SETC2015

JSAE 20159710 / SAE 2015-32-9710

Engine Performance, Emission and Combustion in Common Rail

Turbocharged Diesel Engine from Jatropha Curcas Using Artificial

Neural Network

A.S.Silitongaa,b

*, H.H.Masjukia, Hwai Chyuan Ong

a**,

F. Kusumoa,c

, H.G. Howa, Y.H. Teoh

a,d, T.M.I. Mahlia

e

aDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia

bDepartment of Mechanical Engineering, Medan State Polytechnic,20155 Medan, Indonesia

cCenter for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Universiti Kebangsaan

Malaysia, Bangi, Selangor, 43600, Malaysia dSchool of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia

eDepartment of Mechanical Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia

Copyright © 2015 SAE Japan and Copyright © 2015 SAE International

ABSTRACT

This paper investigates the performance, emission and

combustion of a four cylinder common-rail turbocharged

diesel engine using jatropha curcas biodiesel blends (JCB).

The test was performed with various ratios of jatropha

curcas methyl ester (JCME) in the blends (JCB10, JCB20,

JCB30, and JCB50). An artificial neural networks (ANN)

model based on standard back-propagation algorithm was

used to predict combustion, performance and emissions

characteristics of the engine using MATLAB. To acquire

data for training and testing of the proposed ANN, the

different engine speeds (15003500 rpm) was selected as

the input parameter, whereas combustion, performance and

emissions were chosen as the output parameters for ANN

modeling of a common-rail turbocharged diesel engine. The

performance, emissions and combustion of the ANN were

validated by comparing the prediction dataset with the

experimental results. The results show that the correlation

coefficient was successfully controlled within the range

0.9798–0.9999 for the ANN model and test data. The value

of MAPE (Mean Absolute Percentage Error) was within the

range 1.2373–6.4217 and the Root Mean Square (RSME)

value was below 0.05 by the model, which is acceptable.

This study shows that modeling techniques as an approach

in alternative energy can give improvement advantage of

reliability in the prediction of performance and emission of

internal combustion engines.

INTRODUCTION

Researchers nowadays are focusing on the efforts seeking

renewable energy resources due to limited and increasing

prices of fossil fuels, as well as the harm impacts on the

environment including toxic emissions, GHG and global

warming. Alternative fuel is gaining its popularity for its

ability to solve the issues since it discharges low levels of

pollutants in both gaseous and particulates. As one of

alternative fuels, biodiesel is a renewable and eco-friendly

biofuel. It is regarded as biodegradable with higher cetane

number, no aromatics, very low sulfur, and more than 10%

oxygen compared to diesel fuel [1, 2]. Moreover, the

biodiesel emission characteristics such as carbon monoxide

(CO), hydrocarbons (HC) and particulate matter (PM) are

lesser in the exhaust gas, although NOx emission of

biodiesel is higher due to combustion characteristic [3, 4].

The used of blending biodiesel with diesel fuels have been

widely implemented for internal combustion engine. The

blends are preferred to be used because their availability,

competitive prices and comply with ASTM D7467 standard

[5].

In the practice of biodiesel production, the exploitation of

jatropha curcas as the feedstock for biodiesel is widely

practiced due to its beneficial effects in reducing the fossil

diesel consumption and exhaust emissions [6]. Sahoo et al.

[7] tested jatropha biodiesel-diesel blends (JB0, JB20, JB50

and JB100) in a water-cooled three cylinder tractor engine.

Their results showed that the use of 20% of biodiesel in the

blend improves the brake power for 0.09–2.64%, and

improvement of 0.05–3.8% for 50% biodiesel blend

compared to diesel fuel alone. Besides, the authors also

claimed that the use of the blends also reduces hydrocarbon

(HC) and particulate matter (PM). In another study, El-

Kasaby et al. [8] found that 10% Jatropha biodiesel (JB10)

has the highest brake thermal efficiency compared to the rest

of the biodiesel blended fuels (JB0, JB20, JB30 and JB50),

while JB30 and JB50 produced the lowest CO

concentration. Similarly, Chauhan et al. [9] studied that

preheated Jatropha biodiesel-diesel blend fuel decreases

CO, HC and smoke opacity. Ganapathy et al. [10] tested the

combustion, performance and emission on a single-cylinder,

Greaves Cotton model GL 400 II A, diesel engine. In their

study, Jatropha biodiesel reduced CO, HC and smoke with

heat release rate at 340 oCA, and NOx emission was lower at

350 oCA. Other researchers also had performed experiment

engine performance and emissions on diesel engine. It was

also found that by blending biodiesel with regular diesel fuel

can help in reducing the exhaust emissions without

significantly sacrificing engine’s power and economy [11,

12]. These are the reasons why the use of biodiesel instead

of diesel attracted numerous researches. Another advantage

SETC2015

are including such as lower fuel costs and no engine

modification required.

ANN is computational elements (neurons) working to solve

specific problems and functions. ANN is formed by

interconnecting a group of relatively simple processing

elements called neurons to predict multiple output variables

[13]. The application of ANN for modeling performance,

emission and combustion parameters is the ability to model

a multivariate problem without making complex

dependency assumptions among prediction and

experimental data. Moreover, it is trained to predict

performance, emission and combustion characteristics of

internal combustion engine [14, 15]. ANN can predict those

variables by simulating the results from the training based

on experimental data and validation by the independent data

[15, 16]. Besides, the aim of ANN is to determine the

characterization of engine performance for different

operating conditions and fuel to reduce time and cost

consumed. Some researchers had reported the proficiency of

ANN to predict performance and emission in internal

combustion engine characteristics in their researches.

Ghobadian et al. [17] used ANN to predict brake power,

torque, specific fuel consumption and emissions exhaust of

diesel engine using waste cooking oil. The experimental

results revealed that waste cooking methyl ester-diesel

blends provide better engine performance and improved

emission characteristics. Shivakumar et al. [13] studied that

the performance and emissions can be affected by injection

timing on of a single cylinder with variable compression

ratio. Their result showed the mean relative error (MRE)

between ANN predicted values and experiment data is 8%

which is reliable and acceptable. Maheswari et al. [18]

investigated the performance of an CI engine fueled with a

karanja biodiesel blends. They found that the karanja

biodiesel can improve efficiency engine and reduce HC

emissions. The prediction using ANN was given a low root

mean square error (RSME) that is about 0.950.99 for

performance and emission characteristics.

Since the use of biodiesel in diesel engine has different

physical and chemical properties compared to the fossil

diesel fuel, this is will affect the performance and emissions

of the engine. However the experimental studies and

measurements found in the literatures conclude that the use

of biodiesel-diesel blends are capable as the alternative fuel

leads to the improvement of engine power. This paper is

aimed to compare combustion, engine performance and

emissions between the experimental data and the prediction

using ANN modeling.

METHODOLOGY

BIODIESEL PRODUCTION

The biodiesel used in this research was produced from crude

Jatropha curcas oil (CJCO) by using a laboratory-scale

reactor. A double jacketed reactor equipped with a reflux

condenser with the refrigerator cooling bath and water bath

was used to produce Jatropha curcas biodiesel. The

production used two-stage esterification transesterification

process. The water bath was heated and maintained at 55oC.

A constant stirring speed of 1000 rpm was maintained

throughout the reactions process. The molar ratio of

methanol to oil ratio was 9:1 and the catalyst concentration

of H2SO4 and KOH were 1.0 vol. % and 1.0 wt. % of oil

respectively. All of the processes were carried out for

duration of 2.5 h. At the end of transesterification process,

glycerol and methyl ester were transferred to a separation

funnel and allowed to settle down for 4 h, then methyl ester

was washed with distilled water for three times and dried

using anhydrous sodium sulfate (Na2SO4). Finally, the

methyl ester was filtered and evaporated using rotary

evaporator to obtain the pure biodiesel.

FUEL CHARACTERIZATION

Biodiesel quality was tested according to following the

biodiesel standards ASTM D6751 and EN 14214. ASTM

D7467 standard was used to characterize the biodiesel-diesel

blends. The fuel properties of CJCO, JCME and JCB10,

JCB20, JCB30, and JCB50 were examined for their

viscosity, density, acid value, calorific value, flash point,

cloud and pour point, oxidation stability, water content, etc.

Table 1 shows the summary of the equipment used to

analyze the properties of biodiesel in this study. Each

measurement was repeated for three times and the mean

value was calculated for each sample. After all, the JCME

properties were obtained and the results are compared to

other researchers [6, 9, 11]. The properties and composition

CJCO, the properties JCME and JCB are summarized in

Tables 25. It is observed from Table 2 that properties of

CJCO are quite similar to the results from other researchers

and Table 3 shows fatty acids content of CJCO. The CJCO

has a greater part of unsaturated acid which are 44.5% and

35.4% for oleic acid and linoleic acid respectively. The

unsaturation of oil depends upon the origin of the vegetable

oil and is described by the iodine value [15]. The iodine

value of CJCO obtained in this study was (Table 2) 84.6, as

the value is within the accepted range of 80–130 suggested

by ASTM D6751 [19]. The unsaturation fatty acid chains of

triglycerides is the main parameter of oxidation and

polymerization when vegetable oils are used to produce

biodiesel [6]. The properties of crude oil depend upon the

feedstock, specifically on its fatty acid content [20]. Finally,

the study of biodiesel quality in terms of raw material was

performed based on the reaction conditions and the crude oil

was then tested to achieve better quality of biodiesel.

Table 1: Summary of the equipment

Property Equipment

Kinematic

viscosity StabingerTM viscometer SVM 3000 (Anton Paar)

Density DM40 LiquiPhysics™ density meter (Mettler

Toledo)

Flash point NPM 440 Pensky-martens flash point tester

(Normalab)

Cloud and pour point

NTE 450 Cloud and pour point tester (Normalab)

Calorific value 6100EF Semi auto bomb calorimeter (Perr)

Acid number and Automation titration rondo 20 (Mettler Toledo)

SETC2015

iodine value

Conradsons carbon residue

(100 sample)

NMC 440 micro-Carbone conradson residue

tester (Normalab)

Copper strip

corrosion (3 h at

50◦C)

Seta copper corrosion bath 11300-0 (Stanhope-Seta)

Sulphur content (S 15 grade and S500

grade)

Multi EA 5000 (Analytical jena)

Oxidation stability

at 110oC 873 Rancimat (Metrohm)

Methanol content Agilent 7890 gas chromatograph (Agilent)

Water content 831 KF coulometer (Metrohm)

Carbon CE 440 CHN Elemental Analyzer (EIA)

Table 2: Properties of CJCO

Properties CJCO CJCO [7]

CJCO [13]

Kinematic viscosity at

40oC (mm

2/s)

32.83 52 38

Density at 15oC (kg/m

3) 912.3 921 921.0

Calorific value (MJ/kg) 38.295 39.584

Flash point (oC) 172.5 220 235

Acid value (mg KOH/g) 25.41 30 2.32

Iodine value (I2/100g) 84.6

Table 3: The fatty acid composition of CJCO

Fatty acid Chemical

structure (wt.%)

Lauric (dodecanoic) C12:0 0.1

Myristic (tetradecanoic) C14:0 0.1

Palmitic (hexadecanoic) C16:0 13.0

Palmitoleic (hexadecenoic) C16:1 0.7

Stearic (octadecanoic) C18:0 5.8

Oleic (octadecenoic) C18:1 44.5

Linoleic (octadecadienoic) C18:2 35.4

Linolenic (octadecatrienoic) C18:3 0.3

Arachidic (eicosanoic) C20:0 0.1

Table 4: The properties of JCME

Properties Unit JCME JCME

[12] JCME

[13]

Kinematic

viscosity mm

2/s

4.57 4.34 4.12

Density kg/m3

872.5 881.0

Flash point °C 158.5 130 162

Cloud point °C 1.0 4

Pour point °C 2.0 8

Calorific

value MJ/kg

40.204 39.594

Acid value mg

KOH/g

0.391 0.09

Iodine value g

I2/100g

109.06

CCR m/m 0.03

Sulphur

content ppm

8.016 10 8

Cooper

corrosion -

1a

Oxidation

stability h

10.12

Water

content %vol.

0.0021

Cetane

number -

58.2 52.7 48.13

Methanol

content % m/m

0.061

Carbon %wt. 72.5

Hydrogen %wt. 12.8

Oxygen %wt. 11.8

Table 5: The properties of JCB

Properties Unit JCB10 JCB20 JCB30 JCB50

Kinematic viscosity

mm2/s 3.56 3.67 3.81 4.41

Density kg/m3 832.9 836.6 838.6 852.4

Flash point ºC 80.5 81.5 81.5 85.5

Cloud point ºC 0.0 1.0 1.0 1.2

Pour point ºC 1.0 1.0 2.0 2.0

Calorific

value MJ/kg 44.581 43.481 42.517 40.432

Acid value mg KOH/g 0.115 0.118 0.121 0.285

Iodine

value I2/100 g 30.26 42.71 49.62 53.58

CCR 0.0741 0.0599 0.0571 0.0482

Sulphur

content mg/kg 355.624 280.476 190.573 186.374

Cooper

corrosion _ 1a 1a 1a 1a

Oxidation

stability hours 20.33 18.56 15.48 12.82

Water

content v/v 0.0011 0.0013 0.0015 0.0018

Cetane

number - 52.8 53.9 54.5 55.7

Methanol

content 0.011 0.015 0.018 0.020

Carbon 80.2 78.9 77.3 76.3

Hydrogen 13.3 13.1 13.0 12.9

Oxygen 6.8 7.2 9.5 10.3

SETC2015

Kinematic viscosity is defined as the resistance of liquid to

flow and that is one of the most important fuel features. It

affects the operation of fuel injection, blending formation

and combustion process [20]. The viscosity of jatropha

curcas biodiesel and blends is presented in Table 4. From

this study, the viscosity of JCME was 4.57 mm2/s which is

the highest compared to JCME viscosity from other studies,

which are 4.35 mm2/s [11] and 4.12 mm

2/s [12]. The

viscosity of JCME and JCB were in agreement with ASTM

D6751 (3.55.0 mm2/s) and ASTM D7467 (1.94.5 mm

2/s).

JCB10, JCB20, JCB30 and JCB50 did not vary significantly

with regard the viscosity and the results were as agreed with

the previous studies [6, 21], which also reported that the

increase in length of carbon chain and number of double

bonds can increase the kinematic viscosities of biodiesel.

Density is an important property of biodiesel that affects the

efficiency of atomization of fuel in the combustion system

in which the increase of the fuel density tends to increase the

amount of fuel injected to the combustion chamber [22].

The density values for the fuels from this study, JCME,

JCB10, JCB20, JCB30 and JCB50 are described in Table 5.

The results revealed that the densities of JCB10, JCB20,

JCB30 and JCB50 (832.9 kg/m3, 836.6 kg/m

3, 838.6 kg/m

3

and 852.4 kg/m3

respectively) were higher than diesel (822.2

kg/m3). This density value was matched with Alptekin and

Canakci [23] which reported that the blending up to 20% of

biodiesel with diesel was very close to diesel. Additionally,

they found that there is no significant difference of the

density of the blends with up to 20% of methyl esters. The

density JCB50 was within the recommended limits for

biodiesel diesel blends by ASTM D7467 standard of max.

858 kg/m3 [24].

Flash point is a measure of flammability of fuels and it is an

important safety criterion in transport and storage [25].

Observed from this study, flash point of JCME was not

lower than 120oC and is higher than diesel which 79.5

oC

(Table 4). This is consistent with the studies reported by

Abedin et al. [26] and Gendure et al. [27] who reported that

biodiesel has higher flash point than diesel fuel. This implies

that the main advantage of biodiesel-diesel blends is better

in storing and safer to handle for transportation.

Acid number provides an indication of the level of

corrosiveness and long-term stability of the fuel [28]. As

shown in Table 4, the acid values for JCME and JCB10,

JCB20, JCB30 and JCB50 from this study are fulfilled the

ASTM D6751 and EN 14214 standards, which is below 0.5

mg KOH/g after the transesterification and blending process.

The results from this study are also approved by Tan et al.

[11] and Chauhan et al. [12]. Moreover, the acid values of

JCME and JCB10, JCB20, JCB30 and JCB50 from this

study are within specification of ASTM D7467 standard

which specifies a maximum of 0.3 mg KOH/g.

In this study, calorific value of biodiesel was measured

based on EN 14214 (35 MJ/kg), and the result was in

compliance with the standard. Calorific values of JCME and

JCB10JCB50 were found to be very stable and lower than

diesel fuel (45.448 MJ/kg) due to the oxygen content in the

fuel. It was observed that the calorific value of JCME

(40.204 MJ/kg) was higher than the one presented by

Chauhan et al. (39.594 MJ/kg) [12]. In Table 5, the calorific

value of JCB10, JCB20, JCB30 and JCB50 are ranges from

40.432 to 44.581 MJ/kg, and it is noted that biodiesel-diesel

blends have good calorific value and closed to diesel fuel.

The higher oxygen content in biodiesel could improve the

combustion process and decrease its oxidation potential.

Oxidative stability is one of the major technical issues

affecting the quality of fuel. The content of polyunsaturated

fatty acid chains in biodiesel is a major component that

affects oxidation stability [29]. It was observed that the

oxidation stability values were varied between 12.05 to

20.33 h, while the value for JCME was 10.12, as displayed

in Table 4. These results are also in compliance with the

ASTM D6751 and EN 14214 standards, but lower than

diesel fuel (22.85 h).

The cloud point is the temperature at which wax is first

precipitated and becomes visible when the fuel is cooled

[30]. Meanwhile, the pour point is the temperature at which

the wax is out of the solution and is sufficient to gel the fuel

[25]. As seen in Tables 4-5, both cloud and pour points of

JCME and JCB resulted from this study are quite close to

diesel fuel. This is in regards of the fatty acid composition

of the various individual fatty esters depends on chain

length, hydroxyl group, degree of unsaturation, double

bonds and type of ester, hence the occurrence is because

CJCO has double bond which influences the low

temperature [31]. The cloud and pour point values were

observed higher than the values by Chauhan et al. [12], of

which 4.0oC for cloud point and 8.0

oC for pour point of

JCME. These points, the cloud and pour points are, highly

affected by the proportion of saturated fatty acid esters in

biodiesel [20].

Cetane number is one fuel property which affects the engine

parameters such as combustion, oxidation stability and

exhaust emissions [25]. The biodiesel and biodiesel-diesel

blend cetane number are shown in Table 5. The cetane

number of the fuels in this study was higher than diesel fuel

(49.7), value by Tan et al. [11] (52.7) and by Chauhan et. al.

[12] (48.13). Moreover, the resulted cetane value was in

agreement with the specification standard of biodiesel

(ASTM D6751), which min. 47 and biodiesel-diesel blends

(ASTM D7467) , which min. 40 [24, 25].

EXPERIMENTAL SET UP

The experimental engine to test the performance, emission

and combustion was a four-cylinder, common-rail

turbocharged diesel engine. The engine was coupled with a

150 kW eddy current engine dynamometer and equipped

with a KoBOLD fuel flow meter. Besides, K-type

thermocouple was installed to monitor the fuel flow to the

engine and ambient air temperature, emission exhaust and

lubricant oil. The detail specifications and flow diagram of

the engine test bed are given in Table 6, Fig. 1 and Fig. 2. A

Kistler 6058A piezoelectric sensor was used to measure in-

cylinder pressure was mounted in the head of the first

cylinder by means of a glow plug adapter. The signal of

cylinder pressure was acquired at 0.125 °CA (crank angle)

and the resolution was recorded for averaged a 100

consecutive cycles. The engine was run at 1500 rpm to 4000

rpm with interval of 500 rpm for full load (100% throttle

open). The tests were repeated three times keeping the same

SETC2015

operating conditions. During each experiment, the engine

was warmed up and reached stable condition for about 57

min. The emission exhaust and smoke opacity were

measured by an AVL DICOM 4000 5-gas analyzer, AVL Di

Smoke 4000 portable and Bacharach Analyzer 300 NSX gas

analyzer. The uncertainty measurement of the results are

given in Table 7.

Fig. 1 Schematic diagram of the experiment setup

1. Test engine

2. Eddy current dynamometer

3. Air mass sensor

4. Pressure sensor

5. Data acquisition system

6. Dynamometer controller

7. Bosch gas analyzer

8. AVL gas analyzer9. Smoke opacity meter

Fig. 2 Photo of engine tested

Table 6: Specifications of tested diesel engine

Engine Type Diesel, four strokes, turbocharged,

direct injection

Fuel system High-pressure common-rail

Number of

cylinders

4

Number of

valves per

cylinder

2

Bore 76.0 mm

Stroke 80.5 mm

Displacement 1461 cm3

Compression

Ratio

18.25 : 1

Maximum

power

48 kW @ 4000 rpm

Maximum

torque

160 Nm @ 2000 rpm

Catalytic

converter

No

Table 7: List of instruments and the range accuracy and

percentage uncertainties

Measurement Measurement

range Accuracy

%

Uncertainty

Load ±600 Nm ±0.1 Nm ±0.25

Speed 010,000

rpm ±1 rpm ±0.1

Time ±0.1 s ±0.2

Fuel flow

measurement 0.536 L/hr ±0.1 L/hr ±0.2

Air flow

measurement

0.25-7.83

kg/min

±0.11

kg/min ±2.3

CO 010% by

vol. ±0.001% ±1

NOx 05,000 ppm ±1 ppm ±0.6

Smoke 0100% ±0.1% ±1

EGT sensor 0900°C ±0.3°C ±0.1

Pressure

sensor 025,000

kPa ±10 kPa ±0.5

oCA encoder

012,000

rpm ±0.125° ±0.03

Brake power ±0.04 kW ±0.1

BSFC ±2.7

g/kWh ±1.1

ARTIFICIAL NEURAL NETWORKS (ANN)

Artificial neural network (ANN) is a mathematic nonlinear

computer algorithm that is inspired by the structure or

functional aspects of biological neural systems. ANN

systems are computational function composed of neurons

and are an alternative way to solve specific functions [16].

Moreover, ANN is a massively parallel distributed processor

made up of simple processing units that have a natural

tendency for storing experiential knowledge and making it

available for use [13]. The ANN application in engineering

field has the capability to investigate the characteristics of

performance, emissions and combustion such as fuel

consumption and length time of consumption which from

many different fuel blends and different operating

conditions [32, 33]. Another advantage of using ANN is its

ability to handle large and complex systems with many

interrelated parameters. Furthermore, ANN has ability to

ignore the excess data in large and complex systems with

multidimensional information domain [34]. In this study, an

experimental investigation of the performance, emissions

and combustion of the turbocharged diesel engine was

conducted for different blending ratios (JCB10, JCB20,

JCB30 and JCB50) at full load (100%) without any

modification to the diesel engine. Using experimental data

in this study, ANN model was developed to predict BSFC,

BTE, and EGT for the performance model. The emission

model consisted of the prediction of CO, CO2, NOx and

SETC2015

smoke opacity, while in the combustion model the

predictions were Pmax, CADPmax, RHORmax and

CADROHRmax. The input parameters were JCB percentage

(JCB10, JCB20, JCB30 and JCB50) and engine speed

(15004000 rpm) for the entire model. The application of

ANN for modeling the emission characteristics will develop

strong to predict accuracy of experimental data.

STATISTICAL EVALUATION OF OUTPUT

PARAMETERS

The correlation coefficient (i.e. R) and coefficient of

determination (i.e. R2) are to evaluate the prediction

performance of the proposed ANN [14, 34, 35]. Applying

the statistical evaluation of output parameters, the details of

the ANN setting are presented in Table 7, and the absolute

fraction of variance (R2) is defined as below [14]:

n

i ibk

n

i ibia

YY

YYR

1

2

1

2

2

)(

)(1 (1)

MSE, RMSE and MAPE are commonly used to evaluate the

statistical error in ANN performance [32, 36]. The error

described by RMSE and MAPE are defined, respectively as

follows [32]:

21

1

2)(1

n

iibia YY

nRMSE (2)

1001

1

nY

YYMAPE

n

iia

ibia (3)

Where ‘n’ is the number of pattern in the data set,‘ iaY ’ is

the actual output, ‘ ibY ’ is the predicted output value and

’ kY ’ is the average of the actual value [37, 38]. The

developed ANN model used for the performance of a MAPE

of 5% was taken as a limit for the output parameters.

MODELING AND DESIGN

ANN model contains of three layers which are: input layer,

some hidden layers and an output layer. The information

that set in the input will be sent to the neurons with a certain

weight to the hidden layers. The information from the input

data is processed with the activation functions of the hidden

layer to approximate the non-linear behavior of the input

data set. The summation function that is used to calculate

the net input of the cell is given as below [13] :

bxwNETn

j jji 1 (4)

Where n is the number of input, wj is the connecting line

weight, xj is the input signal and b is an external bias.

The weights modify the connection between layers in

training network continuously until the expected responses

provide. Target value is the network output compared,

calculated and modified where the connecting line weight

and external bias has been minimized. The target is to

minimize the average sum of square errors which is called

as MSE of the output, as follow [16] :

n

j ij ndndn

MSE1

)()(1

(5)

Where: jd represents the experiment data

id is the ANN output or predicted

n is the neuron number of the output data

When the MSE falls below a determined value, or the

maximum number of epochs is exceeded, the training

process is terminated. Then, this trained network can be

used for simulating the system outputs for the inputs that

have not been introduced before [37, 39]. The selection of

an appropriate MATLAB equation significantly affects

network performance as below [37] :

nensig

1

1)(log (6)

nnPurelin )( (7)

The input and output data points in this study were

first normalized given by expression [13] where Zi is the

input or output variable and 21 : is the ranges of the

transfer function [33].

minmax

min

121

ii

iii

zz

zzX (8)

The 30 experimental data were prepared for the training and

testing for ANN modeling. The learning algorithm called

back-propagation was applied for the single hidden layer.

Levenberg–Marquardt (LM) was used for the variants of the

algorithm, while the logsig and purelin transfer function

were normalized between (0, 1) for logsig and (1, 1) for

purelin. The architecture for all the models is shown in Fig.

3. The independent model was initially developed for

performance parameters, emission and combustion

characteristics of diesel engine. The ratio for ANN training

(83% 25 data) and testing (17% 5 data) from the

experimental data were randomly selected for the testing

data and training data, respectively.

Each presented output of the neural network was compared

to the desired output and an error was computed. The

statistical values for the performance, emissions and

combustion model are presented in Tables 810. These

errors were then fed back (back propagated) to the neural

network and used to adjust the weights such that the error

decreases with each iteration. The network was initially

SETC2015

trained with different number of neurons in the hidden layer.

By increasing or decreasing the number of neurons, then

MSE can reach the minimum value. The number of neurons

is the minimum MSE which selected as the optimum

number of neurons in the hidden layer.

Fig. 3 ANN architecture for (a) performance model, (b)

emission model and (c) combustion model

RESULT AND DISCUSSION

ENGINE PERFORMANCE PARAMETER

BRAKE SPECIFIC FUEL CONSUMPTION (BSFC)

The BSFC with speed variation for diesel fuel and different

volume percentages of JCB10, JCB20, JCB30 and JCB50

are presented in Fig. 4. The BSFC trend decreased at

medium speeds (2000 rpm) and increased to high speeds

(4000 rpm) for turbocharged engine. BSFC for JCB10

(208.825 g/kWh) and diesel fuel (215.130 g/kWh) were

lower compared to JCB20, JCB30 and JCB50 (225.925

g/kWh and 228.322 g/kWh and 237.944 g/kWh

respectively) at 2000 rpm. It is believed that the lower BSFC

for JCB10 and diesel fuel was due to the better viscosity and

calorific value they possessed than JCB20JCB50 at the

operation conditions performance at 2000 rpm. Besides,

density of biodiesel was found higher compared to diesel

fuel, and although fuel consumption are the same on volume

basis it resulted in higher BSFC in the case of

JCB20JCB50. Similar trends of inconsistent BSFC values

at high speed for various biodiesel blends are also reported

by other researchers [6, 40]. Oi et al. [41] reported that

increasing volume percentage of biodiesel will decrease

calorific value and it causes higher BSFC to produce the

same amount of energy in the engine compared to diesel

fuel. For instance, JCB10 tends to have lesser BSFC than

JCB20, JCB30 and JCB50.

[Fig. 4]

Fig. 4 The BSFC variation with diesel fuel and

JCB10JCB50 at full throttle

BRAKE THERMAL EFFICIENCY (BTE)

The brake thermal efficiency for diesel fuel and blends of

JCB10, JCB20, JCB30 and JCB50 are shown in Fig. 5. The

results show that BTE have similar trends for all fuels. It

was observed that the BTE of diesel fuel (38.28%) was

slightly higher than the obtained BTE for JCB10 (37.18%)

at 2000 rpm. BTE values of JCB20, JCB30 and JCB50 were

found as 36.49%, 36.12% and 36.34% respectively at 100%

load, which are also lower than BTE of diesel fuel. This is

due to higher kinematic viscosity and density of JCB20,

JCB30 and JCB50, which cause lower volatility of the

blends and hence results in a poor mixture formation and

lower brake torque output. However, this delay the fuel

atomization that heads to reduce fuel air mixing. This study

also observed that JCB10 gave better fuel combustion and

improved the thermal efficiency compared to JCB20, JCB30

and JCB50. This occurrence is agreed with Muralidharan et

al. [42] which reported that the high oxygen content and less

C/H ratio of the blends cause complete combustion which

extends the premixed combustion phase. With higher

viscosity and density in JCB20, JCB30 and JCB50, lower

BTE was resulted due to larger droplet size in the fuel spray.

Other researches have studied cylinder pressure traces to

determine the heat release and any other parameters that

may affect the quality of the fuel-air mixture [9, 26].

(a)

(b)

(c)

SETC2015

Fig. 5 The BTE variation with diesel fuel and

JCB10JCB50 at full throttle

EXHAUST GAS TEMPERATURE (EGT)

The variation of EGT with engine speed for diesel fuel and

different volume percentage of JCB10, JCB20, JCB30 and

JCB50 are shown in Fig. 6. As seen from this figure, the

EGT increases as the speed increases for all test fuels in

common-rail turbocharged engines. This is explained as the

fuel is combusted in the combustion chamber, the unit time

as well as the heat energy are increased to generate faster

engine speed. The highest EGT for diesel fuel and JCB10,

JCB20, JCB30 and JCB50 were observed 515.00oC,

518.74oC, 530.00

oC, 539.13

oC and 550.00

oC respectively at

4000 rpm. According to these values, the EGT was

increased accordingly at higher engine speeds. The EGT for

diesel fuel showed a fairly lower trend compared with JCB

due to the low volatility during ignition delay and the fuel

spray atomizes into small droplets and would lead excessive

smoke (incomplete combustion). Kumar et al. [43] reviewed

that biodiesel-diesel blends have higher EGT and can cause

increase of BSFC of the blends compared to diesel fuel.

Additionally, Usta et al. [44] agreed that biodiesel has longer

ignition delay and slower burning rate that causes the

combustion duration affects the EGT. This is due to the

biodiesel is made from vegetable oil and its lower heating

value are not adequately evaporated during the main

combustion phase and continue to burn in the late

combustion phase [45]. Moreover, Rizwanul Fattah et al.

[22] revealed that higher CN and poor atomization due to

higher viscosity causes the presence of unburnt fuels in the

premixed combustion phase. These unburnt portions

continue can lead to the higher exhaust temperature. In this

study, all JCBs were tested and showed that JCB10 has low

EGT compared to JCB20, JCB30 and JCB50 which is an

indication of good burning of fuel inside cylinder.

Fig. 6 The EGT variation with diesel fuel and

JCB10JCB50 at full throttle

EXHAUST EMISSIONS PARAMETER

CARBON MONOXIDE (CO)

Fig. 7 illustrates the variation of CO emissions with engine

speed for diesel fuel and JCB10, JCB20, JCB30 and JCB50.

The highest CO emission was by diesel fuel (0.360%), then

followed JCB10 (0.163%), JCB20 (0.139%), JCB30

(0.135%) and JCB50 (0.091%) at 1500 rpm. The results

show that CO emission for JCB10, JCB20, JCB30 and

JCB50 were reduced for an average of 54.80% compared to

diesel fuel. It is clear that CO emissions significantly

decrease when the JCME ratio is increased since JCB10,

JCB20, JCB30 and JCB50 have higher cetane number than

diesel fuel. Knothe [46] observed that higher cetane number

causes longer ignition delay time for the biodiesel due to

less volatility leads late premixed combustion and more fuel

early fuel injection. It is acceptable for biodiesel because

higher cetane number is related to the fuel-rich zones

formation, which reduces CO emissions. Moreover, CO

concentration rate increases at low speed due to poor

atomization and uneven distribution of fuel across to the

combustion chamber and can lead to oxygen deficiency and

incomplete combustion [4].

Fig. 7 The comparison of CO emissions for diesel and

JCB10JCB50 at full throttle

CARBON DIOXIDE (CO2)

In general, diesel fuel and biodiesel-diesel blends have

similar trends of which CO2 emissions decrease with an

increase in the biodiesel blends ratio and engine speed. Fig.

8 illustrates the highest CO2 emission which by diesel fuel

(13.70%), follow by JCB10 (13.41%), JCB20 (13.19%),

JCB30 (13.17%) and JCB50 (13.01%) at 1500 rpm.

However, there is a slight increase CO2 for JB10 (11.81%)

compared to diesel fuel (11.79%) at 2000 rpm, which the

occurrence was caused high cetane number and excess

oxygen content in JCB10 developed combustion quality and

decreased fuel delay period compared to diesel fuel. It is

also showed that the average CO2 emission for JCB20,

JCB30 and JCB50 were reduced of 3.94% than diesel fuel.

From the results, JCB10, JCB20, JCB30 and JCB50 can

replace diesel oil in combustion turbocharged engine

without major adjustments and it reduces CO2 emissions.

The reduction CO2 emission for JCB10, JCB20, JCB30 and

JCB50 can be explained by the increasing injection pressure

SETC2015

with constant displacement volume and biodiesel density is

higher than diesel fuel so the amount of fuel mass injected is

larger when the engine is operated with JCB10, JCB20,

JCB30 and JCB50. This is in agreement with Huang et al.

study [47]. They observed that high viscosity and density

cause difficulty for atomization with biodiesel at low engine

speed and it can lead to incomplete combustion which

affects as decreasing in CO2 emissions during the

combustion phase. Canakci [48] and Ozener et al. [3]

investigated engine performance and emission exhaust using

biodiesel-diesel blends. They stated that the increasing

percentage volume of biodiesel-diesel blends produces low

CO2 emissions, as it caused by the availability of oxygen

molecules in biodiesel-diesel. Moreover, in an

environmental view, the use of biodiesel-diesel blends

reduces CO2 emission and pollutant from transportation

sector [43, 49, 50].

Fig. 8 The comparison of CO2 emissions for diesel and

JCB10JCB50 at full throttle

NITROGEN OXIDES (NOX)

NOx emission for diesel fuel and the blends (JCB10, JCB20,

JCB30 and JCB50) are shown in Fig. 9. From the results,

the highest NOx emission was found at JCB50 (1877 ppm)

at 2500 rpm and the lowest was obtained from diesel fuel

(1200 ppm) at 1500 rpm. In average, the increase of NOx

emission of JCB10, JCB20, JCB30 and JCB50 is 12.20%

compared to diesel fuel. This is because JCB10, JCB20,

JCB30 and JCB50 have higher viscosity that reduces

injection timings in the combustion process and leads to

higher NOx emission. Higher density, viscosity, speed of

sound, and bulk modulus leads to early fuel injection can

cause more fuel early fuel injection which leads to early

combustion timing [51]. Therefore, advanced combustion

timing gives higher temperature and increased NOx. In

addition, the double bonded molecule presents in biodiesel

leads higher production of NOx from the biodiesel-diesel

blends compared to diesel fuel [8]. It is caused that biodiesel

have a slightly higher adiabatic flame temperature, which

tend to the increase in NOx production for biodiesel.

Kivevele et al. [50] reported that increasing in NOx emission

is varied by fuel injection parameters and air fuel ratio.

Jindal et al. [52] and Banapurmath et al. [53] reported that

higher injection pressure and longer ignition delay period

may facilitates more NOx emission since high injection

pressure tends to increase nitrogen.

Fig. 9 The comparison of NOx emissions for diesel and

JCB10JCB50 at full throttle

SMOKE OPACITY

The main problem faced by the diesel engine is smoke

opacity, and hence this study examined the smoke emission

level from diesel fuel, JCB10, JCB20, JCB30 and JCB50.

The smoke opacity values in this study were recorded as

7.39% (JCB10), 6.21% (JCB20), 5.85% (JCB30) and 4.37%

(JCB50), which were lower compared to diesel (8.81%) at

2000 rpm (Fig. 10). The average smoke opacity of JCB10,

JCB20, JCB30 and JCB50 was decreased of 37.21%

compared with diesel fuel. This is due to the sufficient

pressure and temperature that can improve complete

combustion and reduce smoke opacity from internal

combustion engines [54, 55]. In addition, the lower emission

of CO is related to the decrease of smoke opacity which

indicates excesses of the oxygen content in the JCB10,

JCB20, JCB30 and JCB50, so that they perform easier to be

burnt at higher temperature when the engine speed increase.

Karabektas [56] and Hwang et al. [49] reported that smoke

opacity and CO emission of biodiesel-diesel blends is lower

than diesel fuel, as it is caused by the higher oxygen content

and cetane number in the blends than diesel fuel only. This

emission is concerned since more smoke opacity can cause

longer ignition delay period and auto ignition capability

compared to diesel fuel. Ushakov et al. [57] observed that

engine performance using biodiesel in turbocharged engine

can reduce smoke opacity and CO emission. The reduction

of smoke opacity from turbocharged engine is as the result

of the delivery of oxygen into chamber to complete the

burning of the hydrocarbon and carbon particles in the fuel.

It is also proven that the increase air to the turbocharged

engine provides easiness in mixing of fuel-air in the

combustion chamber and complete combustion [48, 50].

SETC2015

Fig. 10 The comparison of smoke opacity for diesel and

JCB10JCB50 at full throttle

COMBUSTION PARAMETER AT FULL

THROTTLE

PEAK CYLINDER PRESSURE

The variations of the cylinder pressure and heat release rate

with crank angle (oCA) for diesel fuel and JCB10, JCB20,

JCB30 and JCB50 at different engine speed are plotted on

Fig. 11. The peak cylinder pressure of diesel fuel was higher

compared to JCB10, JCB20, JCB30 and JCB50 at 1500 rpm

and 2000 rpm. However, at 2500-3500 rpm, peak cylinder

pressure values for JCB10, JCB20, JCB30 and JCB50 were

close to diesel fuel; and peak cylinder pressure values for

JCB10, JCB20 and JCB30 at 4000 rpm were higher than

diesel fuel with JCB50 at the lowest since JCB50 has lower

calorific value and higher viscosity compared to the rest of

the tested fuels. At 1500 rpm, the peak cylinder pressure

values were recorded as 114.059 bar at 11oCA (diesel fuel),

103.318 bar at 12oCA (JCB10), 100.964 bar at 13

oCA

(JCB20), 103.140 bar at 13oCA (JCB30) and 97.907 bar at

13oCA (JCB50). From 2500 rpm to 3500 rpm it can be seen

the peak cylinder pressures for JCB10, JCB20, and JCB30

shared the same rising pattern with diesel fuel. A small

increase in peak gas pressure was observed at 4000 rpm as

130.786 bar at 8oCA (JCB10), 131.516 bar at 7

oCA (JCB20)

and 130.898 bar at 8oCA (JCB30). In contrary, decrease as

126.875 bar at 7oCA (JCB50) was occurred due to higher

viscosity content than other fuels which cause longer

ignition delay of blends and slower pressure rise than

JCB10, JCB20 and JCB30. Also, the reduction in ignition

delay is as the regard of the higher oxygen content in

JCB50. Since JCB50 has lower calorific value hence more

fuel needs to be burned in premixed combustion phase and

this may result in slightly lower peak pressure. The high

viscosity and low volatility of the blends result to poor

combustion and cause low peak pressure, as reported by

Buyukkaya [58]. Likewise, Ushakov et al. [57] mentioned

that physical fuel properties such as high density and

viscosity can affect spray characteristics of fuel atomization

and it will reduce combustion stage since it will longer

injection period for each operating cycle. The graph for

JCB10, JCB20 and JCB30 at 4000 rpm has advantage to the

blends in spray atomization and evaporation. These results

are in accordance with Siwale et al. [59] whereas B5 to B20

can improve the combustion process of diesel engines due to

the oxygen content of the blends. In addition, Devan and

Mahalakshmi [45] tested the poon oil based biodiesel and

reported that peak gas pressure of B20 was higher than

B100 due to the lesser viscosity and better spray formation.

Ozsezen et al. [60] confirmed that the oxygen content of the

blends increases the fuel mixing ratio in the cylinder

compared to the only diesel fuel and this condition might

lead to the high cylinder pressure in the combustion

chamber.

RATE HEAT RELEASE (ROHR)

The variations of rate heat release (ROHR) with crank angle

for diesel fuel and JCB10, JCB20, JCB30 and JCB50 at full

throttle (100% load) are shown in Fig. 11. The ignition

delay for diesel was shorter than JCB10, JCB20, JCB30 and

JCB50 at 1500 rpm, and ROHR was increased for JCB10 at

2500 rpm 4000 rpm due to the cetane number and excess

oxygen content as a consequence of the good ignition delay

and complete combustion stage (Table 4). Also, increasing

accumulation of fuel during delay period gave higher ROHR

when running with JCB10. ROHR of JCB10 was slightly

higher than diesel fuel. The obtain values of ROHR were

48.314 J/oCA (JCB10), 47.278 J/

oCA (JCB20) and 46.817

J/oCA (JCB30), which were higher compared to diesel fuel

(45.003 J/oCA) at 13

oCA for 2500 rpm. The premixed

combustion heat release was higher for JCB10, JCB20 and

JCB30 due to higher volatility and good blending with

diesel fuel and air. Long ignition delay of JCB10, JCB20

and JCB30 leads slower ignition delay due to greater fuel

accumulated in the combustion chamber at the time of

premixed burning phase. Besides, the availability of higher

oxygen content is to improve the combustion stage and to

burn fuel complete as well. The lowest ROHR occurred to

JCB50 of 44.602 J/oCA at 13

oCA for 2500 rpm due to the

lower calorific value of JCB50 during the combustion phase

as it reaches unsufficient stoichiometric ratio (excess of

fuel) at higher compression ratio. The low ROHR in JCB50

affects premixed combustion stage hence ignition delay was

shorter compared to JCB10, JCB20, JCB30 and diesel fuel

due to the chemical reactions and composition of biodiesel

at high temperature. Pullen et al. [61] reported that carbon

chain on biodiesel affects the degree of oxidation of the

biodiesel and it causes lesser ignition delay due to higher

cetane number. Sayin et al. [62] investigated that biodiesel-

diesel blends have lower ROHR than diesel fuel due to high

fuel viscosity, which causes deterioration in preparation of

airfuel mixture hence shorter ignition times for the

oxidized fuel.

SETC2015

(a) 1500 rpm

(b) 2000 rpm

(c) 2500 rpm

(d) 3000 rpm

(e) 3500 rpm

(f) 4000 rpm

Fig. 11 Cylinder pressure and ROHR curves for diesel fuel

and JCB at various engine speed at 100% load

MODEL PREDICTION USING ANN

MODEL PREDICTION OF PERFORMANCE

PARAMETERS

The statistical analysis for performance model revealed that

the developed ANN model had low RMSE content of

BSFC, BTE and EGT for the training and testing data (Table

8). For the training data, the resulted values of RMSE were

2.9178%, 2.4316%, and 0.658 for the BSFC, BTE and EGT

respectively, while for the test data were 3.942%, 2.717%,

and 0.977% respectively. Table 8 shows the values of

MAPE, which are 3.5259%, 2.4289% and 1.2373% for

BSFC, BTE and EGT of training data respectively, and

4.7929%, 3.0999%, 1.5311% for test data respectively. The

test data (MAPE values) was found that below the limit

model. The correlation coefficient (R) value for training and

data testing for all the data was close to 1, indicates that

there is a strong correlation in modeling BSFC, BTE and

EGT as depicted in Figs. 1214.

SETC2015

Table 8: Statistical value for the performance model

Variable

Training set Testing set

RMSE R2 MAPE

(%) RMSE R2

MAPE

(%)

BSFC

BTE

EGT

0.029178

0.024316

0.00658

0.9968

0.9922

0.9999

3.5259

2.4289

1.2373

0.03942

0.02717

0.00977

0.9954

0.9941

0.9986

4.7929

3.0999

1.5311

Table 9: Statistical value for the emission model

Variable

Training set Testing set

RMSE R2 MAPE

(%) RMSE R2

MAPE

(%)

NOx

CO

CO2

S.Opc

0.0251

0.0413

0.0463

0.0328

0.9936

0.9832

0.9798

0.9923

3.2789

5.3135

5.7324

4.1137

0.04942

0.02651

0.03235

0.04113

0.9871

0.9967

0.9956

0.9808

6.2131

3.3999

4.5338

6.1724

Table 10: Statistical value for the combustion model

Variable

Training set Testing set

RMSE R2

MAPE

(%) RMSE R

2

MAPE

(%)

Pmax

CADPmax

ROHRmax

CADROHRmax

0.0292

0.0302

0.0473

0.0364

0.9912

0.9937

0.9899

0.9917

5.1027

5.5207

6.1022

4.5723

0.0392

0.0342

0.0436

0.0311

0.9895

0.9905

0.9826

0.9932

6.0765

5.8752

6.4217

5.0233

Fig. 12 Comparison of ANN predicted BSFC with measured

data

Fig. 13 Comparison of ANN predicted BTE with measured

data

Fig. 14 Comparison of ANN predicted EGT with measured

data

SETC2015

MODEL PREDICTION OF EMISSION

PARAMETERS

Table 9 shows the values of RMSE for the training data,

which are 2.5102%, 4.1316%, 4,6371 % and 3,2842 % for

NOx, CO, CO2 and smoke opacity respectively, and 4.942%,

2.6511%, 3.235 and 4.113% respectively for the test data.

The observed MAPE values for the training data were

3.2789 %, 5.3135 %, 5,7324 % and 4,1137% for NOx, CO,

CO2 and smoke opacity respectively, and 6.2131%,

3.3999%, 4,5338% and 6.1724% respectively for the test

data. The increase and decrease percentage accuracy of

MAPE can be specified as error by the measurement of

emission parameters, and MAPE has the range error limits

(±5%) for the emissions. The accuracies of the prediction

for the variable performances are shown in Figs. 1518. The

correlation coefficient (R) for training and data testing for

all the data were found to be close to 1, which indicates a

good reliability in predicting the engine emission.

Fig. 15 Comparison of ANN predicted CO2 with measured

data

Fig. 16 Comparison of ANN predicted CO with measured

data

Fig. 17 Comparison of ANN predicted NOx with measured

data

SETC2015

Fig. 18 Comparison of ANN predicted smoke opacity with

measured data

MODEL PREDICTION OF COMBUSTION

PARAMETERS

The values of RMSE for the training and testing data are

shown in Table 10. Values of 2.9226%, 3.0217%, 4.7351%

and 3,6436% for Pmax, CADPmax, ROHRmax and

CADROHRmax respectively were observed for the training

data, while 3.921%, 3.417%, 4.361 and 3.105% respectively

were observed for the test data. The MAPE values for the

training data were 5.1027%, 5.5207%, 6.1022% and

4.5723% for Pmax, CADPmax, ROHRmax and CADROHRmax

respectively, while for test data were 6.0765%, 5.8752%,

6.4217% and 5.0233% respectively. The predictions for the

combustion parameters are shown in Figs. 1922. The

correlation coefficient (R) for training and data testing for

all the data were close to 1, which indicates a reliable

prediction.

Fig. 19 Comparison of ANN predicted Pmax with measured

data

Fig. 20 Comparison of ANN predicted CADPmax with

measured data

SETC2015

Fig. 21 Comparison of ANN predicted ROHRmax with

measured data

Fig. 22 Comparison of ANN predicted CADROHRmax with

measured data

CONCLUSION

The experimental study was conducted on a four cylinder

common-rail turbocharged diesel engine using Jatropha

curcas biodiesel and its blends with diesel fuel. Resulted

from this study, engine performance values when operated

with JCB10 were higher compared to diesel fuel and others

blends (JCB20, JCB30 and JCB50) at 2000 rpm. The BSFC

of JCB10 was found to be lesser as 208.825 g/kWh

compared to JCB20, JCB30, JCB50 and diesel fuel.

Moreover, the rate heat release of JCB10 was higher of

48.314 J/oCA compared to JCB20, JCB30, JCB50 and diesel

fuel, which concludes the suitability of Jatropha curcas

biodiesel as an alternative fuel in diesel engine.

The application of ANN in this study was developed to

predict engine performance, emission exhaust and

combustion in turbocharged engine based on a model of a

feed-forward back propagation network combine with

logsig/purelin. The best network architecture of ANN

developed the engine performance, emission and the

combustion were 2-8-3, 2-7-4 and 2-6-4 respectively. The

results show that the training algorithm of feed-forward

back-propagation was sufficient enough in predicting BSFC

and EGT outputs for various engine speeds and fuel blends

ratios. Additionally, R values were close to 1 for the entire

model. The RMSE for all the models were smaller than 0.05

and the values of MAPE were within the range 1.2373–

6.4217. The values of correlation coefficient showed a very

strong and positive correlation in both the predicted data and

experimental data. Therefore, the ANN can be applied to

estimate desired output engine parameters. Finally, the

utilization of ANN can be considered as a practical

technique to predict the engine parameters instead of

generalizing in a wide range of experimental studies. For the

further study, ANN can be utterly useful as a tool to

optimize engines using different feedstock in the future.

REFERENCES

[1] Um S, Park SW. Numerical Study on Combustion and

Emission Characteristics of Homogeneous Charge

Compression Ignition Engines Fueled with Biodiesel.

Energy & Fuels. 2010;24(2):916-27.

[2] Sadeghinezhad E, Kazi SN, Badarudin A, Oon CS,

Zubir MNM, Mehrali M. A comprehensive review of

bio-diesel as alternative fuel for compression ignition

engines. Renewable and Sustainable Energy Reviews.

2013;28:410-24.

[3] Özener O, Yüksek L, Ergenç AT, Özkan M. Effects of

soybean biodiesel on a DI diesel engine performance,

emission and combustion characteristics. Fuel.

2014;115:875-83.

[4] Silitonga AS, Masjuki HH, Mahlia TMI, Ong HC,

Chong WT. Experimental study on performance and

exhaust emissions of a diesel engine fuelled with Ceiba

pentandra biodiesel blends. Energ Convers Manage.

2013;76:828-36.

[5] Atadashi IM, Aroua MK, Aziz AA. High quality

biodiesel and its diesel engine application: A review.

Renewable and Sustainable Energy Reviews.

2010;14(7):1999-2008.

SETC2015

[6] Mofijur M, Masjuki HH, Kalam MA, Atabani AE.

Evaluation of biodiesel blending, engine performance

and emissions characteristics of Jatropha curcas methyl

ester: Malaysian perspective. Energ. 2013;55:879-87.

[7] Sahoo PK, Das LM, Babu MKG, Arora P, Singh VP,

Kumar NR, Varyani TS. Comparative evaluation of

performance and emission characteristics of jatropha,

karanja and polanga based biodiesel as fuel in a tractor

engine. Fuel. 2009;88(9):1698-707.

[8] El-Kasaby M, Nemit-allah MA. Experimental

investigations of ignition delay period and performance

of a diesel engine operated with Jatropha oil biodiesel.

Alex Eng J. 2013;52(2):141-9.

[9] Chauhan BS, Kumar N, Du Jun Y, Lee KB.

Performance and emission study of preheated Jatropha

oil on medium capacity diesel engine. Energ.

2010;35(6):2484-92.

[10] Ganapathy T, Gakkhar RP, Murugesan K. Influence of

injection timing on performance, combustion and

emission characteristics of Jatropha biodiesel engine.

Appl Energ. 2011;88(12):4376-86.

[11] Tan P-q, Hu Z-y, Lou D-m, Li Z-j. Exhaust emissions

from a light-duty diesel engine with Jatropha biodiesel

fuel. Energ. 2012;39(1):356-62.

[12] Chauhan BS, Kumar N, Cho HM. A study on the

performance and emission of a diesel engine fueled

with Jatropha biodiesel oil and its blends. Energ.

2012;37(1):616-22.

[13] Shivakumar, Srinivasa Pai P, Shrinivasa Rao BR.

Artificial Neural Network based prediction of

performance and emission characteristics of a variable

compression ratio CI engine using WCO as a biodiesel

at different injection timings. Appl Energ.

2011;88(7):2344-54.

[14] Yusaf TF, Buttsworth DR, Saleh KH, Yousif BF. CNG-

diesel engine performance and exhaust emission

analysis with the aid of artificial neural network. Appl

Energ. 2010;87(5):1661-9.

[15] Atadashi IM, Aroua MK, Abdul Aziz AR, Sulaiman

NMN. Production of biodiesel using high free fatty

acid feedstocks. Renewable and Sustainable Energy

Reviews. 2012;16(5):3275-85.

[16] Canakci M, Ozsezen AN, Arcaklioglu E, Erdil A.

Prediction of performance and exhaust emissions of a

diesel engine fueled with biodiesel produced from

waste frying palm oil. Expert Syst Appl.

2009;36(5):9268-80.

[17] Ghobadian B, Rahimi H, Nikbakht AM, Najafi G,

Yusaf TF. Diesel engine performance and exhaust

emission analysis using waste cooking biodiesel fuel

with an artificial neural network. Renew Energ.

2009;34(4):976-82.

[18] Maheshwari N, Balaji C, Ramesh A. A nonlinear

regression based multi-objective optimization of

parameters based on experimental data from an IC

engine fueled with biodiesel blends. Biomass

Bioenerg. 2011;35(5):2171-83.

[19] Sahoo PK, Das LM. Process optimization for biodiesel

production from Jatropha, Karanja and Polanga oils.

Fuel. 2009;88(9):1588-94.

[20] Canakci M, Sanli H. Biodiesel production from various

feedstocks and their effects on the fuel properties. J Ind

Microbiol Biot. 2008;35(5):431-41.

[21] Ong HC, Masjuki HH, Mahlia TMI, Silitonga AS,

Chong WT, Yusaf T. Engine performance and

emissions using Jatropha curcas, Ceiba pentandra and

Calophyllum inophyllum biodiesel in a CI diesel

engine. Energ. 2014;69:427-45.

[22] Rizwanul Fattah IM, Masjuki HH, Liaquat AM, Ramli

R, Kalam MA, Riazuddin VN. Impact of various

biodiesel fuels obtained from edible and non-edible

oils on engine exhaust gas and noise emissions. Renew

Sust Energ Rev. 2013;18:552-67.

[23] Alptekin E, Canakci M. Characterization of the key fuel

properties of methyl ester–diesel fuel blends. Fuel.

2009;88(1):75-80.

[24] National Renewable Energy Laboratory. Biodiesel

Handling and Use Guide. Fourth ed. Oak Ridge: U.S.

Department of Energy; 2009.

[25] Atabani AE, Silitonga AS, Ong HC, Mahlia TMI,

Masjuki HH, Badruddin Irfan Anjum., Fayaz H. Non-

edible vegetable oils: A critical evaluation of oil

extraction, fatty acid compositions, biodiesel

production, characteristics, engine performance and

emissions production. Renew Sust Energ Rev.

2013;18:211-45.

[26] Abedin MJ, Masjuki HH, Kalam MA, Sanjid A,

Rahman SMA, Fattah IMR. Performance, emissions,

and heat losses of palm and jatropha biodiesel blends

in a diesel engine. Ind Crop Prod. 2014;59:96-104.

[27] Gandure J, Ketlogetswe C, Temu A. Fuel properties of

biodiesel produced from selected plant kernel oils

indigenous to Botswana: A comparative analysis.

Renew Energ. 2014;68:414-20.

[28] Fernando S, Karra P, Hernandez R, Jha SK. Effect of

incompletely converted soybean oil on biodiesel

quality. Energy. 2007;32(5):844-51.

[29] Pullen J, Saeed K. An overview of biodiesel oxidation

stability. Renew Sust Energ Rev. 2012;16(8):5924-50.

[30] Singh SP, Singh D. Biodiesel production through the

use of different sources and characterization of oils and

their esters as the substitute of diesel: A review. Renew

Sust Energ Rev. 2010;14(1):200-16.

[31] Refaat AA. Correlation between the chemical structure

of biodiesel and its physical properties. International

Journal of Environmental Science & Technology.

2009;6(4):677-94.

[32] Çay Y, Çiçek A, Kara F, Sağiroğlu S. Prediction of

engine performance for an alternative fuel using

artificial neural network. Appl Thermal Eng.

2012;37:217-25.

[33] Basheer IA, Hajmeer M. Artificial neural networks:

fundamentals, computing, design, and application. J

MicrobioI Meth. 2000;43(1):3-31.

[34] Kara Togun N, Baysec S. Prediction of torque and

specific fuel consumption of a gasoline engine by

using artificial neural networks. Appl Energ.

2010;87(1):349-55.

[35] Najafi G, Ghobadian B, Tavakoli T, Buttsworth DR,

Yusaf TF, Faizollahnejad M. Performance and exhaust

emissions of a gasoline engine with ethanol blended

gasoline fuels using artificial neural network. Appl

Energ. 2009;86(5):630-9.

[36] Mohamed Ismail H, Ng HK, Queck CW, Gan S.

Artificial neural networks modelling of engine-out

responses for a light-duty diesel engine fuelled with

biodiesel blends. Appl Energ. 2012;92:769-77.

SETC2015

[37] Sayin C, Ertunc HM, Hosoz M, Kilicaslan I, Canakci

M. Performance and exhaust emissions of a gasoline

engine using artificial neural network. Appl Thermal

Eng. 2007;27(1):46-54.

[38] Sözen A, Arcaklioǧlu E. Solar potential in Turkey.

Appl Energ. 2005;80(1):35-45.

[39] Traver M, Atkinson R, Atkinson C. Neural Network-

Based Diesel Engine Emissions Prediction Using In-

Cylinder Combustion Pressure. SAE International.

1999;1532:1–15.

[40] How HG, Masjuki HH, Kalam MA, Teoh YH. An

investigation of the engine performance, emissions and

combustion characteristics of coconut biodiesel in a

high-pressure common-rail diesel engine. Energ.

2014;69:749-59.

[41] Qi DH, Chen H, Geng LM, Bian YZ. Experimental

studies on the combustion characteristics and

performance of a direct injection engine fueled with

biodiesel/diesel blends. Energ Convers Manage.

2010;51(12):2985-92.

[42] Muralidharan K, Vasudevan D, Sheeba KN.

Performance, emission and combustion characteristics

of biodiesel fuelled variable compression ratio engine.

Energ. 2011;36(8):5385-93.

[43] Kumar N, Varun, Chauhan SR. Performance and

emission characteristics of biodiesel from different

origins: A review. Renew Sust Energ Rev.

2013;21:633-58.

[44] Usta N, Öztürk E, Can Ö, Conkur ES, Nas S, Çon AH,

Can AÇ, Topcu M. Combustion of biodiesel fuel

produced from hazelnut soapstock/waste sunflower oil

mixture in a Diesel engine. Energ Convers Manage.

2005;46(5):741-55.

[45] Devan PK, Mahalakshmi NV. Study of the

performance, emission and combustion characteristics

of a diesel engine using poon oil-based fuels. Fuel

Process Technol. 2009;90(4):513-9.

[46] Knothe G. A comprehensive evaluation of the cetane

numbers of fatty acid methyl esters. Fuel.

2014;119(0):6-13.

[47] Huang J, Wang Y, Qin J-b, Roskilly AP. Comparative

study of performance and emissions of a diesel engine

using Chinese pistache and jatropha biodiesel. Fuel

Process Technol. 2010;91(11):1761-7.

[48] Canakci M. Combustion characteristics of a

turbocharged DI compression ignition engine fueled

with petroleum diesel fuels and biodiesel. Bioresource

Technol. 2007;98(6):1167-75.

[49] Hwang J, Qi D, Jung Y, Bae C. Effect of injection

parameters on the combustion and emission

characteristics in a common-rail direct injection diesel

engine fueled with waste cooking oil biodiesel. Renew

Energ. 2014;63:9-17.

[50] Kivevele TT, Kristóf L, Bereczky Á, Mbarawa MM.

Engine performance, exhaust emissions and

combustion characteristics of a CI engine fuelled with

croton megalocarpus methyl ester with antioxidant.

Fuel. 2011;90(8):2782-9.

[51] Agarwal AK, Dhar A. Experimental investigations of

performance, emission and combustion characteristics

of Karanja oil blends fuelled DICI engine. Renew

Energ. 2013;52:283-91.

[52] Jindal S, Nandwana BP, Rathore NS, Vashistha V.

Experimental investigation of the effect of

compression ratio and injection pressure in a direct

injection diesel engine running on Jatropha methyl

ester. Appl Thermal Eng. 2010;30(5):442-8.

[53] Banapurmath NR, Tewari PG, Gaitonde VN.

Experimental investigations on performance and

emission characteristics of Honge oil biodiesel

(HOME) operated compression ignition engine. Renew

Energ. 2012;48:193-201.

[54] Hazar H, Aydin H. Performance and emission

evaluation of a CI engine fueled with preheated raw

rapeseed oil (RRO)–diesel blends. Appl Energ.

2010;87(3):786-90.

[55] Yuste AJ, Dorado MP. A Neural Network Approach to

Simulate Biodiesel Production from Waste Olive Oil.

Energy & Fuels. 2006;20(1):399-402.

[56] Karabektas M. The effects of turbocharger on the

performance and exhaust emissions of a diesel engine

fuelled with biodiesel. Renew Energ. 2009;34(4):989-

93.

[57] Ushakov S, Valland H, Æsøy V. Combustion and

emissions characteristics of fish oil fuel in a heavy-

duty diesel engine. Energ Convers Manage.

2013;65:228-38.

[58] Buyukkaya E. Effects of biodiesel on a DI diesel engine

performance, emission and combustion characteristics.

Fuel. 2010;89(10):3099-105.

[59] Siwale L, Kristóf L, Adam T, Bereczky A, Mbarawa

M, Penninger A, Kolesnikov A. Combustion and

emission characteristics of n-butanol/diesel fuel blend

in a turbo-charged compression ignition engine. Fuel.

2013;107(0):409-18.

[60] Ozsezen AN, Canakci M. Determination of

performance and combustion characteristics of a diesel

engine fueled with canola and waste palm oil methyl

esters. Energ Convers Manage. 2011;52(1):108-16.

[61] Pullen J, Saeed K. Factors affecting biodiesel engine

performance and exhaust emissions – Part II:

Experimental study. Energy. 2014;72:17-34.

[62] Sayin C, Gumus M, Canakci M. Effect of fuel injection

pressure on the injection, combustion and performance

characteristics of a DI diesel engine fueled with canola

oil methyl esters-diesel fuel blends. Biomass Bioenerg.

2012;46:435-46.

ACKNOWLEDGMENTS

The authors would like to acknowledge for the Ministry of

Education of Malaysia And The University Of Malaya,

Kuala Lumpur, Malaysia for the financial support under

High Impact Research Grant (HIRG) entitled clean diesel

technology for military and civilian transport vehicles

M.C/HIR/MOE/ENG/07 (D000007-16001), UMRG:

RP022A-13AET and FRGS: FP009-2014A.

DEFINITIONS/ABBREVIATIONS

Symbol Description

ANN Artificial neural network

BTE Brake thermal efficiency

BSFC Brake specific fuel consumption oCAD Crank angle distance

CJCO Crude Jatropha curcas oil

CO Carbon monoxide

SETC2015

CO2 Carbon dioxide

EGT Exhaust gas temperature

JCME Jatropha curcas methyl ester

JCB Jatropha curcas biodiesel blends

JCB10 Jatropha curcas biodiesel blends 10%

with diesel fuel

JCB20 Jatropha curcas biodiesel blends 20%

with diesel fuel

JCB30 Jatropha curcas biodiesel blends 30%

with diesel fuel

JCB50 Jatropha curcas biodiesel blends 50%

with diesel fuel

MSR Mean square error

NOx Nitrogen oxide

ROHR Rate heat release

TDC Top death center

RMSE Root mean square error

MRE Mean relative error

MSE Mean square error

R Correlation coefficient

MAPE Mean absolute percentage error