Applied Energy 88 (2011) 2663–2676

Contents lists available at ScienceDirect

Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Investigation on the effect of injection system parameters on performanceand emission characteristics of a twin cylinder compression ignitiondirect injection engine fuelled with pongamia biodiesel–diesel blend usingresponse surface methodology

M. Pandian, S.P. Sivapirakasam ⇑, M. UdayakumarDepartment of Mechanical Engineering, National Institute of Technology, Tiruchirappalli 620 015, Tamilnadu, India

a r t i c l e i n f o

Article history:Received 27 April 2010Received in revised form 22 November 2010Accepted 28 January 2011

Keywords:BiodieselDesign of ExperimentsInjection pressureInjection timingNozzle tip protrusionResponse surface methodology

0306-2619/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.apenergy.2011.01.069

⇑ Corresponding author. Tel.: +91 431 250 3408; faE-mail addresses: kalaipands@yahoo.com (M. P

(S.P. Sivapirakasam), muday@nitt.edu (M. Udayakuma

a b s t r a c t

This study is aimed at investigating the effect of injection system parameters such as injection pressure,injection timing and nozzle tip protrusion on the performance and emission characteristics of a twin cyl-inder water cooled naturally aspirated CIDI engine. Biodiesel, derived from pongamia seeds throughtransesterification process, blended with diesel was used as fuel in this work. The experiments weredesigned using a statistical tool known as Design of Experiments (DoE) based on response surface meth-odology (RSM). The resultant models of the response surface methodology were helpful to predict theresponse parameters such as Brake Specific Energy Consumption (BSEC), Brake Thermal Efficiency(BTE), Carbon monoxide (CO), Hydrocarbon (HC), smoke opacity and Nitrogen Oxides (NOx) and furtherto identify the significant interactions between the input factors on the responses. The results depictedthat the BSEC, CO, HC and smoke opacity were lesser, and BTE and NOx were higher at 2.5 mm nozzletip protrusion, 225 bar of injection pressure and at 30� BTDC of injection timing. Optimization of injectionsystem parameters was performed using the desirability approach of the response surface methodologyfor better performance and lower NOx emission. An injection pressure of 225 bar, injection timing of21� BTDC and 2.5 mm nozzle tip protrusion were found to be optimal values for the pongamia biodieselblended diesel fuel operation in the test engine of 7.5 kW at 1500 rpm.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Nowadays the biodiesels, derived from plant vegetable oils, ani-mal fats, used cooking oils etc., are used as fuel in CompressionIgnition Direct Injection (CIDI) engines. Because of use of biodieselin the existing design of a CI engine, the combustion process maynot produce the expected performance and this leads to higherNOx emission [1–6]. Among the various parameters of interest,which have the potential of influencing the performance and NOx

emission, the injection system parameters are fundamental andmodification of these parameters is considered to be a good meth-od of in-cylinder combustion improvement. The effects of injectionsystem parameters were widely studied for biodiesel fuelled CI en-gines. From the literatures [7–25], it is found that the reduction inNOx emission as well as improvement in engine performance andcombustion characteristics can be had by suitably optimizing the

ll rights reserved.

x: +91 431 250 0133.andian), spshivam@nitt.edur).

engine injection parameters when a CI engine is to operate on abiodiesel fuel.

Earlier studies show that the effect of injection system parame-ters has been investigated by the approach of ‘‘varying one param-eter at a time’’. However the combustion process in diesel enginesare highly influenced by the combined effect of various parameterslike air–fuel ratio, injection timing, injection pressure and nozzlegeometries, etc., and operating parameters like load and speed[26]. Hence, a systematic multivariate study could only provide aclear and thorough knowledge on the combustion characteristicsof the engine than the approach by one variable at a time study.

In such multivariate problems, use of non linear techniques likeDesign of Experiments (DoE), fuzzy logic and neural network aresuitable to explore the combined effects of input parameters.Among the mentioned techniques, DoE is the most effective andeconomical technique to evaluate the individual and combined ef-fects of input factors on output responses. Although few studieswere reported using DoE in IC Engine applications, the study oncombined effects between injection system parameters such asinjection timing, injection pressure and nozzle tip protrusion onthe performance and emission characteristics of CI engine was

2664 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

scarce and offered a scope for this study. Win et al. [27] used theTaguchi method of DoE for analyzing the role of operating andinjection system parameters on low noise, emissions and fuel con-sumption (BSFC) and Ganapathy et al. [28] reported the perfor-mance optimization of jatropha biodiesel engine model usingTaguchi approach. Anand and Karthikeyan [29] optimized the en-gine parameters of a Spark Ignition engine with gaseous fuels withthe help of Taguchi methodology. Lee and Reitz [30] used ResponseSurface Method (RSM) to optimize a high speed direct injection die-sel engine equipped with a common rail injection system neglect-ing the interactive effects of the parameters and Satake et al. [31]performed the rapid development of diesel engines using RSMbased optimization of the fuel injection control. Win et al. [32] usedresponse surface methodology to optimize the parameter such asload, speed and static injection timing of a diesel fueled CI engineto reduce noise, fuel consumption and exhaust emissions.

The main objective of this work is to study the individual andcombined effects of injection system parameters on the perfor-mance and emission characteristics of the diesel engine employingpongamia biodiesel–diesel blend as fuel using response surfacemethodology based experimental design and the other objectiveis to determine the optimal values of injection pressure, injectiontiming and the nozzle tip protrusion which would be resulting inimproved performance with lesser NOx emissions without muchpenalty on CO and HC emissions using the desirability approachof numerical optimization.

2. Materials and methods

2.1. Fuel preparation

Pongamia biodiesel was prepared through transesterificationprocess from pongamia oil which was extracted from the seedsof pongamia tree. The formation of methyl esters by transesterifi-cation of vegetable oil requires raw oil, 15% of methanol and 5%of sodium or potassium hydroxide on mass basis. However thetransesterification process requires excess alcohol to drive thereaction very close to completion. A reaction time of 45 min toan hour and reaction temperatures of 55–65 �C were required forcompletion of reaction and formation of esters. The mixture wasstirred continuously and then allowed to settle under gravity in aseparating funnel. Two distinct layers found after gravity settlingfor 24 h. The upper layer was of ester and the lower layer was ofglycerol. The lower layer was separated out and the separated esterwas mixed with some distilled water to remove the catalyst pres-ent in ester and allowed to settle under gravity for another 24 h.The catalyst not dissolved in water, which was separated and re-moved the moisture. The biodiesel thus produced through theabove process was blended with diesel, procured from the nearbycommercial vendor, in a volume ratio of 40:60 to get the biodieseldiesel blend fuel of B40. The B40 blend was chosen because of itssuperior performance over other blend ratios [33–36], which wasconfirmed by the preliminary investigation conducted by theauthors. The fuel blend was prepared just before commencingthe experiments to ensure the mixture homogeneity. The proper-ties of the fuel blend of B40 and diesel have been determined asper the ASTM standards in an industrial testing and analytical lab-oratory, established at Chennai, India. The uncertainty value ofeach of the measured and estimated property is given in Table 1along with the properties of fuels and its ASTM standards.

2.2. Equipment and materials

The experiments were conducted on the twin cylinder watercooled naturally aspirated direct injection compression ignition

engine whose specifications are also prescribed in Table 1. The en-gine is coupled with an alternator manufactured by Kirloskar,which in turn was loaded by the three water heaters each of2.5 kW capacity. The measurements of various parameters weremade only after the engine attained steady state. In each experi-ment, the time for consumption of 40 cc of fuel, load, current andvoltage, the temperatures at salient points and air flow rate werenoted. The smoke opacity of the exhaust gas was measured bysmoke opacimeter (Make: AVL Austria; Model: 437). Exhaust gascomposition was measured using NDIR based exhaust gas analyzer(Make: AVL Austria; Model: 444 DiGas). This analyzer measuresCO2, CO, HC, NOx and O2 in the exhaust gas. The measurementrange and accuracy of the exhaust gas analyzer are given in Table 2.Fig. 1 shows the schematic layout of the experimental setup.

The static injection timing was altered by adjusting the numberof shims under the seat of a mounting flange of the fuel pump.When the shims were added, timing was retarded, and vice versa[27]. Procedure of measurement of static injection timing is as fol-lows: The tank is filled with the fuel in such a way that the level offuel in the tank is about 10 cm above the testing device. The TDC po-sition is marked on the flywheel by bringing the piston to the topmost position of the cylinder. Then the flywheel is turned in anti-clockwise direction till the fuel reaches the testing device. Thisoperation is repeated to note down exactly the moment at whichthe fuel moves through the testing device hole by slowly rotatingthe flywheel and stopped immediately. Then the flywheel isbrought back by 5 mm. This position is marked on the flywheeland that position is called as static injection timing. Thus the staticinjection timing of the engine can be checked with the manufac-turer’s set value. Similar procedure is adopted to measure the staticinjection timing when the shims are added or removed to vary thetimings in comparison with the original injection timing. Thecurvilinear distances on the flywheel are measured by using thread.Then the injection timing angle was calculated in relation with theoriginal injection timing angle. The accuracy of measurement willbe 1�.

The fuel injection pressure was varied by inserting or removingshims under nozzle spring [27]. The fuel pressure was measuredusing the BOSCH standard nozzle tester which had a pressuregauge to measure the pressure in the range of 0–400 bar. The eachdivision in the gauge measures 2 bar, which is also the accuracy ofthe gauge. The usage of copper sealing washers of different thick-ness altered the nozzle tip protrusion [27]. From the engine sup-plier’s manual, the original nozzle tip protrusion is found to be2.5 mm. Then the copper shims of different thicknesses are keptbeneath the fuel injector mountings. Later the nozzle tip protru-sion was improved by the removal of shims of different thicknessin such a way that the protrusion be 4 mm. Similarly addition ofshims was made so that the protrusion can be 1 mm into the com-bustion chamber. The thickness of the shim was measured usingthe digital vernier caliper and the accuracy of vernier caliper is0.01 mm. The experiments were carried out for 80% of load at aspeed of 1500 rpm as that load was found to be economical forthe specified test engine.

2.3. Response surface methodology

Response Surface methodology was employed in the presentstudy for modeling and analysis of response parameters in orderto obtain the characteristics of the engine. The design and analysisof experiment involved the following steps:

� The first step was the selection of the parameters that influencethe performance and emission characteristics. In this study, theinjection timing, injection pressure and the nozzle tip protru-sion were considered as the input parameters.

Table 1Specification of engine and fuel.

A: Engine specificationParameters Details

Make M/s. Rocket Engineering Corporation, Kohlapur, Maharashtra, IndiaBore 80 mmStroke 110 mmCompression ratio 17.5Rated power 7.5 kWRated speed 1500 rpmInjection timing 24� BTDCInjection pressure 200 barNozzle tip protrusion 2.5 mmDynamometer Alternator with water heaters

B: Specification of fuelProperty Diesel Pongamia biodiesel Fuel blend B40 Uncertainty ASTM methods

Kinematic Viscosity @40 �C (mm2/s) 2.6 4.8 3.85 ±0.2 ASTM D445Cetane Number 50 51 51 – ASTM D613Iodine Value NA 112 41 – ASTM D 1959–97Calorific Value (MJ/kg) 42.5 36.5 40.1 ±0.15 ASTM D 240Specific Gravity @15 �C 0.835 0.878 0.859 ±1.5% ASTM D 941Flash Point (�C) 68 172 81 ±0.1 ASTM D93

Table 2Exhaust gas analyzer specification.

Exhaust gas Measurement range Resolution Accuracy

CO 0–10 vol.% 0.01 vol.% <.06 vol.%:±0.03 vol.%P0.6 vol.%:±5% of ind. val.

HC 0–20,000 ppm 62000 ppm:1 ppm vol. > 2000 ppm:10 ppm <200 ppm vol.:±10 ppm vol.P200 ppm vol.:±5% of ind. val.

CO2 0–20 vol.% 0.1 vol.% <10 vol.%:±0.5 vol.%P10 vol.%:±5% of val. M.

O2 0–22 vol.% 0.01 vol.% <2 vol.%:±0.1 vol.%P2 vol.%:±5% of val. M.

NO 0–5000 ppm 1 ppm vol. <500 ppm vol.:±50 ppm vol.P500 ppm vol.:±10% of ind. val.

Fig. 1. Schematic layout of the experimental setup.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2665

2666 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

� The injection timing (denoted by ‘t’) was varied at five levels insteps of 3� from 18� BTDC to 30� BTDC. The injection pressure(denoted by ‘pr’) too was varied at five levels from 150 bar to250 bar in steps of 25 bar. The Nozzle tip protrusion (denotedby ‘l’) was varied at three levels from 1 mm to 4 mm with theinterval of 1.5 mm. The ranges of the input parameters wereselected based on the permissible limits within which the mod-ifications can be made with the existing engine.� The advantage of using Design of Experiments is to evaluate the

performance of the engine over the entire range of variation ofinjection system parameters with minimum number of experi-ments. The design matrix was selected based on the fractionalfactorial design of response surface methodology generatedfrom the software ‘‘Design expert’’ version 7.1.5 of Stat ease,US, which contained 50 experimental runs as shown in Table 3.� As per the run order, the experiments were conducted on the

engine and the responses were fed on the responses column.� A multiple regression analysis was carried out to obtain the

coefficients and the equations can be used to predict the

Table 3Experimental design matrix.

Std Run t (� BTDC) pr (bar) l (mm) BSEC (MJ/kW h)

1 1 21 175 4 15.81122 2 27 225 4 14.50433 3 21 250 1 15.11734 4 24 225 2.5 13.7965 5 18 175 4 16.06286 6 21 250 2.5 14.27687 7 27 150 4 15.25348 8 18 150 2.5 15.69059 9 24 250 4 14.9376

10 10 27 150 1 15.168411 11 18 175 1 16.139312 12 30 250 1 14.535513 13 30 175 2.5 13.879214 14 24 250 1 14.915215 15 27 225 2.5 13.6616 16 21 225 2.5 13.917417 17 27 175 2.5 14.308918 18 27 250 1 14.705719 19 21 225 4 14.839420 20 27 250 4 14.72221 21 30 200 4 14.499622 22 21 250 4 15.067123 23 24 225 4 14.680224 24 30 200 2.5 13.657725 25 18 200 2.5 14.5726 26 18 225 1 15.009927 27 18 150 1 16.464228 28 30 225 1 14.283929 29 24 150 1 15.516430 30 27 200 4 14.929231 31 30 225 4 14.282932 32 21 150 1 15.925933 33 21 175 2.5 14.613234 34 24 200 2.5 14.038135 35 21 200 2.5 14.211336 36 27 200 2.5 13.790737 37 18 175 2.5 14.87538 38 18 225 2.5 14.414439 39 18 150 4 16.642740 40 30 250 1 14.535541 41 21 150 2.5 14.785442 42 30 150 1 14.866143 43 30 150 4 14.951144 44 18 200 4 15.597345 45 24 150 2.5 14.605546 46 24 175 1 15.442647 47 18 200 1 15.529348 48 30 200 4 14.499649 49 18 250 1 15.245350 50 24 175 2.5 14.4524

responses. Using the statistically significant model, the correla-tion between the process parameters and the several responseswere obtained.� Finally, the optimal values of the injection system parameters

were obtained by using the desirability approach of theresponse surface methodology.

2.4. Desirability approach

The real-life problems require optimization with the multipleresponses of interest. Techniques like overlaying the contour plotsfor each response, constrained optimization problems and desir-ability approach are being used. Among them, desirabilityapproach is found to have benefits like simplicity, availability inthe software and flexibility in weighting and giving importancefor individual response. In the present work, response surfacemethodology based desirability approach is used for the optimiza-tion of injection system parameters (i.e. injection timing, injectionpressure and nozzle tip protrusion) for the measured properties of

BTE (%) CO (vol.%) HC (ppm) Smoke (%) NOx (ppm)

23.13 0.62 80 80 17724.57 0.3 61 63 31623.34 0.61 71 71 19926.165 0.39 58 62 29422.53 0.75 90 85 14024.98 0.5 61 71 19023.49 0.55 76 80 26022.94 0.77 90 82 14224.06 0.48 66 70 24823.64 0.63 78 78 26222.99 0.85 83 86 14424.64 0.29 58 60 35025.94 0.37 67 64 36923.61 0.53 66 68 26326.59 0.27 56 60 33225.74 0.45 61 65 22525.62 0.43 69 67 29523.99 0.41 61 63 30524.34 0.47 68 67 20124.47 0.37 63 65 30024.95 0.28 61 64 38323.59 0.51 70 72 19424.34 0.41 64 65 25926.47 0.33 62 62 40124.71 0.54 69 72 15723.49 0.65 71 73 16122.33 0.98 92 90 13925.06 0.23 52 59 37823.38 0.69 84 81 23324.41 0.41 68 66 30225.11 0.23 54 61 42022.89 0.75 88 85 18624.91 0.7 75 73 18025.87 0.44 65 65 26825.33 0.48 67 69 19526.27 0.4 63 64 30824.24 0.72 79 76 14724.98 0.49 65 70 17521.82 0.89 98 92 13024.63 0.29 58 60 35023.82 0.74 87 79 17024.29 0.58 74 74 31623.8 0.5 69 77 30823.25 0.7 81 78 15124.42 0.67 83 77 24623.49 0.57 75 74 24523.14 0.76 76 78 15424.95 0.28 61 64 38323.19 0.68 74 76 15725.11 0.54 71 71 254

Table 4ANOVA for various responses indicating the values of ‘p’.

Source BSEC BTE CO HC Smokeopacity

NOx

Model <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001t <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001p <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001l 0.4938 0.2914 0.0012 0.2783 0.2116 0.5789t p <0.0001 0.0069 –* 0.0198 –* 0.0124p l –* 0.0001 –* –* –* –*

t l –* –* –* –* –* 0.0114p2 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001l2 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0496

* The insignificance of the input parameter over the output responses as the valueof ‘p’ was greater than 0.05.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2667

responses (BSEC, BTE, CO, HC, smoke opacity and NOx). The optimi-zation analysis is carried out using Design Expert software, whereeach response is transformed to a dimensionless desirability value(d) and it ranges between d = 0, which suggests that the response iscompletely unacceptable, and d = 1, which suggests that the re-sponse is more desirable. The goal of each response can be eithermaximize, minimize, target, in the range and/or equal to depend-ing on the nature of the problem. The desirability of the each re-sponse can be calculated by the following equations with respectto the goal of each response.

For a goal of minimum, di = 1 when Yi 6 Lowi; di = 0 whenYi P Highi; and

di ¼Highi � Yi

Highi � Lowi

� �wti

when Lowi < Yi < Highi

For a goal of maximum, di = 0 when Yi 6 Lowi; di = 1 whenYi P Highi and

di ¼Yi � Lowi

Highi � Lowi

� �wti

when Lowi < Yi < Highi

For goal as target, di = 0, when Yi < Lowi; Yi > Highi

di ¼Yi � Lowi

Ti � Lowi

� �wt1i

when Lowi < Yi < Ti

di ¼Yi �Highi

Ti �Highi

� �wt2i

when Ti < Yi < Highi; and

For the goal within the range, di = 1 when Lowi < Yi < Highi anddi = 0; otherwise.

Here ‘‘i’’ indicates the response, ‘‘Y’’ the value of response, ‘‘Low’’represents the lower limit of the response, ‘‘High’’ represents theupper limit of the response, ‘‘T’’ means the target value of the re-sponse, ‘‘wt’’ indicates the weight of the response. The shape ofthe desirability function can be changed for each response by theweight field. Weights are used to give more emphasis to the low-er/upper bounds. Weights can be ranged from 0.1 to 10; a weightgreater than 1 gives more emphasis to the goal, weights less than1 give less emphasis. When the weight value is equal to one, thedesirability function varies in a linear mode. Solving of multiple re-sponse optimizations using the desirability approach involves atechnique of combining multiple responses into a dimensionlessmeasure of performance called the overall desirability function, D(0 6 D 6 1), is calculated by

D ¼ Pni¼1dri

i

� �1=Rri

In the overall desirability objective function (D), each responsecan be assigned an importance (r), relative to the other responses.Importance varies from the least important value of 1, indicated by(+), the most important value of 5, indicated by (+++++). A high va-lue of D indicates the more desirable and best functions of the sys-tem which is considered as the optimal solution. The optimumvalues of factors are determined from value of individual desiredfunctions (d) that maximizes D.

3. Results and discussion

3.1. Analysis of the model

The principal model analysis was based on the analysis of vari-ance (ANOVA) which provides numerical information for the pvalue. The ANOVA for different response parameters such as BSEC,BTE, CO, HC, smoke opacity and NOx emissions were given inTable 4. The models found to be significant as the values of p were

less than 0.05. The quadratic models for the responses were devel-oped in terms of actual factors and are given below as Eqs. (1)–(6).

BSEC ¼ 28:45� 0:24� t � 0:07� pr � 2:08� lþ 7:53� 10�4

� t � pr þ 1:09� p2r þ 0:42� l2 ð1Þ

BTE ¼ 2:93þ 0:26� t þ 0:13� pr þ 2:80� l� 5:56� 10�4

� t � pr þ 2:69� 10�3 � pr � l� 2:93� 10�4 � p2r

� 0:67� l2 ð2Þ

CO ¼ 3:28� 0:3� t � 0:02� pr � 0:16� lþ 3:50� 10�5 � p2r

þ 0:03� l2 ð3Þ

HC ¼ 275:78� 2:70� t � 1:32� pr � 8:78� lþ 6:52� 10�3

� t � pr þ 2:41� 10�3 � p2r þ 1:83� l2 ð4Þ

Smoke opacity ¼ 215:96� 1:18� t � 0:95� pr � 10:18� l

þ 2:02� 10�3 � p2r þ 2:09� l2 ð5Þ

NOx ¼ �412:48þ 8:56� t þ 3:39� pr � 4:74� lþ 0:03� t

� pr þ 0:97� t � l� 9:73� 10�3 � p2r � 3:91� l2 ð6Þ

3.2. Evaluation of the model

The stability of the models was validated using Analysis of Var-iance (ANAVO) presented in Table 4 for the various responses. Theoutput showed that the model was significant with p values lessthan 0.0001. The reference limit for p was chosen as 0.05. Theregression statistics goodness of fit (R2) and the goodness of predic-tion (Adjusted R2) were shown in Table 5 for all the responses. TheR2 value indicates the total variability of response after consideringthe significant factors. The Adjusted R2 value accounts for the num-ber of predictors in the model. Both the values indicate that themodel fits the data very well [37].

3.3. Interactive effect of injection timing and injection pressure

The interactive effect of injection timing and injection pressureon BSEC, BTE, CO, HC, NOx and smoke opacity are depicted inFigs. 2–7 respectively. During advancement of injection timingfrom 18� BTDC to 30� BTDC, the BSEC and the exhaust emissionslike CO, HC and smoke opacity were reduced and BTE and NOx

emission increased. This could be due to the following fact: in-cyl-inder charge temperature and pressure decreased with anadvancement of the injection timing resulting in extended ignition

Table 5Response surface model evaluation.

Model BSEC BTE CO HC Smoke opacity NOx

Mean 14.84 24.30 0.53 70.78 71.48 247.24Std. Deviation 0.15 0.21 0.044 2.84 1.79 13.67Model degree Quadratic Quadratic Quadratic Quadratic Quadratic QuadraticR2 0.9590 0.9709 0.9480 0.9392 0.9591 0.9763Adj. R2 0.9533 0.9661 0.9421 0.9307 0.9545 0.9723Pred. R2 0.9454 0.9594 0.9333 0.9147 0.9461 0.9659

Fig. 2. BSEC variations against injection pressure and injection timing.

Fig. 3. BTE variations against injection pressure and injection timing.

2668 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

delay of the injected fuel. Simultaneously, the penetration of fuelspray enhanced, reaction between fuel and air improved and ulti-mately resulted in premixed or rapid combustion phase of thecombustion process.

Increasing the injection pressure from 150 bar to 225 barincreased BTE and NOx with reduction in BSEC, CO, HC and smokeopacity. Increasing the injection pressure beyond 225 bar, the in-verse trend was noticed at all injection timings. The above result

could be due to the following fact: With increase in injection pres-sure, better atomization of the fuel resulted in the smaller dropletsize; faster evaporation of fuel sprays; and improved reaction be-tween fuel and air. These resulted in comparatively better combus-tion and contributed for higher BTE and NOx emission with lesserBSEC, CO, HC and smoke emissions at all injection timings. Beyond225 bar of injection pressure, faster velocity of the fuel jets causedmost fuel particles to hit the wall of combustion chamber where

Fig. 4. CO variations against injection pressure and injection timing.

Fig. 6. NOx variations against injection pressure and injection timing.

Fig. 5. HC variations against injection pressure and injection timing.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2669

Fig. 7. Smoke opacity variations against injection pressure and injection timing.

Fig. 8. BSEC variations against nozzle tip protrusion and injection pressure.

2670 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

the fuel particles got cooled and not participated in the combustionprocess effectively which would result in incomplete combustion.The above discussions revealed that an injection pressure of225 bar combined with advanced injection timing 30� BTDC pro-duced highest BTE with maximum NOx emissions and lesser emis-sions of CO, HC and smoke emissions while the low injectionpressure (150 bar) combined with the retarded injection timing(18� BTDC) resulted in the opposite trend to that of previous one.

ANOVA for various responses shown in Table 4 indicated thesignificance of the interactions between injection timing and injec-tion pressure on the response parameters such as BSEC, BTE, HCand NOx emissions as the values of p were being less than 0.05.

3.4. Interactive effect of injection pressure and nozzle tip protrusion

The interactive effect of nozzle tip protrusion and the injectionpressure on BSEC, BTE, CO, HC, NOx and smoke opacity are shown

in Figs. 8–13 respectively. As the injection pressure increased from150 bar to 225 bar, there was a reduction in BSEC, CO and HC emis-sions and smoke opacity with increase in BTE and NOx emission.But beyond 225 bar of fuel injection pressure, an opposite trendprevailed in all the response parameters. Further, lesser BSEC, low-er CO and HC emissions and smoke opacity were seen and highervalue of BTE and maximum NOx noticed at 2.5 mm of nozzle tippenetration and at all injection pressures. Irrespective of the fuelinjection pressure at other nozzle tip protrusions like 1 mm and4 mm, the BSEC, CO, HC and smoke opacity values were higherwith low values of BTE and NOx emission.

With reference to the effect of injection pressure, the facts dis-cussed in the paragraph 2 of Section 3.3 could be attributed for theabove stated results.

As far as nozzle tip protrusion is concerned, the shorter protru-sion (1 mm) caused under penetration of the fuel spray and thelonger protrusion (4 mm) caused over penetration of the fuel spray.

Fig. 9. BTE variations against nozzle tip protrusion and injection pressure.

Fig. 10. CO variations against nozzle tip protrusion and injection pressure.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2671

These under and over penetration of the fuel spray caused non-uniform mixing, lesser air utilization and led to incomplete com-bustion. Hence there was higher BSEC, CO, HC emissions and smokeopacity with lesser BTE and lower NOx emission. But at 2.5 mm ofnozzle tip protrusion, there might be an uniform mixing of thespray with the air; faster evaporation of the spray and smooth reac-tions between the fuel spray and air which produced higher BTEand NOx emissions with lower BSEC, CO, HC emissions and smokeopacity at all injection pressures. Figs. 8–13 showed that improvedperformance (lower BSEC and higher BTE) with lower exhaustemissions (CO, HC and smoke opacity) could be had at an injectionpressure of 225 bar and at the nozzle tip protrusion of 2.5 mm.

Although the nozzle tip protrusion and the injection pressurehad strong influence on the performance and emission characteris-tics individually, their interactive effects were insignificant exceptfor BTE, as seen in the ANOVA Table 4, because the values of p weregreater than the reference value 0.05 for all the response parame-ters except BTE.

3.5. Interactive effect of injection timing and nozzle tip protrusion

Figs. 14–19 showed the interactive effect of injection timingand nozzle tip penetration over BSEC, BTE, CO, HC, NOx and smokeopacity. Figs. 14–19 revealed that the BSEC, CO, HC and smokeopacity were decreased while the BTE and NOx were increasedon the advancement of injection timing from 18� BTDC to 30� BTDCat all nozzle tip protrusions. Also the lower BSEC, CO, HC andsmoke opacity with higher BTE and NOx were seen at 2.5 mm ofnozzle protrusion at all injection timings; while high values ofBSEC, CO, HC and smoke opacity with low values of BTE and NOx

observed at other nozzle tip protrusions (1 mm and 4 mm) withall the injection timings.

The contributors for the above results had been discussed elab-orately in Sections 3.3 and 3.4. Table 4 showing the values of AN-OVA for various responses revealed that both the injection timingand nozzle tip protrusion had significant effects on the various re-sponses but their combined effects were insignificant over all the

Fig. 12. NOx variations against nozzle tip protrusion and injection pressure.

Fig. 11. HC variations against nozzle tip protrusion and injection pressure.

2672 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

responses except NOx emission since their values of p were greaterthan the reference value of 0.05 for most of the responses. Due toinsignificant interaction between the injection timing and nozzletip protrusion there was no change in the profile of the graphsfor all the response parameters.

3.6. Optimization

The detailed discussions on the influence of injection systemparameters over performance and emission characteristics re-vealed that the lowest injection pressure of 150 bar, retarded injec-tion timing of 18� BTDC and either 1 mm or 4 mm of nozzle tipprotrusions resulted in very low values of BTE and NOx emissionwith high values of BSEC, CO, HC and smoke opacity values. Aninjection pressure of 225 bar with an advanced injection timingof 30� BTDC and 2.5 mm of nozzle tip protrusion caused higherBTE and NOx with lower BSEC, CO, HC and smoke opacity. As therewas a tradeoff between BTE and NOx and other emissions, it is

essential to optimize the injection system parameters with the goalof minimizing NOx emission and maximizing the BTE in such a waythat no much compromise may take place on the BSEC and otheremissions. The criteria for the optimization such as the goal setfor each response, lower and upper limits used, weights used andimportance of the factors are presented in Table 6. In desirabilitybased approach, different best solutions were obtained. The solu-tion with high desirability is preferred. Maximum desirability of0.98 was obtained at the following injection system parameterslike 21� BTDC of injection timing, 225 bar of injection pressureand 2.5 mm of nozzle tip protrusion which could be consideredas the optimum parameters for the test engine having 7.5 kW asrated power at 1500 rpm.

3.7. Validation of the optimized results

In order to validate the optimized results, the experiments wereperformed thrice at the optimum injection system parameters. For

Fig. 14. BSEC variations against injection timing and nozzle tip protrusion.

Fig. 13. Smoke opacity variations against nozzle tip protrusion and injection pressure.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2673

the actual responses, the average of three measured results wascalculated. Table 7 summarizes the average of experimental values,the predicted values and the percentages of error. The validationresults indicated that the models developed were quite accurateas the percentages of error in prediction were in a good agreement.

4. Conclusion

The following were the conclusions arrived on performing theseveral tests in a twin cylinder diesel engine by varying the injec-tion pressure, injection timing and nozzle tip protrusion at differ-ent levels concurrently:

� The Design of Experiments was highly helpful to design theexperiment and the statistical analysis helped to identify thesignificant parameters which are most influencing on the per-

formance and emission characteristics. This experimentaldesign considerably reduced the time required by minimizingthe number of experiments to be performed and provided sta-tistically proven models for all the responses.� Advancing the injection timing from 18� BTDC to 30� BTDC

helped to reduce the CO, HC and smoke emissions with increasein NOx emission.� Increasing the injection pressure contributed for better BTE

with lesser BSEC at all injection timings with lower CO, HCand smoke emissions and higher NOx. However when too highwas the injection pressure, the results were negated.� At moderate nozzle tip protrusion, lesser BSEC with high BTE was

noticed but shorter and/or longer protrusion led to poor BSEC,BTE with higher CO and HC emissions. Also with moderate pro-trusion and advanced injection timing, high injection pressureyielded better performance than their individual contribution.

Fig. 15. BTE variations against injection timing and nozzle tip protrusion.

Fig. 16. CO variations against injection timing and nozzle tip protrusion.

Fig. 17. HC variations against injection timing and nozzle tip protrusion.

2674 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

Fig. 19. Smoke opacity variations against injection timing and nozzle tip protrusion.

Table 6Optimization criteria and the desirability of responses.

Parameter or response Limits Weights Importance Criterion Desirability

Lower Upper Lower Upper

Injection Timing (� BTDC) 18 30 1 1 5 In range 1Injection Pressure (bar) 150 250 1 1 5 In range 1Nozzle tip penetration (mm) 1 4 1 1 5 In range 1BSEC (MJ/kW h) 13.66 16.64 1 0.1 5 Minimize 0.98BTE (%) 21.82 26.59 0.1 1 5 Maximize 0.97CO (vol.%) 0.23 0.98 1 0.1 5 Minimize 0.96HC (ppm) 52 98 1 0.1 5 Minimize 0.97Smoke opacity (%) 59 92 1 0.1 5 Minimize 0.97NOx (ppm) 130 420 1 0.1 5 Minimize 0.96Combined – – – – 0.98

Table 7Validation test results.

Exp.no

Injection timing(� BTDC)

Injection pressure(bar)

Nozzle tip protrusion(mm)

BSEC (MJ/kW h)

BTE(%)

CO(vol.%)

HC(ppm)

Smoke opacity(%)

NOx

(ppm)

Actual 14.20 25.21 0.49 65 67 22101 21 225 2.5 Predicted 14.12 25.39 0.48 64 66.6 215

% Error 0.005 �0.71 2.08 1.56 0.6 2.79

Fig. 18. NOx variations against injection timing and nozzle tip protrusion.

M. Pandian et al. / Applied Energy 88 (2011) 2663–2676 2675

2676 M. Pandian et al. / Applied Energy 88 (2011) 2663–2676

� Desirability approach of the response surface methodology wasfound to be the simplest and efficient optimization technique. Ahigh desirability of 0.98 was obtained at the optimum injectionsystem parameters viz. 225 bar of injection pressure, 21� BTDCof injection timing with 2.5 mm of nozzle protrusion, wherethe values of the BSEC, BTE, CO, HC, smoke opacity and NOx

emission were found to be 14.12 MJ/kW h, 25.39%, 0.48%,64 ppm, 66.6% and 215 ppm respectively.

Acknowledgement

The authors are grateful to the Director, National Institute ofTechnology, Tiruchirappalli, for extending the laboratory facilitiesto carry out the research.

References

[1] Lapuerta M, Armas O, Rodriguez-Fernandez J. Effect of bio-diesel fuels ondiesel engine emissions. Prog Energy Combust Sci 2008;34:198–223.

[2] Agarwal AK. Biofuels (alcohols and biodiesel) applications as fuels for internalcombustion engines. Prog Energy Combust Sci 2007;33:233–71.

[3] Rao GLN, Sampath S, Rajagopal K. Experimental studies on the combustion andemission characteristics of a diesel engine fuelled with used cooking oil methylester and its diesel blends. Int J Appl Sci Eng Technol 2007;4(2):64–70.

[4] Azam MM, Waris A, Nahar NM. Prospects and potential of fatty acid methylesters of some non–traditional seed oils for use as bio-diesel in India. BiomassBioenergy 2005;29:293–302.

[5] Subramanian KA, Singal SK, Saxena M, Singhal S. Utilization of liquid biofuelsin automotive diesel engines: an Indian perspective. Biomass Bioenergy2005;29:65–72.

[6] Devan PK, Mahalakshmi NV. A study on the performance, emission andcombustion characteristics of a compression ignition engine using of methylester of paradise oil–eucalyptus oil blends. Appl Energy 2009;86:675–80.

[7] Alla GHA, Soliman HA, Badr OA, Abd Rabbo MF. Effect of injection timing on theperformance of a dual fuel engine. Energy Convers Manage 2002;43:269–77.

[8] Bari S, Yu CW, Lim TH. Effect of fuel injection timing with waste cooking oil as afuel in a direction injection diesel engine. Proc Inst Mech Eng Part D: JAutomob Eng 2004;218:93–104.

[9] Buyukkakaya E, Cerit M. Experimental study of NOx emissions and injectiontiming of a low heat rejection diesel engine. Int J Therm Sci 2008;47:1096–106.

[10] Ma Z, Huang Z, Li C, Wang X, Miao H. Effects of fuel injection timing oncombustion and emission characteristics of a diesel engine fuelled withdiesel–propane blends. Energy Fuels 2007;21(3):1504–10.

[11] Mani M, Nagarajan G. Influence of Injection timing on performance, emissionand combustion characteristics of a DI diesel engine running on waste plasticoil’. Energy 2009;34(10):1617–23.

[12] Nwafor OMI, Rice G, Ogbonna AI. Effect of advanced injection timing on theperformance of rapeseed oil in diesel engines. Renewable Energy 2000;21:433–44.

[13] Pandian M, Sivapirakasam SP, Udayakumar M. Influence of injection timing onperformance and emission characteristics of naturally aspirated twin cylinderCIDI engine using bio-diesel blend as fuel. Int J Recent Trends Eng2009;1(5):113–7.

[14] Sayin C, Uslu CanakciM. Influence of injection timing on the exhaust emissionsof a dual fuel CI engine. Renewable Energy 2008;33(6):1314–23.

[15] Sayin C, Canakci M. Effects of injection timing on the engine performance andexhaust emissions of a dual fuel diesel engine. Energy Convers Manage2009;50(1):203–13.

[16] Bakar RA, Semin IsmailAR. Fuel injection pressure effect on performance ofdirect injection diesel engine based on experiment. Am J Appl Sci2008;5(3):197–202.

[17] Benajes J, Molina S, Garcia JM, Novilla R. Influence of boost pressure andinjection pressure on combustion process and exhaust emissions in a HDdiesel engine. SAE International 2004-01-1842; 2004. p. 834–45.

[18] Icingur Y, Altiparmak D. Effect of fuel cetane number and injection pressure ona DI diesel engine performance and emissions. Energy Convers Manage2003;44:389–97.

[19] Puhan SJR, Balasubramanian K, Nagarajan G. Effect of injection pressure onperformance, emission and combustion characteristics of high linoleniclinseed oil methyl ester in a DI diesel engine. Renewable Energy 2009;34(5):1227–33.

[20] Rente T, Gjirja S, Denbratt I. Experimental investigation of the effect of needleopening pressure (NOP) on combustion and emissions formation in a heavyduty DI diesel engine/ SAE International 2004-01-2921; 2004. p. 1692–711.

[21] Venkanna BK, Wadawadagi SB, Reddy CV. Effect of injection pressure onperformance, emission and combustion characteristics of direct injectiondiesel engine running on blends of pongamia pinnatta Linn oil (honge oil) anddiesel fuel. Agricultural Engineering International: The CIGR E-Journal; 2009.p. 11. Manuscript No. 1316.

[22] Roy MM. Effect of fuel injection timing and injection pressure on combustionand odorous emissions in DI diesel engines. ASME J Energy Resources Technol2009;131:032201-1–8.

[23] Lyu MS, Shin BS. Study of nozzle characteristics on the performance of a smallbore high speed direct injection diesel engine. Int J Engine Res2002;3(2):69–79.

[24] Karra PK, Kong SC. Experimental study on effects of nozzle hole geometry onachieving low diesel engine emissions. ASME J Eng Gas Turbines Power2010;132:022802-1–10.

[25] Reddy JN, Ramesh A. Parametric studies for improving the performance of aJatropha oil-fuelled compression ignition engine. Renewable Energy2006;31:1994–2016.

[26] Heywood JB. Internal combustion engine fundamentals. New York: McGrawHill; 1988.

[27] Win Z, Gakkar RP, Jain SC, Bhattacharya M. Investigation of diesel engineoperating and injection system parameters for low noise, emissions, and fuelconsumption using Taguchi methods. Proc Inst Mech Eng Part D: J AutomobEng 2005;219:1237–51.

[28] Ganapathy T, Murugesan K, Gakkar RP. Performance optimization of Jatrophabiodiesel engine model using Taguchi approach. Appl Energy 2009;86:2476–86.

[29] Anand G, Karthikeyan B. An investigation and engine parameters optimizationof a spark ignition engine with gaseous fuels. In: 4th Dessau gas engineconference, WTZ RoBlau gGmbH, Germany, June 2–3, 2005.

[30] Lee T, Reitz RD. Response surface method optimization of a high speed directinjection diesel engine equipped with a common rail injection system. ASME JEng Gas Turbines Power 2003;125:541–6.

[31] Satake K, Monaka T, Yamada S, Endo H, Yamagisawa M, Abe T. The rapiddevelopment of diesel engine using an optimization of the fuel injectioncontrol, Mitsubishi Heavy Industries Limited. Tech Rev 2008;45:6–10.

[32] Win Z, Gakkar RP, Jain SC, Bhattacharya M. Parameter optimization of a dieselengine to reduce noise, fuel consumption, and exhaust emissions usingresponse surface methodology. Proc Inst Mech Eng Part D: J Automob Eng2005;219:1181–92.

[33] Srivastava PK, Verma M. Methyl ester of karanja oil as alternative renewablesource energy. Fuel 2008;87:1673–7.

[34] Agarwal AK, Rajamanoharan K. Experimental Investigations of performanceand emissions of karanja oil and its blends in a single cylinder agriculturaldiesel engine. Appl Energy 2009;86:106–12.

[35] Raheman H, Phadatare AG. Diesel Engine emissions and performance fromblends of karanja methyl ester and diesel. Biomass Bioenergy 2004;27:393–7.

[36] Sureshkumar K, Velraj R, Ganesan R. Performance and Emission characteristicsof a CI engine fuelled with Pongamia pinnata methyl ester and its blends withdiesel. Renewable Energy 2008;33:2294–302.

[37] Lundstedt Torbjorn, Seifert E, Abramo L, Thelin B, Nystrom A, Pettersen J, et al.Experimental design and optimization. Chemom Intell Lab Syst 1998;42:3–40.