Ganji, P. R., et al.: Computational Optimization of Biodiesel Combustion … THERMAL SCIENCE, Year 2017, Vol. 21, No. 1B, pp. 465-473 465

COMPUTATIONAL OPTIMIZATION OF BIODIESEL COMBUSTION USING RESPONSE SURFACE METHODOLOGY

by

Prabhakara Rao GANJI*, V. Rajesh Khana RAJU, S. Srinivasa RAO

Department of Mechanical Engineering, NIT Warangal, Telangana, India Original scientific paper

DOI: 10.2298/TSCI161229031G

The present work focuses on optimization of biodiesel combustion phenomena through parametric approach using response surface methodology. Physical properties of biodiesel play a vital role for accurate simulations of the fuel spray, atomization, combustion, and emission formation processes. Typically methyl based biodiesel consists of five main types of esters: methyl palmitate, methyl oleate, methyl stearate, methyl linoleate, and methyl linolenate in its composition. Based on the amount of methyl esters present the properties of pongamia bio-diesel and its blends were estimated. CONVERGETM computational fluid dynam-ics software was used to simulate the fuel spray, turbulence and combustion phe-nomena. The simulation responses such as indicated specific fuel consumption, NOx, and soot were analyzed using design of experiments. Regression equations were developed for each of these responses. The optimum parameters were found out to be compression ratio – 16.75, start of injection – 21.9° before top dead center, and exhaust gas re-circulation – 10.94%. Results have been compared with baseline case. Key words: CONVERGETM, optimization, response surface methodology,

pongamia biodiesel, variable compression ratio engine

Introduction

Biodiesels are attractive sources of energy to replace fossil fuels used in IC engines. This is because of their desirable attributes such as being sustainable, biodegradable, and car-bon neutral. Biodiesels can also be used in existing CI engines with small or no modifications. Many researchers found that the use of biodiesels can reduce emissions such as unburned hy-drocarbons (UHC), soot, and CO, but a slight increase in NOx was observed. Typically bio-diesels can be assumed to be methyl or ethyl esters but methyl esters were widely used be-cause they show better performance and emission characteristics [1-5].

Simulation of IC engines has gained popularity in recent days due to availability of sophisticated computational facilities and CFD codes for the accurate prediction of spray and combustion phenomena. A suitable 3-D computational model with proper reaction mecha-nism and spray model can accurately predict the combustion characteristics. Physical and chemical properties of biodiesel such as normal boiling point, critical properties, vapor pres-sure, latent heat of vaporization, density, viscosity, thermal conductivity, and surface tension have great impact on the spray and combustion modelling whose accuracy also redefines the –––––––––––––– * Corresponding author, e-mail: ganjiprabhakar@gmail.com, ganjiprabhakar@nitw.ac.in

Ganji, P. R., et al.: Computational Optimization of Biodiesel Combustion … 466 THERMAL SCIENCE, Year 2017, Vol. 21, No. 1B, pp. 465-473

accuracy of numerical predictions. An et al. [6] presented correlations to predict the properties for 5 methyl esters namely methyl palmitate, methyl oleate, methyl stearate, methyl linoleate, and methyl linolenate.

Brakora et al. [7] described a reduced reaction mechanism for biodiesel combustion which consists of 41 species and 150 reactions. The mechanism predicted well as that of the comprehensive mechanism which consists of 264 species and 1219 reactions. They have also developed reaction mechanism for biodiesel blends which consists of 53 species and 156 reac-tions. The reduced mechanism gave the advantage of lesser computational time with reasona-ble accuracy. Brakora and Reitz [8] performed a reduction in the chemical mechanism which suits to the multi fuel component species like biodiesel and its blends; the reaction mechanism contains 69 species and 192 reactions.

A review of pongamia biodiesel combustion from the open literature by Murugesan et al. [9] reveals the following salient features. Pure pongamia biodiesel can be directly used in diesel engines without any alterations with slightly inferior performance than that of diesel. Addition of little amounts of biodiesel to mineral diesel is a worthy strategy for increasing al-ternative fuel consumption and B20 is the best alternative fuel. Brake thermal efficiency of biodiesel (B20) is slightly increased and brake-specific energy consumption is reduced slight-ly when compared with B100. But in comparison with mineral diesel, the BSFC of B20 could not improve at baseline configuration whereas emission characteristics were reduced and at par with diesel in most of the literature available.

Qi et al. [10] analyzed the effect start of injection timing and exhaust gas re-circu-lation (EGR) rate on the combustion and emissions of a direct injection CI engine using split injection strategy with neat soybean biodiesel. The results show that the increase of EGR rate increases the brake specific fuel combustion (BSFC) and soot emission whereas NOx emis-sions were decreased. Results also reveal that BSFC was slightly increased, NOx emissions were evidently decreased, and soot emissions remained almost same with the retarded main injection timing.

Gopal and Kanupparaj [11] conducted experiments on CI engine with blends of pongamia biodiesel and diesel without any modification of existing engine design. The PME 20 (pongamia 20% and diesel 80%) allows for better performance and lower emissions over the various blends and it is also on a par with petro diesel. Wilson [12] optimized diesel en-gine control parameters such as clearance volume, fuel injection pressure (FIP), nozzle-hole diameter, start of injection (SOI), and load using Taguchi design of experiments (DOE) in or-der to get the best performance in terms of NOx and BSFC. They also derived relations be-tween operating parameters and responses and identified that the results of experimental data had a good agreement with the predicted results. It was also analyzed that among all the pa-rameters FIP has a great influence on NOx. They concluded that the DOE is an effective and efficient way to develop a robust design in order to get optimum performance and emissions.

The complexity of the in-cylinder combustion phenomena can be modeled using CONVERGETM [13] CFD software, in which turbulence modeling, combustion modeling, and spray modeling are used simultaneously to predict the performance and emission charac-teristics precisely. Response surface methodology (RSM) is also a proven technique which reduces the number of experiments to be conducted and saves time and human effort. It can also predict the model with reasonable accuracy [14].

There is plenty of literature available on each of these individual effects on perfor-mance and emissions. But the interaction effects of these parameters were not discussed much on the performance and emissions. The present work focuses on CFD simulations of ponga-

Ganji, P. R., et al.: Computational Optimization of Biodiesel Combustion … THERMAL SCIENCE, Year 2017, Vol. 21, No. 1B, pp. 465-473 467

mia biodiesel combustion in a variable compression ratio (VCR) engine and to an-alyze the effect of parameters such as CR, SOI, and EGR on performance and emis-sions. The present study also uses output responses from the CFD study and ana-lyzed using a systematic approach RSM [15]. These effects are studied in the pre-sent simulation study and also aimed to op-timize the operating parameters in order to obtain minimum emissions and improved performance.

The CFD simulation

The engine sector model shown in fig. 1, with the dimensions given in tab. 1, is considered for the present study. The fuel properties are given in tab. 2. The simula-tions are carried out using CONVERGETM CFD software. The models used in CFD package for different physical phenomena are shown in tab. 3. The numerical simula-tion is validated with experimental data and also compared with literature [11] with PB 20 (pongamia methyl ester 20% and 80% diesel) as a fuel. The properties of the selected biodiesel (PB 20) are estimated using MATLAB code based on the correla-tions developed by An et al. [6]. These properties are given as input to numerical simulations.

Validation of model

Comparison of pressure with respect to crank angle for baseline configuration of experimental and simulation model are shown in fig. 2. It can be observed that simulation results are in good agreement with the experimental. The difference in peak pressure for the experimental and simulation is observed to be around 7%. The emissions are also compared for the experi-mental and simulation models in tab. 4. The difference between experimental and simulation is found to be 2% and 8%, respectively, for NOx and soot emissions.

Results and discussion

Three parameters, CR, SOI, and EGR were chosen to optimize the engine configura-tion with a view to attaining better performance and emissions. A total of 17 experiments

Figure 1. Computational domain of VCR engine sector model; (a) near top dead center, (b) near bottom dead center

Table 1. Specifications of the VCR engine

Make Kirloskar

Bore 87.5 mm

Stroke 110 mm

Connecting rod length 234 mm

Compression ratio 17.5:1

RPM 1500

Piston bowl shape Hemispherical

Piston bowl diameter 52 mm

Cylinder head curvature Flat

Injection timing 23° bTDC

Load 3.5 kW (100%)

Fuel injection opening pressure 200 bar

Table 2. Properties of mineral diesel and PB20

Properties Diesel PB20

Density [kgm–3] 830.4 836.4

Calorific value [MJkg–1] 45.2 43.3

Viscosity [cSt] 3.61 4.1

Flash point [C] 77.6 95

Cetane number 48 56

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were conducted and their responses tabulated in tab. 5. The CR is varied from 14 to 18, SOI is varied from 17° bTDC to 26° bTDC and EGR percent-age is varied from 0 to 20 in three lev-els. The RSM was used in the current work for analyzing the output respons-es to obtain the regression equations.

Evaluation of the regression model

The regression models are ana-lyzed based on analysis of variance (ANOVA) which gives the p value for different response parameters such as ISFC, NOx, and soot emissions. The p values for output responses are given in tab. 6. These p values tell us whether the particular parameter is significantly affecting the output responses or not. The reference limit for p value was chosen as 0.05. The models are signifi-cant as their p values are less than 0.05 [25, 26].

It can be observed from tab. 6 that the individual parameters (CR, SOI, and EGR) have significant effect on all the responses since their p value is less than 0.05. Interaction effects of (CR·SOI) and (SOI·EGR) also have significant effect on all the responses. The CR·EGR has no significant effect on the output responses since their p value is greater than 0.05. So the inter-action effects that are significant are only discussed in the present section. The regression statistics goodness of fit, R2, adjusted R2, and predicted R2 for

all the responses is shown in tab. 7. For a model to fit the data well, the difference between predicted and adjusted R2 should be less than 0.2.

The quadratic models have been developed for ISFC, NOx, and soot in terms of in-put parameters in eqs. (1), (2), and (3), respectively.

2 2

2

ISFC 312.5 5.72 CR 4.33 SOI 1.720 EGR 0.114 0.0236 SOI

0.0318 EGR 0.2650 CR SOI 0.0366 CR EGR 0.0031 SOI EGR

CR= + ⋅ − ⋅ + ⋅ + ⋅ − ⋅ −

− ⋅ − ⋅ ⋅ − ⋅ ⋅ − ⋅ ⋅ (1)

Table 3. Key sub models used in the CFD analysis

Turbulence model RNG k-ε [16]

Injection drop distribution χ2 distribution [13]

Drop drag Dynamic drag model [17]

Droplet collision model NTC model [18]

Collision outcomes model Post collision outcomes [19]

Drop turbulent dispersion O’Rourke model [20]

Drop/wall interaction Rebound/slide model [21]

Evaporation model Chiang drop correlations [22]

Spray breakup KH-RT [23]

Combustion SAGE [24]

Figure 2. Validation of pressure vs. crank angle for VCR engine with PB20 as fuel

Table 4. Comparison of emissions for VCR engine with PB20 as fuel

Experimental Simulation

NOx [gkg–1 of fuel] 30.15 29.45

Soot [gkg–1 of fuel] 0.678 0.625

Ganji, P. R., et al.: Computational Optimization of Biodiesel Combustion … THERMAL SCIENCE, Year 2017, Vol. 21, No. 1B, pp. 465-473 469

x2 2 2

NO 410.68 51.27 CR 2.94 SOI 0.417 EGR 0.198 CR SOI

0.033 CR SOI 0.002 SOI EGR 1.72 CR 0.0047 SOI 0.0213 EGR

= − ⋅ − ⋅ − ⋅ + ⋅ ⋅ −

− ⋅ ⋅ − ⋅ ⋅ + ⋅ + ⋅ + ⋅ (2)

2

2 2

Soot 35.34 3.61 CR 0.174 SOI 0.044 EGR 0.0163 CR SOI

0.0011 CR EGR 0.0038 SOI EGR 0.11 CR

0.0047 SOI 0.00154 EGR

= − ⋅ − ⋅ − ⋅ − ⋅ ⋅ −

− ⋅ ⋅ − ⋅ ⋅ + ⋅ +

+ ⋅ + ⋅ (3)

Table 5. Experimental design and their responses

Table 6. The p values for model terms of ANOVA analysis

S. No. Parameter Responses

CR SOI EGR ISFC [gkW–1h–1]

NOx [gkg–1 of fuel]

Soot [gkg–1 of fuel]

1 16 26 0 235.00 40.01 0.81

2 18 17 10 239.48 48.72 0.98

3 18 21.5 20 231.23 51.12 0.64

4 18 26 10 225.12 56.52 0.42

5 16 21.5 10 248.12 32.52 0.95

6 14 17 10 265.27 22.84 2.45

7 14 26 10 260.45 23.52 2.48

8 14 21.5 0 257.15 27.45 2.12

9 16 21.5 10 247.15 32.52 0.95

10 16 21.5 10 248.12 32.52 0.95

11 14 21.5 20 267.16 20.02 2.53

12 16 21.5 10 247.18 32.52 0.95

13 16 21.5 10 247.68 32.52 0.95

14 16 17 20 253.15 26.12 1.54

15 18 21.5 0 224.15 61.15 0.32

16 16 26 20 243.45 30.0 1.25

17 16 17 0 244.15 36.45 1.03

ISFC NOx Soot

Regression model <0.001 <0.001 <0.001

SOI <0.001 <0.001 <0.001

CR <0.001 <0.001 <0.001

EGR <0.001 <0.0001 <0.0001

CR·SOI 0.019 0.005 <0.0001

CR·EGR 0.381 0.187 0.255

SOI·EGR 0.001 0.002 0.02

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Table 7. The RSM model evaluation

Interaction effect of SOI and CR

It can be observed from fig. 3 that at lower CR, the interaction of SOI and CR is sig-nificant, but not fruitful in reducing ISFC. But at higher CR, the interaction is beneficial in re-ducing ISFC. Advancing the injection timing to 26° bTDC increased NOx emissions and re-duced the soot emissions. This is due to the fact that in-cylinder temperature and pressures were low at the time of fuel injection, which increases the ignition delay. At the same time increase in compression ratio resulted in increased in-cylinder temperature and thus NOx emissions were increased, whereas soot emissions are decreased due to efficient combustion. From figs. 4 and 5 it can be interpreted that the combination of high compression ratios and early fuel injection timings resulted high NOx emissions but less soot emissions. Soot emissions were high and NOx emissions were low at retarded injection timings and low compression ratios.

Figure 3. The ISFC variations against SOI and CR

Figure 4. The NOx variations against SOI and CR

Effects of SOI and EGR

Figure 6 shows the interactive effects of injection timing and exhaust gas recircu-lation on ISFC. As the start of injection is advanced from 17° to 26° bTDC, ISFC is decreased. Also, as the EGR is increased from 0% to 25%, ISFC increased. But this phenomenon is dominant at lower EGR. This is due to fact that the EGR reduces the oxygen concentration while also increasing the specific heat of the charge and thus drops the in cylinder temperature which re-sults in inefficient combustion.

Model ISFC NOx Soot

R2 0.9923 0.9976 0.9989

Adjusted R2 0.9823 0.9945 0.9975

Predicted R2 0.9267 0.9617 0.9823

Figure 5. Soot variations against SOI and CR

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From the results shown in figs. 7 and 8, it can be interpreted that the combination of low EGR and early fuel injection tim-ings resulted in high NOx emissions but very little soot emissions. Soot emissions were high and NOx emissions were low at retarded SOI and higher EGR.

Optimization

The previous discussions on the ef-fects of SOI, CR, and EGR on the perfor-mance and emission characteristics re-vealed that the smallest CR of 14 and re-tarded SOI of 17° bTDC resulted in lower

Figure 7. The NOx variations against SOI and EGR

Figure 8. Soot variations against SOI and EGR

NOx, higher ISFC, and higher soot emission. On the other hand, higher CR and advanced SOI resulted in higher NOx, reduced ISFC and decrease in soot values. There is an abso-lute trade-off observed be-tween NOx and soot. So parameters must be optimized in order to get lower emissions without any cost of its performance. In desirability based approach, different solutions were obtained. The solution with highest desirability is preferred. The lowest from the experimental data (in case of emissions/minimization objective) is taken as desirability of 1. The value of desirabil-ity tells us how close the optimum is to the lowest value. Desirability of 0.97 was obtained at the following parameters CR – 16.75, SOI – 21.9° bTDC, and EGR – 10.94%. Table 8 shows the comparison of optimized and baseline cases and it is observed that simultaneous reduction in soot and NOx is attained with some improvement in ISFC. Optimum configuration reduced ISFC, NOx and soot by 2%, 31%, and 34%, respectively.

Figure 6. The ISFC variations against SOI and EGR

Table 8. Comparison of optimized case and base case

ISFC [gkW–1h–1]

NOx [gkg–1 of fuel]

Soot [gkg–1 of fuel]

Base line 235.87 29.45 0.5625

Optimized case 231.17 20.15 0.370

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Conclusions

The following conclusions were drawn based on the numerical simulations using CONVERGETM software. The DOE RSM methodology was employed for designing the min-imum number of simulations and their levels. • The DOE is very useful in analyzing the simulation results in a statistical approach and

eventually help us to determine the most influential parameters that affect the perfor-mance and emissions.

• The NOx emissions were evidently decreased and soot emissions increased with increas-ing rate of EGR.

• Increase in SOI increases NOx and reduces soot and ISFC. • Decrease in CR increases soot emissions and ISFC whereas NOx is reduced. • The interaction effects of CR and SOI were dominant for the responses of NOx and soot. • The interaction effects EGR and SOI were dominant for the responses ISFC and NOx. • The interaction effects CR and EGR were very less significant as compared to other two

combinations. • The optimum set of parameters was found out to be CR – 16.75, SOI – 21.9° bTDC, and

EGR – 10.94%.

Acknowledgment

This work was supported by Center of Excellence (COE), Center for sustainable en-ergy studies of Technical Education Quality Improvement Programme (TEQIP-II), India.

Nomenclature aTDC – after top dead center bTDC – before top dead center BSFC – brake specific fuel consumption CFD – computational fluid dynamics CI – compression ignition CR – compression ratio DOE – design of experiments EGR – exhaust gas recirculation

FIP – fuel injection pressure IC – internal combustion ISFC – indicated specific fuel consumption RPM – rotations per minute RSM – response surface methodology SOI – start of injection VCR – variable compression ratio

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Paper submitted: December 29, 2016 © 2017 Society of Thermal Engineers of Serbia. Paper revised: February 12, 2017 Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. Paper accepted: February 14, 2017 This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.