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Croat. j. for. eng. 37(2016)2 309

Evaluation of Chipping Productivity with Five Different Mobile Chippers at Different

Forest Sites by a Stochastic Model

Mika Yoshida, Simon Berg, Rin Sakurai, Hideo Sakai

Abstract

It is important to evaluate chipping productivity that often differed according to the timing of observations and varied unexpectedly. A variation in production was the major concern of stakeholders for sustainable forest operation to establish regularly attainable production sched-ules on many operational levels. The aim of this study was to estimate the variance of chipping productivity by using a stochastic simulation model to achieve the objective evaluation of chipper performances. Chipping operations of five different kinds of mobile chippers, i.e. three smaller and two middle and larger ones in horse powers, were investigated. Probability dis-tributions of material size and feeding time for chipping in a log-normal distribution were estimated. The estimates were made based on chipping operations performed 2000 or 4000 times by mechanical repetitions. Except for the largest chipper, whose observed productivity was 338 loose m3/hr, all of the observed productivities, varying from 18 to 68 loose m3/hr, were located within a two-sided confidential interval whose difference between both ends was 4 to 10 loose m3/hr. The estimates were, generally, reliable with small variances around the median productivity values in the model. By this stochastic model, chipper productivity could be shown objectively, while the accuracy would be improved more by increasing sample size and accurate material size measurement. It was elucidated that the operations followed by chipping should encompass enough volume capacity to provide stable chipping productivity.

Keywords: biomass energy, feeding operation, forestry operation, stochastic simulation, supply chain management

1. IntroductionThe wood use as a renewable energy resource is

a global trend. In EU, for instance, the target rate of renewable energy share was clearly stated as 20% in 2020(EuropeanCommission2013)andtheshareofwoodybiomassmightaccountfortwo-thirdsofthetarget(EuropeanCommission2015).InCanadaandthe US, the regular share of wood as a renewable en-ergywasalsoreported(GovernmentofCanada2014,USEnergyInformationAdministration2015).Wooddemand as an energy resource enabled theuse oflowerqualitytimbersandloggingresidues.Itwas,therefore,expectedtoprovideadditionalresourcesandprofitforforestcompaniesorforestlandownerswhileimprovingforesthealthandquality.

InJapan,theplantedforestsoccupiedaquarterofland,andabouthalfofthemwereunder50yearsold(JapaneseForestryAgency2015).Itwasamajorcon-cerntomanageandutilizethelowerqualityoftimbersproducedfromsuchplantedforests.Whiletherewereconventionalchipsupplychainsforpulpindustriesusingfixedchippersatfactoriesorstoragesites,somemobilechippershadbeguntobeintroducedtopro-ducewoodchipsatforestroadsidewiththeexpecta-tionofstablechipsupplytopowerplantsasaresource.Oneoftheemergingissuesonmobilechipperswasestimating theproductivityof chippingoperationsbecausetheirworkingconditionsandwoodmaterialsalwayschangedcomparedtofixedchippers.Atthesametime,thesupplychainwithchipping

atforestroadsideforthetimelyproductionofchips

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310 Croat. j. for. eng. 37(2016)2

requiredawell-balancedtransportationandlogisticstofactoryorstoragesitestorealizethemaximumuti-lizationofchipper(TalbotandSuadicani2005),be-causethepriceofchipperswasgenerallyexpensive(YoshidaandSakai2014).Indeed,itwaspossibletodischargechipsonthegroundandtoreloadthemontothe truck, but additional cost would occur in the re-loadingprocess (Stampfer andKanzian 2006) andchipswouldlosequalitybygettingcontaminatedwithsoil(SpinelliandHartsough2001).Therefore,theim-portanceofchippingproductivityestimationofmo-bilechippershasbeengraduallyrecognizedforopti-mizedtransportationscheduling.Itwaswellknownthattheproductivityofwood

chippingwasmainlyaffectedbythetotalsolidvolumeofapieceofmaterialandtherelationshipcouldbeexpressedinvariousways:suchaschippingtimeperton/volumeforaveragepiecesize(SpinelliandHart-sough2001,Assirelli etal.2013); for loadsizeofaloader(Röseretal.2012);forpiecesizeunderamulti-linearregressionanalysis(Ghaffariyanetal.2013);andforbutt-enddiameteroflogs(YoshidaandSakai2014).Previousmodelsofproductivityestimationweregen-erallyanalyzedbyusinglinearregressionfortheaver-ageofmaterialsizes ina forestarea.Furthermore,productivityusuallydifferedaccordingtothetimingofobservations.Itwasalsoprovedthatthereweredif-ferencesbetweenestimatedandactualproductivitiesacrosschippermachines(e.g.SpinelliandMagagnot-ti2010,Ghaffariyanetal.2013).Asthematerialsupplyandtheproductdeliverywerescheduledunderthetypicalproductivity,unexpectedvariancesofchippingproductivitysometimesdiminishedorerodedprofitinitssupplychain.Therefore,therealisticandtypicalvalueofchippingproductivityanditspossiblevari-anceshouldbegraspedinadvanceforprofitableandsustainablemanagementofchipsupplychain.Thevarianceofchippingproductivityseemedtobe

decidedmainlybyfluctuations in feeding timeofapieceofmaterialtoachipper.Comparedtootherfor-estryoperations,suchastimberharvesting,chippingoperationhad less complexoperations (Röser et al.2012),andoperator’seffectcouldberegardedassecon-daryandminutetooverallproduction(SpinelliandMagagnotti2010).Thus,thevarianceofchippingpro-ductivitymightbemainlyderivedfromtheaccumula-tionofuncertaintyinmaterialvolumeandfeedingop-erations,andthemachineryconditionsuchasbladewear(Spinellietal.2014).Theaimofthisstudyistoestimatethevarianceof

chippingproductivitybyusingastochasticsimulationmodelfortheobjectiveevaluationofchipperperfor-mance.Stochasticsimulationmethodwasselectedtoanalyzetheproductivityvariancesincethemethodissuitabletodescribecomplexproblemsincludingin-

teractionsanduncertainty.ThisapproachhadbeenusedinpreviousstudiessuchasGallis(1996),TalbotandSuadicani(2005),Asikainen(2010)andZamora-Cristalesetal.(2013)toanalyzeinteractionsamongproductionandtransportationprocesses.Sincefeed-ingandchippingoperationswere inaninteractiverelationship(Röseretal.2012),thisstudyanalyzedproductivityoffivedifferenttypesofcurrentlyusedmobilechippersconsideringtheinteractionwithinaprocessandverifiedtherobustnessofthemethod.

2. Material and methods

2.1 Time observation and machine selectionToobtainrealdataatforestchippingsite,timeob-

servations recordedby stopwatchwere conductednearbythefivemobilechippersduringoperations.Theobservedproductivechippingsystemfactorsinacycleweredividedintotwomainelements:feedingtochip-perandoperationaldelay.Acycleoffeedingtochipperoperation,whichessentiallydeterminedtheproductiv-ity,wasdefinedasthetimebetweenthestartofgrap-plingbytheloadermovementontothematerialpileafterthepreviousreleasingofmaterialsandtheendofreleasingmaterialsonthefeeder.Theoperationaldelaywastimeofextraoperationsthatincludedcleaningthelandingsite;preparinglogsfrompiles;andremovalofstucklogsinthefeeder,whichrandomlyoccurred.Alloperationaldelayslessthan15minutesweretakenintoconsiderationintheproductivitycalculationforsecur-ingcomparability;theywereusedasoneofthecriteriato classify delays (Samset 1990).Other delays over15minutescouldberegardedasavoidabledelaysthatcouldbeexcludedfromtheanalysisbygivingappro-priateconsiderations.Mechanicalandobservationde-lays were also excluded.Toderiveproductivity,thecommonformulafor

chippingproductivityP (loosem3/hr)wasusedasshownbelow:

1

/n

ii

P d V h=

= ∑ (1)

Where:d densitycoefficientforconvertingsolidvol-

umetochipvolume(loosem3/solidm3)n totalnumberoffeedingoperationsinapro-

ductionperiodVi materialsolidvolumeineachitimeoffeeding

operation(solidm3)h grossworkingtimeofachippingoperation

(hr)consistingofthetimeforfeedingopera-tionsandoperationaldelayslessthan15min-utes.

Evaluation of Chipping Productivity with Five Different Mobile Chippers at Different Forest ... (309–318) M. Yoshida et al.

Croat. j. for. eng. 37(2016)2 311

Inthisstudy,analysiswasmadeoffivedifferenttypesandsizesofmobilechippers:TP250mobileturn-ablediscchipper(expressedasTP250);Farmi380discchipper;YM-400Cdiscchipper;MUS-MAXWT8-XLdrumchipper(expressedasMUS-MAX);andCBIchipmax484VRdrumchipper(expressedasCBI).Theyhadbeenalreadyinactiveuseintheircountriesoforiginandcouldbeconsideredasrepresentative.ThechippermachinedetailsaresummarizedinTable1.TP250wasthesmallestchipperinsizewithengine

powerof53.7kW,anditwaschosenforitssmallsize.Thischipperwasdesignedformanualloadingandvehicle traction,andan independentwheel loaderwasusedtoassistitsloadingoperation.Farmi380andYM-400Chadsimilarenginepowerof140kWand150kW,respectively.Farmi380wasoneoftheattachmentsconnectedtothetractorpowertakeoff(PTO).Ithadafeedingassistantchainconveyorandanintegratedgrappleloader.YM-400Cwasaninde-pendentmachinemountedonafour-wheeledvehicleequippedwithafeedingassistantbeltconveyorandithadtobesupportedbyaseparategrappleloader.Becauseoftheirrelativelysmallersizeandmobility,thesethreechippersseemedtobefittedtosmall-scaleforestryandoperationsalongforestroads.MUS-MAXwasatruckmountedchipperwithafeedingassistantbeltconveyorandanintegratedgrappleloader.Itwaschosenforitsall-in-onesystem.Ithadanenginewith353kWpower,andcouldberegardedasamiddleclass chipper.CBIwas the representative of largechippersbothinsizeandenginepower.Itwasequip-pedwithanengineof570.7kWpowerandabeltcon-veyorinfrontofthefeeder.

Atafeedingoperation,pluralpiecesofmaterialwerefedateveryfeedingcycleintheinvestigationsexceptforYM-400C.AlthoughYM-400Chadafeederbigenoughforpluralpieces,onlyapieceofmaterialwasfedpercyclebecausepluralpieceswerestuckwhenfed.Theinvestigationsiteswerelocatedindif-ferentareasofJapan:TP250,YM-400C,andCBIwereobservedinShimanePrefecture;Farmi380wasob-served inYamagata Prefecture; andMUS-MAX inAkitaPrefecture.TheoperationalsiteswereatpavedlandingsexceptforFarmi380atastoragelanding.

2.2 Material size measurementThenumberoflogsofeachfeedingoperationwas

observedandrecordedbyavideocameratoverifythenumberorquantityoffedmaterials.Thematerialswerepreparedinaccordancewiththeusualpracticeandthesizewasmeasuredduringactualinvestiga-tionsbydifferentmethods.IntheinvestigationsofTP250andYM-400C,logsof4minlengthwereused,andtheirtopandbutt-enddiameterandlengthweremea-suredforthevolumecalculationbySmalian’sformula.IntheinvestigationofFarmi380,shortlogsof2minlengthwereused;thetop-enddiameterof124logswasrandomlymeasuredfromthepiletoestimatethema-terialsizedistribution.Itwaspossibletoregardsuchshortlogsascolumn-shapedtimber,andthevolumewascalculatedbysquareddiametermethodfromtop-enddiameters.IntheinvestigationofCBI,thetotalweightoflogswasmeasuredas7.3wet-tonnesbeforechipping.Theaveragevolumeofapieceofmaterialwascalculatedbythenumberoflogsandtheweightdensitycoefficientof0.6wet-tonne/solidm3assuming

Table 1 Chipper machine details

Chipper TP250 mobile turnable Farmi 380 YM-400C MUS-MAX WT8-XL CBI chip max 484VR

Country of origin Denmark Finland South Korea Austria USA

Chipping type Disc (2 knives) Disc (4 knives) Disc (4 knives) Drum (8 knives) Drum (4 knives)

Mobility Traction Tractor attachment Self-propelled Truck mounted Traction

Engine Internal External Internal Internal Internal

Power, kW 53.7 140.0 150.0 353.0 570.7

Feeder dimension, mm H 260 x W 350 H 380 x W 420 H 500 x W 400 H 600 x W 640 H 762 x W 1,219

Feeding assistant No Chain conveyor Belt conveyor Belt conveyor Belt conveyor

Discharger type Blower Blower Blower Blower Blower

Material type used in the investigation

Sugi and hinoki logs of 4m in length

Sugi short logs of 2m in length

Sugi logs of 4m in length

Sugi and hinoki logs of 4m in length

Tree tops and short logs

Observation place Paved landing Unpaved landing Paved landing Paved landing Paved landing


312 Croat. j. for. eng. 37(2016)2

thatthewet-basedmoisturecontentwas50%moisturebasewhoseoven-dryweightwas0.3oven-drytonnes(GreenhouseGasInventoryOfficeofJapanetal.2014).Thenumberoffedlogsatafeedingoperationwasregardedasthematerialsizedistribution.Intheinves-tigationofMUS-MAX,logsof4minlengthwereused;thiertop-enddiameterswerepreviouslyclassifiedin2cmincrementbytheoperationcontractorforthecal-culationofvolumebyusingavolumetableusedinpractice.Theunit»loosem3«meantacubicmeterofchipvolume.

2.3 Simulation methodTheprobabilitydistributionsoffeedingtimeand

materialsizewereassumedtofollowalog-normaldistributionbecauseallofthefigureswerepositivenumbersandtheoreticallycouldbeinfinite.Toobtainappropriatelocationm and scale sparameters,feedingtime,andmaterialsizedata,theseparameterswerere-sampled by bootstrapping 2000 repetitions onTP250,YM-400C,Farmi380andMUS-MAX,andby4000repetitionsonCBIbecauseofitssmalloriginalsamplesize.ThenormalityofgeneratedparameterswastestedbyShapiro-Wilknormalitytest,andtheadaptabilityofmeanandmedianvalueswasalsoex-amined.Byusingtheseobtainedparameters,chippingpro-

ductivityatjtimeofrepetitionsPj(loosem3/hr)wasexpressedbyusingtwoindependentprobabilitydis-tributions,astheformula(2):

( )j, D

(1 ) ( ) / ( )M F

P d f M g F∈

= − ∑ ∫a (2)

Where:a operationaldelaytimeratioobservedinac-

tualoperations datasetoftrialsindicatingthenumberofop-

eration cycles F andmaterialsMf(M) probabilitydistributionofmaterialsizeata

feedingoperationg(F) probabilitydistributionoffeedingtime.

Thesedenominatorsandnumeratorswereinde-pendent.Thedensitycoefficientdwassetat2.8loosem3/solidm3here(Serupetal.2002).R-languagever.3.1.2(RCoreTeam2014)wasusedforallanalysesandsimulations.

3. Results

3.1 Result of chipping observationInvestigationdetailsaresummarizedinTable2.

TheproductivityofMUS-MAXwashigherthanthatof other three smaller chipperswith the relativelyloweroperationaldelaytimeratio.CBIshowedthehighestproductivity.Inalloftheobservations,therewerenooperationaldelaysexceeding15minutes.ThedelayratioofFarmi380wasgreaterthanthatofothersandthedelaytimeofCBIwasnotobserved.

3.2 Result of simulationFig.1showsthehistogramsandfittedprobability

distributionsforthetimeoffeedingoperationandma-terialsizeofeachchipper.Thefitteddistributionof

Table 2 Investigation details and simulation constraints

ChipperTP250 mobile

turnable Farmi 380 YM-400C

MUS-MAX WT8-XL

CBI chip max 484VR

Total time of investigations, min 64.2 48.6 90.0 60.6 5.16

Observed cycles of feeding operation, cycles 25 63 115 88 13

Number of logs, pieces 80 464 115 569 111

Number of simulation cycles for feeding time function F Î 25 63 115 88 13

Number of simulation cycles for material size M Î 80 464 115 569 13

Representation of material size probability distribution f(M) Log volumeDiameter of a

short logLog volume Log volume

The number of logs at a feeding operation

The number of repetitions i 2000 2000 2000 2000 4000

Average material size, solid m3 0.08 0.02 0.08 0.05 0.09

Operational delay time ratio 0.15 0.43 0.24 0.08 0.00

Productivity from the actual observation, m3/hr 18.7 22.6 18.3 68.2 338.0


Croat. j. for. eng. 37(2016)2 313

Fig. 1 Histograms and fitted probability distributions for the time of feeding operation and material size of each chipper


314 Croat. j. for. eng. 37(2016)2

materialsizeofCBIhadapeakeddistributionhigherthanitshistogram,andthatofMUS-MAXhadagen-tlerpeakcomparedtoitshistogram,whiletheothersweregenerallywell-fittedtolog-normaldistribution.The locationandscaleparametersandp-valuesbyShapiro-WilknormalitytestoftheseparametersareshowninTable3.Althoughthep-valueswerepartlysignificantforFarmi380,MUS-MAXandCBI,allofthelocationandscaleparameterswereconcentratedinaverynarrowrangearoundthemean/medianval-uesasnormaldistribution.Themedianvalueswere,therefore,representedasbothlocationandscalepa-rametersforprobabilitydistribution.Theestimatedproductivitydistributionsaresum-

marizedinTable4,andillustratedvisuallyinFig.2.Productivitiescouldbeestimatedwithvariance,andobjectivelyevaluated.Theproductivitiesfromactualobservationswerewithineachtwo-sidedconfidentialinterval(p =0.05)exceptforCBI,andtheobtainedpro-ductivitydistributionsgenerallyseemedtobereliable.Alloftheproductivitydistributionswerepositivelyskewed,andthemedianvalueswereavailableastherepresentativeproductivity.Theproductivitydistribu-tionofTP250andYM-400Cwassimilartoeachotherdespitetheirdifferencesinmachinesizeandenginepower.Comparing the estimatedproductivities ofYM-400CandFarmi380,thatofFarmi380washigherwhilethemachinesizeandenginepowerweresimilarto each other.

TheproductivityofchippersTP250,YM-400C,andFarmi 380 concentrated in anarrowvariancewithabout4loosem3/hrdifferenceintwo-sidedconfiden-tial interval.TheestimatedproductivityofmiddleclassmachineMUS-MAX,whichhadbiggermechan-icalpowerpotential,alsovariedonlyin10loosem3/hr differencesinthetwo-sidedconfidentialinterval,andtheproductivitywasalsoconcentratedaroundtheme-dianvalueaswellasthoseofotherthreesmallerchip-pers.Onthecontrary,theproductivityofCBI,whoseenginepowerwasaboutdoubleto10timeslargerthanthoseofothers,variedinabout60loosem3/hrdifference

Table 4 Summary of chipping productivity estimation using a sto-chastic modeling method

Chipper TP250 Farmi 380 YM-400C MUS-MAX CBI

Min, m3/hr 13.55 20.12 15.44 58.15 226.77

Median, m3/hr 17.01 23.72 18.40 66.33 278.44

Mean, m3/hr 17.04 23.72 18.41 66.37 270.09

Max, m3/hr 20.63 27.69 23.04 75.83 343.62

Two sided confidential interval, m3/hr

Lower 15.25 21.84 16.42 60.94 250.98

Upper 19.12 25.79 20.51 71.90 309.65

Skewness 0.14 0.15 0.16 0.06 0.20

Kurtosis 0.002 0.11 0.11 –0.018 0.10

The term »m3« means loose m3 here

Table 3 Parameters of log-normal distribution for material size and feeding time, and p-value of Shapiro-wilk normality test

ChipperProbability distribution

Location parameter m Scale parameter s

Median Mean p-value Median Mean p-value

TP250f(M) –2.59 –2.59±0.02 0.25 0.15 0.15±0.01 0.10

g(F) 4.70 4.70±0.06 0.065 0.27 0.27±0.04 0.32

Farmi 380f(M) 2.26 2.26±0.02 0.77 0.23 0.23±0.01 0.47

g(F) 3.47 3.47±0.04 0.014* 0.30 0.30±0.03 0.25

YM-400Cf(M) –2.63 –2.63±0.05 0.74 0.48 0.48±0.04 0.35

g(F) 3.40 3.39±0.03 0.75 0.32 0.32±0.02 0.53

MUS-MAXf(M) –3.06 –3.06±0.007 0.47 0.18 0.18±0.005 0.55

g(F) 3.12 3.61±0.04 0.33 0.38 0.38±0.04 0.00067***

CBIf(M) 2.08 2.07±0.11 5.95e–05*** 0.38 0.37±0.06 2.2e–16***

g(F) 3.12 3.12±0.09 0.073 0.30 0.30±0.06 3.667e–08***

* Significant (p<0.05). Distribution could not clarify its normality by normality test, but median values could be used as representative*** Significant (p<0.001). Distribution could not clarify its normality by normality test, but median values could be used as representative


Croat. j. for. eng. 37(2016)2 315

inthetwo-sidedconfidentialinterval.Indeed,theCBIhadhighpotentialofproductivity,butthislargedif-ferencecouldbeaprobleminproductionschedulingandinvestment.Businessplanningwouldneedim-provement.

4. Discussion4.1 Suggestions for productivity improvementTheoperationaldelaytimeratiosofchippersvar-

iedanditimpliedthepossibilityofproductivityim-provementbecausedelayhadabiginfluenceonchip-ping operation (Spinelli and Visser 2009). TheproductivityofYM-400Cwassimilartothatofsmall-erchipper,TP250.Increasingtheaveragesizeofmate-rialandconsecutivefeedingwereaneffectivewaytoimproveproductivity.AsonlyalogcouldbefedatonefeedingoperationforYM-400Cduetoitsweakpoweroffeedingroller,reducingoperationaldelaysatleasttothedelaytimeratiothatTP250showedwasalsoanotherwaytoimproveproductivity.Farmi380alsohadalargeroperationaldelaytimeratio,whichmighthaveanegativeeffectontheproductivityattheinves-tigationsite,wherematerialpileswerelocatedbehindthefeederentrance.Itwouldbepossibletodecreasesuchoperationaldelaybylocatingthematerialpileinfrontofthefeeder.Theotherthreechippers,TP250,CBIandMUS-MAX,showedsmalleroperationalde-laytimeratios.Theproductivityofbiggerclasschip-perscouldbeimprovedandmadesustainablebypre-paringsufficientlylargeamountsofmaterial.

ThesizeofTP250was,however,smallandmanualfeedingsystemrestrictedlogsizeseveniftheoperatorusedawheelloaderforfeedingassistance.Hence,ithadfewpossibilitiesofproductivity improvement.Conversely,MUS-MAXhadalargerentranceandafeeding-assistantbeltconveyor.Thecapacityseemedtobeusedatmaximumbecausethechippercouldhavemultiplelogsfedatthesametimeatthefeedingoperationduringtheobservation.AsforMUS-MAX,itschippingproductivityimprovementcouldonlybepossiblebyextendingthelengthoftimberlogs.Thelargestmachine,CBI,hadagreaterfeedingspaceforputtingmorematerialontotheconveyorduringtheobservations.Furthermore,utilizationofagrappleloaderwithlargergrapplingcapacitywouldincreasethevolumeofafeedingoperation.Ingeneral,theap-propriateuseandsetupofoperationonsiteareimpor-tanttoachievethefullperformanceofchippers.Itisalsonecessarytodeterminehowthesystemcouldbeimprovedtoincreaseitsperformanceandefficiency.

4.2 Discussion of the modelThere were almost no differences between the

meanandmedianvalues.Log-normaldistributionwasgenerallywell-correlatedtoexpressfeedingtimeandmaterialsizedistributionsbecausechippingop-erationwassimpletoformasupplychainandstableinitsproduction.Thelocationandscaleparametersestimated fromonedataset of an actual operationcouldrepresentfeedingtimeandmaterialsizedistri-butionsatanacceptablelevelbyapplyingre-samplingmethodusedinthisstudy.

Fig. 2 The productivity distribution of each chipper


316 Croat. j. for. eng. 37(2016)2

Theobservedproductivitieswereintherangeoftwo-sidedconfidentialintervalsexceptforCBI.OnepossiblereasonforthisresultontheCBIwasthattheactualnumberofmaterialduringinvestigationmightbemorethanthatrecorded.Thetruematerialsizedis-tributionofCBIintheinvestigationmightbewiderandtheaveragewouldactuallybesmaller.Theproductivityvarianceexpressedbytwo-sided

confidentialintervalscouldbederivedfromthenum-beroftrialsinasimulation .Basedonacommonlawknownasthelawoflargenumbers,theaverageoftheresultsobtainedfromalargernumberoftrialsshouldbeclosetotherealmean.ThetrialnumbersofCBIwassmallerthanothersinthematerialsizedistribution,whichcausedwiderproductivityvarianceofmoreabout60loosem3/hrdifferenceinthetwo-sidedcon-fidentialinterval.Acontinuousoperationwithlargeroperationcyclesandmaterialswas,therefore,prefer-abletoachieveproductivitiesinanarrowvarianceandtoestablishastablechipsupplychainbasedonareli-ablechippingproductivitybyneutralizingthevari-anceinchippingproductivity.ForMUS-MAX,thematerialsizewasmeasuredby

theconventionalmethodusingvolumetablewithadiameterincrementof2cm.Forexample,thevolumeofalogwiththediameterof17.9cmwasactuallycal-culatedasthatofalogwiththediameterof16.0cm.Therefore, thehistogramwasdiscreteandconcen-tratedonsomespecificvolumes.Thevolumeofmate-rialsusedinthisstudyseemedtobeunderestimated,causingdifferencesbetweenthehistogramfromac-tualdataandthefitteddistribution.However, thisunderestimationwasminorinthisstudybecausethesizedistributionwouldnotbevariedenoughtoaffecttheresultofproductivityestimation,andbecausethenumberoffeedingcyclesinacontinuousoperationwasenoughtoneutralizethevariationofeachfeedingoperation.Nevertheless,themeasurementofmate-rialsizewasimportantandshouldbepreciseasmuchaspossibletoprovidetheestimatedchippingproduc-tivity.Forfurtherdevelopmentofthissimulationmeth-

od,itisnecessarytohavealargeandaccuratedatapooloffeedingtime,materialsizedistribution,andoperationaldelaytimeobtainedfrompracticeondif-ferentkindsofchippers.Recently,somegrappleload-ershavebeenequippedwithanautomaticdatacollec-tionsystemtorecordtheiractualproductivetimeratioandfuelconsumption.Suchtechnologyisusefultogetfeeding and operational delay time automatically.Datasharingamongresearchersandstakeholdersofchip supply chain are also useful to increase theamountofverifieddataforobjectivecomparisonofchipperswithacomprehensivedatabase.

4.3 Evaluation of five chippers and suggestionConsideringthehotsysteminwhichchipswere

transporteddirectlyafterchipproduction(Asikainen1998),MUS-MAXwouldhaveanadvantageintermsofinteractionwithtransportationbecausethecapac-ityof typicalsemi-trailers/largetruckswasaround40m3inJapan,sothatchippingtimewouldbelessthanonehourbyMUS-MAXandchippingandtrans-portcouldbeharmonized.Theotherthreesmallerchipperswouldtakemoretimeforchippingandtheywouldbesuitableforthecombinationwithsmallertrucks,whereasalloftruckcapacitiescommonlyusedinJapanseemedtobetoosmallforCBItoshowitspotential.Therefore,forCBI,itwaspreferabletoar-rangeasystemwithnointeractionbetweenchippingoperationandchip transportationsystems.Table4showedthattheproductivitybegantovaryincreas-inglyasthechippersizebecamelarger;therefore,theconditionsofoperationatchippingsite,andquantityandqualityofmaterialshouldbemadeasgoodaspossibleespeciallyforlargeclasschippers.

5. ConclusionsThevarianceofproductivities for each chipper

couldbeestimatedfromrecordedobservations,anditcouldbeneutralizedbyincreasingthenumberoffeed-ingcycles.Thisstochasticmodelapplyinglog-normaldistributionsbothtofeedingoperationsandmaterialsizewasusefultoestimatetheobjectiveproductivityanditsvariance.Accuratematerialsizemeasurementwasindispensabletomakethisestimationmorepre-cise,andtheoptimaloperationalsetupforeachma-chine characteristics shouldbeapplied inorder topresentitsproductivity.Typicalproductivitiescouldberepresentedbythemedianvaluesoftheproductiv-itydistribution,andthesecorrespondedtotheiren-ginepowers.Atthesametime,thevarianceofproduc-tivityshouldbetakenintoaccountwhenchoosingchippersandplanningchipsupplychain.

AcknowledgementsWearegratefultoMr.IshiyamafromForestRealize

Co.,Ltd.,Kaneyama-machiforestowners’association,Kazunoforestowners’association,Sekiouforestown-ers’association,Okidouzenforestowners’associationandShinwa-SangyoCo.,Ltd.forofferingopportuni-tiesofchippingobservation.WewouldliketothankDr.DillonChrimesforhisEnglishrevision.ThisworkwaspartlysupportedbyGrant-in-AidforJSPSFel-lows.


Croat. j. for. eng. 37(2016)2 317

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318 Croat. j. for. eng. 37(2016)2

Received:November2,2015Accepted:March9,2016

Authors’address:

MikaYoshida,PhD.*e-mail:[email protected] UniversityofTsukuba FacultyoflifeandEnvironmentalSciences 1-1-1Tennoudai,Tsukuba-shi Ibaraki,305-8577 JAPAN

Prof.HideoSakai,PhD.e-mail:[email protected],PhD.e-mail:[email protected] TheUniversityofTokyo GraduateSchoolofAgriculturalandLifeSciences 1-1-1,Yayoi,Bunkyo-ku Tokyo,113-8657 JAPAN

Rin Sakurai, PhD.e-mail:[email protected] UniversityofMiyazakiFacultyofAgriculture1-1,Gakuen-Kibanadai-Nishi,MiyazakiCityMiyazaki,889-2192JAPAN

*Correspondingauthor

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