Internal Combustion Engine Analysis of Energy Ecological Parameters

InternalCombustionEngineAnalysisofEnergy EcologicalParametersbyNeutrosophic MULTIMOORAandSWARAMethods

EdmundasKazimierasZavadskas 1,* ,Audrius ˇ Cereška 2,JonasMatijošius 3 , AlfredasRimkus 3 andRomualdasBausys 4

1 LaboratoryofOperationalResearch,InstituteofSustainableConstruction,FacultyofCivilEngineering, VilniusGediminasTechnicalUniversitySauletekioal.11,LT-10223Vilnius,Lithuania

2 DepartmentofMechanicalandMaterialEngineering,VilniusGediminasTechnicalUniversity, Basanaviciausstr.28,LT-03324Vilnius,Lithuania;audrius.cereska@vgtu.lt

3 DepartmentofAutomobileEngineering,VilniusGediminasTechnicalUniversity,Basanaviciausstr.28, LT-03324Vilnius,Lithuania;jonas.matijosius@vgtu.lt(J.M.);alfredas.rimkus@vgtu.lt(A.R.)

4 DepartmentofGraphicalSystems,VilniusGediminasTechnicalUniversitySauletekioal.11, LT-10223Vilnius,Lithuania;romualdas.bausys@vgtu.lt

* Correspondence:edmundas.zavadskas@vgtu.lt;Tel.: +370-688-64910

Received:20March2019;Accepted:4April2019;Published:12April2019

Abstract: Theinvestigationfornewinnovativesolutionstoreducetransportpollutionisapriority fortheEuropeanUnion(EU).Thisstudyincludesenergyandasustainableenvironment,aswellas transport,logistics,andinformationandcommunicationtechnologies.Energyecologicalparameters ofinternalcombustiondependonmanyfactors:fuel,thefuelinjectiontime,enginetorque,etc. Theengine’senergyecologicalparameterswerestudiedbychangingenginetorques,usingdifferent fuels,andchangingthestartofthefuelinjectiontime.Theselectionoftheoptimumparametersisa complexproblem.Multicriteriadecision-makingmethods(MCDM)presentpowerfulandflexible techniquesforthesolutionofmanysustainabilityproblems.Thearticlepresentsanewwayoftackling transportpollution.Theanalysisoftheenergyecologicalparametersoftheexperimentalinternal combustionengineisperformedusingtheneutrosophicmulti-objectiveoptimizationbyaratio analysisplusthefullmultiplicativeform(MULTIMOORA)andstep-wiseweightassessmentratio analysis(SWARA)methods.TheapplicationofMCDMmethodsprovidesuswiththeopportunity toestablishthebestalternativeswhichreflectthebestenergyecologicalparametersoftheinternal combustionengine.

Keywords: internalcombustionengine;fuels;energyecologicalparameters;neutrosophic MULTIMOORA;SWARA

1.Introduction

TheEuropeanUnion(EU)directivesgovernthemandatoryincreaseintheshareofbiofuelsin overallfuelconsumption.Currently,thisisacrucialpoliticalaspectoftheassuranceofEUenergy savingandenvironmentalrequirements.AccordingtotheEUDirective2003/30/ EC,memberstates shallsell10%ofbiofuelsin2020[1].The“polluterpays”principle,whichwastransposedfromEU directivestonationallegislation,isvalidthroughouttheEU[2].Bothheavytransportandpassenger cars[3]withstrictregulationofcarbondioxide(CO2)emissions[4]aresubjecttotheserequirements. Thesearchfornewinnovativesolutionstoreducepollutioninthetransportsectorisapriority EUdirection.Theseincludeenergyandsustainableenvironment,aswellastransport,logistics, andinformation andcommunicationtechnologies[5].TheEUWhitePapersetsouttheprinciplesfor

Energies 2019, 12,1415;doi:10.3390/en12081415 www.mdpi.com/journal/energies

promotingenergysavingandeco-friendlytransportsolutions[6].Oneofthesedirectionscouldbethe usageofdifferentalternativefuelslikehydrogen(H)[7,8]andbiogas[9,10],somedifferentmixtures withgasoline[11,12],orethanol[13].

Biodieselisnowusedininternalcombustionengines.Itisarenewableenergysourcethatcan alsobeproducedfromlocalbiomass.Thisfactemphasizestheimportanceofenergyindependence fromfossilfuels[14,15].Despitemanypositivefeatures,theuseofbiodieselisstilllimited.Itdepends onfuelpropertiessuchasalowercalorificvaluecomparedtodieselandacoldfilterplugging point[16].Thelowercalorificvalueofbiodieselleadstoreducedenginepowerandhigher-viscosity fuelinjectioncharacteristics.

Theabove-listednegativepropertiesofbiodieselcanbeeliminatedifotherfuels(compounds thatcontainoxygen,whichimprovescombustioninthecombustionprocess,generatinglessharmful compounds)areused[17].NitromethaneisusedasfuelinTOPFUELracecars,aero-models, andautomobilemodels[18].Itisagoodoxidantcreatingastableadiabaticflame(2400 ◦C). Thehigher thenitromethanedropletsare,thebetterthecombustionprocessis[19];inmixtures withhydrocarbon–methane,itremainsstable,andthehigheritsconcentrationis,themorestableand betterthecombustionprocessisduetoitsadditionaloxygen(O2)content[20].

Theresearchshowstheimpactoffueltypeonengineperformance;choosingsuchrenewablefuel mixtures,whichwouldensurethatengineperformanceparametersremainasclosetotheplanned onesaspossible,isveryimportant[21–23].Forthisreason,purebiofuelsarerarelyused,becausetheir physic-chemicalpropertiescansignificantlyaffectengineperformance(itsenergyindicators).Various alternativefuelmixturesareusuallyusedtoadjustthepropertiesmentionedabovesothattheyare closetomineraldiesel[24].Therefore,afuelgradationaccordingtonitromethanecontentisused, whichallowschoosingamixture,theinjectionofwhichintotheenginewouldensurethebalanceofits energyandecologicalparameters[25,26].Nitromethanegasisalsofoundinexhaustgases,especially aftertheoxidation–reductionprocessestakingplaceinthecatalyticneutralizer[27,28].

Withtheenginerunningatdifferentloads,requirementsforfuelalsochange.Therefore,identifying theconditions(engineload)andthestartoffuelinjection(SOI)parameters,thepresenceofwhich wouldallowreachingtheoptimumnitromethanecontentinthemixture,becomesessential.

Startoffuelinjectionaffectsthebiodieselcombustionprocessandadjuststhechangeofcombustion indicatorsusingalternativefuelmixtures.Earlierorlaterstartoffuelinjectionallowscompensating forslowerormoreintenseheatreleaseduetothechangedphysic-chemicalpropertiesoffuel[29]. Thecombustionheatreleaseandthedependingpressure,whichcreatesusefulworkinthecylinder, areimportantforminimumfuelconsumptionandmaximumengineefficiencyintimescale.Thestart offuelinjectiontimingalsoaffectsecologicalengineindicators.Inpursuitofthemaximumenergy efficiencyoftheengine,thebeginningoffuelinjectionshouldbebroughtforwardtoacertainlimit, andspecificinjectionstrategiesandpressurecanbecarriedout[30–32].However,havingtheexcellent startoffuelinjection,thecombustiontakesplaceinasmallervolume,closertotheupperpointofthe piston.Thisfactincreasesthecombustiontemperatureandpressure,resultinginhighernitrogenoxide (NOx)contentforminginthecylinder,reducedopacity,andhydrocarbon(HC)emissions[32–35].

Thecomparisonofbrake-specificfuelconsumption(BSFC)andengineefficiency ηe ofengines allowsforthemostobjectiveassessmentofenergyindicatorsoftheinternalcombustionengine[31,36,37]. Theseindicatorsenablecomparingenginesofdifferentstructuresduringtheirrunningonfuelsof variouscomposition.

Themainharmfulcomponentsofexhaustgasesofenginesareproductsofincompletecombustion (carbonmonoxide(CO),incompletelyburntHC,smoke)andNOx [31,38,39].Carbondioxide(CO2) isoneofthegreenhousegascomponentsemittedbyengines.Itsconcentrationdependsonfuel composition(carbon/hydrogenratio—C/Hratio),fuelconsumption,andcombustionprocessinthe engine[31,40,41].TheO2 concentrationintheexhaustenginegasesallowsassessingthefuel/airratio offuelmixtures,whichaffectsfuelcombustion(oxidation)andtheemissionsofharmfulemitted components[31,42,43].

Thereislittleresearchofmixturescomprisingbiodieselandnitromethaneinthescientific literature[1,18].Examiningtheirimpactontheworkoftheinternalcombustionengineandecological characteristicsisveryimportant.Toensuretheoptimumengineoperatingparameters,compatibility ofallthesettings,namely,compatibilityoffuelandtheengine,mustbeguaranteed.Generalresearch oftheseparameterscanensureoptimalresearchresults.

Experimentalresearchwithvariousfuelsanddifferentenginetuningparametersyieldsa multi-criteriaproblemwhichcanbesolvedapplyingmulticriteriadecision-makingmethods(MCDM) methods.TheMCDManalysisisoftenusedtoaddressmultipletechnicalissues,forexample, fortheassessmentandselectionofrenewableenergytechnologies[44–47],developmentofbiomass technologies[48],ecologicalbuildingassessment[49],reductionpotential[50],toevaluatevarious failuremodesmoreprecisely[51],etc.Usingthestep-wiseweightassessmentratioanalysis(SWARA) method,theevaluationfactorsaffectingelectroniclearning[52]logistics[53,54],employeeselection[55], andlogisticproviderselection[56]wereanalyzed.Ratingsofcorporatesocialresponsibilityindicators arebasedonSWARAandadditiveratioassessment(ARAS)[57].Investmentprioritiesinhigh-tech industriesaredeterminedbythestep-wiseweightassessmentratioanalysiscomplexproportional assessment(SWARA-COPRAS)method[58].

Themulti-objectiveoptimizationbyaratioanalysisplusthefullmultiplicativeform (MULTIMOORA)methodaddresseddifferentreal-lifeproblems,suchaslogistics[59],biomedical[60], andbankingoptimization[61]problems.TheMULTIMOORAmethodofanalysiswassuccessfully appliedtotheconstructionproblem[62].ThedifferentaspectsoftheintegrationofMULTIMOORA andSWARAmethodswerestudiedinReference[63].

Inthepresentpaper,anewcombinationofneutrosophicMULTIMOORAandSWARAisproposed. TheanalysisofenergyecologicalindicatorsofinternalcombustionenginesbyMCDMmethodsis theoriginaltopic.Therefore,suchresearchanditsresultsarenewandrelevantnotonlyintheEUbut alsoglobally.

Thepaperaimstoperformexperimentalresearchofenergyecologicalcharacteristicsofthe internalcombustionengineandtoanalyzetheobtainedresultsusingmulti-criteriaanalysismethods. Theultimategoalistodetermineafuelmixture,theinjectionofwhichintotheenginewouldensure thebalanceofitsenergyandecologicalindicators.

2.InternalCombustionEngineResearchStrategy

Reductionofcurrentpollutioninthetransportsectorisanessentialaspectoftheassuranceof energysavingandenvironmentalrequirements.Itisveryimportanttochoosesuchfuelmixturesthat wouldensuregoodengineecologicalenergyparameters.

Theenginealwaysworksatdifferentloadsand,thus,thefuelrequirementschange.Experiments wereperformedbychangingengineload,injectionangle,andfuelmixtures.Duringthelastfew decades,littleresearchonmixturescomprisingbiodieselandnitromethanewasperformed.

Experimentalresearchwithvariousfuelswithdifferentengineloadandvariablestartoffuel injectionparametersyieldsdataconcerningdifferentareas.Tomanagethisinformation,weapplied thepromisingneutrosophicMULTIMOORAmethod[64].Thenewsetofoptionswithminimizing andmaximizingcriteriaispresentedinTable 1

Table1. Internalcombustionengineworkingparameterswithminimizingandmaximizingcriteria.

CriteriaMin/MaxParametersTitles

x1 MinCO,%Carbonmonoxide x2 MaxCO2,%Carbondioxide x3 MaxO2,%Oxygen x4 MinHC,ppmHydrocarbons x5 MinNOx,ppmNitrogenoxides x6 MinSmok.,%Smokiness x7 MinBSFC,g/(kWh)η Brake-specificfuelconsumption x8 Max ηe Effectiveenergyefficiencycoefficient

Duringexperimentalstudies,theenergyefficiencyoftheenginewasassessedbytheparameters BSFCand ηe,andtheeco-efficiencyparameterswereNOx,HC,CO,andCO2 (concentrationinthe exhaustgasandsmoke).TheconcentrationofO2 intheexhaustgashelpsfurtherevaluatethe compositionofthecombustiblemixture.Theseenginecharacteristicswereanalyzedinmanyother studiesthattesteddifferentfuelmixtures[65–68].TheBSFCanalysisprovidestheopportunityto comparefuelconsumptioncostsinkilowatt-hoursforefficientwork.Engineefficiency ηe evaluatesthe efficiencyoffuelenergyconversiontomechanicalenergy,whichvarieswithfuelmixtureandinjection timing[34,69].NOxisahazardousmaterialthatisformedathighcombustiontemperatures[70], butwithincreasingcombustiontemperature,HCandCOandparticulateemissionsarereduced[66,71]. TheCO2 emissionsdependmainlyonfuelconsumptionandfuelcomposition(C/Hratio)[72,73]. Duringthetests,theenginerotatedatthespeedof n = 2000rpm,loadingitatenginetorques(MB) of30,60,and90Nm.Thesetestscorrespondto~17%,~33%,and~50%engineload.Thecalculated energyparametersareshownbelow.

Enginepower,kW[31]: Pe = MB ω,(1)

where ω isequalto2πn.Dieselfuellowerheatingvalue HL_D = 42.5MJ/kg;lowerheatingvalue ofrapeseedmethylester(biodiesel) HL_B = 37.27MJ/kg;nitromethane(NM)lowerheatingvalue HL_NM = 11.3MJ/kg;castoroillowerheatingvalue HL_C = 37MJ/kg. Thelowerheatingvalueoffuelmixturewascalculatedapplyingtheprincipleofadditionality[31]. HL mix = HL B CB 100 + HL NM CNM 100 + HL C CC 100 ,(2) where HL_B, HL_NM,and HL_C representthelowerheatingvalueoffuelsbiodiesel,nitromethane, andcastoroil,respectively,MJ/kg; CB, CNM,and CC;representtheshare(concentration)ofthe massofconsideredfuel,%.

Theeffectiveenergyefficiencycoefficientassessestheshareoffuelenergythatisconvertedtothe effectivepoweroftheengine Pe [31]. ηe = Pe ·3.6 m f mix HL mix .(3)

BSFCassessesthefuelmassthatisusedtodoakilowatt-hourofefficientwork,g/kWh[31]. BSFC = m f mix 1000 Pe .(4)

3.EquipmentandMethodologyofExperimentalResearch

EnginetestswereperformedattheInternalCombustionEnginesLaboratory.Figure 1 presents theprincipalschemeofthelaboratoryequipment.Thelaboratoryequipmentconsistsofthefollowing equipmentgroups:compressionignition(CI)engine,engineloadequipment,airsupplysystem, fuelsupplysystem,andexhaustsystem.Allequipmentgroupswereequippedwithadditional measuringinstruments.

Engine tests were performed at the Internal Combustion Engines Laboratory. Figure 1 presents the principal scheme of the laboratory equipment. The laboratory equipment consists of the following equipment groups: compression ignition (CI) engine, engine load equipment, air supply system, fuel supply system, and exhaust system. All equipment groups were equipped with additional measuring instruments.

Figure 1. The scheme of engine testing equipment: 1—compression ignition engine; 2—shaft; 3— engine load stand; 4—engine brake torque and speed recording equipment; 5—fuel tank; 6—fuel scales; 7—high-pressure fuel pump; 8—start of fuel injection control equipment; 9—crankshaft position sensor; 10—start of fuel injection sensor; 11—start of fuel injection recording equipment; 12— air mass meter; 13—intake air temperature meter; 14—turbocharger; 15—turbocharger pressure meter; 16—air cooler; 17—intake gas temperature meter; 18—exhaust gas recirculation (EGR) valve; 19—exhaust gas temperature meter; 20—exhaust gas concentrations analyzer; 21—smokiness meter.

Figure1. Theschemeofenginetestingequipment:1—compressionignitionengine;2—shaft; 3—engineloadstand;4—enginebraketorqueandspeedrecordingequipment;5—fueltank;6—fuel scales;7—high-pressurefuelpump;8—startoffuelinjectioncontrolequipment;9—crankshaftposition sensor;10—startoffuelinjectionsensor;11—startoffuelinjectionrecordingequipment;12—airmass meter;13—intakeairtemperaturemeter;14—turbocharger;15—turbochargerpressuremeter;16—air cooler;17—intakegastemperaturemeter;18—exhaustgasrecirculation(EGR)valve;19—exhaustgas temperaturemeter;20—exhaustgasconcentrationsanalyzer;21—smokinessmeter.

Themainparametersofthe1.9turbochargeddirectinjection(TDI)engineareshowninTable 2 Theexhaustgasrecirculation(EGR)systemwasdisabledduringthetests.Theaimwastoinvestigate theinfluenceofNMonengineparameters.

The main parameters of the 1.9 turbocharged direct injection (TDI) engine are shown in Table 2. The exhaust gas recirculation (EGR) system was disabled during the tests. The aim was to investigate the influence of NM on engine parameters.

Table2. Themainparametersofthedieselengine.TDI—turbochargeddirectinjection.

Table 2. The main parameters of the diesel engine. TDI—turbocharged direct injection.

Engine

Engine1.9TDI1Z

1.9 TDI 1Z

EnginetypeCompressionignitionengine AirsupplyTurbocharger

Engine type

Air supply

Compression ignition engine

Turbocharger

Preparation of the combustible mixture Internal, with direct fuel injection Displacement volume, cm3 1896

PreparationofthecombustiblemixtureInternal,withdirectfuelinjection Displacementvolume,cm3 1896 Numberofcylinders4

Number of cylinders 4

Compressionratio19.5 Bore,mm79.5 Stroke,mm95.5 Power,kW66(at4000rpm) Torque,Nm180(at2000–2500rpm)

TypeofgasdistributionmechanismSingleoverheadcam(SOHC) EnginecoolingFluid

Compression ratio 19.5 Bore, mm 79.5 Stroke, mm 95.5 Power, kW 66 (at 4000 rpm) Torque, Nm 180 (at 2000–2500 rpm)

Type of gas distribution mechanism Single overhead cam (SOHC) Engine cooling Fluid

Engine brake stand KI-5543 was used for the load setting. The loading stand was connected to the internal combustion engine utilizing a shaft. The engine torque MB (Nm) measurement error was ±1.23 Nm.

Energies

EnginebrakestandKI-5543wasusedfortheloadsetting.Theloadingstandwasconnectedto theinternalcombustionengineutilizingashaft.Theenginetorque MB (Nm)measurementerrorwas ±1.23Nm.

TheintakeairmasswasmeasuredapplyingairflowmeterBOSCHHFM5withanaccuracyof2%. Thepressureofairafterturbochargerintheintakemanifoldoftheenginewasmeasuredwithpressure gaugeDeltaOHMHD2304.0(sensorTP704-2BAI),withameasurementerror ±0.0002MPa.AK-type thermocouple(accuracy ± 1.5 ◦C)wasusedformeasuringintakeairandexhaustgastemperature. Hourlyfuelconsumption m f (kg/h)wasmeasuredusingelectronicscalesSK-5000andastopwatch, withanaccuracyof m f determinationof0.5%.Thestartofthefuelinjectioninthestandardengine controlstheelectronicunit.Theinjectionsteeringalgorithmwasadaptedfordifferentfueltypes. TheinjectiontimingwascontrolledusinganalgorithmofmodulationPWM(Pulsewidthmodulation), whilethesignalgeneratorwasmanagedusingapulsecontrollerTMW1.Thisdevicewasdirectly connectedtothedistributiontypefuelpump,anddisconnectedfromtheengineelectroniccontrolunit. Thecombustionprocesschangesaredependentonthephysic-chemicalpropertiesofthefuel,aswell astheanalysisofthemaximumenergy,andeco-efficiencyanalysiswasperformedbyinjectingfuelvia theapplicationofdifferentalgorithms.Oneoftheleadingfuelinjectionindicatorsisthestartofthefuel injectiontimingmeasuredbythecrankshaftangle(◦CA)beforethetopdeadcenter(TDC).Thestartof thefuelinjectionwascontrolledusingPWM,byformingtheelectroniccontrolsignalofthefuelpump. Startofthefuelinjectionwasregisteredusingapiezosensormountedonthetubeoffuelsupplytothe injectorandwasrecordedwithanAVLDiSystem845device,withanaccuracyof ±0.5 ◦CA.

Pollutantsinexhaustgases(ecologicalparameters)weremeasuredusinggasanalyzersAVL DiGas4000/AVLDiCom4000(forNOx,HC,CO,CO2,O2)andAVLDiSmoke4000/AVLDiCom4000. ThemeasurementrangeandtheaccuracyofgasanalyzersareshowninTable 3.

Table3. MeasurementrangeandaccuracyoftheAVLDiCom4000gasandAVLDiSmoke4000analyzers.

Parameters

Measurements Units Measuring Range Measurement Error AVLDiCom4000

Nitrogenoxide(NOx)concentrationppm(vol.)0–5000 ±1 Hydrocarbon(HC)concentrationppm(vol.)0–2000 ±1 Carbonmonoxide(CO)concentration%(vol.)0–10 ±0.01 Carbondioxide(CO2)concentration%(vol.)0–20 ±0.1 Oxygen(O2)concentration%(vol.)0–25 ±0.01 AVLDiSmoke4000

Smokiness%0–100 ±0.1 Enginerotationfrequency(n)rpm250–9990 ±10 Enginelubricanttemperature ◦C0–150 ±1

Basedieselfuel,inlinewiththeLSTEN590standardandmixtureofbiodiesel,NM,andcastoroil ofdifferentproportions,wasusedforrunningtheengine.Theconcentrationofcastoroilwaschosen accordingtothemethodologyofnitromethaneandethylalcohol-drivenaeromodels.Thecastoroil accountsfor20%oftheweightofNM.NMisahighlyvolatilecompoundwhosepureusecancause enginefailure.

Theexperimentalfuelshadthefollowingproportions:

100%diesel(D100); 100%biodiesel(B100); 94%biodiesel + 5%nitromethane + 1%castoroil(B94NM5C1); 91.6%Biodiesel + 7%nitromethane + 1.4%castoroil(B91.6NM7C1.4); 88%Biodiesel + 10%nitromethane + 2%castoroil(B88NM10C2);

82%Biodiesel + 15%nitromethane + 3%castoroil(B82NM15C3).

Averysmallamountofcastoroilwaschosentoimprovefuellubrication.Itsinfluenceonengine parametersisveryinsignificant.

4.ResultsofExperimentalInvestigationoftheInternalCombustionEngine

Experimentalresearchwascarriedoutusingfuelofsixdifferentcompositions(D100,B100, B94NM5C1,B91.6NM7C1.4,B88NM10C2,andB82NM15C3)inafueltank(Figure 1).Usingthestart offuelinjectioncontrolequipment(PWM),thestartoffuelinjectiontiming(0,4,8,12 ◦CAbefore TDC)waschanged.Enginetorques MB (30,60,90Nm)weredeterminedusingtheengineloadstand. Theaverageoffivetestsofmeasuredenergyandecoindicatorsoftheengineispresentedinthe researchresults.

TheuseofO2-containingfueladdstothecombustionprocess,andimprovestheenergyand environmentalperformanceoftheengine[74].However,astheoxygenateconcentrationincreasesin fuel,thephysic-chemicalpropertiesofthefuelmixturechangeand,consequently,thefuelbecomesmore volatile,changesitsinjectioncharacteristics,reducestheheatcontentofthefuelmixture,andincreases fuelconsumption(Table 4 showstheresultsoftheexperiments).Therefore,itisessentialtoregulate theangleoffuelinjectiontoachieveamorereliablebalancebetweenenergyandecologicalindicators indifferentengineloads.

Table4. Summaryoftheinternalcombustionengineworkingparameters.TDC—topdeadcenter.

Item No. StartofFuelInjection beforeTDC, ◦CA EngineTorques MB,Nm Fuels Criteria x1 x2 x3 x4 x5 x6 x7 x8 1 0

D1000.023.315.9171236.7329.450.257 2B1000.033.415.671264.1389.960.250 3B94NM5C10.033.515.8121922.2403.700.248 4B91.6NM7C1.40.033.515.5112311.8400.870.253 5B88NM10C20.033.515.5102482.3427.800.243 6B82NM15C30.033.515.8102992.5415.400.260 7 60

30

D1000.025.113.51323511.5272.980.310 8B1000.025.213.2112447.5322.050.302 9B94NM5C10.025.113.883673.2322.050.311 10B91.6NM7C1.40.015.213.493902.4325.710.312 11B88NM10C20.025.113.693982.8322.050.322 12B82NM15C30.025.113.684482.5329.450.327 13 90

D1000.026.711.21229422.1254.780.332 14B1000.026.811.2134308.5293.970.331 15B94NM5C10.016.711.6105423.2276.930.361 16B91.6NM7C1.40.016.711.395752.5298.570.340 17B88NM10C20.016.611.5125922.7303.310.342 18B82NM15C30.016.711.596492.5303.310.356 19 4

30

D1000.023.315.9161507.8327.570.259 20B1000.023.415.771594.4384.730.253 21B94NM5C10.023.416.092172.5377.140.265 22B91.6NM7C1.40.023.515.6102601.5389.960.260 23B88NM10C20.023.415.892602.2400.870.259 24B82NM15C30.023.415.893092.2403.700.267 25 60

D1000.024.913.71328013.0262.960.322 26B1000.025.213.3103137.9301.710.322 27B94NM5C10.015.113.894453.1301.710.332 28B91.6NM7C1.40.015.213.784322.1314.970.322 29B88NM10C20.015.013.794692.6308.200.337 30B82NM15C30.015.113.685282.5322.050.335 31 90

D1000.026.511.41048020.0248.160.341 32B1000.016.811.3115527.6276.930.351 33B94NM5C10.016.611.797252.7276.930.361 34B91.6NM7C1.40.016.711.497382.2272.980.372 35B88NM10C20.016.511.6107432.5293.970.353 36B82NM15C30.016.611.588052.3303.310.356

Table4. Cont.

Item No. StartofFuelInjection beforeTDC, ◦CA EngineTorques MB,Nm Fuels

Criteria

x1 x2 x3 x4 x5 x6 x7 x8 37 8

30

D1000.023.316.0162156.5322.050.263 38B1000.023.415.762224.4362.820.268 39B94NM5C10.023.416.282712.2367.470.272 40B91.6NM7C1.40.013.415.793401.5374.670.271 41B88NM10C20.023.415.783272.1398.090.261 42B82NM15C30.023.415.883712.2398.090.271 43 60

D1000.014.913.7144909.6253.650.334 44B1000.015.013.5114257.7292.470.333 45B94NM5C10.015.113.785682.9295.490.339 46B91.6NM7C1.40.015.113.486332.1304.920.333 47B88NM10C20.015.013.896242.6311.550.333 48B82NM15C30.015.013.796842.2314.970.343 49 90

D1000.016.511.51066216.0241.880.350 50B1000.016.811.3138223.1269.130.362 51B94NM5C10.016.511.879442.6269.130.372 52B91.6NM7C1.40.016.711.4119552.1272.980.372 53B88NM10C20.016.611.610 1019 2.5289.520.359 54B82NM15C30.016.611.68 1029 2.3293.970.367 55 12

30

D1000.013.316.0133034.5327.570.259 56B1000.023.515.7213614.7392.640.248 57B94NM5C10.023.516.123892.1360.530.278 58B91.6NM7C1.40.013.415.7104821.4372.240.273 59B88NM10C20.013.415.794322.0384.730.270 60B82NM15C30.013.515.886852.0392.640.275 61 60

D1000.015.013.6146106.7255.910.331 62B1000.015.213.4146415.4286.620.339 63B94NM5C10.015.113.887702.3286.620.349 64B91.6NM7C1.40.015.113.499192.0301.710.337 65B88NM10C20.015.013.898432.2298.570.348 66B82NM15C30.015.113.68 1186 2.2311.550.346 67 90

D1000.016.511.41094210.8230.220.368 68B1000.016.811.212 1285 3.2261.760.372 69B94NM5C10.016.611.88 1180 2.3261.760.382 70B91.6NM7C1.40.016.711.310 1390 2.4276.930.367 71B88NM10C20.016.511.710 1330 2.6276.930.375 72B82NM15C30.016.711.610 1729 2.7289.520.373

TheresultsofTable 4 showthatincreasingtheconcentrationofoxygenatesimprovessomeofthe parameters,whileothersdeteriorate;therefore,itisexpedienttoevaluatethemasawholeusingthe MCDManalysis.

Itiscrucialtoidentifytheimportanceoftheaboveindicators,whichdependsontheengine parametersyouwanttohighlight.Ifyouaregoingtohighlighttheeco-engineparameters, theappropriatemixtureofoptimumfuelandexceptionallyself-containedoxygenateswillshow bettercombustioncharacteristics,whichinturnwillallowthecompletecombustioncycletotakeplace onadditionalO2.Thisfactresultsinfewerincompletecombustionproductsandbetterenvironmental performance.However,thelowercalorificvalueofthemixtureresultsinhigherfuelconsumption[21].

Whenchangingengineload,itincreasesfuelconsumptionandincreasesexhaustgasemission. Therefore,inordertofindtheoptimummixtureofoxygenates,itisessentialtoevaluatethebalance betweenengineperformanceandeco-indicators,wherethepreferredpriorityoftheindicatorsallows toensurethecompatibilityofallengineparametersdependingontheloadandtheadvanceangleof theengine,allowingtochangethetimeoffuelinjection,anddirectlyaffectingthecombustionprocess. Anexpertsurveywascarriedouttoassesstheimportanceofengineperformanceandenvironmental performance,duringwhichexpertsputapriorityrankingofindicators[75].

Twelveexperts,includingadoctor,scientist,andengineers,mostofthemteachingatahigher schoolandwithexperiencebetweenthreeand45years,wereinterviewed.

These12expertsreachedaconsensusconcerningtherankingorderofthecriteriaandprovideda personalevaluationofpairwiserelativeimportancedata,presentedinTable 5

Table5. Evaluationoftherelativeimportancebycriteriapairs.

Experts

PairwiseEvaluationofCriteriaRelativeImportance x1↔2 x2↔3 x3↔4 x4↔5 x5↔6 x6↔7 x7↔8

10.170.400.030.210.170.180.40 20.110.150.000.070.030.130.36 30.150.120.330.400.030.670.03 40.130.180.110.200.130.120.73 50.130.130.930.070.070.130.27 60.160.110.500.050.130.090.50 70.110.190.470.090.070.160.17 80.140.170.080.160.170.120.19 90.190.100.110.190.190.220.20 100.120.440.060.120.270.110.33 110.110.270.100.100.300.090.27 120.130.670.040.130.270.100.18

5.MCDMAnalysisUsingSWARAandNeutrosophicMULTIMOORAMethods

ExperimentalinternalcombustionenginetestsandlargequantitiesofresultsshowedthatMCDM analysisisrequiredtoanalyzetheresults.Foranalysis,multi-criteriaanalysismethodsSWARA andneutrosophicMULTIMOORAwereused.TheSWARAmethodallowsustoefficientlyintegrate individualexpertopinionsconcerningthepairwiseimportanceofthecriteria.TheMULTIMOORA methodisgovernedbyatripleobjectivewhichprovidesadditionalrobustnessandstability.

5.1.AssessmentoftheWeightsbySWARAMethod

TheassessmentofthecriteriaimportanceisacrucialaspectconsideringallMCDMproblems. Theweightsofthecriteriarepresentthedegreeoftheinfluenceoftheparticularcriteriononthefinal rankingofthealternatives.Theweightdeterminationproblemwasconsidered,applyingconsistent andgradualpairwisecomparisonofcriteriacorrespondingtoinfluenceintheSWARAmethod[76–78]. TheSWARAmethodisgovernedbyexperts’evaluationconcerningtheimportanceofthecriteria[77]. TheessenceoftheSWARAmethod,whichisappliedtocalculatecriteriaweights,canbeexpressed bythefollowingsteps: 1. Determinationofthesetofcriteria; 2. Criteriarankingperformedbyexpertevaluation(themostcrucialweightisplacedinfirst position); 3. Theaveragevalueofcomparativeimportanceisobtained s j; 4. Thebenefitsofthecomparativeimportancearecalculated k j = s j + 1; 5. Transitionalweightsarerecalculated q j = q j 1 k j ; 6. Thefinalweightsarenormalizedas w j = w j n j=1 w j ,where n isthenumberofcriteria.

Table 6 presentsallintermediateresultsobtainedapplyingtheSWARAmethod:theaveragevalues ofcriteriarelativecomparativeimportance,coefficientsofcriteriarelativecomparativeimportance, converted(intermediate)criteriaweights,andfinalcriteriaweights.

Table6. Criteriaweightingbystep-wiseweightassessmentratioanalysis(SWARA)method.

Criteria

AverageValuesof Comparative Importance Criteria, sj↔j+1

Coefficientsof Comparative Importance Criteria, kj

Recalculated (Intermediate) CriteriaWeights, qj

FinalCriteria Weights, wj

x1 –110.2108 x2 0.13751.13750.87910.1853 x3 0.24421.24420.70660.1489 x4 0.231.230.57440.1211 x5 0.14921.14920.49990.1054 x6 0.15251.15250.43370.0914 x7 0.17831.17830.3680.0776 x8 0.30251.30250.28260.0596 4.7443

5.2.TheNeutrosophicMULTIMOORAMethod

TheneutrosophicMULTIMOORAmethodwasconstructedapplyingchiefconstituents. Thesingle-valued neutrosophicsetmodelstheinitialinformationrepresentationandprovidesthe algebraicbasisoftheoperations,andthetraditionalcrispMULTIMOORAapproachintroducedin Reference[79]offersthetheoreticalframeworkfortherankingofthealternatives.Thetraditional crispMULTIMOORAmethod,whichinitiallywasproposedbyoneoftheauthors,wasquite intensivelyappliedtosolvevariousproblems,andthisapproachunderwentdifferentextensions[64,80]. Thisnewproposedapproach,namelymulti-objectiveoptimizationbyaratioanalysisplusthefull multiplicativeformsingle-valuedneutrosophicset(MULTIMOORA-SVNS),wasappliedtoperform internalcombustionengineanalysisofenergyecologicalparameters.

TheessenceoftheMULTIMOORA-SVNSconsistsoftheapplicationofthreeobjectives: ratiosystem,referencepointtechnique,andfullmultiplicativeform.Thefinalrankingofthe alternativeswasperformedintegratingtheresultsofthesethreeobjectivesbytherulesofthe dominancetheory.

Traditionally,likeallMCDMmethods,theproposedapproachwasinitiatedbytheconstructionof thedecisionmatrix X.The xij elementsofthismatrixrepresentthe ith criteriaof jth alternative,andthis matrixcanbeexpressedby X = x11 x1m xn1 xnm .(5)

Theratiosystemwasappliedtotheformationofthefirstobjectiveoftheproposedmethod. Atthefirststep,thenormalizationofthedecisionmatrixisinitiallyperformedbythevector normalizationapproach. X∗ = xij m i=1 x2 ij

.(6)

Afternormalization,theneutrosophicationstepfortheelementsofthedecisionmatrixisperformed. Thecrispvaluesaretransformedintosingle-valuedneutrosophicmembersapplyingconversionrules whichwerediscussedinReference[62].

Afterthisstep,theneutrosophicdecisionmatrixisconstructed.Thefirstobjectiveofthe neutrosophicMULTIMOORAapproachiscalculatedasfollows:

,(7)

where g elementscorrespondtomembersofthecriteriatobemaximized,and n g correspondsto membersofthecriteriatobeminimized.Here,single-valuedneutrosophicmembershavetheform (x∗ n)1 = (tn1, in1, fn1).Thefollowingalgebraicoperationswereappliedfortheneutrosophicnumbers: λ(x ∗ n)1 = 1 (1 tn1)λ , (in1)λ , ( fn1)λ ,(8) (x ∗ n)1 ⊕ (x ∗ n)2 = (tn1 + tn2 tn1 tn2, in1 in2, fn1 fn2),(9) x ∗ n1 c = ( fn1,1 in1, tn1).(10)

       

max i D ri wi(x ∗ n)ij i





Thereferencepointcanbeexpressedasfollows: ri = max j wi(x ∗ n)ij ,(12) forthecaseofthecriteriatobemaximized,and,inthecaseofthecriteriaminimization, ri = min j wi(x ∗ n)ij .(13)

Thescorefunctionisutilizedtoperformacomparisonoftheneutrosophicmembers. S((x ∗ n)1) = 3 + tn1 2in1 fn1 4 .(14)

Therefore,inthecaseof S((x ∗ n)1) S((x ∗ n)2),,(15) (x∗ n)1 issmallerthan (x∗ n)2,and (x ∗ n)1 (x ∗ n)2.(16)

Thedistancemeasurebetweentwosingle-valuedneutrosophicsetsisintroducedasfollows: D((x ∗ n)1, (x ∗ n)2) = 1 3 (tn1 tn2)2 + (in1 in2)2 + ( fn1 fn2)2 .(17)

Thefullmultiplicitiesformisthemainconcernofthethirdobjectiveintheproposedapproach. Therefore,theoverallutilityiscalculatedforeachdecisionalternative,whichcanbeexpressed asfollows:

U j = S A j S B j .(18) Here, A j and B j componentsarecalculatedas A j = g Π i=1 wi(x ∗ n)ij, B j = n Π j=g+1 wi(x ∗ n)ij.(19)

Thefirstcomponent A j representstheproductofcriteriaof jth alternativetobeingmaximized, andthesecondcomponent B j correspondstotheproductofcriteriaof jth alternativetobeingminimized. Themultiplicationoftheseparatesingle-valuedneutrosophicmembersisperformedasfollows: (x ∗ n)1 ⊗ (x ∗ n)2 = (tn1 · tn2, in1 + in2 in1 · in2, fn1 + fn2 fn1 · fn2).(20)

ThefinalsummarizationofallthreeobjectivesoftheneutrosophicMULTIMOORAmethodis performedapplyingthedominancetheoryofBrauersandZavadskas[62].

6.ResultsofMCDMAnalysis

ExpertresultsofMCDManalysisusingneutrosophicMULTIMOORAandSWARAmethodswere determined.Experimentalresultsaredividedinto12tasks.Thefirstproblemcorrespondstothefirst caseoftheexperiments(lines1–6inTable 4),wherethefirstproblemcorrespondstostartoffuel injectionbeforeTDC,0(◦CA);enginetorque,30(Nm),thesecondproblemcorrespondstostartoffuel injectionbeforeTDC,0(◦CA);enginetorque,60(Nm),etc.Theother11problemsareconstructedin thesameway.TheresultsarepresentedinTables 7–9,andTables A1–A22.

Table7. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophicmulti-objective optimizationbyaratioanalysisplusthefullmultiplicativeform(MULTIMOORA)approach (firstproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.658960.969720.08364 A20.694720.971040.14482 A30.679730.971450.09233 A40.710010.970531.29141 A50.665940.972160.06575 A60.649950.969210.05566

Table8. Thefinalrankingsofthealternativesbythedominancetheoryoftheneutrosophic MULTIMOORAapproach(firstproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank

Theotherresultsarepresentedintheannexedtables. Table 9 summarizestheresults.

Table9. Summaryoftheengineperformanceofthevehicle.WASPAS—weightedaggregatedsum productassessment.

Item.No. StartofFuelInjection beforeTDC, ◦CA EngineTorque MB,Nm Fuels Places Neutrosophic MULTIMOORA WASPAS 1 0

D10066 2B10022 3B94NM5C133 4B91.6NM7C1.411 5B88NM10C244 6B82NM15C355 7 60

30

D10066 8B10055 9B94NM5C132 10B91.6NM7C1.411 11B88NM10C243 12B82NM15C324 13 90

D10066 14B10055 15B94NM5C111 16B91.6NM7C1.422 17B88NM10C244 18B82NM15C333 19 4

30

D10066 20B10032 21B94NM5C121 22B91.6NM7C1.414 23B88NM10C243 24B82NM15C355 25 60

D10066 26B10055 27B94NM5C13–42 28B91.6NM7C1.411 29B88NM10C223 30B82NM15C33–44 31 90

D10066 32B10055 33B94NM5C121 34B91.6NM7C1.413 35B88NM10C244 36B82NM15C332 37 8

30

D10066 38B10033 39B94NM5C122 40B91.6NM7C1.411 41B88NM10C244 42B82NM15C355 43 60

D10066 44B10055 45B94NM5C111 46B91.6NM7C1.434 47B88NM10C243 48B82NM15C322 49 90

D10066 50B10055 51B94NM5C111 52B91.6NM7C1.434 53B88NM10C243 54B82NM15C322

Table9. Cont.

Item.No. StartofFuelInjection beforeTDC, ◦CA EngineTorque MB,Nm Fuels Places Neutrosophic MULTIMOORA WASPAS 55 12

D10053 56B10066 57B94NM5C115 58B91.6NM7C1.422 59B88NM10C231 60B82NM15C344 61 60

30

D10066 62B10055 63B94NM5C111 64B91.6NM7C1.433 65B88NM10C222 66B82NM15C344 67 90

D10066 68B10055 69B94NM5C111 70B91.6NM7C1.433 71B88NM10C222 72B82NM15C344

7.Discussion

TheaimoftheresearchusingneutrosophicMULTIMOORAandSWARAmethodswasto determineafuelmixturewhichwouldensurethebalanceofengineenergyandecologicalparameters. Whenanalyzingtheresultsoftheresearch,itisnecessarytotakeintoaccounthowdifferent fuelmixturesunderdifferentoperatingconditionsoftheengineinfluenceditsenergyandecological characteristics.Forthispurpose,anoptimumscalewasusedthatrevealedhowfuelmixturesvaryin differentSOI,i.e.,prioritizingfuelselection.Thehighestprioritywasgiventofuel,whichwasfirst ontheoptimumscale,whilethelowestprioritywasgiventothefuelinsixthplace.Thismethodof assessmentallowstheidentificationoftheoptimumuseoffuelmixturesatdifferentengineloads andSOI.

TheoptimalmixturechangebasedonfuelSOIwasobservedataverylow(~17%)engineload (30Nm)(Figure 2).TherankingofmixturesuponchangingSOIataverylow(~17%)engineload (30Nm)ispresentedinFigure 2.Thedieselwastheleastefficientfuelatthefuelinjectionadvance angleof8◦.Thistrendwasobservedintheapplicationofbothmethods(weightedaggregatedsum productassessment(WASPAS)andneutrosophicMULTIMOORA).Thisfactcanbeexplainedbya lowamountoffuelsuppliedatlowload,duetophysic-chemicalproperties[15].Highnitromethane content(15%)duetoincreasedevaporationrateoffuelandlowdropletdispersiondidnotallow usingitasoptimal(thirdplaceinapplicationoftheneutrosophicMULTIMOORAmethodandsecond placeusingWASPASmethod),althoughhavingdelayedtheinjectionangleto12◦,itfelltothefifth placeinapplicationofbothmethodsduetohigherevaporationtime.Severaltrendsofchangeofthe biodieseland5%NMmixturewereobserved.Attheinjectionanglesof0◦ and4◦,amorehomogeneous combustiblemixturewasnotformedduetoworseevaporationcomparedtoNMevaporation[15] incomparisontoanglesof8◦ and12◦.Thistrendwasobservedintheanalysisintheapplication ofbothmethods,buttheMULTIMOORAmethodwasmoreconsistentattheadvanceangleof12◦ Comparedtootherfuels,biodieselhasthehighestviscosity[16],andtheoptimalperformancewas achievedat0◦ fuelinjectionadvanceangle(secondplaceintheapplicationofbothmethods).However, whenincreasingthefuelinjectionadvanceangle,alowerdropletbecomesanadvantage,whilea heavieroneburnsworseinthiscase,formingareasofthefattermixture[81].Thistrendwasobserved byincreasingthefuelinjectionadvanceangle,andbiodieselbecametheworstmixtureat4◦ and 8◦.Whenfurtherincreasingtheinjectionangleupto12◦,thefuelhadenoughtimetoevaporate

angle of 12°. Compared to other fuels, biodiesel has the highest viscosity [16], and the optimal performance was achieved at 0° fuel injection advance angle (second place in the application of both methods). However, when increasing the fuel injection advance angle, a lower droplet becomes an advantage, while a heavier one burns worse in this case, forming areas of the fatter mixture [81]. This trend was observed by increasing the fuel injection advance angle, and biodiesel became the worst mixture at 4° and 8°. When further increasing the injection angle up to 12°, the fuel had enough time to evaporate and form a homogeneous mixture in individual zones, resulting in improved efficiency of biodiesel (third place in the application of the WASPAS method and second place using the neutrosophic MULTIMOORA method). A small addition of NM (7%) slightly changed the properties of biodiesel, improving them because of decreasing viscosity of the mixture, whereby this mixture was found to be optimal. This mixture was optimal at all fuel injection angles, but when increasing the fuel injection advance angle to 12° in the application of the neutrosophic MULTIMOORA method, its optimality parameters deteriorated (fourth place). Similar trends were observed analyzing a 5% NM mixture; with the increasing injection advance angle, the mixture approached its optimal efficiency due to the NM evaporation properties (at 8° in application of both methods; at 12°, it ranked second in the application of the WASPAS method, and first using the neutrosophic MULTIMOORA method).

andformahomogeneousmixtureinindividualzones,resultinginimprovedefficiencyofbiodiesel (thirdplaceintheapplicationoftheWASPASmethodandsecondplaceusingtheneutrosophic MULTIMOORAmethod).AsmalladditionofNM(7%)slightlychangedthepropertiesofbiodiesel, improvingthembecauseofdecreasingviscosityofthemixture,wherebythismixturewasfoundtobe optimal.Thismixturewasoptimalatallfuelinjectionangles,butwhenincreasingthefuelinjection advanceangleto12◦ intheapplicationoftheneutrosophicMULTIMOORAmethod,itsoptimality parametersdeteriorated(fourthplace).Similartrendswereobservedanalyzinga5%NMmixture; withthe increasinginjectionadvanceangle,themixtureapproacheditsoptimalefficiencyduetothe NMevaporationproperties(at8◦ inapplicationofbothmethods;at12◦,itrankedsecondinthe applicationoftheWASPASmethod,andfirstusingtheneutrosophicMULTIMOORAmethod).

Figure2. Enginetorqueof30Nm.

Figure 2. Engine torque of 30 Nm.

Atthelow(~33%)engineload(60Nm)(Figure 3),purediesel(sixthplace)andbiodiesel(fifthplace at0–4◦ and12◦ advanceangles)wereobservedtobetheleastoptimalmixturesintheapplication ofbothmethods.Thisfactcomesasaresultofthelowerleveloftheirevaporationcomparedto otherfuels.Thealreadyestablishedtrendsofoptimalitywerealsoobserved;attheadvanceangle of4–12◦,lowerNMcontent(5%)allowedensuringtheoptimalefficiency(firstandsecondplace usingbothmethods)duetohigherfuelconsumptioncomparedtotheloadof30Nm.Furthermore, the7%NMmixtureremainedoptimal(attheanglesof0◦ to8◦,itrankedfirstusingtheWASPAS method,and,attheadvanceangleof12◦,decreasingoptimalitywasobservedintheapplicationofthe neutrosophicMULTIMOORAmethod(thirdplace)).Thebiodieselpropertiesalsoimprovedthrough increasingitsevaporationlevelanddistributingthefuelmixturemoreevenly.Uponincreasingthe NMconcentrationinmixtures,theoptimaltrendsremainedsimilar(examiningmixtureswith10%NM content).Amixturewith15%NMcontentremainedtheleaststableintermsofitsoptimalefficiency.

At the low (~33%) engine load (60 Nm) (Figure 3), pure diesel (sixth place) and biodiesel (fifth place at 0–4° and 12° advance angles) were observed to be the least optimal mixtures in the application of both methods. This fact comes as a result of the lower level of their evaporation compared to other fuels. The already established trends of optimality were also observed; at the advance angle of 4–12°, lower NM content (5%) allowed ensuring the optimal efficiency (first and second place using both methods) due to higher fuel consumption compared to the load of 30 Nm. Furthermore, the 7% NM mixture remained optimal (at the angles of 0° to 8°, it ranked first using the WASPAS method, and, at the advance angle of 12°, decreasing optimality was observed in the application of the neutrosophic MULTIMOORA method (third place)). The biodiesel properties also improved through increasing its evaporation level and distributing the fuel mixture more evenly. Upon increasing the NM concentration in mixtures, the optimal trends remained similar (examining mixtures with 10% NM content). A mixture with 15% NM content remained the least stable in terms of its optimal efficiency.

Figure 3. Engine torque of 60 Nm.

Figure 3. Engine torque of 60 Nm.

Figure3. Enginetorqueof60Nm.

At average (~50%) engine loads (90 Nm) (Figure 4), the 5% NM mixture became optimal at all fuel injection advance angles (0°, 4°, 8°, 12°) (first place in application of the SWARA analysis method), while fuel diesel and biodiesel were the least optimal (sixth and fifth places, respectively).

Ataverage(~50%)engineloads(90Nm)(Figure 4),the5%NMmixturebecameoptimalat allfuelinjectionadvanceangles(0◦,4◦,8◦,12◦)(firstplaceinapplicationoftheSWARAanalysis method),whilefueldieselandbiodieselweretheleastoptimal(sixthandfifthplaces,respectively). Engineperformance parametersdifferedchaoticallywithothermixtures,whichwasrelatedtothe unevendistributionofthemixture.

Engine performance parameters differed chaotically with other mixtures, which was related to the uneven distribution of the mixture.

At average (~50%) engine loads (90 Nm) (Figure 4), the 5% NM mixture became optimal at all fuel injection advance angles (0°, 4°, 8°, 12°) (first place in application of the SWARA analysis method), while fuel diesel and biodiesel were the least optimal (sixth and fifth places, respectively). Engine performance parameters differed chaotically with other mixtures, which was related to the uneven distribution of the mixture.

Figure4. Enginetorqueof90Nm.

Figure 4. Engine torque of 90 Nm.

Figure 4. Engine torque of 90 Nm.

TheconductedMCDManalysisrevealedthatthebestresultswereobtainedwithfuelB94NM5C1 becausethisfuelrankedfirstinsevencases,whichaccountsformorethan50%ofallresearchresults.

The conducted MCDM analysis revealed that the best results were obtained with fuel B94NM5C1 because this fuel ranked first in seven cases, which accounts for more than 50% of all research results.

The conducted MCDM analysis revealed that the best results were obtained with fuel B94NM5C1 because this fuel ranked first in seven cases, which accounts for more than 50% of all research results.

Accordingtotheanalysis,fuelB88NM10C2rankedsecondandthird.Theremainingplaceswere sharedbytheotherfuels,withD100takinglastplace.

According to the analysis, fuel B88NM10C2 ranked second and third. The remaining places were shared by the other fuels, with D100 taking last place.

According to the analysis, fuel B88NM10C2 ranked second and third. The remaining places were shared by the other fuels, with D100 taking last place.

8.Conclusions

8. Conclusions

8. Conclusions

Experimentalresearchofenergyecologicalparametersoftheinternalcombustionenginewas performed.Duringtheexperiments,threevariableswereused:fuelinjectionangle,enginetorque, andfuelcomposition.

Experimental research of energy ecological parameters of the internal combustion engine was performed. During the experiments, three variables were used: fuel injection angle, engine torque, and fuel composition.

Experimental research of energy ecological parameters of the internal combustion engine was performed. During the experiments, three variables were used: fuel injection angle, engine torque, and fuel composition.

Manyinternalcombustionengineoperatingcharacteristicsobtainedbyexperimentalstudies requireanalysisusingMCDManalysismethods.TheMCDManalysiswasperformedtodetermine theprioritysequenceoftheenergyecologicalparametersoftheinternalcombustionengine.

Inthepresentpaper,anewcombinationofneutrosophicMULTIMOORAandSWARAwas proposed.Whenevaluatingtheenergyandecologicalaspectsoftheperformanceoftheengine, itshouldbenotedthatthefuelmixmadefrombiodieselrapeseedmethylester(RME)andNMwas significantlybetterthanconventionalfueldieselorbiodieselaftertheinjectionangleadjustment. Accordingtothesummarizedresultsoftheanalysis,thebestenergyecologicalworkingparameters wereobtainedusingfuelB94NM5C1andtheworstwereobtainedusingfuelD100.Fuelmixturesthat containbiofuelsmakeitpossibletocompletelyreplacefossilfuelswithoutdamagingtheperformance oftheengine.Thearticlepresentsanewwaytoreducedieselinternalcombustionenginepollution.

TheMCDManalysiscanbeusedtodetermineafuelmixturethatprovidestheoptimalenergy andenvironmentalperformanceoftheinternalcombustionengine,dependingontheengineload.

TheMCDManalysismethodscanbeappliedtoothersimilartechnicalproblems.

AuthorContributions: Conceptualization,E.K.Z.;methodology,R.B.;software,R.B.;validation,R.B.;formal analysis,E.K.Z.,A.C.,andR.B.;investigation,A.R.andJ.M.;resources,A.R.andJ.M.;datacuration,A.R.and J.M.;writing—originaldraftpreparation,A.R.andJ.M.;writing—reviewandediting,E.K.Z.,A.C.,andR.B.; visualization,A.C.;supervision,E.K.Z.;projectadministration,E.K.Z.

Funding: Thisresearchreceivednoexternalfunding. ConflictsofInterest: Theauthorsdeclarenoconflictsofinterest.

AppendixA

TableA1. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(secondproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.607960.971250.25486 A20.655950.972360.44675 A30.688920.971141.43573 A40.758710.971036.99981 A50.681030.969521.31034 A60.676740.968811.45132

TableA2. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(secondproblem).

TableA3. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(thirdproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.523460.972150.10886 A20.583750.972260.26165 A30.747810.968416.86442 A40.743820.971247.00071 A50.729240.970934.63154 A60.735930.969026.69463

TableA4. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(thirdproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A16566 A25655 A31121 A42412 A54344 A63233

TableA5. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(fourthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.605260.97094-50.25886 A20.700920.971961.31613 A30.703710.969921.58452 A40.689550.970831.58631 A50.692940.97094–51.23244 A60.679930.969511.01255

TableA6. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(fourthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A164–566 A22633 A31221 A44312 A534–544 A65155

TableA7. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(fifthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank

A10.532260.97154–60.10936 A20.581350.97154–60.22835 A30.740220.970233.68874 A40.744310.970154–65.75761 A50.738830.969514.16332 A60.729940.969723.78993

TableA8. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(fifthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A164–666 A254–655 A32343–4 A414–611 A53122 A64233–4

TableA9. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(sixthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank

A10.481260.972460.05856 A20.707850.971351.21695 A30.728710.970024.40382 A40.726120.968615.14241 A50.719940.971043.55924 A60.721630.970634.27633

TableA10. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(sixthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A16666 A25555 A31222 A42111 A54444 A63333

TableA11. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(seventhproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.589550.972260.20936 A20.677340.970941.16815 A30.713110.96961-23.35301 A40.589460.96961-21.99593 A50.690930.971251.62084 A60.698620.970332.34572

TableA12. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(seventhproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A15666 A24455 A311–211 A461–233 A53544 A62322

TableA13. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(eighthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.623160.970930.25666 A20.662850.97104-60.47825 A30.704010.970221.79202 A40.697420.97104-61.91441 A50.696430.97104-61.47654 A60.691040.969711.58623

TableA14. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(eighthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A16366 A254–655 A31221 A424–613 A534–644 A64132

TableA15. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(ninthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank

A10.589560.972260.20936 A20.677350.970941.16815 A30.713110.96961–23.35301 A40.689440.96961–21.99593 A50.690930.971251.62084 A60.698620.970332.34572

TableA16. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(ninthproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A16666 A25455 A311–211 A441–233 A53544 A62322

TableA17. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(10thproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.709630.972051.49725 A20.520860.973560.06186 A30.624550.968813.99492 A40.745020.969734.26201 A50.747610.970243.49813 A60.710440.969422.09694

TableA18. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(10thproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank A13555 A26666 A35122 A42311 A51433 A64244

TableA19. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(11thproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.619060.971250.30046 A20.678640.970840.80665 A30.713710.969512.81921 A40.690930.971461.21753 A50.698320.970421.43302 A60.675250.970630.84084

TableA20. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(11thproblem).

TheNeutrosophic RatioSystem TheNeutrosophic ReferencePoint TheNeutrosophicFull MultiplicativeForm FinalRank A16566 A25455 A31111 A43633 A52222 A64344

TableA21. Therankingsofthealternativesusingtheseparateobjectivesoftheneutrosophic MULTIMOORAapproach(12thproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjective

S(Qi) Rank max|D(ri wi(x * n)ij)| Rank Uj Rank A10.619060.971250.30046 A20.678640.970840.80665 A30.713710.969512.81921 A40.690930.971461.21753 A50.698320.970421.43302 A60.675250.970630.84084

TableA22. Thefinalrankingsofthealternativesusingthedominancetheoryoftheneutrosophic MULTIMOORAapproach(12thproblem).

TheFirstObjectiveTheSecondObjectiveTheThirdObjectiveFinalRank

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