research article optimization of performance and emission...

Hindawi Publishing CorporationJournal of EngineeringVolume 2013 Article ID 915357 8 pageshttpdxdoiorg1011552013915357

Research ArticleOptimization of Performance and Emission Characteristics ofDiesel Engine with Biodiesel Using Grey-Taguchi Method

Goutam Pohit and Dipten Misra

Department of Mechanical Engineering Jadavpur University Kolkata West Bengal 700032 India

Correspondence should be addressed to Goutam Pohit gpohitgmailcom

Received 18 December 2012 Revised 18 January 2013 Accepted 29 January 2013

Academic Editor Chin-Ping Tan

Copyright copy 2013 G Pohit and D Misra This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Engine performances and emission characteristics of Karanja oil methyl ester blended with diesel were carried out on a variablecompression diesel engine In order to search for the optimal process response through a limited number of experiment runsapplication of Taguchi method in combination with grey relational analysis had been applied for solving a multiple responseoptimization problem Using grey relational grade and signal-to-noise ratio as a performance index a particular combination ofinput parameters was predicted so as to achieve optimum response characteristics It was observed that a blend of fifty percent wasmost suitable for use in a diesel engine without significantly affecting the engine performance and emissions characteristics

1 Introduction

Rudolf Diesel the father of Diesel engine demonstrated thefirst use of vegetable oil in compression ignition engine Heused peanut oil as fuel in his engine Because of the increasein the crude oil prices and limited reserve of fossils fuelsthere has been a renewed focus on usage of vegetable oils assuitable alternative to diesel fuelThey are known as biodieselessentially an ester of vegetable oil A brief discussion of someimportant research findings related to this field is presentedbelow

Carraretto et al [1] conducted experiment on a CIengine firstly on a test bench and later on an urban busThey observed that with biodiesel there was an increase inspecific fuel consumption and emission of oxides of nitrogen(NO119909) However carbon monoxide (CO) carbon dioxide

(CO2) emissions were reduced Raheman and Phadatare [2]

observed that Karanja oil methyl ester blended with dieselcould be a suitable alternative fuelTheir emission study indi-cated that CO and NO

119909were reduced by a good percentage

compared to diesel Agarwal [3] produced biodiesel fromRatanjyot (Jatropha) Karanja Nagchampa and Rubber byusing both methyl and ethyl alcohol He also claimed that

biodiesel could be a better alternative to petroleum dieselsince there was no need for engine modification

Raheman and Ghadge [4 5] carried out experimentusing Mahua biodiesel and its blends in a Ricardo E6engine They varied the compression ratio from 18 to 20 Ithad been observed that with an increase in percentage ofbiodiesel brake-specific fuel consumption increased whilebrake thermal efficiency decreased Rao et al [6] concludedfrom his investigation that the vegetable oils were promisingalternative fuels for agricultural diesel engines Howeverthese vegetable oils exhibited slightly inferior performance inrespect to higher smoke emission Kalbande and Vikhe [7]studied the performance of Jatropha and Karanja biodieseland their blends with diesel The efficiency of Karanjabiodiesel was found to be higher for B20 (20 biodiesel and80 diesel) and B40 (40 biodiesel and 60 diesel) blendamong different combination of Karanja bio-diesel blendIn case of Jatropha B60 and B80 delivered the maximumefficiency Fontaras et al [8] investigated the combustion andemission characteristics of biodiesel using soybean biodieselin a diesel passenger car complying EURO 2 emissionstandard They observed that there had been a problem ofcold starting while using soybean biodiesel

2 Journal of Engineering

Godiganur et al [9] observed that after trans-esterifica-tion mahua oil showed a similar kind of property as that ofdiesel Among the different blends 20 (B 20) blend wasfound to be the most suitable Baiju et al [10] producedKaranja oil ethyl ester and Karanja oil methyl ester fromKaranja oil Both of them showed good emission charac-teristics except presence of NO

119909being on the higher side

They also claimed that the methyl ester exhibited a betterperformance than ethyl ester In a study made by Sahooet al [11] Jatropha Karanja and Polanga methyl esterswere blended with diesel The maximum power output wasobtained from B 50 blend The smoke emission was found tobe reduced in case of biodiesel at full throttle However COand NO

119909emission increased a bit compared to diesel

Murugesan et al [12] observed that methyl ester ofKaranja oil could be directly used in CI engines withoutany modification In case of biodiesel brake-specific fuelconsumption was found to be higher than that of diesel andthe emission characteristics were reduced They noted thatthe B 20 Blend was the most suitable alternative for dieselDuraisamy et al [13] carried out experiment by mixing themethyl esters of Jatropha Pongamia Mahua and Neem seedoil In engine performance study the B 40 biodiesel showeda thermal efficiency almost equal to that of diesel Emissionstudy indicated a reduction of hydrocarbon (HC) and carbonmonoxide (CO) at any percentage mix but increased in NO

119909

and smoke densityThe review of the literature clearly indicated that

researchers have put sincere attempt to find out the suitablealternative to diesel fuel without going through major enginemodification In most cases they varied different inputparameters such as load blend of fuels and compressionratio one at a time and observed the performance andemission characteristics of the engine However it may bepointed out that the number of input parameters was morethan one and response of the system was not unidirectionalIn other words for a few responses lower values were betterwhile for others higher values were better As a result thestudy became a multiresponse optimization problem thatrequired a systematic approach to ascertain the number ofexperiments to be made in order to cover the entire domainof input parameters

Based on the above-mentioned observations an attemptwas made to determine an optimum combination of inputparameters that maximizes response characteristics Designof experiment was carried out in such a fashion that thenumber of experiments to be carried out should beminimumwhile output data to be maximum In the present investiga-tion Karanja biodiesel was taken as a fuel for experimen-tation The performance test of biodiesel was conducted ona Kirloskar-made single-cylinder variable-compression-ratioengine

The objective of the study was to determine the opti-mum blend of Karanja biodiesel and diesel oil that wouldresult in a better engine performance along with minimumemission characteristics FollowingGrey-Taguchi approach amultiresponse problem was converted into a single one usingweighting factors of grey relational analysis Lastly validationof the result was carried out by actual experimentation

Table 1 Design factors and their levels

Design factor Levels1 2 3 4 5

Load in kg (119860) 4 8 12 16 20Blend (119861) B 0 B 25 B 50 B 75 B 100CompressionRatio (119862) 14 1 15 1 16 1 17 1 18 1

Table 2 Fuel property table of diesel and Karanja oil methyl ester

Property Diesel Karanjabio-diesel

ASTMstandards

Specific gravity 0824 0880 D 3142-05Density (gmcc) 0717 0766 D 1298API gravity 4024 293 D 4052Ash content () 0060 0094 D 874Water content () 0070 166 D 2709Carbon residue () 0080 0530 D 189Flash point (∘C) 66 190 D 6450Pour point (∘C) 15 4 D 5949Fire point (∘C) 72 395 D 3828Calorific value (kcalkg) 1005623 809524 D 5453-93Viscosity (cSt) 42 323 D 2171Cetane number 48 5661 D 613

2 Methodoloy

In order to determine the optimum blend of Karanjabiodiesel and diesel on engine performance and emissioncharacteristics of a variable compression ignition enginethree major input parameters namely load (119860) blend offuels (119861) and compression ratio (119862) were considered tobe main design factors Each factor was further subdividedinto five levels as shown in Table 1 The levels and theirranges were selected based on the previous findings asdescribed on the open literature All together eight response(output) parameters were analysed three of them belongedto performance characteristics of the engine namely brakepower (BP) brake-specific fuel consumption (BSFC) andbrake thermal efficiency (BTE) The rest five responses wereemission characteristics of the engine namely CO CO

2

O2 NO119909 and HC The relevant fuel properties of diesel

and Karanja biodiesel were tested as per ASTM standard(Table 2)

Since there were many input and output variables alarge number of experiments had to be conducted to coverentire domain A well-designed experiment could producesignificantly more information with fewer runs comparedto an unplanned experimentation Accordingly Taguchirsquosparameter design method was adopted to understand theeffect of different input parameters on response Howeverconventional Taguchimethod could effectively establish opti-mal parameter settings for single performance characteris-tics Sincemultiple performance characteristics with conflict-ing goals were present Grey-Taguchi method was adopted

Journal of Engineering 3

to generate a single response from different performancecharacteristics

21 Taguchi Analysis TheTaguchi method developed by DrTaguchi involved reduction of variation in a process throughrobust design of experiments A standard orthogonal arraycould be selected for designing the experimental plan basedon the total number of degree of freedom number of factorand level of each factor In the present study an orthogonalarray (L25) was considered having 25 rows correspondingto the total number of tests (24 degrees of freedom) with 3columns of input parameters each having 5 levels

211 Grey Relational Analysis Signal-to-noise ratio (119878119873) isa measure used in science and engineering for comparingthe level of a desired signal to the level of background noiseSince the present study aimed at optimizing eight responseparameters it might so happen that the higher 119878119873 ratio forone performance characteristic may exhibit a lower 119878119873 ratiofor another characteristicTherefore the overall evaluation ofthe 119878119873 ratio was required for the optimization of multipleperformance characteristics Grey relational analysis [14 15]was found to be an efficient tool for analyzing this kind ofproblem It was used to determine the key factors of thesystem and their correlations The key factors were identifiedby the input and output sequences

In the present paper the experimental results were firstnormalized in the range between zero and one Afterwardsthe grey relational coefficients were obtained from thenormalized experimental data to express the relationshipbetween the desired and actual experimental data Lastlythe overall grey relational grade was obtained by averag-ing the grey relational coefficients corresponding to eachselected process response The evaluation of the multipleprocess response was based on the grey relational gradeThis method was employed to convert a multiple responseprocess optimization problem into a single response problemwith the objective function of overall grey relational gradeThe corresponding level of parametric combination with thehighest grey relational grade was considered as the optimumprocess parameter

Therefore when the target value of the original sequencewas ldquothe higher-the-betterrdquo the original sequence was nor-malized as follows

119909119894(119896) =

119910119894(119896) minusmin119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (1)

When the purpose was ldquothe lower-the-betterrdquo the originalsequence was normalized as follows

119909119894(119896) =

max119910119894(119896) minus 119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (2)

119910119894(119896) is the original reference sequence 119909

119894(119896) is the sequence

for comparison 119894 = 1 2 119898 119896 = 1 2 3 119899 with 119898 119899being total no of experiments and responses min119910

119894(119896) is the

smallest value of 119910119894(119896) and max119910

119894(119896) is the highest value of

119910119894(119896)

Here 119909119894(119896) was the value after the grey relational genera-

tion An ideal sequence was 1199090(119896) The grey relational grade

revealed the relational degree between the experimental runsequences [119909

0(119896) and 119909

119894(119896) 119894 = 1 2 119898]

The grey relational coefficient 120585119894(119896) could be calculated as

120585119894(119896) =Δmin + 120595ΔmaxΔ119900119894(119896) + 120595Δmax

(3)

where

Δ0119894=10038171003817100381710038171199090 (119896) minus 119909119894 (119896)

1003817100381710038171003817 (4)

was the difference of the absolute value between 1199090(119896) and

119909119894(119896) Δmin Δmax were the minimum and maximum values

of the absolute differences (Δ0119894) of all comparing sequences

The purpose of distinguishing coefficient 120595 (0 le 120595 le 1) wasto weaken the effect of Δmax when it became too large In thepresent analysis the value of 120595 was taken as 05

After averaging the grey relational coefficients the greyrelational grade 120574

119900was be calculatedThe higher value of grey

relational grade was considered to be the stronger relationaldegree between the ideal sequence 119909

0(119896) and the given

sequence 119909119894(119896) The ideal sequence 119909

0(119896) was supposed to be

the best process response in the experimental layout Thusthe higher relational grade implied that the correspondingparameter combination was closer to the optimal

22 Grey Relational Grade Generation With respect toincrease in blend of fuel engine performances exhib-ited demising nature while emission characteristics showedincreasing trend Since reduction of engine emission could beachieved by means of different types of external equipmentssuch as exhaust gas recirculation (EGR) the analysis wascarried out in such a way that the performance of the enginedid not suffer even when diesel was replaced by blend ofKaranja biodiesel and diesel oil

Accordingly while converting multiple grey relationgrades the value of weighting factor in engine performancewas taken higher than that of emission characteristics Whenappropriate weighting factors 120573 was used with the sequencevalues the general form of grey relational grades became

120574119900=

119899

sum

119896=1

120585119894(119896) 120573120574

119894 sum120573 = 1 (5)

In the present case the following values of weighting factorshad been taken for different responses brake power =03 brake-Specific fuel consumption = 03 brake thermalefficiency = 03 CO emission = 001 HC emission =001 CO

2emission = 003 O

2emission = 001 and

NO119909emission = 004

The different sequence value of weighting factor (120573) couldbe specified from experience or appropriate weights could becomputed by processes such as singular value decompositionusing preliminary grey relational grade values One shouldnote that the use of weighting factors would not be equivalentto changes in the sequence value units used or the choicemade for sequence normalization [15 16]


Burette

RPM indicator

Load indicator

Load regulator

Electronic dataacquisition system

Fuel

W

N

Dynamometer Engine

Water

Exhaust

AirGas analyser

W load sensor N engine speed sensor

Figure 1 Schematic diagram of experimental setup

3 Experimental Setup

The engine was directly coupled to an eddy current dyna-mometer using flexible coupling (Figure 1) The output of theeddy current dynamometer was fixed to a strain gauge loadcell for measuring load applied to the engine A gas analyzerwas used for the measurement of carbon monoxide (CO)oxides of nitrogen (NO

119909) unburned hydrocarbon (HC)

oxygen (O2) and carbon dioxide also CO was measured

as percentage volume and NO119909 HC was measured as n-

hexane equivalent parts per million (ppm) A glass burettewas provided at the fuel tank for the measurement of fuelconsumption by volume per minute For this purpose astopwatch was used to measure the diesel and biodiesel fuelseparately The engine was subjected to different loads (4 kg8 kg 12 kg 16 kg and 20 kg) corresponding to load rangingfrom 20 at the lowest level and 100 at the highest levelKnowing the dynamometer shaft length (0185m) torqueapplied on the engine was determined All the experimentswere carried out at a rated speed of 1500 rpm maintaining23∘ BTDC (before top dead centre) for both diesel andbiodiesel The experiments were conducted using B 0 (0Karanja 100 diesel) B 25 (25 Karanja 75 diesel) B 50(50 Karanja 50 diesel) B 75 (75 Karanja 25 diesel)and B 100 (100 Karanja) under different load conditionson the engine and the results are presented in Table 4The compression ratios (CR) were varied (14 1 15 1 16 117 1 and 18 1) During the experiment whenever fuel waschanged the fuel lines were cleaned and the engine wasleft to operate for 30min to stabilize at its new conditionFigure 2 shows the whole engine assembly used for theexperimentThe specifications of the engine and eddy currentdynamometer are given in Table 3 The engine exhaust (COHC CO

2 O2 and NO

119909) was analyzed and calculated by AVL

DIG AS 444 gas analyzer fitted with DIGAS SAMPLER atthe exhaust Specification of the gas analyzer is furnished inTable 3

Table 3 Specifications of engines and instruments

Specifications of the engineManufacturer Kirloskar Oil Engines LtdModel TV 1Type Four stroke water cooledNo of cylinder OneRated power 52 kW 1500RPMCompression ratio 11 1 to 18 1Bore 875mmStroke 110mmInjection timing 23∘ before TDCMethod of loading Eddy current dynamometer

Specifications of thedynamometer

Manufacturer Saj Test Plant Pvt LtdModel AG10Type Eddy current water cooled

Specifications of the AVLgas analyzer

Manufacturer AVL India Pvt LtdType DiGas 444Model 5 gas analyzer

4 Results and Discussions

Different combinations of three input variables namely loadcompression ratio (CR) and blends were considered andeight output responses (output) were obtained In order tosearch for the optimal process condition through a limitednumber of experiment Taguchirsquos L25 orthogonal array hadbeen selected Therefore total number of experiments con-ducted was 25 (119894 = 25)


Load indicator

Engine head

RPM indicator

Fly wheel

Load regulator

Dynamometer

Figure 2 The engine assembly used for the experiment

minus2

minus4

minus6

minus8

minus2

minus4

minus6

minus8

1 2 3 4 5Design parameter level

Blend of fuel


Compression ratio

minus2

minus4

minus6

minus8Mea

n of

119878119873

ratio


Load

Figure 3 The main effect plots for 119878119873 ratio

Following grey relation methods experimental resultswere normalized in the range between zero to one Howeverit was noted that out of eight responses shown in columns5 to 12 of Table 4 higher target values of three responses(BP BTE and O

2) were better while those for the rest

five responses lower values were desirable Accordinglyduring normalization of data target values of BP BTE andO2parameters were calculated using (1) and the rest were

obtained from (2) Furthermore using (3) grey relationcoefficients 120585

119894(119896) were evaluated for each response

In order to determine the grey relational grades (4)had been used Considering appropriate weighing factorsthe overall grey relation grade thus obtained is shown inTable 5

41 Analysis of Signal-to-Noise Ratio Since the traditionalmethod could not capture the variability of the results signal-to-noise ratio was introduced to analyze the grey relationgradeThe signal-to-noise ratio for overall grey relation gradewas calculated from (6) presented below Since the mainaim of the experiment was always to determine the highestpossible 119878119873 ratio for the result the higher-the-better (HB)

criteria was sort for A high value of 119878119873 implied that thesignal was much higher than the random effects of the noisefactors

119878119873 = minus10 log[ 1119873119894

119873119894

sum

119906=1

1

1199102119906

] (6)

where 119894 = experiment number 119906 = trial number and 119873119894=

number of trials for experiment 119894The analysis of the output response was done by minitab

software Table 6 shows the average of the selected charac-teristics for each level of the design factors The graphicalrepresentation of 119878119873 ratio for three factors load blend andcompression ratio is shown in themain effect plot (Figure 3)If the line for a particular parameter is nearly horizontalthe parameter has less significant effect on response On theother hand a parameter for which the line has the highestinclination will have the most significant effect It had beenobserved from the plot that parameter119860 (load) had the mostsignificant effect among the three parameters


Table 4 Experimental results of engine performances and emissions

No ofexp

Factors Engine performance Emission characteristicsLoad() Blend Compression

ratioBP(kw)

BSFC(gmkw-hr) BTE CO

(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410

The optimum process parameter combination corre-sponding to minimum emission and better engine perfor-mance was indicated by the maximum value for signal-to-noise ratio for each input parameter Thus from Table 6 andFigure 3 the optimum process parameter combination wasfound to be A5B3C4 that is load at 100 blend of fuel atB 50 (50 Karanjal 50 diesel) and a compression ratio of17 1

42 Confirmation Tests After the optimum process param-eter was selected from the 119878119873 ratio plot the objective wasto predict the result and verify it by actual experimentationFirst corresponding to optimum level of process parametersthe estimated 119878119873 ratio (120574) was evaluated using the followingequation

120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)

where 120574119898is the total mean of 119878119873 ratio 120574

119894is the mean of

119878119873 ratio for optimum level and 119900 is the number of the main

design factors that affect the output responses Following(7) the estimated value of 120574 corresponding to A5B3C4 wasobtained as minus128942

In order to verify our estimated value an experimentwas actually carried out with A5B3C4 combination Thecorresponding 119878119873 ratio of the grey relational grade wasfound to be minus155769 as shown in Table 7 The values of greyrelation grade are also mentioned in the table

In addition an initial parameter combination of A3B3C3(load 60 blend of fuel B 50 and compression ratio 16)had been chosen as it lay at the mean level Again an actualexperiment was conducted with this combination and thevalue of 120574 thus obtained was also shown in Table 7 It hadbeen observed that the increase in the 119878119873 ratio from theinitial parameter combination to the optimal parameters was038181

5 Conclusion

In this experimental study the effect of Karanja oil methylester diesel fuel blends (B 0 B 25 B 50 B 75 B 100) on engine


Table 5 Calculated grey relational coefficient of all responses and grey relational grade with weightage

Weightingfactor 03 03 03 001 001 003 001 004 Total = 1

No of expGrey relational coefficient

BP BSFC BTE CO HC CO2 O2 NO119909

Overall greyrelation grade

1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081

performance and exhaust emissions were investigated Theengine performance and emission characteristics had beenanalysed in the context of applicability of blend of Karanja oilmethyl ester with conventional diesel as a suitable alternativefuel resource

In the study an attempt was made to optimize the engineresponses comprising of eight different parameters whenthree input parameters were varied simultaneously Since theinvestigation clearly indicated possibility of a large numberof test combinations design of experiment was carried outusing Taguchi method to limit the number of experimentsby the formation of orthogonal array yet without sacrificingsignificant information

Complexity of the optimization problem was evidentfrom the fact that the responses were not unidirectionalSubsequently multiresponse problem was converted into asingle one with the application of weighting factors of greyrelational analysis and optimum solution was obtained fromthe test data

Table 6 Response for the signal-to-noise ratio

Level Load (119860) Blend of fuel (119861) Compression ratio (119862)1 minus8011 minus4144 minus45952 minus5448 minus4280 minus44073 minus3425 minus4038 minus41414 minus2071 minus4217 minus37575 minus1795 minus4072 minus3849Delta 6216 0242 0838Rank 1 3 2The total mean SN ratio (120574

119894) = minus415

Finally finding of experimental study was validated withthe result obtained through actual experimentation It wasconcluded that B 50 blend was found to be most suitableblend for diesel engine without significantly affecting the


Table 7 Results of confirmation test

Initialparametercombination

Optimal parameter combinationPrediction(120574)

Experimentation

Level 119860311986131198623 119860511986131198624

119878119873 ratio minus193950 minus128942 minus155769Grey relationgrade 079988 0846867 0835825

engine performance and emissions characteristics corre-sponding compression ratio and engine load being 17 and80 respectively

Conflict of Interests

The authors of the paper do not have a direct financialrelation with the commercial identity mentioned in theirpaper that might lead to a conflict of interests for any of theauthors

References

[1] C Carraretto A Macor A Mirandola A Stoppato and STonon ldquoBiodiesel as alternative fuel experimental analysis andenergetic evaluationsrdquo Energy vol 29 no 12ndash15 pp 2195ndash22112004

[2] H Raheman and A G Phadatare ldquoDiesel engine emissions andperformance from blends of karanja methyl ester and dieselrdquoBiomass and Bioenergy vol 27 no 4 pp 393ndash397 2004

[3] A K Agarwal ldquoBiofuels (alcohols and biodiesel) applications asfuels for internal combustion enginesrdquo Progress in Energy andCombustion Science vol 33 no 3 pp 233ndash271 2007

[4] H Raheman and S V Ghadge ldquoPerformance of compressionignition engine with mahua (Madhuca indica) biodieselrdquo Fuelvol 86 no 16 pp 2568ndash2573 2007

[5] H Raheman and S V Ghadge ldquoPerformance of diesel enginewith biodiesel at varying compression ratio and ignition tim-ingrdquo Fuel vol 87 no 12 pp 2659ndash2666 2008

[6] Y V H Rao R S Voleti V S Hariharan and A V S JuldquoJatropha oil methyl ester and its blends used as an alternativefuel in diesel enginerdquo International Journal of Agriculture andBiological Engg vol 1 pp 32ndash38 2008

[7] S R Kalbande and SDVikhe ldquoJatropha and karanja biofuel analternative fuel for diesel enginerdquo ARPN Journal of Engineeringand Applied Sciences vol 3 pp 7ndash13 2008

[8] G Fontaras G Karavalakis M Kousoulidou et al ldquoEffectsof biodiesel on passenger car fuel consumption regulated andnon-regulated pollutant emissions over legislated and real-world driving cyclesrdquo Fuel vol 88 no 9 pp 1608ndash1617 2009

[9] S Godiganur C H Suryanarayana Murthy and R P Reddyldquo6BTA 59 G2-1 Cummins engine performance and emissiontests using methyl ester mahua (Madhuca indica) oildieselblendsrdquo Renewable Energy vol 34 no 10 pp 2172ndash2177 2009

[10] B Baiju M K Naik and L M Das ldquoA comparative evaluationof compression ignition engine characteristics usingmethyl andethyl esters of Karanja oilrdquo Renewable Energy vol 34 no 6 pp1616ndash1621 2009

[11] P K Sahoo L M Das M K G Babu et al ldquoComparativeevaluation of performance and emission characteristics ofjatropha karanja and polanga based biodiesel as fuel in a tractorenginerdquo Fuel vol 88 no 9 pp 1698ndash1707 2009

[12] A Murugesan C Umarani R Subramanian and NNedunchezhian ldquoBio-diesel as an alternative fuel for dieselenginesmdasha reviewrdquo Renewable and Sustainable Energy Reviewsvol 13 no 3 pp 653ndash662 2009

[13] M K Duraisamy T Balusamy and T Senthilkumar ldquoExperi-mental investigation on amixed biodiesel fueled direct injectiondiesel enginerdquo International Journal of Applied EngineeringResearch vol 5 no 2 pp 253ndash260 2010

[14] Y S Tarng S C Juang andCH Chang ldquoTheuse of grey-basedTaguchi methods to determine submerged arc welding processparameters in hardfacingrdquo Journal of Materials Processing Tech-nology vol 128 no 1ndash3 pp 1ndash6 2002

[15] A K Sood R K Ohdar and S S Mahapatra ldquoImprovingdimensional accuracy of fused deposition modelling processedpart using grey Taguchi methodrdquoMaterials and Design vol 30no 10 pp 4243ndash4252 2009

[16] Y Kuo T Yang and G W Huang ldquoThe use of a grey-basedTaguchi method for optimizing multi-response simulationproblemsrdquo Engineering Optimization vol 40 no 6 pp 517ndash5282008

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Godiganur et al [9] observed that after trans-esterifica-tion mahua oil showed a similar kind of property as that ofdiesel Among the different blends 20 (B 20) blend wasfound to be the most suitable Baiju et al [10] producedKaranja oil ethyl ester and Karanja oil methyl ester fromKaranja oil Both of them showed good emission charac-teristics except presence of NO

119909being on the higher side

They also claimed that the methyl ester exhibited a betterperformance than ethyl ester In a study made by Sahooet al [11] Jatropha Karanja and Polanga methyl esterswere blended with diesel The maximum power output wasobtained from B 50 blend The smoke emission was found tobe reduced in case of biodiesel at full throttle However COand NO

119909emission increased a bit compared to diesel

Murugesan et al [12] observed that methyl ester ofKaranja oil could be directly used in CI engines withoutany modification In case of biodiesel brake-specific fuelconsumption was found to be higher than that of diesel andthe emission characteristics were reduced They noted thatthe B 20 Blend was the most suitable alternative for dieselDuraisamy et al [13] carried out experiment by mixing themethyl esters of Jatropha Pongamia Mahua and Neem seedoil In engine performance study the B 40 biodiesel showeda thermal efficiency almost equal to that of diesel Emissionstudy indicated a reduction of hydrocarbon (HC) and carbonmonoxide (CO) at any percentage mix but increased in NO

119909

and smoke densityThe review of the literature clearly indicated that

researchers have put sincere attempt to find out the suitablealternative to diesel fuel without going through major enginemodification In most cases they varied different inputparameters such as load blend of fuels and compressionratio one at a time and observed the performance andemission characteristics of the engine However it may bepointed out that the number of input parameters was morethan one and response of the system was not unidirectionalIn other words for a few responses lower values were betterwhile for others higher values were better As a result thestudy became a multiresponse optimization problem thatrequired a systematic approach to ascertain the number ofexperiments to be made in order to cover the entire domainof input parameters

Based on the above-mentioned observations an attemptwas made to determine an optimum combination of inputparameters that maximizes response characteristics Designof experiment was carried out in such a fashion that thenumber of experiments to be carried out should beminimumwhile output data to be maximum In the present investiga-tion Karanja biodiesel was taken as a fuel for experimen-tation The performance test of biodiesel was conducted ona Kirloskar-made single-cylinder variable-compression-ratioengine

The objective of the study was to determine the opti-mum blend of Karanja biodiesel and diesel oil that wouldresult in a better engine performance along with minimumemission characteristics FollowingGrey-Taguchi approach amultiresponse problem was converted into a single one usingweighting factors of grey relational analysis Lastly validationof the result was carried out by actual experimentation

Table 1 Design factors and their levels

Design factor Levels1 2 3 4 5

Load in kg (119860) 4 8 12 16 20Blend (119861) B 0 B 25 B 50 B 75 B 100CompressionRatio (119862) 14 1 15 1 16 1 17 1 18 1

Table 2 Fuel property table of diesel and Karanja oil methyl ester

Property Diesel Karanjabio-diesel

ASTMstandards

Specific gravity 0824 0880 D 3142-05Density (gmcc) 0717 0766 D 1298API gravity 4024 293 D 4052Ash content () 0060 0094 D 874Water content () 0070 166 D 2709Carbon residue () 0080 0530 D 189Flash point (∘C) 66 190 D 6450Pour point (∘C) 15 4 D 5949Fire point (∘C) 72 395 D 3828Calorific value (kcalkg) 1005623 809524 D 5453-93Viscosity (cSt) 42 323 D 2171Cetane number 48 5661 D 613

2 Methodoloy

In order to determine the optimum blend of Karanjabiodiesel and diesel on engine performance and emissioncharacteristics of a variable compression ignition enginethree major input parameters namely load (119860) blend offuels (119861) and compression ratio (119862) were considered tobe main design factors Each factor was further subdividedinto five levels as shown in Table 1 The levels and theirranges were selected based on the previous findings asdescribed on the open literature All together eight response(output) parameters were analysed three of them belongedto performance characteristics of the engine namely brakepower (BP) brake-specific fuel consumption (BSFC) andbrake thermal efficiency (BTE) The rest five responses wereemission characteristics of the engine namely CO CO

2

O2 NO119909 and HC The relevant fuel properties of diesel

and Karanja biodiesel were tested as per ASTM standard(Table 2)

Since there were many input and output variables alarge number of experiments had to be conducted to coverentire domain A well-designed experiment could producesignificantly more information with fewer runs comparedto an unplanned experimentation Accordingly Taguchirsquosparameter design method was adopted to understand theeffect of different input parameters on response Howeverconventional Taguchimethod could effectively establish opti-mal parameter settings for single performance characteris-tics Sincemultiple performance characteristics with conflict-ing goals were present Grey-Taguchi method was adopted







119909119894(119896) =

119910119894(119896) minusmin119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (1)


119909119894(119896) =

max119910119894(119896) minus 119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (2)




119894(119896) is the



119910119894(119896)




0(119896) and 119909

119894(119896) 119894 = 1 2 119898]


120585119894(119896) =Δmin + 120595ΔmaxΔ119900119894(119896) + 120595Δmax

(3)

where

Δ0119894=10038171003817100381710038171199090 (119896) minus 119909119894 (119896)

1003817100381710038171003817 (4)














120574119900=

119899

sum

119896=1

120585119894(119896) 120573120574

119894 sum120573 = 1 (5)


2emission = 003 O

2emission = 001 and




Burette

RPM indicator

Load indicator

Load regulator


Fuel

W

N

Dynamometer Engine

Water

Exhaust

AirGas analyser









2 O2 and NO












Load indicator

Engine head

RPM indicator

Fly wheel

Load regulator

Dynamometer


minus2

minus4

minus6

minus8

minus2

minus4

minus6

minus8


Blend of fuel


Compression ratio

minus2

minus4

minus6

minus8Mea

n of

119878119873

ratio


Load










119878119873 = minus10 log[ 1119873119894

119873119894

sum

119906=1

1

1199102119906

] (6)






No ofexp


ratioBP(kw)


(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410



120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)







5 Conclusion








1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















119909119894(119896) =

119910119894(119896) minusmin119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (1)


119909119894(119896) =

max119910119894(119896) minus 119910

119894(119896)

max119910119894(119896) minusmin119910

119894(119896) (2)




119894(119896) is the



119910119894(119896)




0(119896) and 119909

119894(119896) 119894 = 1 2 119898]


120585119894(119896) =Δmin + 120595ΔmaxΔ119900119894(119896) + 120595Δmax

(3)

where

Δ0119894=10038171003817100381710038171199090 (119896) minus 119909119894 (119896)

1003817100381710038171003817 (4)














120574119900=

119899

sum

119896=1

120585119894(119896) 120573120574

119894 sum120573 = 1 (5)


2emission = 003 O

2emission = 001 and




Burette

RPM indicator

Load indicator

Load regulator


Fuel

W

N

Dynamometer Engine

Water

Exhaust

AirGas analyser









2 O2 and NO












Load indicator

Engine head

RPM indicator

Fly wheel

Load regulator

Dynamometer


minus2

minus4

minus6

minus8

minus2

minus4

minus6

minus8


Blend of fuel


Compression ratio

minus2

minus4

minus6

minus8Mea

n of

119878119873

ratio


Load










119878119873 = minus10 log[ 1119873119894

119873119894

sum

119906=1

1

1199102119906

] (6)






No ofexp


ratioBP(kw)


(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410



120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)







5 Conclusion








1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Burette

RPM indicator

Load indicator

Load regulator


Fuel

W

N

Dynamometer Engine

Water

Exhaust

AirGas analyser









2 O2 and NO












Load indicator

Engine head

RPM indicator

Fly wheel

Load regulator

Dynamometer


minus2

minus4

minus6

minus8

minus2

minus4

minus6

minus8


Blend of fuel


Compression ratio

minus2

minus4

minus6

minus8Mea

n of

119878119873

ratio


Load










119878119873 = minus10 log[ 1119873119894

119873119894

sum

119906=1

1

1199102119906

] (6)






No ofexp


ratioBP(kw)


(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410



120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)







5 Conclusion








1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Load indicator

Engine head

RPM indicator

Fly wheel

Load regulator

Dynamometer


minus2

minus4

minus6

minus8

minus2

minus4

minus6

minus8


Blend of fuel


Compression ratio

minus2

minus4

minus6

minus8Mea

n of

119878119873

ratio


Load










119878119873 = minus10 log[ 1119873119894

119873119894

sum

119906=1

1

1199102119906

] (6)






No ofexp


ratioBP(kw)


(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410



120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)







5 Conclusion








1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











No ofexp


ratioBP(kw)


(vol)HC

(ppmvol)CO2(vol)

O2(vol)

NO119909

(ppm vol)1 2000 0 14 1223 632784 13521 035 58 48 142 1172 2000 25 15 1138 624431 14207 036 70 4 1503 1053 2000 50 16 1200 509584 18029 017 43 38 1553 2184 2000 75 17 1190 474778 20317 008 17 4 1545 4005 2000 100 18 1150 599332 17734 006 11 42 1536 5966 4000 0 15 2330 365421 23414 006 25 56 1364 7027 4000 25 16 2260 362915 24445 007 41 56 1333 7268 4000 50 17 2250 345877 26562 004 37 54 1371 8439 4000 75 18 2342 337708 28564 005 13 6 1315 96310 4000 100 14 2443 399555 26601 021 23 58 133 24711 6000 0 16 3403 271750 31485 001 22 73 1142 115412 6000 25 17 3351 293691 30207 002 31 74 1128 131613 6000 50 18 3475 271973 33780 002 39 72 115 113014 6000 75 14 3282 327154 29485 01 32 78 1086 85915 6000 100 15 3431 317996 33424 007 18 73 1162 98316 8000 0 17 4469 255027 33550 002 7 11 1925 12317 8000 25 18 4497 255320 34747 004 34 95 859 124018 8000 50 14 4319 283192 32442 008 55 96 86 129819 8000 75 15 4326 287354 33569 005 30 93 905 133720 8000 100 16 4412 299319 35510 005 21 9 957 146321 10000 0 18 5272 277365 30848 032 16 45 1469 48222 10000 25 14 5570 284662 31165 073 84 116 529 120923 10000 50 15 5411 287639 31940 03 65 113 606 135024 10000 75 16 5281 299598 32197 017 39 112 657 136025 10000 100 17 5539 300610 35357 012 29 111 681 1410



120574 = 120574119898+

119900

sum

119894=1

(120574119894minus 120574119898) (7)







5 Conclusion








1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















1 033765 0333333 0333333 0514286 0430168 0586592 043927 0982634 03734452 0333333 0338321 0340414 0507042 037931 0644172 0417464 1 03780413 033642 0425944 0386093 0692308 0516779 0660377 0405343 0857323 04172564 0335961 0462224 0419843 0837209 0793814 0644172 0407235 0697125 04354045 0333886 0354246 0382147 0878049 0905882 0628743 0409384 0580342 0387426 0406158 0631127 0476157 0878049 0681416 0538462 0455316 0532132 05126117 0400941 0636456 0498413 0857143 0531034 0538462 0464714 0522308 05170828 0400289 067522 0551299 0923077 0562044 0549738 0453247 0479181 05441289 0407054 0695535 0612821 09 0865169 0517241 047035 044177 057079910 0414748 0566512 0552407 0642857 0706422 0527638 0465644 082704 052790211 0505474 0918667 0732055 1 0719626 0458515 0532418 039294 069824112 0499549 0830083 0674644 0972973 0616 0454545 0538165 0359259 064984813 0513915 0917668 0864036 0972973 0546099 0462555 0529189 0398474 073843114 0491898 0723657 0646022 08 0606299 0439331 0556175 0473831 06092115 0508724 0749971 0840584 0857143 0777778 0458515 0524418 0436095 06821116 0667872 1 0848695 0972973 1 1 0333333 0974175 085366717 0673761 0998456 0935102 0923077 0587786 0384615 0678988 0374311 082777218 0638985 0870237 0781823 0837209 0445087 0381818 0678328 0362714 073005919 0640462 0853863 0850008 09 0626016 0390335 0649907 0355311 074854620 0656787 0810048 1 09 0733333 039924 0619893 0333333 078588421 0881464 0894241 0702223 0537313 0810526 0606936 0426129 0642992 080683322 1 0864383 0716782 0333333 0333333 0333333 1 0380819 080958223 0933053 0852765 0754927 0553846 0398964 0339806 0900645 0352911 079952324 0884278 0809076 0768489 0692308 0546099 034202 0845036 0351086 077879125 0986204 0805587 0986363 0765957 0636364 0344262 0821176 0342238 0875081






119894) = minus415






Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













Experimentation

Level 119860311986131198623 119860511986131198624





References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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