电气工程与自动化专业暖通空调系统中英文资料外文翻译文献

外文翻译文献暖通空调系统中英文资料外文翻译文献外文文献：HVACsystemoptimization––condenserwaterloopAbstractThispaperpresentsamodel-basedoptimizationstrategyforthecondenserwaterloopofcentralizedheating,ventilationandairconditioning(HVAC)systems.Throughanalyzingeachcomponentcharacteristicsandinteractionswithinandbetweencoolingtowersandchillers,theoptimizationproblemisformulatedasthatofminimizingthetotaloperatingcostofallenergyconsumingdeviceswithmechanicallimitations,componentinteractions,outdoorenvironmentandindoorcoolingloaddemandsasconstraints.Amodifiedgeneticalgorithmforthisparticularproblemisproposedtoobtaintheoptimalsetpointsoftheprocess.SimulationsandexperimentalresultsonacentralizedHVACpilotplantshowthattheoperatingcostofthecondenserwaterloopcanbesubstantiallyreducedcomparedwithconventionaloperationstrategies.Keywords:CentralizedHVACsystem;Condenserwaterloop;Model-basedoptimization;Geneticalgorithms;Simulationsandexperiments1.IntroductionAtypicalcentralizedheating,ventilationandairconditioning(HVAC)systemiscomprisedofacondenserwaterloopandchilledwaterloopthat,togetherwithchillersandindoorairloops,provideacomfortenvironmentfortheconditionedspace.Theprocessofacondenserwaterloopconsistsofchillercondensers,pumps,coolingtowersandfans[1].TheschematicdiagramofacondenserwaterloopisshowninFig.1.Chillercondenserstransfertheindoorcoolingloadandtheheatgeneratedbythecompressorsintothecondenserwater.Pumpsprovidetheenergytocirculatewaterbetweenthechillercondensersandthecoolingtowers.Theheatisrejectedtotheambientairthroughheattransferandevaporationbythecoolingtowers.SincethecondenserwaterloopisamainfunctionblockofHVACsystems,itsenergyconsumptioncontributessignificantlytotheoveralloperatingcost.Efficientoperationofindividualdevicesaswellasthewholecondenserwaterloophasbeenintensivelystudiedinrecentyears.Amongmanypublishedresearchresults,CassidyandStack[2]showedthatvaryingthespeedofcoolingtowerfanscanreduceenergyconsumptionatpartloadconditions.BraunandDoderrich[3]proposedasystematicapproachtofindanearoptimalvariablespeeddrive(VSD)fanspeedbasedonparametersestimatedfromdesigndata.ThismethodwasfurtherextendedbyCascia[4]tosimplifythecomponentmodelandprovideequationsfordeterminingthesetpointsofnearoptimalcontrol.However,allthesemethodswerebasedontheassumptionthatthecondenserwaterflowrateisunchanged.Byconsideringtheeffectsofcondenserwaterflowrateontheperformanceofthechillercondensersandcoolingtowers,SheltonandJoyce[5]recommendedafixedcondenserwaterflowrate(1.5gpm/ton)asaruleofthumbforsystemoperation.Later,Kirsner[6]showedthathighcondenserwaterflowrate(3gpm/ton)hasgoodperformanceatfullloadcondition,whilelowcondenserwaterflowrate(1.5gpm/ton)hasadvantagesatpartloadconditions.Unfortunately,systematicdeterminationofthewaterflowrateunderdifferentout-doorenvironmentandcoolingloadsisstillanopenquestion.Anotherimportantvariabletobeconsideredincondenserwaterloopoptimizationisthecondenserwatersupplytemperature.Schwedler[7]usedseveralexamplestodemonstratethatthelowestpossibleleavingtowerwatertemperaturedoesnotalwaysconservesystemenergy.Nevertheless,hisresultswerenotconclusiveasonlyhalfspeedandfullspeedfanconditionswereconsidered.Inthispaper,anoveloptimizationstrategyforthecondenserwaterloopispresented.Ourobjectiveistominimizethetotalenergyconsumptionofthecondenserwaterloop.Basedonthemathematicalmodelsofrelatedcomponents,theoperatingcharacteristicsofcoolingtowers,theeffectsofdifferentambientenvironmentandtheinteractionsbetweenchillersandcoolingtowers,theenergyefficiencyofthecondenserwaterloopcanbemaximizedbybothvariablewaterflowrateandairflowrate.Amodifiedgeneticalgorithmisusedtosearchforoptimalvaluesoftheindependentvariables.SimulationandexperimentalresultsonacentralizedHVACpilotplantdemonstratethatasignificantoperatingcostcanbesavedbytheproposedmethod.
2.ProblemformulationInthecondenserwaterloop,therearethreetypesofdeviceswhichconsumeenergy,namelychillers,pumpsandfans.Therefore,theobjectivefunctionistominimizethetotalenergyconsumptionofthesedevices.Thepowerconsumptionsofthechillers,pumpsandfansaregiven,respectively.Notethattheperformanceofthecondenserwaterloopisaffectedbyseveralfactors,suchasthephysicallimitationsofindividualcomponents,interactionsamongthemandtheoutdoorenvironment.Thesefactorshavetobeconsideredinsolvingtheoptimizationproblem.Themathematicalformulationsandphysicalexplanationsoftheseconstraintsaregivenbelow.2.1.MechanicalconstraintsAsPpumpandPfanareinfluencedbymw;jandma;kmonotonically,thephysicallimitationsformw;jandma;kareConstraint(1)2.2.CoolingtowerconstraintThecoolingtowerconstraintisgivenas[10]Constraint(3)whereKisthetotalnumberofoperatingcoolingtowersandmw;kisthewaterflowratetoeachcoolingtower.Withoutlossofgenerality,inanalyzingthecoolingtowerperformance,itisassumedthatthecondenserwaterisevenlydistributedineachcoolingtowerTherearetwofactorsaffectingcoolingtowerperformanceinConstraint(3),oneismw;jvs.ma;kandtheotherisTCWRvs.Twb.Tosimplifytheanalysis,itisassumedthatTCWRandTwbareconstantsindiscussingtheeffectofmw;jvs.ma;k.Fig.2showsfivecurvesofequalheatrejectionrate[11],wherethex-axisispercentageofwaterflowrateatfullloadandthey-axisispercentageofairflowrateatfullload.Thesecurvesofequalheatrejectionratearedividedintothreeportions.Portion(1):theairflowrateisverysmallandthewaterflowratemustbeverybiginordertoachieveagivenheatrejectionrate.Inthiscase,theairflowrateistoosmalltoexchangeheatefficientlywiththecondenserwater.Theoutletairflowwetbulbtemperatureisalmostthesameasthatoftheinletwater.Portion(2):theairflowrateisverybig,whilethewaterflowrateisverysmall,theheatex-changeissaturatedandtheoutletwatertemperatureisnearlyequaltotheambientairwetbulbtemperature.Portion(3):theheatrejectionrateofthecoolingtowerincreaseswitheitherincreasedairflowrateorincreasedwaterflowrateandviceversa.Apparently,theenergyefficientoperatingrangemustlieinsidePortion(3).Inthisportion,areducedairflowrateleadstoalowerfanpowerconsumption,butthewaterflowratehastobeincreased,resultinginanincreasedpumppowerconsumption.Similarly,areducedwaterflowratelowersthepumppowerconsumptionbutresultsinanincreasedfanpowerconsumption.Constraint(3)limitsthevalueofmw;jandma;kduetothecoolingtowercharacteristics.ThetermTCWRTwbinConstraint(3)reflectstheeffectofTwbonthecoolingtowerperformance.Assumingthecoolingtowerheatrejectionrateandcondenserwatersupplytemperaturearekeptconstant,theoptimaloperatingpointofcoolingtowerschangesifTwbchanges.Fig.3givesanexamplewherethecoolingtowerheatrejectionrateisassumedtobeafixedvaluefordifferentwetbulbtemperaturesofambientair,20and25LC,respectively.Theoptimaloperatingpointsarelabeledaspentagonstoindicatethecorrespondingpowerconsumptionofthefansandpumps.Whilethecurvesoffanpowerconsumptionarethesamefordifferentwetbulbtemperatures,thecondenserwaterflowratechangeswithchangingairflowrateandoutdoorenvironmentforaconstantcoolingtowerheatrejectionrate.Theoptimalairflowrateis85%ofthefullloadat25℃and50%at20℃.Foranoptimaloperatingpoint,thepowerconsumptionis12%ofthefullloadat20℃wetbulbtemperature.Iftheairflowrateiskeptat85%ofthefullloadat20℃insteadof50%,thecombinedpowerconsumptionofthefanandpumpis19%ofthefullload.Comparedwith12%ofthefullloadattheoptimalpoint,almost7%oftheenergyofthefullloadcouldbesavedwithvaryingthemassflowratesofwaterandair.2.3.InteractionconstraintsThevariableTCWSinfluencesboththechillerpowerconsumptionandthecoolingtowerperformance.Constraint(4)Thistemperatureisalsorestrictedbyboundariesthatareoftenprovidedbychillermanufacturersforsafeoperationofthechillers.Ithasbeengenerallyacknowledged[3,5–7,12–16]thatadecreasingTCWSresultsinanincreasingCOPandlowerenergyconsumptionofthechillers.However,alowerTCWSleadstoasmallerTCWRandthenhigherma;kandmw;kforfixedQandTwb.Asma;kandmw;kincrease,thefanpowerandcondenserwaterpumppowerincreasecubically.Fig.4illustratesthetrade-offbetweenthechillerandcoolingtowerfanpowerassociatedwithanincreasingtowerairflowrate[2].Here,afixedcondenserwaterflowrateisassumed.Astheairflowrateincreases,thefanpowerincreases.Atthesametime,thereisareductioninthecondenserwatersupplytemperature,resultinginalowerchillerpowerconsumption.Ontheotherhand,TCWR,inturn,affectstheheatexchangeefficienciesinthecoolingtowers.Whenthecondenserwatersupplytemperaturedecreases,thecondenserwaterreturntemperaturealsodecreasesforthesamecoolingload.Thisresultsinlowerefficienciesofthecoolingtowerunderthesameambientwetbulbtemperature,astheenthalpydifferencebetweenambientairandcondenserwaterbecomessmaller.Theoptimaloperatingpointoccursatapointwheretherateofpowerincreaseinthefansandpumpsisequaltotherateofpowerreductioninthechillers.3.OptimizationalgorithmIntheoptimizationproblem,i,j,k,ma;kandmw;jareindependentvariables,Twb,TCHWS,TCHWRandmCHWarevariablesthatcanbemeasuredandQ,TCWSandTCWRarevariablestobedeter-minedbyconstraints.Asthisoptimizationproblemisacombinatorialoptimizationproblemwithnon-linearconstraintsandcontainsbothcontinuousanddiscretevariables,conventionalgradientbasedoptimizationmethodscannotbeapplieddirectly.Anexhaustivesearchmethodoranexhaustivesearchmethodcombinedwithconventionalgradientbasedmethodscanbeappliedtofindtheoptimalsolutions,eventhoughitisimpracticalinrealtimeapplicationsforsuchacomplicatedproblemduetoitstimeconsumingnature.Geneticalgorithmsforproblemsolvingarenotnew,butitisonlyveryrecentlythattheyareimplementedinindustryapplications[17–20].Thegeneticalgorithmismoreattractivethanotheroptimizationalgorithmsinseveralaspects:Itcanhandleproblemconstraintsbysimplyembeddingthemintothechromosomeencodingprocedure.Itisfeasibletosolvemulti-model,non-differentiable,non-continuousproblemsetc.,sinceitisindependentofthefunctiongradient.Itisveryeasytounderstandandinvolvesverylittlemathematics.Ithasimplicitparallelcomputationfeatures,whichmakeitmoreefficientthantheexhaustivesearchmethods.Theimplementationofamodifiedgeneticalgorithmforthisparticularproblemcanbedividedintofourphases:encoding,constructionoffitnessfunction,evolutionandtermination.3.1.EncodingThefirststepforageneticalgorithmisencoding.Itisaprocessoftransformingaseriesofprobleminputsintoaserialofcodesthatcanbeeasilyinterpretedandusedinevaluatingtheinformationitrepresentsbythefitnessfunction.Inthisapplication,bothdiscretevariables(i,j,k)andcontinuousvariables(ma;k,mw;j)areconvertedintobinarystringsandareconnectedtogethertoformachromosome.Forthediscretevariables,eachbitrepresentsthestatusofeachcomponent.Forexample,‘‘1’’standsforeitherachiller,apumporafanbeingstagedon,while‘‘0’’isforoff.Forthecontinuousvariables,suchasthemassflowratesofairandwater,theupperandlowerboundsoftheirbinarystringsstandforminimumandmaximumvaluesinConstraint(1).Thelengthsofthebinarystringsaredeterminedbythecontrolprecisionofthecorrespondingvariables:themoreprecisesetpointcontrol,thelongerbinarystring.3.2.ConstructionoffitnessfunctionInordertofulfillConstraints(2)–(5),penaltyfunctionsarecommonlyusedtopenalizeaninfeasiblesolution.Inthisstep,apenaltyfunctionisaddedifanyconstraintcannotbefulfilled.Thefitnessfunctionisexpressedinthefollowingequation.wherev1,v2andv3arethepenaltymultipliers,whichshouldbelargepositivenumbers.Withthisfitnessfunction,theminimalsystempowerconsumptionwithoutviolatinganyconstraintshasthemaximumfitnessvalue.Thefitnessvalueswillbeusedasguidesforevolution.3.3.EvolutionTheevolutionconsistsofthreemajorfunctions:selection,crossoverandmutation[17].Thesefunctionsareperformedforeachgenerationtoproducethenextgenerationwithimprovedfitnessvalues.Selectionistheprocessofdeterminingthenumberoftimesthataparticularindividualischosenforreproduction.The‘‘roulettewheel’’selectionmethod[17]isadoptedintheapplicationbasedonlinearscaledfitnessvalues.Crossoverisabasicfunctiontoproducenewindividualswhichhavesomepartsofbothparentsgeneticmaterial.Asinglepointcrossovermethodisadoptedhereandshownbythefollowingexample.Parent1:111111‘‘crossoveratthesecondbit’’Newindividual1:110000Parent2:000000)Newindividual2:001111Mutationisarandomprocesswhereonebitofabinarystringisflippedtoproduceanewindividual.Singlebitmutationisusedintheexamplebelow.Originalindividual:111111‘‘mutationatthefifthbit’’Newindividual:111101Thecrossoverandmutationpointsareallselectedrandomlyineachgeneration.TheprobabilityofcrossoverandmutationareselectedaccordingtotherecommendationsinRefs.TheevolutionprocedureofthemodifiedgeneticalgorithmisillustratedinFig.5.ThemajordifferenceswiththesimplegeneticalgorithmgiveninRef.[17]are:Torestrictthesearchingspacebyknowledgefromthepreviousoptimization.Thereducedsearchingspacereducescomputingtime.2.Tokeeptheindividualwiththebestfitnessvalueineachgeneration.Thisoperationpreventstheoptimalresultsfrombeinglostinthesubsequentevolutions.Theparametersettingsinthemodifiedgeneticalgorithmarelistedasfollows:Numberofindividualsinageneration:100;Maximumnumberofgenerations:500;Precisionofeachcontinuousvariable:28;Generationgap:0.9;Probabilityofcrossover:0.7;Probabilityofmutation:0.01.3.4.TerminationThecomputationofthegeneticalgorithmisterminatedwhenthefollowingcriteriaarereached.Themaximumnumberofgenerationsisreached;Thefitnessvalueofthebestindividualconvergestoacertainasymptote.Eachnewoptimalresultiscomparedwiththecurrentoperatingsetpointsbeforebeingputintoforce.Thisisasafetymeasuretopreventuncertaintiesofthegeneticalgorithmduetoinsufficientevolutiontime.Ifsuchaconditionoccurs,thesystemwilloperateatthepresentsetpointswithoutanychangesuntilthenextsamplingperiod.中文译文：暖通空调系统的优化––冷却水循环摘要本文提出了一种基于模型的集中加热、通风和空调（HVAC）系统的冷却水循环的优化策略。通过分析冷却塔和制冷机之间的每个组件的特性和相互作用，提出一个优化问题：所有的能源消耗与机械限制，组件交互，室外环境和室内冷负荷需求为约束设备的总运营成本最小化。改进遗传算法这一特定问题，提出以获得该过程的最佳设定值。仿真和实验结果表明，冷凝水回路的运行成本与传统的经营策略相比可大幅减少。关键词：集中式空调系统，基于模型的优化，遗传算法，模拟和实验，冷凝水回路1、简介一个典型的集中供热，通风和空调（HVAC）系统由冷冻水回路和冷凝水回路，连同冷水机组和室内空气循环组成，提供空调空间一个舒适的环境。冷凝水回路包括冷水机组冷凝器，水泵，冷却塔和风机[1]。冷凝水环路的示意图示（图.1）。制冷机的冷凝器转移室内制冷负荷和由压缩机产生的进入冷凝器的水的热量。泵提供冷水机组冷凝器和冷却塔之间水循环需要的能量。热量通过冷却塔的热传导和蒸发排放到周围空气中。由于冷却水回路是暖通空调系统的主要功能模块，其能耗显著体现整体经营成本。各个设备以及整个冷凝器水环路的有效操作已被广泛研究。近年来，许多研究的成果被发表。Cassidy和Stack[2]表明，不同的冷却塔风扇的速度可以降低能源消耗在部分负荷条件。Braun和Doderrich[3]提出了一种系统的方法来找到基于设计数据估计参数的近似最优变速驱动（VSD）的风扇转速。这个方法是由Cascia[4]进一步扩展，简化了组件模型，并提供方程确定的近似最优控制的设定点。然而，所有这些方法都基于这样的假设：该冷凝水的流速保持不变。通过考虑冷却水流量对冷水机组冷凝器性能的影响，根据经验进行系统操作的规则，Shelton和Joyce[5]建议冷却塔使用一个固定的冷凝水流量（每分钟1.5加仑/吨）。后来，Kirsner[6]表明，高冷凝器水流量（3加仑/吨）具有良好的性能在满负荷状态，而低冷凝水流量（每分钟1.5加仑/吨）具有的优势在部分负荷条件下。不幸的是，系统地确定不同的户外环境下的水流量和冷却负荷仍然是一个悬而未决的问题。在冷却水回路优化要考虑的另一个重要变量是冷凝器供水温度。Schwedler[7]用几个例子来证明尽可能低的离去塔水温并不总是节省系统能量。尽管如此，他的结果不是决定性的，因为只有半速和全速风扇的条件进行了审议。在本文中，为冷却水回路提出一种新型的优化策略。我们的目标是尽量减少冷凝水回路的总能耗。根据相关部件的数学模型，冷却塔的操作特性，不同的周围环境中和冷却器和冷却塔之间的相互作用的影响，在冷凝器水环路的能量效率可以通过两个变量的水的流速和空气最大化流速。一种改进的遗传算法用于搜索自变量的最优值。集中式空调试验工厂仿真和实验结果表明，一个显著的经营成本可节省所提出的方法。图1.冷凝水回路的框图。2、问题描述在冷凝器水环路中，有三种类型的消耗能量，即制冷器，泵和风扇装置。因此，目标函数是尽量减少这些设备的总能耗该冷水机组，水泵和风机的功率消耗给出这和注意，冷凝器水环路的性能受若干因素影响，如各个部件的物理限制，以及它们和室外环境的相互作用。这些因素都在求解该优化问题上加以考虑。这些约束的数学公式和物理解释如下。2.1机械约束由于Ppump和Pfan受mw;j和ma;k的单调的影响，mw;j和ma;k的物理限制在于约束条件1所产生的冷却必须遵循Pchiller约束条件2K表示工作中冷却塔的总数，mw;k表示每个冷却塔水的流速，在不失一般性，分析了冷却塔的性能情况下，它假定该冷凝水被均匀地分布在每个冷却塔中。在条件3中，还有两个影响冷却塔性能的因素，其中一个是mw;j对ma;k，，另一个是TCWR对.Twb。为了简化分析，假定TCWR和Twb是mw;j对.ma;k.所产生影响的常量。图2表示出5条平等的散热率[11]曲线，其中x轴是在满负荷下水流量百分比，y轴是在满负荷下空气流量百分比。这些平等的散热率曲线被分为三个部分。•部分（1）:为了达到给定的散热率，空气流速是非常小的，水的流速必须是非常大的。在这种情况下，空气流量太小而不能高效地与冷凝器水进行热交换。出口气流的湿球温度与出口气流的湿球温度几乎是相同的。图.2冷却塔的性能图3.最佳工作点在不同的湿球温度•部分（2）:空气流速是非常大的，而水的流量很小，热交换是饱和的并且出口水温几乎等于周围空气的湿球温度。•部分(3):冷却塔的散热速率随对空气流速或增加水的流动速率增加而增加，反之亦然。显然，能量高效的工作范围必须位于内部部分（3）。在该部分中，降低空气流速导致较低的风扇功耗，但水的流速必须增加，从而增加泵的功率消耗。类似地，还原水流量降低泵的功耗，但导致增加风扇的功耗。由于冷却塔的特点，条件约束（3）限制了mw;j和ma;k的比值在约束条件（3）中，术语TCWR_Twb反映了Twb对冷却塔性能的影响。假设冷却塔排热速率和冷凝器的供水温度保持恒定，如果Twb改变，冷却塔的最佳工作点也改变。图3给出一个例子，其中在冷却塔的排热率被假设为一个固定值，用于环境空气分别不同的湿球温度，20℃和25C。最佳工作点被标记为五边形来表示风机和泵相应的功率消耗。当对于不同的湿球温度风扇功耗的曲线是相同的情况下，冷凝器水流量随着空气流量和室外环境的恒定冷却塔散热速率的改变而改变。在25摄氏度下最佳的空气流量为满负载的85%,在20℃下最佳的空气流量为满负载的50％。在20℃湿球温度下，最佳点功耗为满负载的20％。如果空气流速保持在满负载的85％，在20℃，而不是50％，风扇和泵的组合能耗是满负载的19％。与满载时的最佳点的12％相比，接近7％满负荷能量可以用不同的水和空气的质量流率保存。2.3互动约束可变量TCWS影响既制冷功率消耗和冷却塔的性能约束条件4这个温度也受到了