to use. Neural networks have demonstrated a more accurate empirical estimate over conventional methods.The advantages of using neural networks are illustrated in a rockburst prediction example and an open stope dilution example.2 ROCKBURST PREDICTIONThe first example of a potential situation where neural networks could be useful in the mining industry isthe prediction of rockbursts through physical inputs. To quote directly from the Ontario Ministry ofLabor“.we do not have the ability to predict when and where rockbursts will occur, and the experts in the fieldagree that we are not close to make such predictions” 1. Between 1984 and 1993 eight undergroundminers were killed in Ontario due to rockbursts. This accounted for approximately 10% of underground fatalities during this period. If neuralnetworks were to have success in predicting where rockburstsoccur, additional ground support, remote equipment, and/or design modifications could reduce or possibly eliminate fatalities due to rockburst. As safety is the primary responsibility of mining engineers, thepotential for neural networks to assist in predicting rockburst inputs should be investigated.In 1995, a joint project was completed by Goldcorp Inc. and Canmet called “Development of EmpiricalDesign Techniques in Burst Prone Ground at A. W. White Mine” 2. Part of the study was to collect inputinformation on rockburst, caving, ground wedge, and roof fall failures at the A. W. White Mine between1992 and 1995. This resulted in a failure database consisting of 88 ground failures with correspondinginputs for each failure. The six inputs collected for each failure were RMR 3, Q 4, span 5, SRF2,RMR adjustment, and depth. These input factors were set up and run in a neural network with 73 examplesbeing used for training and 15 examples being used to test the network. The output factor, stability, can beone of four failures2 - PUN-RF (potentially unstable roof fall), PUN-GW (potentially unstable groundwedge), BUR (rockburst), and CAV (cave). A brief description of the input and output factors are listed below.Input factorsRMR - The RMR system, initially developed by Bieniawski in 19733, bases rock mass quality on fiveparameters.These parameters are: Uniaxial compressive strength of the rock Rock quality designation (RQD) Spacing of discontinuities Condition of discontinuity Ground water conditions.These factors are given a numerical value and totalled together to get an RMR value. This value will be anumber between 0 and 100 with zero being very poor rock and 100 being extremely good rock. The groundwater conditions were assumed to be dry conditions.Q -The Q factor refers to the rock quality tunnelling index 4. Developed in 1974, by Barton, Lien and Lunde,from the Norwegian Geotechnical Institute, the Qfactor is based on six factors, which are: RQD - rock quality designation Jn -joint set number Jr -joint roughness number Ja -joint alteration number Jw - joint water reduction factor SRF - stress reduction factor.The actual Q formula is Q= RQD/Jn Jr/JaJw/SRF.The Jw/SRF factor was assumed to be 1.0 for this study because dry conditions are assumed. Stress is factoredthrough modelling and strain measurements. The Q factor ranges on a logarithmic scaleranging from 0.001 to1,000 where 0.001 is extremely poor rock and 1,000 is virtually perfect rock.Span5 - the meaning of span refers to the width of an underground opening in plan view. Span can bedetermined through the largest diameter of a circle within an underground excavation.SRF 2 - refers to the adjusting of RMR values relative to stress ratios and previous history of groundconditions. It does not refer directly to SRF used in the calculation of Q. Stress criteria is based upon the ratioof induced stress overunconfined compressive strength (UCS) of the rock.Output FactorsBurst refers to a stope in which a rockburst has occurred. A rockburst is an instantaneous rock failure in orabout an excavated area characterized/accompanied by a shock or tremor in the surrounding rock.PUN-RFrefers to potentially unstable ground with respect to a roof fall. A stope is considered potentiallyunstable if any of the following conditions occur2: The opening may exhibit strong discontinuities having orientations that form potential wedges in the back. Extra ground support may have been installed to prevent a potential fall of ground. Instrumentation installed in the stope has recorded continuing movement of the stope back. There may be an increased frequency of ground working or scaling.PUN-GW refers to a stope considered potentially unstable due to the likelihood of a ground wedge failure.This is a subset of PUN-RF collected separately to identify areas where jointing may result in wedge failures.Cave refers to when uncontrolled ground failures result in caving.3 NEURAL NETWORK ANALYSISThe above inputs and outputs were run on a neural network to see if a neural network could predictresults from the input data and also to see which inputs had the greatest effect on output prediction. A two layernetwork consisting of 13 nodes was run for 10105 cycles reaching a 1.69 percent error. Seventy threeobservations were used to train the network. The remaining 15 observations were used to test the networks predicting ability.The results of the neural network showthat the network correctly predicted all outputs from the training. The reason that this is not surprising is that the network used these 73 observations for prediction training.However, the neural network also predicted burst conditions on the test data which was new data for the neuralnetwork. The network appears to have trouble distinguishing between PUN-GW and PUN-RF but predictedburst conditions on every occasion. The fact that burst conditions were predicted on each occasion waspromising with respect to the possibility that neural networks may be a useful tool to predictrockbursts.It appears from this database, that SRF has the most significant effect on predictingrockbursts. The bias node,Q, and adjusted RMR are also significant while RMR, span, and depth appear to have a lesser effect. It is notsurprising that SRF has the most significance as it is a factor given to rock according to its previousburst proneness. A larger database with stable openings included is necessary to gain confidence in neural network predictions and the influence of input factors.This example, by simultaneously analyzing several inputs, shows that neural networks can provide an effectivetool for predicting rockbursts. Further work varying error, the number of nodes, layers and cycles couldimprove the network using this database. However, a larger database with more input factors could make aneural network a more effective burst predicting tool that could be practically applied in the mining industry.4 NEURAL NET/FORMULA DILUTION PREDICTION COMPARISONIn an effort to compare neural net results with conventional formulae estimates, neural net predictions werecompared with three formulas developed through a database collected from the Ruttan Mine. The formulasbeing compared are from three stope configurations:isolated stopes, echelon stopes, and rib stopes 6.Isolated Stopes (61 observations)Dil(%) = 5.9 0.08(RMR) 0.010(ER) + 0.98(HR)Echelon Stopes (44 obs)1 Dil(%) = 8.8 0.12(RMR) - 0.l8(ER) + 0.8(HR)Rib Stopes (28obs)Dil(%) = 16.1 - 0.22(RMR) - 0.1l(ER) +0.9(HR)Where:DIL(%) - Stope Dilution (%), ie. 10%, DIL(%) = 10RMR - CSIR Rock Mass Rating (%), ie. 60%, RMR = 60ER- Exposure Rate as Volume removed(metres cubed)/mth/stope width (m)HR - Hydraulic Radius (m) of exposed stope wallNeural nets were developed from the same databases that these formulae were developed. The neural netpredictions on unseen data (not in the original databases) were compared with the formulae estimates. Thiswas done to provide insight into the effectiveness of neural net predictions compared with statisticallydeveloped formulae estimates. The neural networks were not optimized.The neural networks were trained using one hidden layer of two unseen nodes. Each neural net was trainedon the entire original database for each stope configuration. The neural net trained on the rib stope databasewas trained to eight percent error, the neural nets trained on the echelon and isolated stope databases wereeach trained to 10 percent training error. These neural nets were then used to predict dilution on new unseendata and compared with the respective formula dilution estimates. The differences in the actual dilutionfrom the neural net and formula predictions were compared for each stope and the combined averages of thestopes for each stope configuration. Figure 1 charts the average percent error between the neural net andformula predictions.AVERAGE PERCENT ERROR OF NEURAL NET PREDICTION (SERIES 1) AND FORMULA PREDICTION (SERIES 2)Fig. 1. Average Neural Net / Formula Error Over Actual Average DilutionFor the unseen rib stope data, the neural net had an average error of 3.2 percent dilution and the formulahad an average error of 5.1percent dilution. For the echelon unseenstope data the neural net had an averageerror of 1.8 percent while the formula had an average error of 3.9 percent. For the two unseen isolatedstopes the neural net had an average error of 0.9 percent while the formula had an average error of 0.6percent. As the rib stope and echelon unseen databases were significantly larger than the isolated stopeunseen database the neural network showed a clear improvement over the formula estimates. The improvedperformance of the neural net predictions in this example over the statistically derived formulas suggests thatneural nets can have better predictions than conventional formula predictions.5 CONCLUSIONThe Goldcorp/Canmet example shows that neural networks can provide an effective tool for predicting rockbursts. Further work varying error, the number of nodes, layers and cycles could improve thenetworkusing this database. However, a larger database with more input factors would make a neural network a more effective burst predicting tool. Additional inputs for each failure may include: induced stress (map3d), hydraulic radius, presence of raises, microseismic data, faults or dikes, ground support; type of heading, and if active mining is in the vicinity.The improved accuracy of the neural net dilution predictions over the statistically derived dilution formulas suggest that neural nets can produce more accurate estimates over conventional empirical methods. The need to having adequate amounts of input data was demonstrated as the training error improved on the smaller stope dilution databases but the accuracy of predictions on unseen data decreased for the smaller databases.Besides improved accuracy, the added neural net advantages of multiple inputs and the continuous ability to retrain the neural nets should improve empirical estimates in the mining industry.REFERENCES1. Caron, M., 1995, Ontario Ministry of Labour Rockburst Recommendations, Ontario Ministry of Labour.2. Mah, P., 1995, Development of Empirical Design Techniques in Burst Prone Ground at A.W. WhiteMine; Canmet Project No.: 1-91 80.3. Bieniawski, Z. T., 1989, Engineering Rock Mass Classifications, New York; John Wiley & Sons.4. Barton, Lien, Lunde, 1974, Classification of Rock Masses for the Design of Tunnel Support, Roc Mechanics Vol. 6, No. 4,7 pp.5. Lang, B., Pakalnis, R., Vongpaisal, S., 1991, Spa n Design in Wide Cut and Fill Stopes at Detour Lake. Mine, 93rd. AGM CIMM, paper # 142, Vancouver.6. Pakalnis, R, 1986, Empirical Stope Design at the Ruttan Mine, Sherritt Gordon Mines Ltd., Universityof British Columbia, Canada, 276 pp.中文译文采矿工业中实用的神经网络应用米勒.L-泰特，R.帕卡尔尼斯（不列颠哥伦比亚大学采矿与矿物加工工程学院，加拿大范库弗峰）摘要:采矿工业很大程度上依赖实验数据分析从而进行设计和预测。神经网络是一系列使用了并行处理类似于人脑的计算机程序，从而分析数据来得到趋势和之间的相互关系。采矿工业中的两个实用神经网络程序是岩爆预测和采场贫化评估。本文概述了对鲁坦矿一些研究的神经网络数据分析结果，研究原型分别是1995年黄金企业公司/加拿大矿产与能源技术中心有关岩爆的研究和1986年不列颠哥伦比亚大学/加拿大矿产与能源技术中心有关露天采场矿石贫化的岩爆研究。1 引言矿井设计的许多方面都是基于实验数据。神经网络以先前的结果为基础分析数据并进行预测。与传统的以实验为基础的设计方法相比，它有着许多优点。这些优点如下：神经网络可以简单地使用多重输入来分析数据。通过使用多重隐藏的层次和节点，神经网络可以审查输入间的联合影响。当新的数据被获得时，神经网络可以很容易地被重新训练，从而使得它成为一个动态性与灵活性更强的基于观察的评估方法。神经网络软件不是很贵且易于使用。神经网络已经被证明了较之传统的方法它是一个更加精确的基于观察的评估方法。神经网络的优点在岩爆预测和露天采场贫化例子中得到阐明。2 岩爆预测神经网络在采矿工业使用具有良好前景的第一个例子是通过自然输入进行的岩爆预测。以下是直接引自安大略劳工部的一段话：“我们没有能力预测岩爆发生在什么时间什么地点，这个领域的专家也认为我们离做出这种预测还有很远的距离。”119841993年间在安大略有8名井下矿工死于岩爆。这个数字大约占了同时期井下事故死亡人数的10%。如果神经网络当时可以成功地预测岩爆的发生地点，那么更多的地面支持、远程设备，或者是改进设计可以减小甚至有可能消除岩爆带来的恶性事故。由于安全是采矿工程人员首要的责任，故神经网络可以协助预测岩爆带来的可能性应当被检验。1995年，黄金企业公司和加拿大矿产与能源技术中心公司完成了一个被称为“基于实践的设计技术在A.W.怀特矿易爆区域的研究进展”的联合项目2。这个研究的一部分就是收集19921995年间发生在A.W.怀特矿有关岩爆、冒顶、地面楔和放顶事故的输入信息。从而形成了一个由88个地面事故组成的事故数据库，它里面的每一个失败都有相类似的输入。对每个事故收集到的6个输入因素是：RMR3，Q4，span5，SRF2，RMR调节，深度。这些输入因素被建立并运行于一个神经网络中，该网络有73个用来训练的实例和15个用来测试网络的实例。输入元素或稳定性可能是以下四个失败之一：PUN-RF(顶板垮落的潜在不稳定性)，PUN-GW(地面楔的潜在不稳定性)，BUR(岩爆)，CAV(采掘)。一个有关输入和输出因素的简要描述被罗列如下。2.1输入因素RMRRMR系统最初是在1973由Bieniawsk发展出来的，它建立于岩体质量的五个参数上。这些参数是：岩块的单轴抗压强度、岩石质量指标（RQD）、不连续间隙、不连续条件、地下水条件。这些因素被赋予了一个数值并且被计算到一起从而得到一个RMR值。这个值是一个介于0100的数，其中0表示质量非常差的岩体，100表示质量极好的岩体。此时地下水条件被假设为干燥状态。QQ因素指的是表征可开挖的岩石指数。它是由来自挪威土木技术学院的巴顿、李恩和鲁恩德共同提出的，建立于以下六个要素之上：RQD岩石质量指标；Jn节理面数目；Jr节理粗糙程度；Ja节理蚀变数目；Jw节理处水减少因素；SRF应力减小因素。Q的准确公式是：QRQD/Jn Jr/JaJw/SRF。因为假定了干燥条件所以本次研究Jw/SRF因素被假定为1.0。应力通过模型和变形值被因子化。Q系数在一个从0.001到1000的范围内变化，其中0.001表示品质极差的岩石，1000表示实质上品质非常理想的岩石。Spanspan意思是指平面图上一个地下缺口的宽度。它可以通过一个地下硐室的最大半径来确定。SRF指相比于应力系数和先前地下条件而进行的RMR值调整。它并不直接代表使用在Q计算公式中的那个SRF。应力准则是基于采动应力与不受限制的岩石抗压强度的比值。2.1输出因素冲击指的是一个发生了岩爆的采矿场。岩爆是一个已开挖区域内发生的岩石突然失效，同时伴随了围岩的自然或人为震动。PUN-RF指考虑到顶板垮落的潜在不稳定地域。如果一个采矿场有如下一些情况发生则被认为是潜在不稳定：缺口呈现出强烈的有着可能在后面形成楔的方向不连续。额外的地面保护可能需要加入以防止地表塌陷的可能发生。安装在采场的仪器记录了连续的采场后面的运动。地表运动和升高的频率可能会增加。PUN-GW指由于地面有楔形失效的可能性考虑到潜在不稳定的采矿场。它是PUN-RF的一个子集，被用来单独地识别出可能导致楔形失败的区域。Cave指当不可控地表失效导致的挖掘发生。3 神经网络分析上面的输入和输出都运行于神经网络中，以观察一个神经网络能否从输入数据中预测出结果，同时也要观察哪些输入对输出预测有最大影响。一个由13个节点组成的两层神经网络被运行了10105次，从而达到了1.69%的错误率。共有73次观察被用来训练这个网络。剩下的15次观察被用来测试的预测能力。神经网络的结果显示出网络可以正确地预测出所有来自于培训的输出。这并不令人惊讶，因为网络为进行预测培训共用了73次观察。然而，神经网络也可以通过一些对网络而言完全是新的测试数据预测出岩爆条件。网络似乎在区分PUN-GW和PUN-RF方面存在问题，但是每次均可以预测出岩爆状况。冲击状况每次均可以成功预测的事实做出了这样的承诺：就潜力而言，神经网络有可能是一个预测岩爆的有效工具。从数据库显示出SRF是对岩爆预测最重要的影响。偏心节点Q和调整过的RMR同样重要的影响，而RMR，span和深度则表明有较小的影响。SRF是最重要的影响并不令人惊奇，因为它是根据先前的冲击倾向并考虑了岩石本身的一个因素。一个包含稳定开度的更大数据库有必要建立，以取得神经网络在预测方面和输入因素影响作用的更大信心。通过同步
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号