Fuel Consumption in Timber Haulage€¦ · Fuel Consumption in Timber Haulage Radomír Klvač, Josef Kolařík, Marcela Volná, Karel Drápela Abstract – Nacrtak The paper presents

Originalscientificpaper–Izvorni znanstveni rad

Croat. j. for. eng. 34(2013)2 229

Fuel Consumption in Timber Haulage

Radomír Klvač, Josef Kolařík, Marcela Volná, Karel Drápela

Abstract – Nacrtak

The paper presents an assessment of road timber transport by trucks, which included 132 truck-and-trailer units – three types of trucks (Tatra, Mercedes Benz and Iveco) with a selec-tion of trailers in the Czech Republic. The main aim of this work was to establish the effect of hauling distance in the individual types of timber-transport units on the fuel consumption per 100 km and on the specific fuel consumption per one transported cubic metre of timber. Any decrease of fuel consumption per unit of production can enhance environmental profile of secondary transport. Freight transport recorded conspicuous changes in the last ten years, and the analysis presented in this work provides important information useful in the planning and organization of road timber transport. During the study period, obsolete and inadequate truck-and-trailer units were continuously replaced with new units, which resulted in a con-siderable reduction in fuel consumption per unit of production (0.5 L/m3 ub).

Keywords: haulage road, timber transport, truck, truck-and-trailer unit, fuel consumption

transportunitsandclassifiedthemintothefollowinggroups:vehiclecharacteristics,trailercharacteristics,roadgeometry,roadsurface,goalspeed,gearchange,drivingbehavior,weatherandroadsurfaceconditions.Theabovefactorsoftechnicalandtechnological

characterhaveaconsiderableinfluenceontheaveragefuelconsumptionof timber truck-and-trailerunits,whichmaybedoubleascomparedwiththecommonroadgoodstransportbytrucks(Devlin2010).Thenumberofinformationsystemsspecializedin

goodsorbustransportationishighintheCzechRe-publicbutthenumberofinformationsystemsspecial-izedintimbertransportislow.Haulingtimberfromtheroadsidelandingfeaturesproblemssuchashet-erogeneityofthetransportedmaterial,difficultutiliza-tionofvehiclesattheirreturnrun,seasonalcharacterofoperations,climaticeffects–alltheseresultinginahighrateof»empty«drives.Dataprocessing,trans-portoptimizationandnecessityofflexibleresponsetounexpectedsituationsputhighrequirementsbothontheinformationsystemandontimberhaulagemanag-ers.Thisiswhyaninformationsystemwasdesigned,whichtriestorespondtotheabsenceofinformationsystemsinthefieldoftimberhaulage(Klvač2006).Fromtheeconomicpointofview,theshareoftim-

berhaulageintotaltimbersupplychaincostsmayreachmorethan30%(Favreau2006).Hementionsthat

1. Introduction – UvodTimbertransportfromtheroadsidelandingtothe

customerrepresentsaverydemandingphaseinthechainoftimbersupplyintermsofenergyandcost.Itischaracterizedbyseveralspecificfactorsthatinflu-enceitsimplementationanddifferentiateitfromthegoodstransportbytrucks.Ingeneral,wecansaythatitisaone-wayhaulage,whereitisverydifficultorevenimpossibletoutilizethetimber-transportunitinitsreturnrun.Themachinesarespecificallydesignedandcanbeusedonlytoalimitedextentforthehaul-ageofothergoods.Also,theyhavetodrivealargerpartofthehaulingdistanceonforestroads.Holzleit-ner(2009)andHolzleitneretal.(2011)studiedtheop-erationoftimber-transportunitsbyusingtheGPS/GISsystemandconcludedthattheshareoftheirtravelonforest roadswas14%.Themachinesoftenhave todrivedeepintotheforestsandhavetobeadaptedac-cordingly.Theyhavetoworkindifficultfieldcondi-tionsandthereforetheyareveryfrequentlyaffectedbythemaswellasbyextremeseasonalweather.Thisiswhythetrucksareoftenequippedwiththemulti-ple-wheeldriveandheavy-dutyengines.Thesespe-cifictechnologicalrequirementsconsiderablyincreasefuelconsumptionoftimber-transportunits.Svenson(2011)mentionedarangeoftechnicalfac-

torsdirectlyaffectingthefuelconsumptionoftimber-

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230 Croat. j. for. eng. 34(2013)2

transportisthebiggestcostiteminroundwoodcostsinCanada.InSweden,Svenson(2011)saysthat35%oftotaltransportationcostsarerelatedtothefuelcon-sumptionoftimbertrucks.Economicdataprovidedby the contractor of timber-transport units,whichwerethesubjectofourstudy,demonstratedthatdieselfuelsaccounted for thehighest share in total costs(30%),followedbydepreciationandleasing(20%)andrepairsandmaintenance(16%).Wages(15%),over-headcosts(13%)andothercosts(5%)followed.Theobjectiveofimplementationoftheinformationsystemwastoconductabasicanalysisofindividualtypesoftimber-transportunitsandbasedontheacquireddatatofindprimaryrelationsaffectingtransportefficiencyandthustofindwayshowtoreducethecostoftimberhaulage.Anydecreaseoffuelconsumptionperunitofpro-

duction can enhance environmental and economyprofileofsecondarytransport.Asthefuelcostmakesthelargestpartoftotaltimberhaulagecosts,theaimofthisworkistoanalyzethefuelconsumptionintheindividualtypesoftruck-and-trailerunitsusedintim-bertransport.Anyreplacementofobsoleteandinad-equatetruck-and-trailerunitsbynewmoreefficientunitscanresultinaconsiderablereductionoffuelcon-sumptionperunitofproduction.

2. Material and methods – Materijali metode

A»tailormade«informationsystemwasdesignedin2003,whichcanreceiveordersplacedbycustomers,supportthedecision-makingprocessofdispatchersbyusingsuitabletruck-and-trailerunits(TTU),makere-cordsofhaulingperformance,monitorproductioninprogressandsummarizedataintheformofdatabases.In2004,thesystemwascharacterizedintheformofdiagramssothatdesignerswouldbecapableofmeet-ingcustomerrequirements(Klvač2006).Thisinforma-tionsystemwasdesignedforlargercompanieswithagreaternumberofvehiclesdislocatedonremotework-places.Allworkplaceshadanaccesstothesystemviaclientandworkedwithdataonmultiplelevelsrelatedtothepositionincompanyorbusinessinterrelation-ship.Eachposition/clienttypehadcentrallysetrightsandresponsibilitiesinthesystem. Atimbertransportcompanyimplementedthesystematthebeginningof2005anddataoneachindividualtransportationcasestartedtoberecordedfromtheendofthesameyear.ThedatawassummarizedforeachTTUinmonthlyintervalsforpurposesofanalyticalassessmentbythecompanymanagement. Themonthly indicators ofTTUswereusedinthisstudy.

Thestructureoftheassesseddatarelatedtothisstudywasasfollows:

Þ Truck-and-trailerunit,inventorynumberpro-videdfornon-commutabilityofdata,

Þ TTUoperationalcentre,Þ Trailer,inventorynumber,Þ Totaltraveldistance,km,Þ Travelunloaded,km,Þ Travelloaded,km,Þ Backhauling,%ofkilometersdrivenloaded,Þ Volumeoftransportedtimber,m3ub;softwoodandhardwood,

Þ Numberofloadspermonthandperday,Þ Averagesizeofload,m3ub,Þ Averagehaulingdistance–onewaydistance,km,Þ Fuelconsumptioninliterspermonth.Parametersthatwerecalculatedbasedontheabove

datawereasfollows:Þ Averagefuelconsumptionperunitofproduc-tion,llm3ub

Þ Averagefuelconsumptionper100km,ll100kmAlldatawerecheckedatfirstandrecordscontain-

inggrosserrorscausedbyhumanfactoratrecordingwereeliminated.Thenthedatawereimportedandorganizedwithinthespreadsheetsoftware(MicrosoftExcel)andsubsequentlysummarizedforindividualtypesofTTUs.Intheperiod2005–2009,considerablechangesoccurredinthefleetoftimbertransportunitswithobsoleteTTUsbeingputoutofoperationandreplacedbynewTTUswherenecessary.Oldandtech-nicallyunfitLiazTTUsweretakenoutofservicefirst.AstheamountofdataontheseTTUswasnotrepre-sentative,theLiaztypeofTTUwasnotstatisticallyevaluated in this study. Types of truck-and-trailerunitsassessedinthisstudywereIveco(representedbymodelsASTRA,MP260andSTRALIS),Tatra(rep-resentedbyTatra815only),MercedesBenz(models3344,3341,2644and3348).Thedatawereaggregatedandanalyzedaccordingtotruckmanufacturers.Theinitialanalysiswasmadewiththeuseofpivot-

ing(contingency)tablesandgraphs.GraphPadPrism5(Motulsky2007)wasusedfornon-linearregressions.Thesoftwareenablesaveryflexiblechoiceofthere-gressionmodel,ithasverygoodgraphicalcapabilitiesandprovidesthepossibilitytocomputeanddrawcon-fidenceintervalsofthemodel.Prism5caneliminateoutlierswiththeROUTmethod(MotulskyandBrown2006).Thismethodisbasedonanewrobustnon-lin-earregressioncombinedwithoutlierrejection.Itisanadaptivemethodthatgraduallybecomesmorerobustasthemethodproceeds.Pressetal.(1988)basedtheir

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robustfittingmethodontheassumptionthatvariationaround the curve followsaLorenziandistributionratherthanaGaussiandistribution.TheMarquardtnon-linearregressionalgorithmwasadaptedtoac-commodate theassumptionof aLorenzian (ratherthanGaussian)distributionofresiduals.Afterfittingacurveusingrobustnon-linearregression,athresholdisneededfordecidingwhenapointisfarenoughfromthecurvetobedeclaredanoutlier.AllmethodologyisdescribedindetailinMotulskyandBrown(2006).Theauthorsstatethattheirmethodidentifiesoutliersfromnon-linearcurvefitswithreasonablepowerandfewfalsepositives(lessthan1%).Inallcases,thelogarithmicfunctionusedforthe

regressionmodelwasinthefollowingform: y = a ln(x) + b (1)Where:

x explaining(independent)variable,y explained(dependent)variable,a, b coefficients.Therespectivestatisticalassessmentsinclude a,b

coeffi cientsestablishedbytheregressionanalysis,95%confidenceinterval(shadedinthegraphs),R2–deter-minationcoefficient,numberofanalyzedpointsandnumberofoutliers.Therespectivedependenciesarepresentedinsum-

marydiagramsinMicrosoftExcel,inwhichonlyre-gressioncurveswereplotted.

3. Results – RezultatiThetotalnumberofassessedrecords(i.e.monthly

performancesofvarioustruck-and-trailerunits)was

2548.ThetotalnumberofTTUsassessedintheperiod2005–2009was134andtheunitswereoperatedatdifferentplacesintheCzechRepublic.Intheperiod2003–2004,wemonitoredonly21trucks;thisnumberincreasedin2005to90.Inthefollowingyears,thefleetwasgraduallyrenewedandsomeoldvehicleswereputoutofoperation.Thisiswhythenumberoftrucksmonitoredin2006,2007and2008was87,80and71,respectively.In2009,theprocessofrenewalwascom-pletedandthefinalnumberoftruckswas51ofwhich45wereMercedesBenz.Intheperiodunderstudy(Table1),morethan3.4

millioncubicmetersoftimberwerehauledfromtheroadsidelandingtotheconversiondepot,directlytocustomersortothesidingrailway.Theywererecord-edandassessed-softwoodaccountedfor92%andhardwoodfor8%ofthetotalvolume.Totaldieselcon-sumptionofmonitoredTTUswas6.8millionliters.The fuel consumption is not broken down to theamount used directly in timber haulage and theamountusedindirectly, i.e.drivingto theworkingplaceordrivingtotheworkshopforrepair.Theshareof»emptykilometers«inthetotalnumberofdrivenkilometerswas47%.AveragebackhaulingofTTUs(loadedvehicles)was53%.Thepresentedvaluesrep-resentandsummarizeatotalof136292casesoftimbertransport.Theaveragehaulingdistancewaschanginginthe

courseofyearsdependingonactivitiesofthecom-panyoperatingthetrucks.From2005,thenumberoftimberyardswasdecreasingandtheamountoftim-berhandledattheroadsidelandingwasincreasingaswellasthetimberhaulagefromthelandingdirectlytothecustomer.Theaveragehaulingdistancewas

Table 1 Mean values for all monitored TTU typesTablica 1. Značajke promatranih kamionskih skupova

Volume of transported timber, m3 – Obujam transportiranoga drva, m3 3 418 171Softwood – Crnogorica 3 161 533

Hardwood – Bjelogorica 256 638

Total distance, km – Ukupno prijeđena udaljenost, km 11 032 534Empty kilometers – Vožnja praznim kamionom, km 5 172 109

Kilometers driven loaded – Vožnja punim kamionom, km 5 860 425

Fuel consumption, l – Potrošnja goriva, l 6 811 604 – –

Number of cycles – Broj turnusa 136 292 – –

Average fuel consumption, l/m3 – Prosječna potrošnja goriva, l/m3 2.19 – –

Average consumption, l/100 km – Prosječna potrošnja goriva, l/100 km 67.4 – –

Average hauling distance*, km – Prosječna udaljenost turnusa*, km 45.05 – –

* One way distance – * U jednom smjeru

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232 Croat. j. for. eng. 34(2013)2

Table 2 Trends of important indicators in all TTU types in the studied periodTablica 2. Trendovi i važne karakteristike promatranih kamionskih skupova u vremenu istraživanja

Year – Godina 2005 2006 2007 2008 2009

Average fuel consumption, l/m3 – Prosječna potrošnja goriva, l/m3 2.32 2.06 1.87 2.67 3.08

Average fuel consumption, l/100 km – Prosječna potrošnja goriva, l/100 km 69.51 68.4 70.94 61.22 61.36

Average hauling distance*, km – Prosječna udaljenost turnusa*, km 39.31 37.6 36.87 65.76 74.21

Average size of load, m3 – Prosječni obujam tovara, m3 20.59 23.45 25.96 26.13 27.57

Average backhauling**, % – Prosječna transportna udaljenost punoga kamiona**, % 53 48 48 52 49

* One way distance – * U jednom smjeru** % of kilometers driven loaded – ** Udio s obzirom na udaljenost turnusa

Table 3 Outputs and indicators of individual TTU typesTablica 3. Tehničke karakteristike promatranih kamionskih skupova

TTU type – Model kamionskoga skupa IVECO TATRA MB*

Average fuel consumption, l/m3 – Prosječna potrošnja goriva, l/m3 2.26 1.93 2.71

Average fuel consumption, l/100 km – Prosječna potrošnja goriva, l/100 km 66.74 72.25 58.31

Average hauling distance**, km – Prosječna udaljenost turnusa **, km 48.97 28.98 76.11

Average loads per day – Prosječan broj turnusa po danu 2.96 3.25 2.98

Average size of load, m3 – Prosječan obujam tovara, m3 25.21 22.84 28.38

Average backhauling*** – Prosječna transportna udaljenost punoga kamiona *** 51 46 55

Total, km – Ukupno, km 903 845 4 014 736 6 055 543

Volume of hauled timber, m3 – Obujam transportiranoga drva, m3 285 683 1 701 892 1 408 446

* MB: Mercedes-Benz** One way distance – **U jednom smjeru*** % of kilometers driven loaded – *** Udio s obzirom na udaljenost turnusa

transport units is presented in Table 3,where theprominentindicatoristheloadsize.Backhauling considerably affects transport effi-

ciency;averagebackhaulingincreaseddependingonaveragehaulingdistance,whichwas favorablyaf-fectedbytheeasiercoordinationofloadsbydispatch-ers.Overshorthaulingdistances, timber transportfromtheforestisoperatedmoreorlessinone-waydirection; backhauling is often unrealistic and thetrucksareadditionallyburdenedbydrivingtotheirworkplaceandtorepairormaintenanceworkshops.Thisiswhyitsefficiencyisbelow50%.Withthein-creasingofthehaulingdistance,thepossibilityoffind-ingsuitablebackhaulingincreasesandtheeffectofdrivingtotheworkplaceorrepairisminimized(Fig.1).Extremelylowvaluesmostlyresultedfromloadingintowagons(whenthevehiclewasusedforloadingwagons)anditsnumberofemptykilometersincreaseddue to frequentdriveswithin the terminal (timberyard).Ontheotherhand,extremelyhighvaluesre-sultedfromanearlyidealrelationwhenemptykilo-

increasingtowardstheendofthestudyperiod–seeTable2.Thelowestdistancewasachievedin2007duetotheKyrillgaledisasterwhenasubstantialpartofallTTUswereconcentratedtoworkinaffectedareas,where the trucksmostly transported timber overshorthaulingdistances,whichconsiderablyaffectedtheannualaveragehaulingdistance.Theaveragesizeof loadwasmarkedly increasingduring theyearsthankstochangesinthefleetbecausethenewlyusedTTUsofMercedesBenztypefeaturedaconsiderablyhighercapacitythantheotherassessedTTUtypes(Table3).Table2showsthattheincreasingaveragehauling

distanceresultedintheincreasingaveragefuelcon-sumptionperunitofproductionandthatthefleetre-newalbrought agradualdecrease in the fuel con-sumptionper100km. In2008and2009,when theMercedesBenztypeofTTUstartedtodominatethefleet, the average fuel consumption per 100 kmdroppeddramaticallyby9%.Adetailedsurveyofin-dicatorsandoutputsbyindividualtypesoftimber

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Fig. 1 Dependence of backhauling on hauling distance (all TTU types)Slika 1. Udio vožnje punim kamionom po turnusu (svi promatrani modeli kamionskih skupova)

Fig. 2 Relation between fuel consumption per 100 km and hauling distance for the Iveco type of TTUSlika 2. Odnos između potrošnje goriva na 100 km i duljine turnusa za kamionski skup Iveco

Fig. 3 Relation between fuel consumption per 100 km and hauling distance for the Tatra type of TTUSlika 3. Odnos između potrošnje goriva na 100 km i duljine turnusa za kamionski skup Tatra

Fig. 4 Relation between fuel consumption per 100 km and hauling distance for the Mercedes-Benz type of TTUSlika 4. Odnos između potrošnje goriva na 100 km i duljine turnusa za kamionski skup Mercedes-Benz

metersrepresentedonlydrivingonforestroadsandveryshorttravelsforanotherload.Detailsofregres-sionanalyzeswereasfollows:Best-fitvaluesa=8.352,

b=19.19;Std.Errora =0.1920,b=0.6994;95%Confi-denceIntervalsa=7.976to8.728,b =17.82to20.56;R square0.4500;Outliers(excluded,Q=1.0%)3.

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Table 4 Results of regression analyses of the relation of fuel consumption per 100 km and hauling distanceTablica 4. Rezultati regresijske analize potrošnje goriva na 100 km i duljine turnusa

TTU type

Model kamionskoga skupa

* Regression coefficients of equation

* Regresijski koeficijenti jednadžbe

y = a × ln(x) + b

Border coefficients, 95%

Granični koeficijenti, 95 %

Confidence Intervals – Faktor pouzdanosti

R2 Range of × value

Raspon × vrijednosti

a b a b

Iveco –15.47 123.4 –17.14 ; –13.81 117.2; 129.6 0.6102 10 – 132

Tatra –13.96 116.7 –15.30 ; –12.61 112.3 ; 121.0 0.2390 10 – 131

MB –10.42 101.7 –11.25 ; –9.576 98.12 ; 105.3 0.4397 12 – 178

* x – hauling distance – Duljina turnusay – fuel consumption per 100 km – Potrošnja goriva na 100 km

Table 5 Results of regression analyses of the relation of fuel consumption per unit of production (m3) and hauling distanceTablica 5. Rezultati regresijske analize potrošnje goriva po jedinici proizvodnje (m3) i duljine turnusa

TTU type

Model kamionskoga skupa

* Regression coefficients of equation

* Regresijski koeficijenti jednadžbe

y = a × ln(x) + b

Border coefficients, 95%

Granični koeficijenti, 95 %

Confidence Intervals – Faktor pouzdanosti

R2 Range of × value

Raspon × vrijednosti

a b a b

Iveco 0.9842 –1.399 0.8887; 1.080 –1.756 ; –1.043 0.6601 10 – 132

Tatra 1.335 –2.444 1.280; 1.391 –2.624 ; –2.265 0.6311 10 – 131

MB 1.531 –3.749 1.460; 1.601 –4.048 ; –3.450 0.7025 12 – 178

* x – hauling distance – Duljina turnusay – fuel consumption per unit of production, m3 – Potrošnja goriva po jedinici proizvodnje, m3

Fig. 5 Relation between fuel consumption per 100 km and hauling distance for all types of TTUsSlika 5. Odnos između potrošnje goriva na 100 km i duljine turnusa za sve promatrane kamionske skupove

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Croat. j. for. eng. 34(2013)2 235

Fig. 6 Relation between fuel consumption per unit of production (m3) and hauling distance for the Iveco type of TTUSlika 6. Odnos između potrošnje goriva po jedinici proizvodnje (m3) i duljine turnusa za kamionski skup Iveco

Fig. 8 Relation between fuel consumption per unit of production (m3) and hauling distance for the Mercedes-Benz type of TTUSlika 8. Odnos između potrošnje goriva po jedinici proizvodnje (m3) i duljine turnusa za kamionski skup Mercedes-Benz

Fig. 7 Relation between fuel consumption per unit of production (m3) and hauling distance for the Tatra type of TTUSlika 7. Odnos između potrošnje goriva po jedinici proizvodnje (m3) i duljine turnusa za kamionski skup Tatra

3.1 Average fuel consumption in relation to driven distance including the effect of up-loading and unloading and proportion of time spent on forest roads – Prosječna potrošnja goriva po prijeđenom kilometru uključujući utovar, istovar te udio vožnje šumskom cestomAveragefuelconsumptionper100kmismarkedly

higherintheolderTTUtypessuchasIvecoandTatrainparticular(seeTable3).Itisalsosynergyaffectedbyuploadingandunloadingtimesaswellasbythehaul-ingdistance.Ifthehaulingdistanceisshorter,theav-erageconsumptionper100kmismarkedlyhigherthanoverlongerdistancesduetotheeffectofupload-ingandunloading.Duringtheuploadingandunload-ing,theengineofthetruck(energysource)drivesthehydrauliccraneandtheconsumptionoffuelthusin-creaseswithoutachange indrivenkilometers.Ac-cordingtocompanyworkers(personalcommunica-tion), the loadingtimewasdifferentwhenloadingstemsor timbershortenedto transportation length(max.35min.)andwhenloadingstackedassortmentsupto8m(max.50min.).Thesecondeffectistheproportionoftimespent

onforestroads.Theshorterjourneymeantahigherproportionoftraveltimespentonforestroads.Thetrucks have a higher fuel consumption on forest

roadsduetoharshterrainconditions,limitedspeed(lowergear)andworseroadqualitythatdecreaseswiththeincreasinghaulingdistance.Noneofthese

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Fig. 9 Relation between fuel consumption per unit of production (m3) and hauling distance for all types of TTUsSlika 9. Odnos između potrošnje goriva po jedinici proizvodnje (m3) i duljine turnusa za sve promatrane kamionske skupove

twoaspectscanbeeliminatedtodeterminetheinflu-enceofeachseparately.Inotherwords,asthetwoaspectsareinseparablepartoftimberhaulage,theassessmentwasmadeincludingtheimpactofthemboth.Botheffectsalsocorrespondtotheaveragenumber

ofdailydeliveredloadswithrespecttohaulingdis-tancei.e.:4deliveriesat10.7kmaveragehaulingdis-tance,3at38kmand2at200km,respectively.Regressionequationsoffuelconsumptionforthe

respectiveTTUsarepresentedinFigs.2–4includingdiscernedoutliersandincludingconfidenceintervalof95%reliability.Theregressionequationsareplottedinacomprehensivegraph(Fig.5)forthecomparisonofindividualTTUtypes.TheregressioncurvesaredrawnintheintervalofhaulingdistancesinwhichTTUtypeswereoperating.TheresultsofregressionanalysesforindividualtypesoftimbertransportunitsarepresentedinTable4.

3.2 Average fuel consumption per unit of production (hauled cubic meter) – Prosječna potrošnja goriva po jedinici proizvodnje (prevezeni kubni metar)Inthiscase,too,therespectivetypesoftruck-and-

trailerunitswereassessedseparately(Figs.6–8).Theaveragefuelconsumptionperunitofproduction(m3)wasconspicuouslydifferentintheindividualTTU

types,thereasonforthedifferencebeingmainlytheeffectofhaulingdistanceandthesizeofTTUload.Thegreaterthehaulingdistance,thehigherwasthefuelconsumptionperunitofproduction;atthesametime,thegreaterthevehiclecapacity,thelowerwastheaveragefuelconsumption.Thetwofactorsactinsynergyandthereareotherimpactstobeexpected,too,suchasseasonalcharacterofthework,effectoftheoperator,etc.Resultsofregressionanalysesforindividualtypesoftimbertransportunitsarepre-sentedinTable5.ThecomprehensivediagraminFig.9showsre-

gressionequationsfortherespectivetypesoftimbertransportunits.Theregressioncurvesareplottedonlywithinthehaulingdistanceintervalinwhichtheval-uesused in theregressionanalysisoccurred.Tatratypetrucksshowedunambiguouslythehighestfuelconsumptionperunitofproduction.

4. Discussion and conclusion – Rasprava sa zaključcima

Theabovegraphs(Figs.2–5)showthedepen-denceoffuelconsumptionper100kmonaveragehaulingdistanceoftheindividualTTUtypes.Theaveragehaulingdistancerangedfrom10–180km.OlderIvecoandTatratrucksinparticularhadacon-siderablyhigherfuelconsumptionper100km,which

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boards,extraairhorns,extralampsandotherun-necessaryaccessories.Averagefuelconsumptionperunitofproduction

(m3)isfirstofallaffectedbythehaulingdistanceandbytheloadsize–thetwofactorsactinginsynergy.Thehigheristhevehiclecapacity,theloweristheaverageconsumptionperunitofproduction,andthegreateristhehaulingdistance,thehigheristhefuelconsumptionperunitofproduction.Furthertotheabove,theTatraTTUswouldhavethelowestfuelconsumptionperunitofproductioniftheaveragevaluesofTTUtypes fromglobalassessmentwerecomparedwithoutamoredetailedanalysis(seeTa-ble3).Nevertheless,thisviewoftheproblemwouldberathernaïve,becausethesearetheaveragevaluesfortheentire5-yearmonitoringperiodandtheyarerelatedtoanaveragehaulingdistancecalculatedforthewholeperiodofthestudy.Therefore,itisneces-sarytocomparetheaveragefuelconsumptionbasedondatapresentedinFig.9.TheTatratruck-and-trail-erunithasthehighestaveragefuelconsumptionperunitofproductioninrelationtothehaulingdistance,thelikelyreasonbeingtheaveragesizeofloadbutalsotheconstructionof themachine,which isde-signedfordifficult,inaccessibleterrainsandisfittedwitholderenginetypes.Bycontrast,theMercedes-BenzTTUsexhibited

thehighestaveragefuelconsumptionperunitofpro-duction(approx.3literspercubicmeter)intheglob-alassessment(Table3),whichresultedfromthelonghaulingdistanceinthemonitoredperiod.However,itcanbeconcludedfromFig.9thattheMercedes-BenzTTUsaremoreeconomicalintermsoffuelcon-sumptionperunitofproductionwiththeloadsizeplayingonceagainthemostimportantrole.TheloadsizeintheMercedes-BenzTTUswasapproximately5m3greaterthanintheTatraTTUs.Furthertotheabove,itcanbeconcludedthatthetimbertransportunitscannotbeevaluatedonlyaccordingtosumma-rizeddata(Table3)butthatmoredetailedanalyses,suchasinFigs.6–8,areabsolutelynecessary.Theissueofrelationsbetweentheindividualin-

dicatorsisverycomplexanditwouldbecertainlyusefultoconductadetailedsurveywithintherespec-tive typesof truckse.g. inrelationtohaulingdis-tance,loadingcapacity,trailertypeorregioninwhichtheTTUoperated.Allactivitiesconnectedwiththedetailedcharacterizationof these relationsare fo-cusedonfueleconomy.ThisdirectionisalsoobviousfromtheactivitiesofFPInnovation,wheretheso-called StarTrackwasdesigned aimed at reducingmachineweightandprovidingmaximumloadingcapacity.Thespecificationsplacedontheresearch

supposedlyresultedfromthefactthattheirhaulingdistanceswererelativelyshort(38km)andloadingandunloadingwasmorefrequent.Thus,duetomorefrequentloadingandunloading,thefuelconsump-tionincreasedalthoughitdidnotshowinthetraveldistance.Theaveragefuelconsumptionper100kmofMercedes-BenzTTUswasmarkedly lower, be-causethehaulingdistanceswereapparentlyhigher.Anotheraspectaffecting the fuelconsumption to-getherwiththisfactorwastheproportionofdrivingonforestroads,whichdecreaseswiththeincreasinghaulingdistance i.e. loading is limitedwithinoneworkingday.Fig.5showsthatthefuelconsumptionper100kmdecreaseswith the increasinghaulingdistance.Thethirdveryimportantfactoristheen-ginecategory.MercedesBenzTruckswereEuro3andEuro5class,whichshouldguaranteelowerfuelcon-sumption.However,thisisnotasvisibleasexpectedandfurtherdetailedanalysisofMercedesBenztruckis necessary.Other impacts, such as the seasonalcharacterofwork,locality(roadquality,relations),humanfactorinloading/unloading,equipmentop-erators,drivers,etc.couldnotbeidentifiedbuttheirinfluencecanbeanticipatedatleasttosomeextent.Theauthorsconsiderthatthevolumeofdataisrep-resentative forestimating themeanvaluesof fuelconsumption.Svenson(2011)informsthatinSwedentheaver-

agefuelconsumptionper100kmis58litersbutdoesnotmention thehaulingdistance,whichcouldbecorrespondingto65kmaccordingtotheresultsofourstudy.Althoughthevalueishighlyspeculative,itmightberealisticforsuchavastcountryasSwedenevenifitisby40%higherthantheaveragehaulingdistanceof45kmestablishedinthisstudy.Itishow-everfullycomparablewithvaluesrecordedin2008and2009,whenthetimbertransportcompanythatprovidedthedatafocusedonlongerhaulingdistanc-es.SimilarconditionsasintheCzechRepubliccanbe expected inAustria, whereHolzleitner (2009)claimstheaveragehaulingdistanceof51km,whichisinlinewiththevaluesdetectedinthisstudy.Fuel consumption can be reduced in different

ways.Consideratedrivingmayconsiderablyreducethefuelconsumption.Byaprogramthatcanmonitorthedrivingregime,theTomTomCorporationcanidentifyinappropriatedrivingmannersanddemon-strateamoreeconomicalregime(personalcommu-nicationTomTom).Lofroth andLindholm (2005)mention furtherpossibilitiesof fueleconomy,e.g.thathaulagetruckscanreducetheirfuelconsump-tionby5–10%simplybyfittingawinddeflectorandby removing all unnecessary items such as sign-

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238 Croat. j. for. eng. 34(2013)2

truckconsideredthelocaloperatingconditionsandincluded the following requirements: heavy-dutyaluminumrims,smallerfueltank(buttherightsizeforoneshift),aluminumcabprotector,centraltireinflation(CTI),on-boardweighing,in-cabauxiliaryheater,on-boardcomputer,singletractorframerail,lightweight multi product semi-trailer and roadmaintenancemanagementsystem.Alltheseinnova-tionsresultedinthefollowingimprovements:

Þ TheStarTruckhadahigherpayloadby9.8%andconsumedonly1%morefuel,

Þ TheStarTrucktransported8.6%moreproductsperliteroffuel,

Þ TheStarTruckfuelcostpertonwasby8%low-erthaninthecontroltruck,

Þ Tirewearwasby40%lowerintheStarTruckduetoCTI.(Anon.2012)

Thereducedfuelconsumptionperunitofproduc-tionaimsatmitigatingtheenvironmentalpollutioncausedbyemissionsofgreenhousegases (GHGs).Fuelconsumptionbytrucksisoneofthelargestcon-tributorsoftheseemissions.Komor(1995)informsthatintheU.S.A.,trucksaccountforover80%ofthefreightenergyuseand19%ofUSoilconsumption.Planstoimprovethetechnicalefficiencythroughnewtechnologies, careful driving and optimal drivingconditionscanincreasetheefficiencyby50to70%.Bandivadekaretal.(2008)believethattheincreaseintheconsumptionofoilfortransportintheU.S.A.isachallengingenvironmentalproblemthatneedstobeaddressed in terms of reducing fuel consumptionbasedondrivers’behaviorratherthanconcentratingontheimprovementofvehicleperformancethroughnewpropulsiontechnologiesandnewfuels in theshorterterm.OthermethodsleadingtoreducedfuelconsumptionaredecisionsupportsystemsanduseoftelemetryincombinationwithGPS/GIS.AnexamplemaybethestudypublishedbyDevlinetal.(2007).TheamountoftimberextractedintheCzechRe-

publicperyearisabout15millionm3.Adequatefleetchanges,improvedoptimizationandtechnicalmodi-ficationsmaybeusedtoreducefuelconsumptionperunitofproductionby0.5–1.0liter.Thiswouldbringareductionoffuelconsumptionintimberhaulageby0.75–1.5millionlitersofoilintheCzechRepublic.Devlin(2010)claimsthateachliterofoilburntinthetruck-and-trailerunitisresponsiblefor2.67kgofcar-bondioxideemittedintotheatmosphere.BasedontheemissionfactorsestablishedbyLewis(1997),wecanstatethateachliterofoilisresponsibleforaddi-tional0.25kgCO2emittedduringtheproductionanddistribution. Thus, saving 1.5million liters of oilequivalentwouldresultinareductionofCO2emis-

sionsintotheatmosphereof4.4milliontons.Theun-ambiguousconclusionisthatoptimizationanduseofadequate TTU types in timber transport from theroadsidelandingcansignificantlycontributetothemitigationofthenegativeimpactofforestmachineryontheenvironment.

AcknowledgementThepaperwaspreparedwithintheframeworkof

researchprojectsoftheMinistryofEducationoftheCzechRepublicnos.MSM6215648902andOC10041,MendelUniversity internalproject IGAandof theCOSTActionFP0902.Theauthorsalsowishtoexpresstheirthankstocontractorsforprovidingthepossibil-ityofdatacollection.

5. References – LiteraturaAnon., 2012:FP Inovation.TimberTransportResearch–FERIC’sStarTruckProject.Logging-onnewsletter.Availableon http://www.loggingon.net/timber-transport-research-ferics-star-truck-project_news_op_view_id_43

Bandivadekar,A.,Cheah,L.,Evans,C.,Groode,T.,Hey-wood,J.,Kasseris,E.,Kromer,M.,Weiss,M.,2008:ReducingthefueluseandgreenhousegasemissionsoftheUSvehiclefleet.EnergyPolicy36(7):2754–2760.

Devlin,G.,2010:Fuelconsumptionoftimberhaulageversusgeneralhaulage.Harvesting/TransportationNo. 22.CO-FORD,6p.

Devlin,J.G.,McDonnell,K.,Ward,S.,2007:TimberhaulageroutinginIreland:ananalysisusingGISandGPS.JournalofTransportGeography16(1):63–72.

Favreau,J.,2006:Sixkeyelementstoreduceforesttranspor-tationcost.FERIC.Availableonhttp://www.forac.ulaval.ca/fileadmin/docs/EcoleEte/2006/Favreau.pdf

Holzleitner,F.,2009:Analyzingroadtransportofround-woodwithacommercialfleetmanager.In:PrknováH(ed)Formec2009.KostelecnadČernýmilesy:CzechUniversityofLifeSciencesPrague,p.173–181.ISBN978-80-213-1939-4.

Holzleitner,F.,Kanzian,Ch.,Stampfer,K.,2011:Analyzingtimeandfuelconsumptioninroadtransportofroundwoodwithanonboardfleetmanager.EurJForestRes130(2):293–301.

Klvac,R.,2006:DraftofInformationsystemfortimberhaul-age.InCharvátK(ed)InformationSystemsinAgricultureandForestry.Praha:ČZUPraha,p.1-8.ISBN80-213-1494-X.

Komor,P.,1995:ReducingenergyuseinUSfreighttrans-port.TransportPolicy2(2):119–128.

Lofroth,C.,Lindholm,E.L.,2005:Reducedfuelconsump-tiononroundwoodhaulagerigs.Skogforsk.Resultatno.23.

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Motulsky,H.J.,2007:GraphPadPrismVersion5.0.Regres-sionGuide.GraphPadSoftware.Inc..SanDiego.294p.

Motulsky,H.J.,Brown,R.E.,2006:Detectingoutlierswhenfittingdatawithnonlinearregression–anewmethodbasedonrobustnonlinearregressionandthefalsediscoveryrate.BMCbioinformatics.Availableonhttp://www.ncbi.nlm.nih.gov/pubmed/16526949.

Press,W.H.,Teukolsky,S.A.,Vettering,W.T.,Flannery,B.P.,1988:NumericalRecipesinC.TheArtofScientificCom-puting.NewYork.CambridgeUniversityPress.

Svenson,G.,2011:Theimpactofroadcharacteristicsonfuelconsumption for timber trucks. InAckermanP,HamH,GleasureE(eds)Proceedingsof4thForestEngineeringCon-ference: Innovation inForestEngineering–Adapting toStructuralChange.StellenboschUniversity,p.172.ISBN978-0-7972-1284-8.

Sažetak

Potrošnja goriva pri prijevozu drvnih sortimenata

U ovom je radu istraživana pristupačnost drvnih sortimenata prijevozu kamionskim skupovima, a istraživala su se 132 kamionska skupa i tri modela kamiona (Tatra, Mercedese Benz i Iveco) s različitim vrstama kamionskih prikolica.

Svako smanjenje potrošnje goriva po jedinici proizvodnje može povećati okolišni i ekonomski profil sekundar-noga prijevoza. S obzirom na to da na gorivo otpada najveći dio troškova koji nastaju pri prijevozu drvnih sorti-menata, cilj je ovoga rada bio analizirati potrošnju goriva promatranih kamionskih skupova korištenih za prijevoz. Svaka zamjena zastarjeloga i neučinkovitoga kamionskoga skupa novim učinkovitijim kamionskim skupom može rezultirati značajnim smanjenjem potrošnje goriva po jedinici proizvodnje.

Glavni je cilj ovoga rada bio ustanoviti na koji način prijevozna udaljenost (duljina jednoga turnusa) kod promatranih kamionskih skupova utječe na potrošnju goriva na 100 km te na specifičnu potrošnju goriva po pre-vezenom kubnom metru drva.

Dizajniran je informacijski sustav koji može primati narudžbe od naručitelja i koji pruža potporu dispečerima pri donošenju odluka da bi se odabrao najpogodniji kamionski skup. Sustav također bilježi podatke o pojedinom turnusu, zbraja ih te ih pohranjuje u baze podataka.

Početna je obrada podataka napravljena usporedbom velikoga broja tablica i grafikona. Za nelinearnu regresi-ju koristili smo se programom GradhPad Prism 5. Taj program omogućuje vrlo fleksibilan izbor regresijskoga modela, ima vrlo dobre grafičke mogućnosti i moguće je ubaciti i ucrtati intervale pouzdanosti pojedinih modela. Navedeni program eliminira ekstreme metodom »ROUT«.

U vrijeme istraživanja više od 3,4 milijuna kubnih metara drva prevezeno je od pomoćnoga stovarišta do glavnoga stovarišta, krajnjega korisnika ili do željezničke pruge. U ukupnom obujmu prevezenoga drva udio je crnogorice bio 92, a bjelogorice 8 %. Ukupan utrošak goriva za promatrane kamionske skupove iznosio je 6,8 milijuna litara.

Na potrošnju goriva po jedinici proizvodnje (m3) najviše utječu duljina turnusa i obujam tovara. Ta dva čimbenika djeluju u sinergiji. Što je veći obujam tovarnoga prostora kamionskoga skupa, manja je prosječna po-trošnja goriva po jedinici proizvodnje, dok s druge strane, što je veća udaljenost pojedinoga turnusa, veća je i prosječna potrošnja goriva po jedinici proizvodnje.

Zastarjeli i neadekvatni kamionski skupovi tijekom istraživanoga razdoblja stalno su zamjenjivani novim i učinkovitijim, zbog čega je primijećeno značajno smanjenje prosječne potrošnje goriva (0,5 l/m3) po jedinici proi-zvodnje.

Smanjenje potrošnje goriva po jedinici proizvodnje u konačnici znači smanjenje emisije stakleničkih plinova te ublažavanje štetnoga utjecaja na okoliš. Sagorijavanjem jedne litre goriva u motoru kamionskoga skupa u at-mosferu se ispušta 2,67 kg ugljičnoga dioksida te bi se smanjenjem potrošnje goriva za 1,5 milijuna litara sman-jila i emisija ugljičnoga dioksida u atmosferi za 4,4 milijuna tona. Nedvosmisleni je zaključak ovoga rada da se pri odabiru kamionskih skupova za prijevoz drvnih sortimenata, tj. njihovom optimizacijom, može značajno pridoni-

R. Klvač et al. Fuel Consumption in Timber Haulage (229–240)

240 Croat. j. for. eng. 34(2013)2

jeti ublažavanju negativnih utjecaja šumskih strojeva na okoliš. Cestovni je promet u posljednjih deset godina zabilježio velike promjene, a analiza predstavljena u ovom radu daje važne informacije korisne u planiranju i orga-nizaciji cestovnoga prijevoza drvnih sortimenata.

Ključne riječi: šumska cesta, prijevoz drvnih sortimenata, kamionski skup, potrošnja goriva

Received(Primljeno):February18,2013Accepted(Prihvaćeno):August06,2013

Authors’address–Adresa autorâ:

Assoc.prof.RadomírKlvač,PhD.*e-mail:[email protected]říke-mail:[email protected]áe-mail:[email protected]ápela,PhD.e-mail:karel.drapela@mendelu.czMendelUniversityinBrnoFacultyofForestryandWoodTechnologyDepartmentofForestandForestProductsTechnologyZemedelska361300BrnoCZECHREPUBLIC

*Correspondingauthor–Glavni autor