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



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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



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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.



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min

min

min

max

min

max

( )*( )y y

y x x yx x

−= − +

− (1)

Tangent-Sigmoid transfer function is chosen for hidden layer which is represented by following

equations.

1 exp( )( )

1 exp( )

xb x

x

σ

σ

− −=

+ − (2)

'( ) [(1 ( ))(1 ( )]2

b x b x b xσ

= + − (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 2MSE= 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)

zj= hidden unit j

voj

= bias on hidden units

w

ok= bias on output unit k

vij

= weight connection between ith input node and

jth hidden node

wjk

=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.



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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



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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



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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



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