Development of back propagation neural network model to predict performance and emission

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME
85
DEVELOPMENT OF BACK PROPAGATION NEURAL NETWORK
MODEL TO PREDICT PERFORMANCE AND EMISSION
PARAMETERS OF A DIESEL ENGINE
Shailaja M,
Assistant Professor, Department of Mechanical Engineering
JNTUH, College of Engineering,
Jagtial,Karimnagar, Andhra Pradesh, INDIA.
V.Vijaya Kumar,
Assistant Professor, Department of Mathematics
Sree Chaitanya Institute of Technological Sciences
Karimnagar, Andhrapradesh, INDIA
Chandragiri Radha Charan,
Assistant Professor, Electrical and Electronics Department
JNTUH, College of Engineering,
Jagtial, Karimnagar, Andhra Pradesh, INDIA,
Dr.A V Sitarama Raju,
Department of Mechanical Engineering JNTUH College of Engineering,
Kukatpallyl, Hyderabad, Andhra Pradesh, INDIA,
ABSTRACT
In this paper, development of Back Propagation Neural Network (BPNN) is proposed to
predict performance and emission parameters of a diesel engine fuelled with sunflower oil and its
blends with petro-diesel. Short term tests were conducted at various loads and with various blends of
fuel to collect data. Various combinations of network parameters were investigated by varying
number of hidden nodes, learning rate (η), momentum factor (α), and training algorithm. The
developed neural network is able to predict brake specific fuel consumption (bsfc), torque, brake
thermal efficiency, Hydro Carbon (HC) and Carbon Monoxide (CO) emissions within acceptable
range of correlation coefficients, with load and percentage of blends as inputs.
Keywords: Back Propagation Algorithm; Correlation coefficient; Diesel Engine; Performance
Parameters; Emission Parameters.
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN
ENGINEERING AND TECHNOLOGY (IJARET)
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 3, April 2013, pp. 85-92
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2013): 5.8376 (Calculated by GISI)
www.jifactor.com
IJARET
© I A E M E

86
I. INTRODUCTION
A. Suitability of Sunflower oil in Diesel Engines
Stringent regulations on automobile emissions and rapid depletion of fossil fuels inspired
researchers to think towards alternative fuels. One of the best substitutes for petro-diesel is vegetable
oils and/or their methyl esters as vegetable oils as properties of vegetable oils are very much similar to
petro-diesel and requires no or little engine modifications. In the present work, sunflower oil and its
blends with petro-diesel in various proportions are used as fuels. S Kalligeros et al.[1] investigated use
of sunflower oil bio diesel in diesel engines and reported satisfactory results of emissions compared to
petro-diesel. Abuhabaya A et al. [2] prepared bio diesel from sunflower oil and tested in diesel engine
and testified decrease in emissions with increase in proportion of bio diesel in blend. The following
table presents comparison of properties of diesel and sunflower oil.
TABLE 1. Comparison of Properties of Diesel and Sunflower Oil
Property Diesel Sunflower oil
Density (Kg/m3
) 0.832 0.918
Calorific Value (kJ/kg.) 44800 43500
Flash Point (°°°°C) 62 274
Fire Point (°°°°C) 175 371
B. Artificial Neural Networks (ANNs)
Artificial Neural Networks has been motivated right from their inception by the recognition
that the brain computes in an entirely different way from the conventional digital computers.
This interconnected group of artificial neurons that uses a mathematical model or computational
model for information processing based on a connectionist approach to computation. In most cases an
ANN is an adaptive system that changes its structure based on external or internal information that
flows through the network. In more practical terms neural networks are non-linear statistical data
modeling tools. They can be used to model complex relationships between inputs and outputs or to
fine patterns in data.
C. Applications of ANN in the Field of Internal Combustion Engines
ANNs are widely used as a tool for prediction of performance parameters such as efficiency,
specific fuel consumption etc. and fault diagnosis in internal combustion engines.
Michael L. Traver et al. [3] developed a model to predict emissions of a diesel engine based on in-
cylinder pressure derived variables. A.M.Frith et al. [4] investigated on adaptive control of gasoline
engine air-fuel ratio using artificial neural networks and reported successful results. F.Gu et al. [5]
attempted to develop a RBF neural network model for cylinder pressure reconstruction in internal
combustion engines and reported that RBFNN is best suited for the data. Robert.J.Howlett et al. [6]
investigated on accurate measurement of air fuel ratio with back propagation algorithm and reported
that the predicted values are in good agreement with experimental values. Radial basis function neural
network is found to produce good results to predict toxicity and exhaust gas components as per
investigations by Brzozowska et al. [7]. Adnan Parlak et al.[8] developed ANN to predict specific fuel
consumption and exhaust temperature with Levenberg-Marquardt algorithm and reported relative
error for prediction in the range of 1% - 9%. Recurrent neural network was developed and validated to
predict NOx (nitrogen oxides) emissions and error lower than 2% was reported by Arise et al. [9].
Hidayat Oguz et al.[10] investigated on development of ANN for the prediction of power, torque, fuel
consumption with speed and type of fuel as inputs and reported that BPNN with Tangent-Sigmoid
activation function was best suited for data. M.Ghazikhani et al. [11] developed ANN to predict soot

87
emissions and reported 6.4% mean absolute percentage error. Sumita Deb Barma et al.[12] developed
BPNN to predict performance parameters with Levenberg-Marquardt algorithm and reported
satisfactory results. R.Manjunatha et al. [13] compared performance of radial basis function neural
networks and BPNN and concluded radial basis function neural network is best suited for the data to
predict emissions.
II. DISCRIPTION OF EXPERIMENTAL SETUP
A single cylinder Direct Injection type four Stroke water cooled vertical diesel engine test rig
developing 3.5 kilo Watts at approximately 1500 RPM coupled to AC alternator with loading bank for
experimentation purpose. AC alternator is fixed to engine flywheel and the engine is mounted on a
mild steel channel frame and further mounted on anti-vibration mounts. Panel board is used to fix the
burette with 3-way cock digital RPM indicator and u-tube manometer.Load is varied by varying
resistance. The fuel is supplied from the main fuel tank to the measuring burette. An air drum is fitted
on the panel frame and connected to engine through an air base. The air drum facilitates a magnified
orifice and pressure pick up points are connected to end u-tube manometer limbs. The difference in
manometer readings is taken at different loads.
Emissions CO and HC are measured using a gas analyzer which measures with a good accuracy.
III. EXPERIMENTAL WORK
Experiments were carried with eight different proportions of diesel, sunflower oil combinations as
fuels which are given below.
1. 0% of sunflower oil and 100% of diesel
With each fuel, steady state short term tests were conducted at six load settings over entire range of
engine operation and observations were recorded for emissions and to calculate performance
parameters. Short term experiments were conducted to collect data. For accuracy each observation is
recorded three times and averaged. Using conventional formulae brake specific fuel consumption,
torque and brake thermal efficiency were calculated. An exhaust gas analyzer is used to record CO
and HC emissions.
Development of Back Propagation Neural A neural network is capable of approaching a
nonlinear function to significant desirable degree of accuracy. A feed forward network with back
propagation algorithm is chosen. Back propagation algorithm is most popular for supervised training
of multilayer perceptron as it is simple to compute locally and it performs stochastic gradient descent
in weight space [14].
Two input parameters (load and blend percentage) and five output parameters (brake specific
fuel consumption, torque, brake thermal efficiency, Carbon Monoxide and Hydro Carbon emissions)
are chosen. Data were collected over entire range of engine operation comprising of 46 sets. Entire
data is divided into two sets namely training set and test set. Training set consists of 87% of data (40
sets) and test set consists of 13% of data (6 sets).
Before training, it is often useful to scale the given data so that they always fall within a specified
range which facilitates network for better training. Data is normalized in the range [-1 1] by using
following formula.

88
min
min
min
max
min
max
( )*( )
y y
y x x y
x x
−
= − +
−
(1)
Tangent-Sigmoid transfer function is chosen for hidden layer which is represented by following
equations.
1 exp( )
( )
1 exp( )
x
b x
x
σ
σ
− −
=
+ −
(2)
'( ) [(1 ( ))(1 ( )]
2
b x b x b x
σ
= + − (3)
Network performance is measured in terms of error function. Mean Squared Error (MSE) is one of
the network error functions. It measures the network's performance according to the MSE.
N1 2
MSE= e(k)
K=1N
∑ (4)
; where e(k)=t(k)-a(k)
t(k) is the experimental output, a(k)is the output predicted by ANN and N is number of elements in
output vector. MSE is set as 0.001. Best BPNN is arrived after numerous trials by varying network
parameters such as number of nodes in hidden layer starting from 3 nodes-20 nodes, number of
hidden layers 1 -2, learning rate,α (0.0001-0.3), momentum factor,η (0.75-0.98) and training
algorithm (gradient descent back propagation, gradient descent with adaptive learning rate back
propagation, gradient descent with momentum back propagation, and Levenberg-Marquardt back
propagation). Application of algorithm for back propagation theorem is presented in the following
steps [15].
1. Initialize weights (from training algorithm).
2. For each set of input perform steps 3 to 5.
3. For i=1… n; set activation of input unit, xi.
4. For j=1,……, p;
n
z =v + x voj i ij-inj i=1
∑ (5)
5. For k=1,……., m
p
y =w + z w-ink ok j jkj=1
∑ (6)
y(k)=f(y )-ink (7)
z j
= hidden unit j
voj
= bias on hidden units
wok = bias on output unit k
vij= weight connection between ith
input node and
jth
hidden node
w jk =weight connection between jth
hidden node and kth
output node
y (k) = output unit k
Variation of correlation coefficient with number of nodes in hidden layer is shown in following Fig. 2.
Maximum value of Regression, R (0.9953) may be observed at 7 nodes in hidden layer.
Based on performance, supervised learning with feed forward network by Levenberg-Marquardt back
propagation neural network, one hidden layer with seven neurons, learning rate α=0.0001, momentum
factor, η=0.9 is reported as best suitable network. Schematic block diagram is shown in Fig.1.

89
TABLE II :TRAINING DATA SET
S
.No.
Blend
Load
(amps)
SFC
(kg/
kwhr)
Torque
(N-m)
Brake Thermal
Efficiency
(percentage)
HCs
(parts
per million)
CO
(percentage)
1 0 0 0 0 0 22 0.057
2 0 2.4 0.566 4.297 14.2 22 0.057
3 0 5.1 0.339 9.261 24.68 22 0.057
4 0 7.8 0.262 14.524 30.44 22 0.057
5 0 10.6 0.233 19.848 34.27 22 0.057
6 0 14.6 0.195 25.102 41.02 22 0.057
7 0.1 0 0 0 0 19 0.054
8 0.1 5.1 0.347 9.243 23.45 19 0.054
9 0.1 7.8 0.273 14.54 29.75 19 0.054
10 0.1 10.6 0.248 19.875 32.78 19 0.054
11 0.1 14.6 0.227 25.138 35.73 19 0.054
12 0.2 0 0 0 0 15 0.049
13 0.2 2.4 0.578 4.524 14.31 15 0.049
14 0.2 4.9 0.353 9.49 23.43 15 0.049
15 0.2 10.2 0.247 20.343 33.46 15 0.049
16 0.2 12.8 0.225 25.485 33.66 15 0.049
17 0.25 0 0 0 0 16 0.046
18 0.25 2.4 0.618 4.545 13.52 16 0.046
19 0.25 5 0.359 9.718 23.27 16 0.046
20 0.25 10.9 0.235 21.779 35.46 16 0.046
21 0.25 13 0.229 26.008 36.44 16 0.046
22 0.3 0 0 0 0 15 0.046
23 0.3 2.4 0.639 4.3 13.19 15 0.046
24 0.3 4.9 0.359 9.03 23.48 15 0.046
25 0.3 12.9 0.226 25.8 37.29 15 0.046
26 0.4 0 0 0 0 15 0.046
27 0.4 2.3 0.638 4.3 13.55 15 0.046
28 0.4 4.9 0.381 9.4 23.16 15 0.046
29 0.4 7.5 0.281 14.9 30.56 15 0.046
30 0.4 12.9 0.236 25.7 36.4 15 0.046
31 0.5 0 0 0 0 14 0.045
32 0.5 2.3 0.663 4.31 16.35 14 0.045
33 0.5 4.9 0.38 9.3 23.01 14 0.045
34 0.5 7.6 0.296 14.9 29.04 14 0.045
35 0.5 10.2 0.255 20.3 34.02 14 0.045
36 1 0 0 0 0 15 0.043
37 1 2.4 0.661 4.459 14.61 15 0.043
38 1 4.9 0.399 9.379 24.19 15 0.043
39 1 7.6 0.305 14.79 31.63 15 0.043
40 1 10.2 0.271 20.108 35.6 15 0.043

90
TABLE III.TEST DATA SET
S.
No.
Blend
Load
(amps)
Specific
Fuel Consumption
(kg/kWhr)
Torque
(N-m)
Brake Thermal
Efficiency
(percentage)
HCs
(parts per
million)
CO
(percentage)
1 0.1 2.4 0.584 4.267 13.91 19 0.054
2 0.2 7.6 0.28 14.92 29.57 15 0.049
3 0.25 7.6 0.293 14.99 28.52 16 0.046
4 0.3 7.6 0.283 14.9 29.72 15 0.046
5 0.3 10.2 0.254 20.4 33.1 15 0.046
6 0.4 10.2 0.257 20.3 33.43 15 0.046
7 0.5 12.9 0.237 25.61 36.79 14 0.045
8 1 12.9 0.246 25.642 39.01 15 0.043
IV. RESULTS
The economic structure that resulted in minimum error and maximum efficiency during
training as well as testing is selected as the final form of the BPNN model. BPNN with 2-7-5
architecture is trained and validated .Performance plot is shown in Fig.3(a). Regression analysis plots
for training, validation and testing are presented in Fig. 3 (b), Fig. 3(c) and Fig. 3(d) respectively.
Comparison of experimental values with predicted from BPNN is presented in Fig.5 to Fig.10.
Correlation coefficients, R for outputs of specific fuel consumption is 0.99409, Torque is 0.99526,
Brake Thermal Efficiency is 0.9952, HC is 0.90193 and CO is 0.89137 which are satisfactory and in
acceptable range(shown in Fig. 4 - Fig.8).
Fig.1 Architecture of Back Propagation Neural Network (BPNN)
Fig.3 (a) Performance plot of BPNN Fig. 3(b) Regression analysis of
Training of BPNN

91
After training BPNN is further validated with unseen data during training and predicted
values from BPNN are in very good agreement with experimental values.Fig.9 to Fig.13 shows
comparison of experimental and predicted values by BPNN.
Average error of specific fuel consumption, Torque, Brake Thermal Efficiency, Hydro Carbon,
Carbon monoxide for prediction of various outputs is shown in Table IV.
V. CONCLUSIONS
Short-term experimental data with Levenberg Marquardt back propagation neuron model is
used to predict performance and emission parameters of a diesel engine. The BPNN has been
simulated in MATLAB 7.0 ®. As mentioned in results the correlation coefficients (R) by using
adaptive learning has been obtained as R1= 0.99526, R2 =0.99409, R3 =0.9952, R4 =0.90193 and R5
=0.89173 respectively for outputs which are in acceptable range. Further work may be extended to
conduct medium term and long term tests on the diesel engine.
ACKNOWLEDGMENT
Authors are grateful to authorities of JNTUH College of Engineering Jagtial for permitting to
carry out the experiments in Thermal Engineering laboratory.
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S.No. Prediction Parameter
Average Error
(Percentage)
1. Specific Fuel Consumption (kg/kW-hr) 1.854
2. Torque (N-m) 3.498
3. Brake Thermal Efficiency (percentage) 2.187
4. Hydro Carbon 5.71
5. Carbon monoxide 2.837

92
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