An Experimental Study of Variable Compression Ratio Engine Using Diesel Blend - A Computing Approach

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-3, Issue-8, Aug- 2017]
https://dx.doi.org/10.24001/ijaems.3.8.12 ISSN: 2454-1311
www.ijaems.com Page | 886
An Experimental Study of Variable
Compression Ratio Engine Using Diesel Blend -
A Computing Approach
S. Vinodh1
, A. Bragadeesh2
, P. Nishanth3
1
Assistant professor, Department of Aeronautical, Mahendra Engineering College, Namakkal, India
2,3
Student, Department of Aeronautical, Mahendra Engineering College, Namakkal, India
Abstract— Increase in the scarcity of the fossil fuels,
prices and global warming have generated an interest in
developing alternate fuel for engine. Technologies now
focusing on development of plant based fuel, plant oils and
plant fats as alternative fuel. The present work deals with
finding the better compression ratio for the honne oil
diesel blend fueled C.I engine at variable load and
constant speed operation. In order to find out optimum
compression ratio, experiments are carried out on a single
cylinder four stroke variable compression ratio diesel
engine. Engine performance tests are carried out at
different compression ratio values. The optimum
compression ratio that gives better engine performance is
found from the experimental results. Using experimental
data Artificial Neural Network (ANN) model was
developed and the values were predicted using ANN.
Finally the predicted values were validated with the
experimentally.
Keywords— biofuel, ANN, feed forward back
propagation, variable compression ratio diesel engine,
optimization.
I. INTRODUCTION
Predicting and analysis of chemical reactions of fuels are
important tasks because they can unlock insights into the
complicated dynamics of systems which are difficult or
expensive to test experimentally. Optimisation of the
compression ratio of diesel fuelled with variable
compression ratio C.I engine at various compression ratios
and concluded that at compression ratio 14.8
improvements in performance and emission characteristics
were attained [9]. Investigation of the performance and
emission parameters of methyl ester of cottonseed oil
blended with diesel in a single cylinder variable
compression ratio diesel engine at a constant speed of
1500rpm and concluded that the highest brake thermal
efficiency and lowest specific fuel consumption were
observed for 5% biodiesel blend for compression ratio of
15and 17and 20% biodiesel blend for compression ratio of
19 and also the maximum NOx emission were observed for
20% biodiesel blend at a compression ratio of 17and also
observed CO emissions and smoke in the full range of
compression ratio and loads [8]. The Variable
Compression Ratio SI engine performance and emission
using pure petrol, pure LPG, Ethanol blends and kerosene
blends and observed that the LPG is a promising fuel at all
loads lesser carbon monoxide emission compared with
other fuels tested and also found that petrol mixed with
ethanol at 10% by volume is better at all loads and
compression ratios [8]. Investigation of the performance
parameters and exhaust emission parameters using
different percentage of blends of Honne oil with diesel at
various compression ratios. The experimental results were
used to train the Artificial Neural Networks (ANNs) which
is used to predict the Engine performance and emission
characteristics of the engine. ANN results shows good
correlation between the ANN predicted values and the
experimental values for various engine performance values
and the exhaust emissions [7]. Investigation of the output
parameters of CI engine using diesel and sunflower oil
blends and experimentally calculated values can be used
for training several neural networks using MATLAB. They
calculated the trained error values by comparing the output
values given by these trained networks with experimental
values. The Maximum percentage error between
experimental and theoretical values is 0.1691 and
suggested that the ANN can be used as a powerful tool to
optimize the input parameters in the C.I engine [3].
Biodiesel (palm oil methyl ester) blends in a direct
injection diesel engine study shows that the lower blends
of biodiesel increases the brake thermal efficiency, reduces
the fuel consumption and produce lower engine emissions
than neat diesel [4]. The performance and emission
characteristics of neat diesel (ND) and neat honne oil
(H100) as fuel in DI diesel engine and result shows, at
maximum load, brake thermal efficiency and NOx
emission decreased whereas emissions like CO, HC
increased [5]. The performance characteristics of variable
compression ratio diesel engine using ethanol blends with
diesel [1]. The artificial neural network modelling of a

diesel engine using nakthamala oil biodiesel fuel to predict
the exhaust emissions of C.I.engine. The experimental
results reveals that blends of nakthamala oil methyl ester
(NOME) provide improved emission characteristics and
ANN results showed good correlation between the ANN
predicted values and the desired values for various engine
exhaust emissions [2].
From the above studies it is clear that biodiesel can be
used as an alternate for diesel without modifications in the
C. I Engine and ANN can be used as a prediction
algorithm to get better correlation with the experimental
values.
II. TRANSESTERIFICATION
Transestrification if the process of using alcohol(e.g.
Methanol, ethanol or butanol),in the presence of a catalyst,
such a sodium hydroxide or potassium hydroxide, to break
the molecule of the renewable oil chemically into methyl
or ethyl ester of the renewable oil, with glycerol as a by
product. Biodiesel, defined as the mono-alkyl esters of
fatty acids derived from vegetable oil or animal fat, in
application ass an extender for combustion in diesel
engines, has demonstrated a number of promising
characteristics, including reduction of exhaust emissions.
Transesterified, renewable oils have proven to be a viable
altemative diesel engine fuel with characteristics similar
to those of diesel fuel. The transesterification reaction
proceeds with or without catalyst by using primary or
secondary monohydric aliphatic alcohols having 1-8carbon
atoms as follows:
III. OF A DIESEL ENGINE USING NAKTHAMALA
OIL BIODIESEL FUEL TO PREDICT THE
EXHAUST EMISSIONS OF C.I. ENGINE.
Triglycerides + Monohydric alcohol = Glycerine +
Mono-alkyl esters.
A. Properties of Calophylluminophyllum oil
In this present work the calophylluminophyllum oil methyl
ester is produced by transesterification process.
Transesterification is a chemical reaction between
triglyceride and alcohol in the presence of
catalyst.Transesterification makes the viscosity lowered.
The purified calophylluminophyllum oil methyl ester was
then blended with petroleum diesel in various
concentrations for preparing biodiesel blends to be used in
CI engine for conducting experiments. The properties of
calophyllum inophylum oil are listed in Table I
Table I: Properties of Calophylluminophyllum oil
Properties Units HSD Honne Oil
Density At 30℃ kg/m3
830 910
Kinematic Viscosity mm2
/s 3.12 (32.48±2)
At 40℃
Flash Point ℃ 56 120
Fire Point ℃ 75 160
Pour Point ℃ (-16±1) (-08±1)
Heating Value kJ/kg 43 000 39 100
IV. EXPERIMENTAL INVESTIGATIONS
Variable compression ratio diesel engine test rig was
shown in the Fig.1 and its specifications are given bellow.
Bore in mm - 80
Stroke in mm - 110
Rated speed in rpm - 1500
Rated power output in KW - 5
Method of Loading -Eddy current
Compression ratio - 13:1 to 20:1
Diameter of orifice in mm - 20
Type of ignition -compression ignition
Method of starting - manual cranking
Method of cooling - water cooling
No of cylinder - 1
Fig. 1: Variable compression ratio diesel engine test rig
experimental setup
A. Experimental procedure
The experiment is carried out on a single cylinder four
stroke variable compression ratio vertical diesel engine test
rig which has coupled to eddy current dynamometer.
 Initially the engine was started by manual
cranking in proper direction and the
decompression lever is used for easy cranking.
 The engine is allowed to run to attain the steady
state.
 Set the engine to a particular compression ratio.
 Apply various load and note down the time taken
for 10CC fuel consumption.

 Repeat the experiment for various compression
ratios and corresponding readings are noted.
The following engine tests were performed in single
cylinder variable compression ratio diesel engine test rig
with diesel, and calophylluminophyllum methyl ester
blended with diesel for various concentrations and finds
the performance and Emission characteristic is studied.
The best performance was obtained for IC engine using
diesel blend at 25% of bio-diesel and 75% of diesel.
Thus the tests are carried out for varying the compression
ratios (14:1 to 18:1) and various blend concentrations as
follows.
25% of calophylluminophyllum methyl ester +75% of
diesel.
B. Performance curves
The following performance curves were plotted using
the values obtained
 Load VsSpecific Fuel Consumptions (SFC)
 Load Vs Brake Thermal Efficiency
 Load Vs Mechanical Efficiency
V. EXPERIMENTAL RESULTS AND DISCUSSION
The performance tests are carried out on variable
compression ratio (VCR) engine under different
compression ratio (C.R) and loads using Diesel as fuel and
the results are tabulated in the Table II.
The performance tests are carried out on variable
compression ratio (VCR) engine under different
compression ratio (C.R) and loads using 25% honne oil
diesel blend as fuel and the results are tabulated in the
Table III.
Table II: Performance observations for 100% diesel
C.R LOA
D
B.P TFC SFC Meff ITE BTE IP
Kg KW Kg/hr Kg/KW-hr
Hr
% % % KW
14:1 1 0.3151 0.4788 1.5194 22.270 24.059 5.3580 1.4151
14:1 2 0.6238 0.6156 0.9868 36.190 22.794
9
8.2495 1.7238
14:1 4 1.2276 0.7358 0.5993 52.741 25.753
2
13.582
6
2.3271
14:1 6 1.8365 1.005 0.5475 62.54 23.774
0
14.868
6
2.9365
15:1 1 0.3173 0.464 1.4624 19.382 28.720
8
5.5669
3
1.6373
15:1 2 0.6238 0.5801 0.9299 32.094 27.277
7
8.7545
8
1.9438
15:1 4 1.2356 0.7358 0.5954 48.350 28.276
4
13.671
7
2.5556
15:1 6 1.8378 0.9427 0.5129 58.198 27.269
2
15.870
3
3.1578
16:1 1 0.3183 0.4713 1.4805 19.794 27.778
2
5.4986 1.6083
16:1 2 0.6254 0.5915 0.9457 32.654 26.362
4
8.6083 1.9154
16:1 4 1.2316 0.7735 0.6280 48.842 26.539
0
12.962 2.5216
16:1 6 1.8414 0.9731 0.5284 58.804 26.196
3
15.404 3.1314
17:1 1 0.3173 0.4132 1.3021 22.711 27.527
7
6.2520 1.3973
17:1 2 0.6254 0.5801 0.9275 36.674 23.932 8.7771 1.7054
17:1 4 1.2372 0.7938 0.6416 53.393 23.762
8
12.687 2.3172
17:1 6 1.8390 1.0056 0.5468 63.001 23.631
6
14.888 2.9190
18:1 1 0.3183 0.4022 1.2634 22.931 28.099
9
6.4437 1.3883
18:1 2 0.6278 0.5485 0.8735 36.980 25.200
6
9.3194 1.6978
18:1 4 1.2396 0.7182 0.5794 53.673 26.178
2
14.050 2.3096
18:1 6 1.8378 0.9141 0.4974 63.202 25.895 16.36 2.9078
Table III: Performance observations for 25% of Calophylluminophyllum + 75% diesel
C.R LOAD B.P TFC SFC Meff ITE BTE IP
Kg KW Kg/hr Kg/KW-hr % % % KW
14:1 1 0.3149 0.4502 1.4296 23.4174 27.4106 6.4188 1.3449
14:1 2 0.6254 0.5746 0.9186 37.7824 26.4375 9.9887 1.6554
14:1 4 1.2235 0.7015 0.5733 54.2952 29.476 16.004 2.2535
14:1 6 1.830 0.9577 0.5231 63.9930 27.4093 17.5401 2.860
15:1 1 0.3163 0.4404 1.3920 24.7863 26.5951 6.5919 1.2763
15:1 2 0.6198 0.5485 0.8849 39.2346 26.4311 10.370 1.5798
15:1 4 1.2343 0.7015 0.5681 56.2610 28.7087 16.151 2.1948
15:1 6 1.840 0.942 0.5123 65.7169 27.2571 17.912 2.800
16:1 1 0.3171 0.4372 1.3785 25.4310 26.176 6.6569 1.2471

16:1 2 0.6250 0.5485 0.8775 40.1959 26.0167 10.457 1.5550
16:1 4 1.2340 0.7015 0.5685 57.0251 28.3057 16.141 2.1640
16:1 6 1.8359 0.9282 0.5054 66.3700 27.3386 18.1446 2.765
17:1 1 0.3171 0.4249 1.3396 26.0578 26.2873 6.8492 1.2171
17:1 2 0.6238 0.5485 0.8792 40.9399 25.4946 10.437 1.5238
17:1 4 1.2364 0.6935 0.5603 57.8744 28.2699 16.3610 2.1364
17:1 6 1.8378 0.942 0.51297 67.1269 26.6495 17.8890 2.7378
18:1 1 0.3165 0.3969 1.2539 21.5854 33.9041 7.31835 1.4665
18:1 2 0.6254 0.5535 0.8849 36.4609 28.4393 10.3692 1.7154
18:1 4 1.2316 0.6935 0.5630 53.0504 30.7200 16.2974 2.3216
18:1 6 1.8358 0.8619 0.4696 62.7400 31.1450 19.5404 2.9253
A. Performance curves for 25% honne oil and 75% diesel blend.
Fig. 2: Specific fuel consumption Vs Load
Fig. 3: Mechanical Efficiency Vs Load
Fig. 4: Brake Thermal Efficiency Vs Load

B. Summary
Based on the experimental data and the graphs were drawn
for the different compression ratio and load with SFC,
mechanical efficiency and brake thermal efficiency.The
Fig 2, 3 and 4 shows that the SFC is almostequal for all the
compression ratio. But the mechanical and brake thermal
efficiency are more for the compression ratio of 18:1 than
other values.
VI. ARTIFICIAL NEURAL NETWORK
The first and arguably most simple type of artificial neural
network is the feed neural network. Information are moves
in only one direction forwards in this network: Data goes
through the hidden nodes (if any) and to the output nodes
from the input nodes. There are no cycles and loops in this
network. Feed forward networks can be constructed from
different types of units, e.g. binary McCulloch-Pitts
neurons, the simplest example being the preception.
Continuous neurons, frequently with sigmoidal activation,
are used in the context of the back propagation of error.
Fig.5: Simple feed forward back propagation network
In the model, 80%of the data set was randomly assigned as
the training set, while the remaining 20%of the data are
put aside for prediction and validation. From all of the
trained networks, any ones could provide this condition,
from which the simplest network was chosen to have a
more precise investigation into the model. The correlation
between the predicted values by the ANN model and the
measured values resulted from experimental tests are very
high.
A. Prediction output responses using ANN
Table IV: Artificial Neural Network
Output responses ANN
Brake power 2-3-2-1
SFC 2-2-3-1
Mechanical efficiency 2-2-2-1
The training algorithm of Feed Forward Back-Propagation
isselected for predicting engine parameters. The input
response is selected as Compression Ratio and Load also
Speed and volume of fuel consumed are taken as constant
and the outputs are tabulated in Table V
Table.V: Comparison experimental and predicted values
of Brake Power
C.R LOAD BRAKE POWER (KW) % of
error
Kg
EXPERIMENTAL
VALUES
PREDICTED
VALUE
14:01 1 0.3149 0.2952 6.26
14:01 2 0.6254 0.6056 3.17
14:01 4 1.2235 1.2041 1.59
14:01 6 1.83 1.8102 1.08
15:01 1 0.3163 0.2965 6.26
15:01 2 0.6198 0.6005 3.11
15:01 4 1.2343 1.2145 1.60
15:01 6 1.84 1.8202 1.08
16:01 1 0.3171 0.2973 6.24
16:01 2 0.625 0.6052 3.17
16:01 4 1.234 1.2142 1.60
16:01 6 1.8359 1.8161 1.08
17:01 1 0.3171 0.2973 6.24
17:01 2 0.6238 0.604 3.17
17:01 4 1.2364 1.2166 1.60
17:01 6 1.8378 1.818 1.08
18:01 1 0.3165 0.2967 6.26
18:01 2 0.6254 0.6056 3.17
18:01 4 1.2316 1.2118 1.61
18:01 6 1.8358 1.816 1.08
The below Fig. 6 (a), (b), (c), (d), (e) shows that the
predicted neural network (2-3-2-1) for brake power. It is
seen that the predicted values are very close to the
experimental results and the maximum error is 6.26%.
(a)

(b)
(c)
(d)
(e)
Fig. 6: Comparison of Predicted and Experimental Values
of brake power
Then again the input response is selected as Compression
Ratio and Load also Speed and volume of fuel consumed
are taken as constant. Output response is Specific fuel
consumption and it is tabulated in Table VI
Table VI: Comparison experimental and predicted values
of Specific Fuel Consumption
C.R
LOAD
SPECIFIC FUEL
CONSUMPTION (kg/kWhr) % of
error
Kg
EXPERIMENTAL
VALUES
PREDICTED
VALUE
14:01 1 1.4296 1.4093 1.42
14:01 2 0.9186 0.8988 2.16
14:01 4 0.5733 0.5539 3.38
14:01 6 0.5231 0.5033 3.79
15:01 1 1.392 1.3722 1.42
15:01 2 0.8849 0.8656 2.18
15:01 4 0.5681 0.5483 3.49
15:01 6 0.5123 0.4925 3.86
16:01 1 1.3785 1.3587 1.44
16:01 2 0.8775 0.8577 2.26
16:01 4 0.5685 0.5487 3.48
Table VI contd.: Comparison experimental and predicted
values of Specific Fuel Consumption
C.R
LOAD
SPECIFIC FUEL
CONSUMPTION
(kg/kWhr)
LOAD
% of
error
kg
EXPERIMENTAL
VALUES
kg
16:01 6 0.5054 0.4856 3.92
17:01 1 1.3396 1.3198 1.48
17:01 2 0.8792 0.8594 2.25
17:01 4 0.5603 0.5405 3.53
17:01 6 0.51297 0.49317 3.86
18:01 1 1.2539 1.2341 1.58
18:01 2 0.8849 0.8651 2.24
18:01 4 0.563 0.5432 3.52
18:01 6 0.4696 0.4498 4.22
predicted neural network (2-3-2-1) for SFC.It is seen that
the predicted values are very close to the experimental
results and the maximum error is 3.86%.
(a)

(b)
(c)
(d)
(e)
Fig. 7: Comparison of Predicted and Experimental
Values of SFC
Then again input response is selected as Compression
Ratio and Load also Speed and volume of fuel consumed
are taken as constant. Output response is Mechanical
Efficiency and the outputs are tabulated in Table VII
Table VII: Comparison experimental and predicted values
of brake thermal efficiency
C.R LOAD
MECHANICAL EFFICIENCY
(%) % of
error
kg
EXPERIMENTAL
VALUES
PREDICTED
VALUE
14:01 1 23.4174 22.4644 4.07
14:01 2 37.7824 36.8294 2.52
14:01 4 54.2952 53.3422 1.76
14:01 6 63.993 63.04 1.49
15:01 1 24.7863 23.8333 3.84
15:01 2 39.2346 38.2816 2.43
15:01 4 56.261 55.308 1.69
15:01 6 65.7169 64.7639 1.45
16:01 1 25.431 24.478 3.75
16:01 2 40.1959 39.2429 2.37
16:01 4 57.0251 56.0721 1.67
16:01 6 66.37 65.417 1.44
17:01 1 26.0578 25.1048 3.66
17:01 2 40.9399 39.9869 2.33
17:01 4 57.8744 56.9214 1.65
17:01 6 67.1269 66.1739 1.42
18:01 1 21.5854 20.6324 4.42
18:01 2 36.4609 35.5079 2.61
18:01 4 53.0504 52.0974 1.80
18:01 6 62.74 61.787 1.52
predicted neural network (2-3-2-1) for mechanical
efficiency. The values are more accurate with the
maximum error of 3.84%..
(a)
(b)

(c)
(d)
(e)
Fig.8: Comparison of Predicted and Experimental Values
of Mechanical Efficiency.
VII. CONCLUSION
The performance test on VCR engine is carried out for the
engine fueled with 25% biodiesel blend and neat diesel.
The experimental results of both the fuels are compared.
Thus it is understood thatthe25 % biodiesel blend produces
better performance than base fuel at the compression ratio
of 18:1
The training algorithm of Feed Forward Back-Propagation
is selected for predicting engine parameters. The input
responses selected are Compression Ratio and Load by
taking speed as constant. Then, the ANN results are
validated with experimental results and found satisfactory.
Thus it can be concluded that the training algorithm of
feed forward back-propagation is sufficient in predicting
engine performance parameters.
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