冷链物流外文翻译文献综述Word格式文档下载.docx

冷链物流外文翻译文献综述Word格式文档下载.docx

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Abstract

Qualitycontrolandmonitoringofperishablegoodsduringtransportationanddeliveryservicesisanincreasingconcernforproducers,suppliers,transportdecisionmakersandconsumers.Themajorchallengeistoensureacontinuous„coldchain‟fromproducertoconsumerinordertoguarantyprimeconditionofgoods.Inthisframework,thesuitabilityofZigBeeprotocolformonitoringrefrigeratedtransportationhasbeenproposedbyseveralauthors.However,uptodatetherewasnotanyexperimentalworkperformedunderrealconditions.Thus,themainobjectiveofourexperimentwastotestwirelesssensormotesbasedintheZigBee/IEEE802.15.4protocolduringarealshipment.Theexperimentwasconductedinarefrigeratedtrucktravelingthroughtwocountries（SpainandFrance）whichmeansajourneyof1,051kilometers.Thepaperillustratesthegreatpotentialofthistypeofmotes,providinginformationaboutseveralparameterssuchastemperature,relativehumidity,dooropeningsandtruckstops.Psychrometricchartshavealsobeendevelopedforimprovingtheknowledgeaboutwaterlossandcondensationontheproductduringshipments.

1.Introduction

Perishablefoodproductssuchasvegetables,fruit,meatorfishrequirerefrigeratedtransportation.Foralltheseproducts,Temperature（T）isthemostimportantfactorforextendingshelflife,beingessentialtoensurethattemperaturesalongthecoldchainareadequate.However,localtemperaturedeviationscanbepresentinalmostanytransportsituation.Reportsfromtheliteratureindicategradientsof5°

Cormore,whendeviationsofonlyafewdegreescanleadtospoiledgoodsandthousandsofEurosindamages.Arecentstudyshowsthatrefrigeratedshipmentsriseabovetheoptimumtemperaturein30%oftripsfromthesuppliertothedistributioncentre,andin15%oftripsfromthedistributioncentretothestores.Royetal.analyzedthesupplyoffreshtomatoinJapanandquantifiedproductlossesof5%duringtransportationanddistribution.Thermalvariationsduringtransoceanicshipmentshavealsobeenstudied.Theresultsshowedthattherewasasignificanttemperaturevariabilitybothspatiallyacrossthewidthofthecontaineraswellastemporallyalongthetrip,andthatitwasoutofthespecificationmorethan30%ofthetime.Inthoseexperimentsmonitoringwasachievedbymeansoftheinstallationofhundredsofwiredsensorsinasinglecontainer,whichmakesthissystemarchitecturecommerciallyunfeasible.

Transportisoftendonebyrefrigeratedroadvehiclesandcontainersequippedwithembeddedcoolingsystems.Insuchenvironments,temperaturesriseveryquicklyifareeferunitfails.Commercialsystemsarepresentlyavailableformonitoringcontainersandtrucks,buttheydonotgivecompleteinformationaboutthecargo,becausetheytypicallymeasureonlytemperatureandatjustonepoint.

Apartfromtemperature,waterlossisoneofthemaincausesofdeteriorationthatreducesthemarketabilityofperishablefoodproducts.Transpirationisthelossofmoisturefromlivingtissues.Mostweightlossofstoredfruitiscausedbythisprocess.Relativehumidity（RH）,Toftheproduct,Tofthesurroundingatmosphere,andairvelocityallaffecttheamountofwaterlostinfoodcommodities.Freewaterorcondensationisalsoaproblemasitencouragesmicrobialinfectionandgrowth,anditcanalsoreducethestrengthofpackagingmaterials.

Partiesinvolvedneedbetterqualityassurancemethodstosatisfycustomerdemandsandtocreateacompetitivepointofdifference.Successfultransportinfoodlogisticscallsforautomatedandefficientmonitoringandcontrolofshipments.Thechallengeistoensureacontinuous„coldchain‟fromproducertoconsumerinordertoguarantyprimeconditionofgoods.

TheuseofwirelesssensorsinrefrigeratedvehicleswasproposedbyQingshanetal.asanewwayofmonitoring.SpecializedWSN（WirelessSensorNetwork）monitoringdevicespromisetorevolutionizetheshippingandhandlingofawiderangeofperishableproductsgivingsuppliersanddistributorscontinuousandaccuratereadingsthroughoutthedistributionprocess.Inthisframework,ZigBeewasdevelopedasaverypromisingWSNprotocolduetoitslowenergyconsumptionandadvancednetworkcapabilities.Itspotentialformonitoringthecoldchainhasbeenaddressedbyseveralauthorsbutwithoutrealexperimentation,onlytheoreticalapproaches.Forthisreason,inourworkrealexperimentationwiththeaimofexploringthelimitsofthistechnologywasapriority.

ThemainobjectiveofthisprojectistoexplorethepotentialofwirelessZigBee/IEEE802.15.4motesfortheirapplicationincommercialrefrigeratedshipmentsbyroad.Asecondaryobjectivewastoimprovetheknowledgeabouttheconditionsthataffecttheperishablefoodproductsduringtransportation,throughthestudyofrelevantparametersliketemperature,relativehumidity,light,shockingandpsychrometricproperties.

2.MaterialsandMethods

2.1.ZigBeeMotes

FourZigBee/IEEE802.15.4motes（transmitters）andonebasestation（receiver）wereused.AllofthemweremanufacturedbyCrossbow.Themotesconsistofamicrocontrollerboard（Micaz）togetherwithanindependenttransducerboard（MTS400）attachedbymeansofa52pinconnector.TheMicazmotehostsanAtmelATMEGA103/128LCPUrunningtheTinyOperatingSystem（TinyOS）thatenablesittoexecuteprogramsdevelopedusingthenesClanguage.TheMicazhasaradiodeviceChipconCC24202.4GHz250KbpsIEEE802.15.4.PowerissuppliedbytwoAAlithiumbatteries.

Thetransducerboardhostsavarietyofsensors:

TandRH（SensirionSHT11）,Tandbarometricpressure（IntersemaMS5534B）,lightintensity（TAOSTSL2550D）andatwo-axisaccelerometer（ADXL202JE）.Alaptopcomputerisusedasthereceiver,andcommunicateswiththenodesthroughaMicazmountedontheMIB520ZigBee/USBgatewayboard.

EachSensirionSHT11isindividuallycalibratedinaprecisionhumiditychamber.Thecalibrationcoefficientsareusedinternallyduringmeasurementstocalibratethesignalsfromthesensors.TheaccuraciesforTandRHare±

0.5°

C（at25°

C）and±

3.5%respectively.

TheIntersemaMS5534BisaSMD-hybriddevicethatincludesapiezoresistivepressuresensorandanADC-InterfaceIC.Itprovidesa16bitdatawordfromapressureandT（−40to+125°

C）dependentvoltage.Additionallythemodulecontainssixreadablecoefficientsforahighlyaccuratesoftwarecalibrationofthesensor.

TheTSL2550isadigital-outputlightsensorwithatwo-wire,SMBusserialinterface.Itcombinestwophotodiodesandananalog-todigitalconverter（ADC）onasingleCMOSintegratedcircuittoprovidelightmeasurementsovera12-bitdynamicrange.TheADXL202Emeasuresaccelerationswithafull-scalerangeof±

2g.TheADXL202Ecanmeasurebothdynamicacceleration（e.g.,vibration）andstaticacceleration（e.g.,gravity）.

2.2.ExperimentalSetUp

Theexperimentwasconductedinarefrigeratedtrucktravelingduring23h41m21sfromMurcia（Spain）toAvignon（France）,adistanceof1,051km.Thetrucktransportedapprox.14,000kgoflettucevar.LittleGemin28palletsof1,000×

1,200mm.Thelettucewaspackedincardboardboxeswithopeningsforaircirculation.

Thelengthofthesemi-trailerwas15mwithaCarrierVector1800refrigerationunitmountedtothefrontofthesemi-trailer.Forthisshipmentthesetpointwas0°

C.

Thetruckwasoutfittedwiththewirelesssystem,coveringdifferentheightsandlengthsfromthecoolingequipment,whichwasatthefrontofthesemi-trailer.Fourmotesweremountedwiththecargo（seeFigure1）:

mote1wasatthebottomofthepalletsinthefrontsideofthesemi-trailer,mote2wasinthemiddleofthesemi-trailer,mote3wasintherearatthetopofthepallet,andmote4waslocatedasshowninFigure1,aboutathirdofthedistancebetweenthefrontandtherearofthetrailer.Motes1,2and3wereinsidetheboxesbesidethelettuce.Theprograminstalledinthemotescollectsdatafromallthesensorsatafixedsamplerate（7.2s）,witheachtransmissionreferredtoasa“packet”.TheRFpowerintheMicazcanbesetfrom−24dBmto0dBm.Duringtheexperiment,theRFpowerwassettothemaximum,0dBm（1mWapproximately）.

2.3.DataAnalysis

AspecializedMATLABprogramhasbeendevelopedforassessingthepercentageoflostpackets（%）intransmission,bymeansofcomputingthenumberofmultiplesendingfailuresforagivensamplerate（SR）.Amultiplefailureofmmessagesoccurswhenevertheelapsedtimebetweentwomessagesliesbetween1.5×

m×

SRand2.5×

SR.Forexample,withasamplerateof11s,asinglefailure（m=1）occurswheneverthetimeperiodbetweenconsecutivespacketsislongerthan16.5s（1.5×

1×

11）andshorterthan27.5s（2.5×

11）.Thetotalnumberoflostpacketsiscomputedbasedonthefrequencyofeachfailuretype.Accordingly,thetotalpercentageoflostpacketsiscalculatedastheratiobetweenthetotalnumberoflostpacketsandthenumberofsentpackets.

Thestandarderror（SE）associatedtotheratiooflostpacketsiscomputedbasedonabinomialdistributionasexpressedinEquation1,wherenisthetotalnumberofpacketssent,andpistheratiooflostpacketsintheexperiment.

2.4.AnalysisofVariance

FactorialAnalysisofVariance（ANOVA）wasperformedinordertoevaluatetheeffectofthetypeofsensorintheregisteredmeasurements,includingT（bymeansofSensirionandIntersema）,RH,barometricpressure,lightintensityandaccelerationmodule.ANOVAallowspartitioningoftheobservedvarianceintocomponentsduetodifferentexplanatoryvariables.TheSTATISTICAsoftware（S