Application of Taguchi’s methods

8/2/2019 Application of Taguchis methods

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Int. J. Vehicle Design, Vol. x, No. x, xxxx 1

Copyright 200x Inderscience Enterprises Ltd.

Application of Taguchis methods to investigatefactors affecting emissions of a diesel engine runningwith tobacco oil seed methyl ester

Adnan Parlak*Yildiz Technical University,

Naval Architech and Marine Engineering,stanbul, TurkeyE-mail: [email protected]*Corresponding author

Hlya KarabaSakarya University,Vocational School of Akyazi,Sakarya, 54000, Turkey

brahim zsert, Vezir Ayhan and dris CesurTechnical Educational Faculty,Sakarya University,Sakarya, 54187, TurkeyE-mail: [email protected]: [email protected]

Abstract: Different kinds of vegetable oils and their methyl/ethyl esters have beentested in diesel engines. However, studies reporting the effects of tobacco seed oiland Tobacco Seed Oil Methyl Ester (TSOME) on emissions of diesel engines arelimited. One of the most important issues is to investigate parameters that affectthe yield of biodiesel and their interactions on emissions of a diesel engine. TheTaguchi method is a useful tool for this purpose. Two different catalysts (KOHand NaOH), four different blends (B10, B20, B50 and B100) and four enginespeeds were used during full-load tests. Optimal catalyst type, engine speed andTSOME blends on exhaust emissions were determined using Taguchis technique.The Taguchi design method revealed that choosing right catalyst and the blend rateare important two factors in view of minimisation of pollutant emissions.

Keywords: Taguchi method; catalyst; emissions; tobacco; methyl ester.Reference to this paper should be made as follows: Parlak, A., Karaba, H.,zsert, I., Ayhan, V. and Cesur, I. (xxxx) Application of Taguchis methods toinvestigate factors affecting emissions of a diesel engines running with TSOMEblends,Int. J. Vehicle Design, Vol. x, No. y, pp.xxxxxx.

Biographical notes: AUTHOR PLEASE SUPPLY CAREER HISTORY OF NOMORE 100 WORDS FOR EACH AUTHOR.

AuthorpleasesupplyE-mail id forremainingauthors.

IJVD 0(0) 07 Parlak et al.indd 1IJVD 0(0) 07 Parlak et al.indd 1 1/18/2012 11:55:08 AM1/18/2012 11:55:08 AM


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2 A. Parlak et al.

1 Introduction

Vegetable oils are renewable, non-toxic, biodegradable, and have low-emission profiles butNOx (Hasimoglu et al., 2008; Altin et al., 2001; Karaosmanolu et al., 2000). Significantstudies have been conducted on the effects of various raw vegetable oils, vegetables oilmethyl/ethyl esters on diesel engines. While some researchers focused on the effects ofvegetable oils and their esters on the performance (Nwafor and Rice, 1996; Sapaun et al.,1996; Mc Donnel et al., 2000), some of the other studies have been focused on emissionscharacteristics of engine fuelled with vegetable oil methyl esters (Murayama et al., 1984;Scholl and Sorenson, 1993; Ali et al., 1995; Arregle et al., 1999). Among them, Murayamaet al. (1984) reported that vegetable oils and methyl ester of rapeseed oil offered lowersmoke and oxides of nitrogen (NO

X) emissions. Scholl and Sorenson (1993) reported that

carbon monoxide, oxides of nitrogen (NOX) and smoke emissions were slightly lower for

soybean ester than that of diesel, whereas hydrocarbon (HC) emission showed 50% reductioncompared with diesel.

The effects of vegetable oil fuels and their methyl esters (raw sunflower oil, raw cottonseedoil, raw soybean oil and their methyl esters, refined corn oil, distilled opium poppy oil andrefined rapeseed oil) on a direct injected, four stroke, single-cylinder diesel engine exhaustemissions was investigated by Altin et al. (2001). Niemi et al. (2002) reported that the carbonmonoxide emission was higher at all loads for different speeds with preheated-mustard oil asfuel. The authors concluded that emissions decreased with preheating the oil.

Kalligeros et al. (2003) analysed the emission characteristics on a stationary dieselengine fuelled with sunflower oil methyl ester/diesel blends. They observed decreases in

particulate matter, carbon monoxide, hydrocarbon and nitrogen oxide emissions. Doradoet al. (2003) tested the use of methyl ester of used olive oil as fuel in a direct-injection

diesel engine. They reported that carbon monoxide, carbon dioxide, oxides of nitrogen andsulphur dioxide emissions decreased by 59%, 8.6%, 32% and 57%, respectively, and thatthe smoke emission was low. They concluded that the methyl ester of olive oil could beused as fuel.

Labeckas and Slavinskas (2006) analysed the emission characteristics of four stroke,four-cylinder, direct injection, unmodified, naturally aspirated diesel engine when operatingon neat Rapeseed Methyl Ester (RPE) and its 5%, 10%, 20% and 35% blends with dieselfuel. They found that carbon monoxide, hydrocarbon and visible emissions have decreasedwhile an oxide of nitrogen emissions increased for methyl ester compared with diesel.

As can be seen from the literature, there are various kinds of vegetable oil methyl/ethylesters, which were investigated their effects on performance and emission characteristics.Among them, Tobacco Seed Oil (TSO) was also considered as a potential alternative fuel forcompression ignition engines (Parlak et al., 2009; Usta, 2005a, 2005b).

There is less study TSO and TSOME on performance and emissions has not been tested indiesel engines, yet. In studies conducted by Usta (2005a, 2005b), blends containing TSOMEin 10%, 17.5% and 25% proportions by volume were tested in a turbocharged-diesel enginesfor the engine loads of 50%, 75% and 100%. The author reported that maximum power wasobserved with 17.5% TSOME blend. The increase in brake power and thermal efficiencywere about 3% and 2%, respectively. The results also showed that the addition of TSOME tothe diesel fuel reduced CO and SO

2emissions while causing slightly higher NO

Xemissions.

In his other study, the author reported that TSOME addition (up to 25% in volume) did notcause any significant variation in the engine performance. On the contrary, the TSOME



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Application of Taguchis methods 3

blends resulted in slightly higher torque and power than the diesel fuel at full load owing toits slightly higher density and viscosity.

Investigation of the parameters, which affects the yield of biodiesel on engineemissions of a diesel engine is important. The optimum operating parameters for a givensystem can be determined using experimental techniques but experimental procedurewill be time consuming and expensive when the number of parameters is in the order of20, 30, etc., like in the case of IC engines (Murugesan et al., 2009). The most commonoptimisation techniques used for engine analysis are simplex method (Shroff and Hodgetts,1974), response surface method (Satake et al., 2008), Artificial Neural Network (ANN)(Parlak et al., 2006), Genetic Algorithm (GA) (Alonso et al., 2007) and Taguchi method.Taguchis technique has been popular for parameter optimisation in Design of Experiments(DOE) for decades. Basic principle of Taguchi methods is to develop an understanding ofindividual and combined effects of variety of design parameters from a minimum number

of experiments (Sava and Kayikci, 2007).Nowadays, some researches begin to use Taguchi method for optimisation in

internal-combustion engines. Among them, Win et al. (2005) applied the Taguchi methodfor optimising the diesel engine operation and injection-system parameters for low noise,emission and fuel consumption. Anand and Karthikeyan (2005) applied Taguchi methodto optimise engine design and operating parameters for improving the engine efficiencywithout considering the combustion parameters. Murugesan et al. (2009) carried out anoptimisation analysis of direct-injection diesel engine run by Jatropha biodiesel using athermodynamic model in combination with Taguchi method.

However, there is no study using this technique in optimisation of some important factorsaffecting exhaust emissions of a diesel engines running with TSOME. In the experimentaldesign, the parameters affecting the emissions are chosen as engine speed, catalyst type and

the amount of TSOME in the blend. The conditions, which maximise the brake torque andthe conditions, which minimise the brake-Specific Fuel Consumption (SFC) and exhaustemissions were investigated.

2 Materials and methods

2.1 Production of TSOME

Transesterification method was used for producing TSOME. The weight of the oil, alcoholand catalyst was measured by 0.0001 g sensitivity. Base catalyst was chosen because acidvalue is found about around 1% in the oil analyses. Methyl alcohol in 99% purity was usedfor transesterification process. Measured and premixed methyl alcohol and catalysts (KOH

and NaOH) mixtures were poured on glass beaker, then the catalyst is stirred until all catalystwas completely resolved with alcohol. After TSO was heated up to desired temperature,the prepared alcoholcatalyst mixture was added to TSO to start the transesterificationreaction by using the heating bath and glass balloon (1 lt) of Buchi rotary evaporator. Thetemperature sensitivity of the heating bath was 0.1C. The mixture was stirred for 1 h andthen it was taken into the separatory funnel and waited until esterglycerin separation istaken place.

After the separation process, glycerin, which was moved to bottom of the separatoryfunnel was removed. It is necessary to clean the TSOME by sterile purified water to separate



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4 A. Parlak et al.

remaining glycerin, mono- and di-glycerides. Hot-sterile purified water was added to

TSOME in the separatory funnel for the cleaning process, then the separatory funnel wasshaken upside down by repeating four times. After this process, TSOMEWater mixturewas left to settle for 12 h and then pure water and glycerin glimmers were removed fromthe separatory funnel. After all this stages, remaining TSOME was separated from all ofthe unwanted particles by using an centrifuge separatory device called NUVE NF400 in4000 rpm and 3600 RCF for 1 h. Finally, TSOME sample was dried by heating up to 110 Cfor 30 min. Table 1 shows the analysis of methyl ester.

Table1 Analysis ofmethyl ester

Method KOH NaOH

Ester content, % (m/m) prEN 14103 96.5 97.0

Carbon residue, % (m/m) ENISO10370 0.17 0.17Copper band corrosion (3 h at 50C) ENISO2160 1.0 1.0Cold filter plug point, C EN116 7.0 10.0Water content, mg/kg ENISO12937 300.0 400.0Total contamination, mg/kg EN12662 20.0 23.0Idoine value, g iodine/100 g EN1411 122.0 118.0Methanol content, % (m/m) EN1410 0.20 0.18Triglyceride content, % (m/m) EN1410 0.11 0.11Di-glycerites content, % (m/m) EN1410 0.20


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Figure 1 Test setup

Table 2 Specification of the test engine

Engine type Super star water cooled

Bore [mm] 108Stroke [mm] 100Cylinder number 1

Stroke volume [l] 0.92Injection pressure [MPa] 17.5Injection advance [CA, BTDC] 35Maximum speed [rpm] 2500Cooling type Water Injection type DI

Table 3 The errors in parameters and total uncertainties

Parameters Systematic errors,

Load, N 0.1Speed, rpm 1.0Time, s 0.1

Temperature, C 1.0Fuel consumption, g 0.1NOx, ppm 5% of measured valueCO, % 5% of measured valueHC, ppm 5% of measured valueSmoke, % 1

Total uncertainty, %Specific fuel consumption 1.5Brake torque, Nm 1.1



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6 A. Parlak et al.

2.3 Design of experiment

The present experiments were designed to apply the Taguchis methods to establish theeffects of two catalysts, five TSOME blending rates and four engine speeds for the purposeof determining optimal conditions of the exhaust emissions. The three design parameters(factors) and their levels are given in Table 4. Optimum experimental conditions, whichmaximise the brake torque and minimise the emissions of SFC, NOx, CO, CO

2, HC and

smoke were determined by Taguchis methods. In experimental design, as the effects ofengine speed on performance is well-known in comparison witht the catalyst types andthe amount of TSOME in blend, four engine speeds, which is important for the engine

performance are selected.

Table 4 Design factors and their levels

Symbols Factors 1 Level 2 Level 3 Level 4 Level

A Catalyst type KOH NaOH B Engine speed, rpm 1000 1400 1800 2200C Blends, % m/m 10 20 50 100

Basic principle of Taguchi methods is to develop an understanding of individual andcombined effects of variety of design parameters from a minimum number of experiments.Taguchi method uses a generic Signal-to-Noise (S/N) ratio to quantify the present variation.There are several S/N ratios available depending on the type of characteristics includingLower is Better (LB), Nominal is Best (NB), and Higher is Better (HB). Since thelower SFC and exhaust emissions are vital in the engine tests, the S/N ratio for the LBcharacteristics is related to the present study, which is given by

S/N = 10log 2

1

1 n

i

i

yn =

(1)

Similarly, higher-brake torque are vital in the engine tests, the S/N ratio for the HBcharacteristics is given as following, which is given by Sava Kaykci (2007)

S/N = 10log2

1

1 1n

i in y=

(2)

where n is the number of repetition in a trial under the same design conditions,yirepresents

the measured value, and subscript i indicates the number of design parameters in theOrthogonal Array (OA). In the Taguchi method, a design parameter (factor) is considered

to be signifi

cant if its infl

uence is large compared with the experimental error as estimatedby the Analysis of Variance (ANOVA) statistical method given by equations (3)(7) shownbelow. If this is the case, the design parameter is a critical factor in determining the optimalsolution to the design problem:

SST= ( )

22

1

/N

i

TS N i

N=

(3)



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SSA

=2 2

1

AK

i

i Ai

A T

n N=

(4)

v= N 1 (5)

Vfactor

=vfactorSS (6)

Ffactor

= factorV

errorV(7)

where, SST

is the sum of squares owing to total variation,Nis the total number of experiments,SS

Arepresents the sum of squares owing to factorA,K

Ais number of levels for factorA.A

i

stands for the sum of the total ith level of the factorA, nAi

is the number of samples forithlevel of factorA. Tis the sum of total (S/N) ratio of the experiments, v is the degrees offreedom, V

factoris the variance of the factor, SS

factorrepresents the sum of squares of the factor

andFfactor

is theFratio of the factor.In Taguchi methods, the levels of factors given in the ANOVA are meaningful according

to 90% and 99% confidence intervals. Design of experiments is composed consideringthese confidence limits. Required the minimum experimental layout according to Taguchimethods are given in Table 5.

Table 5 Experimental layout

Exp. NoFactors

[A] [B] [C]

1 1 1 12 1 1 23 2 1 34 2 1 45 1 2 36 1 2 47 1 2 18 2 2 29 1 3 3

10 1 3 4

11 2 3 112 2 3 213 1 4 114 1 4 215 2 4 316 2 4 4



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8 A. Parlak et al.

3 Results and discussion

3.1 Determination of optimal operation conditions by Taguchi method

In this study, the effects of using TSOME blends, which is yielded for KOH and NaOHcatalyst on exhaust emissions of a direct-injection diesel engines are studied. To investigatethe factors of catalyst type, engine speed and mass base TSOME percentage in the blend(blending rate), Taguchis methods were used.

B10, B20, B50 and B100 blends of TSOME on exhaust emissions were tested forcomparison with data of standard-diesel engine. Two different catalysts (KOH and NaOH)and four different blends were used during full-load tests. Optimal catalyst type, engine speedand TSOME blend affecting emissions of diesel engine were determined using Taguchistechnique. Although the engine was tested for seven engine speeds for full-load testing, four

engine speeds was only taken for experimental design and in determining optimal conditionsfor performance as the solution is rather simplified.Experiments were designed considering the requirements of the increase in the brake

torques and the necessity of lowering the SFC and exhaust emissions. Table 6 shows ANOVAfor the parameters considered. According to the results of ANOVA, it is shown that enginespeed, blending rate and catalyst type affects the SFC within a 99% confidence level. Theeffect of blend rate on SFC, brake torque and brake power are found meaningful comparingwith the others factors.

Table 6 The Analysis of Variance (ANOVA)

FactorsSum of squares,

SS

Degree of

freedom v

Variance,

VTF

factor

TORQUE [A] Catalyst 0.70 1 0.70 44.21***[B] Speed 1.15 3 0.38 24.11***[C] Blends, % 0.41 3 0.14 8.65***Total 2.27 7 0.32Error 0.13 8 0.02

SFC [A] Catalyst 0.50 1 0.50 33.19***[B] Speed 1.76 3 0.59 38.96***[C] Blends, % 1.76 3 0.59 38.92***Total 4.02 7 0.57Error 0.12 8 0.02

NOX

[A] Catalyst 0.22 1 0.22 10.47**[B] Speed 18.99 3 6.33 295.30***[C] Blends, % 0.70 3 0.23 10.89***Total 19.91 7 2.84Error 0.17 8 0.02

HC [A] Catalyst 0.14 1 0.14 0.03[B] Speed 527.38 3 175.79 37.88***[C] Blends, % 77.73 3 25.91 5.58**Total 605.25 7 86.46Error 37.12 8 4.64



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FactorsSum of squares,

SS

Degree of

freedom v

Variance,

VTF

factor

CO [A] Catalyst 4.05 1 4.05 2.164463

[B] Speed 98.99 3 33.00 17.63***

[C] Blends, % 20.20 3 6.73 3.59*Total 123.24 7 17.61Error 14.97 8 1.87

CO2

[A] Catalyst 0.54 1 0.54 10.66**[B] Speed 0.76 3 0.25 5.01**[C] Blends, % 0.09 3 0.03 0.61Total 1.39 7 0.20Error 0.40 8 0.05

Smoke [A] Catalyst 0.00 1 0.00 0.000574[B] Speed 11.01 3 3.67 1.803275[C] Blends, % 19.77 3 6.59 3.23*Total 30.78 7 4.40Error 16.28 8 2.04

*At Least 90% confidence.**At Least 95% confidence.***At Least 99% confidence.

Table 7 shows S/N values of the factors according to experimental layout designed byTaguchi methods. Observed values in the table are the real values, which were calculatedusing the experimental data. According to Taguchi techniques, to determine the optimalconditions and to compare the results with the expected conditions, it is necessary to perform

a confirmation experiment. If the generated design fails to meet the specified requirement,the process must be reiterated using a new system until the required criteria are satisfied.In the study, the required confirmation tests were done and the results were found completelyconfidence with the observed values.

3.1.1 Brake torque and specific fuel consumption

Optimal conditions were determined by using Taguchi method for brake power. As thegenerated design has not been included in the main experimental layout, the process wasre-iterated until the required criteria are satisfied. After confirmations test carried out in99% confidence level, the optimum design parameter (factor) combination were found asA

1B3C

3(KOH- 2200d/d-B50) for brake torque and as A

1B

2C

1(NaOH-1400 rpm-B10) for

SFC. Figures 2 3 show S/N values of factor levels for the brake torque and SFC. Sincethis combination of design parameter had already been included in the main experimentallayout (see Experiment 7 in Table 5) there was no need to carry out an extra confirmationexperiment. According to the ANOVA, catalyst type and blend rate of TSOME aremeaningful within 99% confidential level. Higher torque was obtained with KOH catalystand B50 TSOME blend. Similarly, lower SFC was measured with KOH catalyst and B10TSOME.

Table 6 The Analysis of Variance (ANOVA) (continued)



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10 A. Parlak et al.

Figure 2 S/N values of factor levels for brake torque

Figure 3 S/N values of factor levels for brake SFC

3.1.2 Exhaust emissions

According to ANOVA in Table 6, catalyst type and blend rate are significant on NOxemissions within 95% and 90% confidence level, respectively. The catalyst type and TSOME

blend rate strongly affect NOx, HC and CO above 90% confidence level. The effect ofcatalyst type on smoke emission is not observed above 90% confidence level. The changeof NOx strongly depends on the engine speed. ANOVA confirm that engine speed affect the

NOx emissions within 99% confidence limit. Same effects were observed with the pollutantemissions of HC, CO within 99% confidence level. However, the effects of TSOME blendon emissions were meaningful within 90% confidence level.

S/N values of factor levels of smoke, NOx, HC, CO and CO2

emissions for B10, B20,B50, B100 blends of TSOME are shown in Figures 48. After confirmations test carried outin 99% confidence level, the optimum design parameter (factor) combination were found as



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Exp.

no.

TORQUE(Nm)

SFC(g/kWh)

NO

X(ppm)

HC(ppm)

CO(%)

CO

2(%)

Smoke,%

Observed

value

S/N

ratio

Observed

value

S/N

ratio

Observed

value

S/N

ratio

O

bserved

value

S/N

ratio

Observed

value

S/N

ratio

Ob

served

v

alue

S/N

ratio

Observed

value

S/N

ratio

1

51.2

0

34.1

9

255.2

6

48.1

4

939.0

0

59.4

5

11.0

0

20.8

3

0.3

6

8.8

7

9.4

0

19.4

6

75.0

0

37.5

0

2

51.3

8

34.2

2

254.3

7

48.1

1

978.0

0

59.8

1

9.0

0

19.0

8

0.3

3

9.6

3

9.2

0

19.2

8

65.0

0

36.2

6

3

48.7

6

33.7

6

270.8

9

48.6

6

955.0

0

59.6

0

10.0

0

20.0

0

0.3

4

9.3

7

9.6

6

19.7

0

77.0

0

37.7

3

4

48.4

1

33.7

0

284.1

8

49.0

7

1005.0

0

60.0

4

9.0

0

19.0

8

0.3

2

9.9

0

9.5

0

19.5

5

71.0

0

37.0

3

5

55.5

0

34.8

9

247.8

9

47.8

9

1289.0

0

62.2

1

11.0

0

20.8

3

0.2

7

11.3

7

9.3

0

19.3

7

58.0

0

35.2

7

6

51.2

0

34.1

9

268.6

9

48.5

9

1310.0

0

62.3

5

9.0

0

19.0

8

0.2

4

12.4

0

1

0.0

0

20.0

0

56.0

0

34.9

6

7

51.8

8

34.3

0

246.2

5

47.8

3

1221.0

0

61.7

3

15.0

0

23.5

2

0.2

7

11.3

7

9.9

2

19.9

3

67.0

0

36.5

2

8

51.8

5

34.2

9

255.8

9

48.1

6

1269.0

0

62.0

7

14.0

0

22.9

2

0.2

6

11.7

0

1

0.3

0

20.2

6

65.0

0

36.2

6

9

56.5

7

35.0

5

256.6

9

48.1

9

1298.0

0

62.2

7

7.0

0

16.9

0

0.2

0

13.9

8

9.6

3

19.6

7

58.0

0

35.2

7

10

54.0

7

34.6

6

275.6

6

48.8

1

1321.0

0

62.4

2

6.0

0

15.5

6

0.1

7

15.3

9

1

0.1

0

20.0

9

46.0

0

33.2

6

11

53.5

6

34.5

8

260.4

3

48.3

1

1189.0

0

61.5

0

11.0

0

20.8

3

0.2

4

12.4

0

1

0.1

9

20.1

6

70.0

0

36.9

0

12

53.5

6

34.5

8

263.9

9

48.4

3

1197.0

0

61.5

6

10.0

0

20.0

0

0.2

0

13.9

8

1

0.6

0

20.5

1

62.0

0

35.8

5

13

54.0

7

34.6

6

266.0

2

48.5

0

987.0

0

59.8

9

4.0

0

12.0

4

0.2

3

12.7

7

9.6

0

19.6

5

70.0

0

36.9

0

14

54.0

7

34.6

6

267.1

8

48.5

4

999.0

0

59.9

9

3.0

0

9.5

4

0.2

0

13.9

8

9.6

0

19.6

5

69.0

0

36.7

8

15

51.1

6

34.1

8

293.3

7

49.3

5

987.0

0

59.8

9

2.0

0

6.0

2

0.1

3

17.7

2

1

0.1

6

20.1

4

59.0

0

35.4

2

16

48.7

6

33.7

6

307.8

3

49.7

7

1009.0

0

60.0

8

1.0

0

0.0

0

0.1

0

20.0

0

9.8

2

19.8

4

34.0

0

30.6

3

Table 7 Experimental layout and results with calculated S/N ratios for performance and

emission parameters



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12 A. Parlak et al.

A2B

4C

4(NaOH- 2000d/d-B100) for smoke, HC and CO, A

2B

1C

1(NaOH-1000 rpm-B10) for

NOx and A1B1C3(KOH-1000 rpm-B50) for CO2. While maximum performance was obtainedwith KOH catalyst, the lowest emissions were found with NaOH catalyst except CO

2.

Figure 4 S/N values of factor levels for smoke emission

Figure 5 S/N values of factor levels for NOX

Figure 6 S/N values of factor levels for HC



13/16


Figure 7 S/N values of factor levels for CO

Figure 8 S/N values of factor levels for CO2

4 Conclusions

The present study has applied the Taguchi method to investigate the three well-known factorson the performance parameters. The conditions, which maximise the brake torque and theconditions, which minimise the brake-SFC and emissions were investigated. The conclusionof this study can be summarised as follows:

The Taguchi design method revealed that choosing right catalyst type and blend rateare important in view of maximisation of brake torque and minimisation of SFC

increase rate and exhaust emissions.

Although KOH is found the best catalyst for brake torque, NaOH is the better catalystfor SFC and emissions.

The ANOVA results indicated that TSOME contributed to increase combustionefficiency up to 50% blend in the mixture. This reason caused to increase the

performance in the blends of 10%, 20% and 50% blends. This effect also caused todecrease smoke, HC and CO emissions.



14/16

14 A. Parlak et al.

As the oxygen content in fuel blend increased, the heat content of the blend decreased

considerably and these factor lead to decrease performance and decrease SFC for100% TSOME.

Taguchi Method is a powerful tool for experimental design so as to minimising theexperiments number, which will be conducted. The method gives to a media to findinteractions among the factors affecting the performance parameters and optimalconditions for the performance.

Acknowledgement

This work has been supported by The Scientific and Technological Research Council ofTurkey and was performed within The Support Programme for Scientific and TechnologicalResearch Projects (1001) with the Project Number of105M259.

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Nomenclature

Ai

Sum of the total ith level of the factorAANOVA Analysis of VarianceANN Artificial Neural NetworkBTDC Before Top Dead CentreCA Crank AngleCO Carbon MonoxideCO

2Carbon Dioxide



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Ffactor

Ratio of the factorGA Genetic AlgorithmHB Higher is Better HC HydrocarbonK

ANumber of levels for factorA

KOH Potassium HydroxideLB Lower is Better N Number of experimentsn

AiNumber of samples forith level of factorA

NB Nominal is BetterNaOH Sodium HydroxideNOx Nitrous Oxides,RCF Relative Centrifuge ForceRPM Revolution per Minute

RPE Rapesed Methyl Ester SFC Specific Fuel Consumption (g/kWh)SO

2Sulphur Dioxide, ppm

S/N Signal/NoiseSS

ASum of squares due to factorsA

SSfactor

Sum of squares of the factorSS

TSum of squares due to total variation

vtotal

Degrees of freedomv Variance of the factory

iMeasured value

T Sum of total (S/N) ratio of experimentsTSOME Tobacco Seed Oil Methyl Ester


Application of Taguchi’s methods

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