孙村矿1.8Mt-a新井初步设计

摘要本设计包括三个部分：一般部分、专题部分和翻译部分。一般部分为孙村矿1.8Mt/a新井初步设计，共分10章：1.矿区概述及井田地质特征；2.井田境界和储量；3.矿井工作制度、设计生产能力及服务年限；4.井田开拓；5.准备方式—采区巷道布置；6.采煤方法；7.井下运输；8.矿井提升；9.矿井通风与安全技术；10.矿井基本技术经济指标。孙村煤矿位于山东省泰山东侧的新泰市新汶办事处孙村镇境内，地处新汶煤田东部，位居山东新汶矿业集团公司腹地。东与张庄煤矿，西与良庄煤矿相邻，南依蒙山山系，北与莲花山相望，柴汶河自东向西流经井田之上。东北距新泰市9km，西距磁窑68km，西北距济南168km。孙村井田位于莲花山和蒙山山脉两大分水岭之间，地面为平缓的丘陵地带，地面标高在+165～+210m之间。井田西部地形较平坦，东部起伏较大。总的趋势是西北部地形较低，东南部较高。煤系地层大部为冲积层所掩盖，只有溪沟中略有出露。奥陶系石灰岩广泛出露于井田南部区域。井田中南部有柴汶河纵贯东西。工业场地建在柴汶河南岸的小平原之上，该工业场地内主斜井井口标高为+175.5m。20世纪90年代围绕千米北立井建设的北立井工业场地位于柴汶河北岸，井口标高为199.5m。在井田内可采煤层中，2#煤层平均厚度3.5m，是井田内的首采煤层和主采煤层。4#煤层是主采煤层，平均厚度2.8m。该煤层赋存稳定，结构简单，全井田范围内均可采。矿井最大相对瓦斯涌出量为5m3/t，属于低瓦斯矿井。煤尘具有一定的爆炸性和自然发火危险性。矿井采用斜井多水平上山开拓，采用采区式和中央并列式混合通风。一矿一面，采煤方法为长壁综采一次采全高开采。煤炭运输采用胶带输送机，辅助运输采用蓄电池电机车牵引矿车。矿井年工作日为330d，每天净提升时间16h。矿井工作制度为“三八”制。专题部分题目是承压水与安全开采的技术与施工。翻译部分是一篇关于煤与瓦斯突出灰色-神经网络预测模型的建立研究的论文，英文原文题目为：Establishmentofgrey-neuralnetworkforecastingmodelofcoalandgasoutburst。关键词：斜井；采区；综采；千米立井

AbstractThisdesignincludesthreeparts:thegeneralpart,thespecialsubjectpartandtranslationpart.ThegeneralpartisanewpreliminarydesignofSuncunwellsthatannualoutputis1.8Mt.Thedesignincludestenchapters:1.Mineoverviewandminefieldgeologicalfeatures;2.Theminefieldboundaryandthereserves;3.Minesystemofwork,designproductioncapacityandservicelife;4.Minefieldextension;5.Preparation-withthedistrictroadwaylayout;6.Miningmethods;7.Undergroundtransport;8.Minehoist;9.Mineventilationandsafetytechnology;10.Minebasictechnicalandeconomicindicators.SuncuncoalmineislocatedintheterritoryoftheSuncuntownofTaishaninShandongProvince,eastsideoftheXintaiCityXinwenoffice,locatedintheeasternpartoftheXinwencoalfield,ThehinterlandoftheShandongXinwenMiningGroupCompany.Intheeastside,itisadjacenttoZhangzhuangcoalmine,inthewestside,itisadjacenttoLiangzhuangcoalmine,Mengshanmountaininthesouthsideoftheattachment,ThenorthsidewiththeLotusHillseparated,ChaiwenRiverflowsthroughtheminefieldabovetheeasttowest.NortheastfromtheXintaiCity9km,westfromCiyao68km,northwestfromtheJinan168km.SuncunminefieldislocatedintheLotusHillandMengshanMountainsbetweenthetwowatershed,thegroundisthegentlehillyareas,groundelevationof+165to+210m,minefieldwesternterrainisrelativelyflat,minefieldeasternundulating.Thegeneraltrendisnorthwestlowerterrain,southeasternhigher.Coalmeasuresstratainmostconcealedbyalluvium,onlyslightlyexposedgully.Ordovicianlimestonewidelyexposedintheminefieldinthesouthernregion.ChaiwenRiverrunsthroughtheminefieldincentralandsouthern.IndustrialsitesarebuiltintheplainofthesouthbankofChaiwenRiver,Theindustrialsite,themainslopewellheadelevationis+175.5m.IndustrialsiteislocatedinthenorthbankofChaiwenRiverinthe1990saroundthousandmetersnorthshaftconstruction,wellheadelevationis199.5m.Minefieldwithinthecoallayer,#2coalseamaveragethicknessof3.5m,itisminefield'sfirstcoallayerandmaincoalseam.#4seamisthemaincoallayer,withanaveragethicknessof2.8m.Thecoalseamisstable,simplestructure,canbeminingwithintherangeofthewholeminefield.Minemaximumrelativegasemissionis5m3/t,alow-gascoalmine.Thedusthasexplosiveandnaturalignitionrisk.Mineusingslopeandmultiple-levelrisedevelopment,miningareaventilationandcentralparallelventilationaremixedused.Onlyoneworkingfaceworking,miningmethodforafullymechanizedlongwallminingheight.Coaltransportionbybeltconveyor,auxiliarytransportionbybatteryelectriclocomotivetractiontramcar.Mineworkingdaysfor330days,dailynethoisttimefor16hours.Minesystemofworkforthe"38"system.SpecialsubjectpartisabouttheresearchofTechnologyandConstructionofConfinedWaterandSafeMining.Translationpartisaboutanarticleoncoalfractalcharacteristicsandcoalandgasoutburst.TheoriginaltitleoftheEnglishtext:StudyonRelationshipsbetweenCoalFractalCharacteristicsandCoalandGasOutburst.Keywords:slope;district;fullymechanizedmining;thousandmetersverticalshaft

目录一般部分1矿井概况与地质特征 页英文原文Establishmentofgrey-neuralnetworkforecastingmodelofcoalandgasoutburstYangSheng-qianga,SunYana,b,ChenZu-yuna,YuBao-haia,XuQuanaaStateKeyLaboratoryofMineResourceandSafeExploitation,SchoolofSafetyEngineering,CUMT,Xuzhou221008,ChinabDepartmentofPublicManagement,ShanghaiTradeUnionPolytechnic,Shanghai201415,ChinaAbstract:Greycorrelationanalysiswasmadewithrespecttofactorsaffectingcoalandgasoutburstandtheinputparametersofartificialneuralnetwork(ANN)determined.ThenfivedominantfactorswerechosenforgreycorrelationanalysisastheinputparametersbasedontheimprovedBPalgorithm,andneuralnetworkforecastingmodelofcoalandgasoutburstestablished.Thenetworkwastrainedbyusingthestudysamplesfromtheinstancesoftypicalcoalandgasoutburstmines,andcoalandgasoutburstinstancesofYunnanEnhongminewereusedasforecastingsamples.Thecomparisonbetweentheresultsfromnetworkforecastingwiththatofthetraditionalmethodsindicatesthatthismethodcanmeettherequirementforcoalandgasoutburstforecast.Keywords:coalandgasoutburst;greycorrelationanalysis;grey-neuralnetwork1.IntroductionInchina,coalhasawidedistributionandthelandformsofcoalfieldsarecomplex.Thecoalproductionisthreatenedbywater,fire,coaldust,rooffall,gasoutburst,andsoon.Ofthesefactors,gasoutburstisthemostseriousdangerousonetocausegreateconomiclossandkillcoalminers.So,gasoutburstforecastingbecomesparticularlyimportant[1].Becausetheinherentmechanismofcoalandgasoutburstissocomplicatedandlotsofuncertainandfuzzyproblemsexistbetweeneffectfactorsandaccidents,boththetraditionalforecastingtechnologiesbasedonexperienceandthestatisticalforecastingtechnologiesbasedonmathematicalmodelarerestrictedinthefieldapplication.Grey-neuralnetworkforecastingmethodsofcoalandgasoutburstisappliedinthispaper.2.Analysisofeffectfactors2.1.Initialvelocityofgas(Δp)Theinitialvelocityofgasisoneoftheriskindexesforcoalandgasoutburst[2-3].Itshowstheblow-offvelocityofgasfromcoal.Thisindexreflectshowquicklythegasreleasesfromcoalseams.Δpisrelatedtothegascontentofcoal,structureandsurfacepropertyofpore.Toalargedegree,themovementanddestructivepowerofgasisdecidedbydesorptionandblow-offabilityofgasincoalduringthedevelopingprocessofcoalandgasoutburst.2.2.Consistentcoefficientofcoal(f)Theconsistentcoefficientofcoalisakindofrelativeindexesofcoalparticles’mechanicalstrength.Itsvaluereflectscoal’sphysicalandmechanicalpropertiesandisalsoanimportantparameterinvolvedincoalandgasoutburst.Generally,thebiggerthefis,themoredifficulttheoutbursthappensunderthesamegaspressureandgroundstress.2.3.GaspressureGroundstresscontrolsgaspressurefieldandpromotescoal-bodytobedestructedbygas.Theincreasedpressureinsurroundingrockdeterminesventilationpropertyofcoalseamsandleadstoincreasepressuregradientwhichisfavorableforthecoalandgasoutbursttohappen.Thecontentofgaspressureisanimportantsymbolofgascompressiveenergy’svalue.2.4.ThicknessofsoftsublayerThedeeperthecoalseamis,themorefrequentthegasoutbursthappens.Boththeoutbursttimesandthescalesincreasewiththeincreaseofcoalseamthickness,especiallythethicknessofthesoftsublayer.Becausethereasonoflowmechanicalstrengthofcoalandbadventilationproperty,muchcontentandpressureofgasexistsinthechangeareaofcoalbedthickness.2.5.Coal-bodydestructiontypeGroundstresses,includingself-weightstress,structuralstress,anddisturbancestress,getthesurroundingrock’sorcoal-body’selasticpotentialenergydowork,makingthecoal-bodydestroyedanddisplaced.Coal-bodydestructiontypereferstothecoal-bodydestructiondegreeofcoal-bodystructureunderstructuralstress.Accordingtothedestructiondegree,itcanbedividedintofivetypes:1-non-destructivecoal;2-destructivecoal;3-strongdestructivecoal;4-pulverizedcoal;5-completelypulverizedcoal.2.6.MiningdepthViewingfromtheregionalmetamorphismofcoal,thedepthisthemainreasonlignitechangingintoanthracitebecause,withincreaseofdepth,thepressureandtemperatureincreases.Thedeeperthedepthis,thehigherthecoalrankis.Thehugethicknesscovermakesthegasbeformedandprotected,mostofwhicharemethaneandsoon.Sotheoutburstintensityofcoalwillincreasewiththeincreaseofminingdepth.2.7TheGasContentofCoalseamGasisfromthecoalseam,strata,goborproductionprocessduringmineexcavation.Thehigherthegascontentofcoalseamis,themoregaswilleffuseintotunnelsandworkingfacesduringcoalseamexcavation.Asaresult,thethreatofthegasaccidentwillbemoreserious.Basedontheabovefactors,thispapercollectedsometypicaldatafromeightoutburstminesinChinaasthesamplesetsofgreyrelationanalysismodel.Valuesofvariousfactorsareshownintable1.Table1.OriginaldataofeachinfluencingfactorSampleNumberoutburstscales(t)InitialVelocity(Δp)Consistencecoefficient(f)GasPressure(MPa)Softstratificationthickness(m)Coal-bodyDestructionTypeMiningDepth(km)GasContentofCoalSeam(m3/t)1150.0019.000.312.761.2030.62010.02220.606.000.240.952.0050.44513.04315.1018.00030.46210.3640.005.000.611.171.6110.3959.04576.508.000.361.251.4130.7459.01610.208.000.592.801.8230.42510.2570.007.000.482.001.1010.4609.508110.2014.000.223.950.9330.5438.233.GreyrelationanalysisInfact,aseriesofeffectfactorsofcoalandgasoutburstarenon-timeseries,soit’sreasonabletogeneratetheintervalvaluesfromoriginaldata.Thispapermakesthefactorthatisoutburstscalesofgasasthecontrollerseriesandothervariousfactorsassubsequenceofgreyrelationanalysis.Gettingtheintervalvaluesfromoriginaldataaccordingtotheformula:,thencalculatingtheabsolutedifferencesΔi(k)ofonesample’sintervalvaluesbetweencontrollerseriesandsubsequencebytheformula:.Correlationcoefficientseriesξi(k)canbegottenbytheCalculationFormulaandthenthecorrelationdegreebetweencontrollerseriesandsubsequencecanbegottenthoughtheformula:.TheResolutionCoefficientρis0.5.Theresultsareshownintable2.Table2.RelationaldegreetaxisofinfluencingfactorsInfluencingfactorsCorrelationdegreeOrderingInitialVelocityΔp0.791Consistencecoefficientf0.585GasPressurep0.684Softstratificationthickness0.547Coal-bodyDestructionType0.703MiningDepth0.782GasContentofCoalSeam0.556Therelatingsequenceofvariousfactorsforgasoutburstscalesshowstheaffectingdegreeonthegasoutburstscales.Accordingtothecalculationresultsofcorrelationdegreeabove,correlationdegreethatvariousfactorsforgasoutburstscalesordersareasfollows.Initialvelocity>miningdepth>coal-bodydestructiontype>gaspressure>consistencecoefficient>gascontentofcoalseam>softstratificationthickness.4.ModelbuildingComplicatednonlinearrelationexistsbetweenvariousfactorsandgasoutburstscales.Usingartificialneuralnetworktoforecastthehappeningcoalandgasoutburstcanreducethehumandisturbance,maketheresultmoreobjective,andshowtheconnectionofinputandoutputvariablestruly[4-5].4.1.DeterminationofinputelementsTheaccuracyofneuralNetworkisn’tproportionaltothenumberofchosenfactors.Iffactorsareoverabundant,thelearningspeedofnetworkwillreduceandthelearningprocesswillbecomecomplicatedanddifficulttocontrol.Atthesametime,thenumberofeffectfactorcan’tbetooless,otherwiseit’llmakeresultsdependonsomepartsoffactorstoomuch.WechosefivedominantfactorsinGraycorrelationanalysis:InitialVelocity,MiningDepth,Coal-bodyDestructionType,GasPressure,andConsistentcoefficientasinputneurons.Inaddition,weintroducetheimprovedBPalgorithmofArtificialNeuralNetworkandadoptneuralnetworkfunctionstorehouseofMATLABtowritetheprocedureandestablishedneuralnetworkpredictingmodelofanalysisofcoalandgasoutburst.4.2.ClassificationoftheforecastresultsAccordingtotheactualconditionsofcoalmine,thecoalseamcanbedividedintotwotypes:outburstcoalseamandnon-outburstcoalseam.Inordertoimprovetheactualapplicability,thispaperdividedtheoutburstsituationintothreetypes:smallamountoutburst(below50t,calledsmall);commonoutburst(between50tand100t,calledmedium);massoutburst(above100t,calledlarge).TheoutputvalueofNeuralNetworktrainingcan’tbe0or1,sotheoutputvaluesaresetintofourkindsasfollows:[1,0,0,0]standsfor"non";[0,1,0,0]standsfor"small";[0,0,1,0]standsfor"medium";[0,0,0,1]standsfor"large".4.3.DeterminationofnetworkstructureAccordingtotheanalysisabove,wechosefivemaineffectfactorsofcoalandgasoutburstandtheycanalsoserveasinputnodesofthemodel.Therearefouroutputnodesandtheoutputvalueis0or1.Thenumberofthehiddenlayernodesisveryimportant.Ifthenumberofthenodesistooless,thenetworkcan’testablishcomplicatedjudgmentboundary;Ifthenumberofthenodesisoverabundant,thenetworkwilllosethesummarizingandjudgingability[6].Thenumberof11,9and16aretriedinthispaper.Finallythebestnumberofhiddenlayerischosenbycomparingthenetworkperformance.Thetransferfunctionbetweendifferentlayersischosenbysigmoidfunctionandthewholeinterconnectionisusedbetweendifferentlayers.NetworkstructureofNeuralNetworkisshowedinfigure1.Fig..1.BPnetworkstructurechartofcoalandgasoutburstforecast4.4.CollectionandnormalizationofdataTypicalcoalandgasoutburstinstancesmustbechosenassampleandtheirdatamustbestandardizedbeforenetworktraining.ThispaperchosefiveeffectfactorsfromtypicalcoalandgasoutburstminesinChinaaslearningsamplesshownintable3.Table3.OriginaldataofcoalandgasoutburstinstancesSampleNumberInitialVelocity(Δp)Consistencecoefficient(f)GasPressure(MPa)Coal-bodyDestructionTypeMiningDepth(km)outburstscales119.000.312.7630.620Big26.000.240.9550.445Small318.000.161.2030.462Small45.000.611.1710.395Non58.000.361.2530.745Medium68.000.592.8030.425Small77.000.482.0010.460Non814.000.223.9530.543Big911.000.282.3930.515Small104.800.601.0520.477Non116.000.240.9530.455Medium1214.000.342.1640.510Small134.000.581.4030.428Non146.000.421.4030.426Big154.000.512.9050.442Big1614.000.243.9535.52Small174.000.531.6524.38Non186.000.543.9555.43Big197.400.370.7547.40Medium203.000.511.4034.00NonInputnodes’parametervaluesoftheBPnetworkaredifferentandthevaluesdivergegreatly,sothevaluesneedtobenormalizedinordertopreventtheinformationofsmallvaluesfrombeingweakenedbybigones.Generally,variousvaluesarenormalizedbetween0and1.Butitisn’tanappropriatemethodforthiscase.Becausethefunctionsigmoidcurvechangesissmoothbetween0and0.1orbetween0.1and0.9.Sothegoodnormalizedvaluerangeshouldbe[0.10,0.90].Theformulaofcansatisfythenormalizedrequirements.Quantificationaldatacanbenormalizedusingtheabovemethod.4.5.TrainingofBPNeuralNetworkforcoalandgasoutburstpredictionInthispaper,theBPtoolfunctionsintheANNtoolboxofMATLABsoftwareisapplied,andapplicationsofsomeimportanttoolfunctionsaredemonstrated[7-10].Theinputtinglayerhasfivenervefibresbecausetheinputtingsamplesare5-dimensionalinputtingvector.Aftermanytimespilotcalculation,thehighperformancenetworkwillbegottenifthenumberofhiddenlayeris11.Theoutputtinglayerhasfourneuronsbecauseoffouroutputtingdata.Sothenetwork’sstructureis5-11-4.ThetransferfunctionbetweendifferentlayersistheS-shapedtangentfunction.Fig.2.TrainingerrorcurveofnetworkThetrainingfunctionistraingdxandthelearningrateisadaptedbynetworkitself,calledimprovedBPalgorithm.Thetrainingerrorcurveofnetworkisshowninfigure2.Fromthefigurewecangetthatthenetworkconvergesafter14,166timesiterativecalculationsandthenetworkalsocanidentifythestudylearningsamplecompletelyandaccurately.Complicatednonlinearrelationissetupbetweenvariousfactorsandgasoutburstscales.4.6.CoalandgasoutburstforecastingbytrainednetworkEightcoalandgasoutburstinstancesofYunnanEnhongcoalminewereusedasforecastingsamples.Thedetaildataareshowedintable4.Whenthenetworkoutputtingvalueiscloseto[1,0,0,0],thescaleofcoalandgasoutburstis"small",whenthenetworkoutputtingvalueiscloseto[0,1,0,0],thescaleis"Medium",whenthenetworkoutputtingvalueiscloseto[0,0,0,1],thescaleis"big".Networkoutputisasfollows:TheexpectedoutputtingvaluesofBPnetworkshouldbeall[0,1,0,0],fromforecastresults,theyareallconsistentwithactualcoalminesituation.Sothemethodhascertainpracticability.TheErrorcurveisshowninfigure3.Theforecastingresultscalculatedbygrey-neuralnetworkandothermethodsareshownintable5,inwhich“You”meanshavingtheriskofcoalandgasoutburstwhile“Wu”meanshavingnoriskofoutburst.Fig.3.ForecasterrorcurveofnetworkTable4.InstancesofcoalandgasoutburstofYunanEnhongmineSamplenumberInitialvelocityemissionRobustnesscoefficientGaspressure(MPa)DestroythetypeofcoalMiningdepth/×102mstrengthofoutburst(t)1110.372.134.1211212.10.492.034.129311.50.281.934.0710411.80.362.334.0315510.80.302.233.969612.40.381.834.109.3711.80.571.634.0836.8810.00.551.534.0510.8Table5.Comparisonofforecastresultsbetweengrey-neuralnetworkandotherforecastmethodsSamplenumberInitialvelocityemissionRobustnesscoefficientGaspressure(MPa)DestroythetypeofcoalMiningdepth/×102mKSCBP1110.372.134.7920YouYouSmall212.10.492.034.1218.0YouYouSmall311.50.281.931.416.8YouYouSmall411.80.362.332.414.2YouWuSmall510.80.302.232.819.4YouYouSmall612.40.381.834.717.5YouYouSmall711.80.571.633.916.1WuYouSmall810.00.551.531.820.5WuYouSmallSingle-targetmethodandcomprehensivetargetmethodarerespectivelyinsteadbySandC.Fromtable5,partofthepredictionresultsbysingle-targetmethodandcomprehensivetargetmethodrespectivelyarediscrepantwiththerealityandtheycan'treflecttheoutburstriskdegree.Thereasonisthatthosemethodscouldnotpostthecomplicatedrelationshipbetweeninfluencingfactorsofcoalandgasoutburst.ButthepredictionresultsbyBPnervenetworkaremoreaccuratethanothers.Sothemethodpresentedinthepaperhascertainpracticability.5.Conclusions1)Sevenmaineffectfactorsofcoalandgasoutburstareanalyzed,andactualdataineighttypicalcoalandgasoutburstminesinChinaarecollected.ThroughtheGreycorrelationanalysis,wegetthecorrelationorderofeffectfactorsaffectingcoalandgasoutburst.2)Neuralnetworkforecastingmodelofcoalandgasoutburstisbuilt.Accordingtotheresultofgreycorrelationanalysis,InputElementsinGrey-neuralNetworkForecastingModelaredetermined.3)20Chinesetypicalexamplesofcoalandgasoutburstarecollectedtotrainthenetworkmodel.Thetrainedgray-neuralnetworkmodelhasbeenappliedinYunnanEnhongcoalmineandthencheckedusefulnessandaccuracy,showingthatthegrey-neuralnetworkmodelissuitableforpredictingcoalandgasoutburst.AcknowledgementsProjectsupportedbytheNaturalScienceHallofYunnanProvince(No2005IT02)andbytheNaturalScienceKey-Foundation(No50834005).References[1]G.Wen,S.Xu,Anewprogressofpreventingandcuringcoalminegasoutburst.MiningSafety&EnvironmentalProtection,27(2002).[2]S.Ma,E.Wang,CoalandGasforecastmethods.Mining&ProcessingEquipment,5(2001)71-72.[3]C.Jiang,Forecastmodelandindexesofcoalandgasoutburst.JournalofChinaUniversityofMining&Technology,27(1998)373-376.[4]J.Deng,GreySystemsTheoryCourse.Wuchang:HuazhongUniversityofScienceandTechnology,1989.[5]J.Deng,GreyControlSystem.Wuchang:HuazhongUniversityofScienceandTechnology,1997.[6]B.Xu,Z.Liu,MATLABEngineeringMathematicsandApplications.Beijing:TsinghuaUniversityPress,2000.[7]H.Yu,IntelligentDiagnosisonNeuralNetwork.Beijing:MetallurgicalIndustryPress,2000.[8]Y.Pan,C.Zhai,Anevaluationoftheroadtrafficsafetybasedonthegreyclusterandneuralnetwork.JournalofChongqingJiaotongUniversity,4(2005)101-104.

中文译文煤与瓦斯突出灰色-神经网络预测模型的建立杨胜强1，孙岩12，陈祖云1，于宝海1,徐泉11.矿产资源与安全开采国家重点实验室，安全工程学院，中国矿业大学，徐州221008，中国2.公共管理系，上海工会理工学院，上海201415，中国摘要：对煤与瓦斯突出影响因素进行灰关联分析，以此确定人工神经网络的输入参数。并应用改进的BP算法，选择灰关联分析的5个优势因子作为输入参数，建立了煤与瓦斯突出预测的神经网络模型。选用典型突出矿井的煤与瓦斯突出实例作为学习样本，对网络进行训练学习，并以云南恩洪煤矿的煤与瓦斯突出实例作为预测样本，将经过网络预测的结果与传统方法的计算结果进行对比。结果表明该灰色-神经网络模型能够满足煤与瓦斯突出预测的要求。关键词：煤与瓦斯突出；灰关联分析；灰色-神经网络1.简介我国煤炭分布范围广泛、埋藏地形复杂，煤炭生产一直受到各种灾害，如瓦斯、涌水、火灾、煤尘及冒顶等的威胁，其中尤以瓦斯事故后果最为严重。由于瓦斯事故带来的人员伤亡和经济损失在煤矿事故中占据十分重要的位置，因此，瓦斯事故的预测方面的研究就显得十分重要。煤与瓦斯突出的内在机理极为复杂，突出影响因素与突出事故之间相关规律存在一定的不确定性和模糊性，基于经验的传统预测技术和基于数学建模的统计预测方法的应用已受到了很多的限制。文章采用基于灰色关联分析的神经网络方法来对煤与瓦斯突出进行预测。2.煤与瓦斯突出影响因素分析2.1瓦斯放散初速度（Δp）煤的瓦斯放散初速度是预测煤与瓦斯突出危险性的指标之一[2-3]，该指标反映了含瓦斯煤体放散瓦斯快慢的程度。Δp的大小与煤的瓦斯含量、孔隙结构和孔隙表面性质与大小有关。在煤与瓦斯突出的发展过程中，瓦斯的运动和破坏力，在很大程度上取决于含瓦斯煤体在破坏时瓦斯的解吸与放散能力。2.2煤的坚固性系数（f）煤的坚固性系数是煤颗粒本身力学强度的一种相对指标，其数值大小也是煤层物理力学性质的重要反映，是煤与瓦斯突出现象所涉及到的重要参数之一。通常情况下，在相同的瓦斯压力和地应力条件下，煤的坚固性系数越大，越不容易发生突出。2.3瓦斯压力地应力控制瓦斯压力场，促进瓦斯破坏煤体，围岩中应力的增加，决定了煤层的透气性，造成瓦斯压力梯度增高，对突出有利。瓦斯压力的大小是煤体含瓦斯压缩能高低的重要标志。2.4软分层煤体厚度煤层越厚特别是软分层越厚，瓦斯突出越频繁，突出次数和突出强度随着煤层厚度，特别是软分层的厚度的增加而增加。因为在煤层厚度变化区域煤的力学强度低，透气性差，瓦斯含量和瓦斯压力较大。2.5煤体破坏类型地应力（包括自重应力、构造应力和采动应力）使围岩或煤体的弹性潜能做功，使煤体破坏和位移。煤的破坏类型是指煤体结构受构造应力作用后的煤体破坏程度，根据其破坏程度，一般分为5类：1-非破坏煤；2-破坏煤；3-强烈破坏煤；4-粉碎煤；5-全粉煤。2.6开采深度从煤质区域变质的角度看，在由褐煤向烟煤无烟煤演变过程中主要是由于盖层的增厚，使其地层温度升高，压力增大。因此盖层愈厚，煤的变质程度亦愈高，巨厚的盖层使其以甲烷为主的变质气体产物得以大量产生并予以保护。因此，随着开采深度的增加，煤层突出强度就会增强。2.7煤层瓦斯含量瓦斯是在矿井采掘过程中，从煤层、岩层、采空区放出的和生产过程中产生的。煤层的瓦斯含量越高，开采煤层时涌入井巷和工作面的瓦斯就越多，瓦斯灾害的威胁也越大。根据以上所考虑的各种因素，论文收集了在中国的8个比较具有代表性的突出矿井的实测数据作为灰色关联分析的模型样本集。各因素的取值见表1。表1各影响因素的原始数据样本序号突出强度/t放散初速度Δp坚固性系数f瓦斯压力/Mpa软分层厚度/m煤体破坏类型开采深度/km煤层瓦斯含量/(m3/t)1150.0019.000.312.761.2030.62010.02220.606.000.240.952.0050.44513.04315.1018.00030.46210.3640.005.000.611.171.6110.3959.04576.508.000.361.251.4130.7459.01610.208.000.592.801.8230.42510.2570.007.000.482.001.1010.4609.508110.2014.000.223.950.9330.5438.233.煤与瓦斯突出影响因素的灰色关联分析事实上，煤与瓦斯突出的影响因素实质为一个非时间序列，因此采用原始数据区间化比较合理。文章把煤与瓦斯突出的强度作为灰色关联分析的母序列，而把其他因素作为灰色关联分析的子序列。依据下述公式从原始数据中获得间隔值：，然后利用公式计算样本中母序列和子序列之间的绝对值。相关系数ξi(k)可通过计算公式得到：，母序列与子序列之间的关联度可通过公式：获得。其中分辨系数ρ=0.5。结果见表2。表2各影响因素的关联度影响因素关联度顺序放散初速度Δp0.791坚固性系数f0.585瓦斯压力p0.684软分层厚度0.547煤体破坏类型0.703开采深度0.782煤层瓦斯含量0.556关联度计算分析可得各子序列与母序列之间的关联度,即各影响因素对瓦斯突出强度的关联度排序为：放散初速度>开采深度>煤体破坏类型>瓦斯压力>坚固性系数>瓦斯含量>软分层厚度。4.煤与瓦斯突出预测灰色-神经网络模型的建立煤与瓦斯突出与其影响因素之间存在着复杂的非线性关系，采用人工神经网络进行煤与瓦斯突出预测，能减少人为的干扰，从而更具有客观性，并且具有极强的非线性逼近能力，能真实刻画出输入变量与输出变量之间的关系[4-5]。4.1灰色-神经网络模型输入元的确定神经网络的准确性与影响因素选择的数量并不成正比。如果影响因素的选择过分充裕，会导致神经网络的工作速度降低，工作过程会变得十分复杂并难以控制。与此同时，影响因素的数量也不能过少，否则会导致结果过多取决于部分影响因素。根据前面灰色关联分析可知，煤与瓦斯突出危险性影响因素最主要有以下5个优势因子：瓦斯放散初速度、坚固性系数、瓦斯压力、煤体破坏类型和开采深度。此外，我们采用人工神经网络的改进BP算法和神经网络的MATLAB函数库来写程序，并建立煤与瓦斯突出预测灰色-神经网络模型。4.2煤与瓦斯突出预测结果的划分从实际情况看，煤层可分两种：一种是煤与瓦斯突出，另一种是不突出。为了加强对实际的应用性，将突出情况又细分成3种情况：少量突出（50t以下，“小”）；一般突出（50~100t，“中”）；大量突出（100t以上，“大”）。由于神经网络训练的输出值，不可能为0或1，所以设置4类期望输出值为：[1,0,0,0]代表“无”；[0,1,0,0]代表“小”[0,0,1,0]代表“中”；[0,0,0,1]代表“大”。4.3网络结构的确定根据上述分析，由于煤与瓦斯突出的主要影响因素有5个，这就决定了输入端点数为5个。输出节点有4个,输出值为0和1。隐含层节点数的多少非常重要，节点数太少，网络不能建立复杂的判决界，节点数太多，则会使判决界包封训练点，从而使网络失去概括判断能力[6]。首先采用隐含层节点数为11个，观察其网络性能。再分别取9和16，并与11时进行预测性能比较，找出最适合网络的训练和收敛。层间转移函数采用Sigmoid函数，各层之间采用全互联方式。神经网络网络结构如图1所示。.图1煤与瓦斯突出预测BP网络结构4.4数据的采集及归一化在进入网络训练之前，首先要