Abstract

This article focuses on the use of a rule-based Mamdani-type fuzzy expert system for the prediction of Pmax, HRRmax, ID, and CD as combustion parameters, BTE and BSFC as engine performance parameters, and CO, CO2, HC, and NOx as exhaust emission parameters of fuel blends formed by blending waste bread bioethanol with gasoline in different proportions. For modeling of 55 test conditions created by being operated test engine with 11 different test fuels under five different engine loads. As a result of the study, while combustion parameters were predicted with correlation coefficients in the range of 0.948–0.973% for waste bread bioethanol-gasoline blends, correlation coefficients for engine performance and exhaust emission parameters were in the range of 0.968–0.977% and 0.955–0.991% respectively. Similarly, the ranges of correlation coefficients obtained for sugar beet bioethanol-gasoline blends with fuzzy expert system were as follows: 0.967–0.971% for engine performance parameters, 0.955–0.978% for exhaust emission parameters, and 0.951–0.964% for combustion parameters. These results prove that costly and labor-intensive engine tests can be predicted with minimum effort and high accuracy with the developed model.

References

1.
Quader
,
M. A.
,
Ahmed
,
S.
, and
Ahmed
,
S.
,
2014
, “
Trends of Energy Consumption and Driving Forces for Mitigation of CO2 Emissions in Malaysia: A Multi-Sectoral Analysis
,”
5th Brunei International Conference on Engineering and Technology (BICET 2014)
,
Bandar Seri Begawan
,
Nov. 1–3
.
2.
Lee
,
D.-Y.
,
Ebie
,
Y.
,
Xu
,
K.-Q.
,
Li
,
Y.-Y.
, and
Inamori
,
Y.
,
2010
, “
Continuous H2 and CH4 Production From High-Solid Food Waste in the Two-Stage Thermophilic Fermentation Process With the Recirculation of Digester Sludge
,”
Bioresour. Technol.
,
101
(
1
), pp.
S42
S47
.
3.
Najafi
,
G.
,
Ghobadian
,
B.
,
Moosavian
,
A.
,
Yusaf
,
T.
,
Mamat
,
R.
,
Kettner
,
M.
, and
Azmi
,
W. H.
,
2016
, “
SVM and ANFIS for Prediction of Performance and Exhaust Emissions of a SI Engine With Gasoline–Ethanol Blended Fuels
,”
Appl. Therm. Eng.
,
95
, pp.
186
203
.
4.
Ilangkumaran
,
M.
,
Sakthivel
,
G.
,
Syam Kumar
,
U.
,
Vasudevan
,
M.
,
Venkatesh
,
S.
, and
Viswanathan
,
B.
,
2015
, “
Development of Fuzzy Logic Model to Predict the Engine Performance of Fish Oil Biodiesel With Diethyl Ether
,”
Int. J. Ambient Energy
,
36
(
3
), pp.
142
154
.
5.
Dey
,
S.
,
Reang
,
N. M.
,
Majumder
,
A.
,
Deb
,
M.
, and
Das
,
P. K.
,
2020
, “
A Hybrid ANN-Fuzzy Approach for Optimization of Engine Operating Parameters of a CI Engine Fueled With Diesel-Palm Biodiesel-Ethanol Blend
,”
Energy
,
202
, pp.
117813
.
6.
Uslu
,
S.
, and
Celik
,
M. B.
,
2020
, “
Performance and Exhaust Emission Prediction of a SI Engine Fueled With I-Amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based Optimization
,”
Fuel
,
265
, pp.
116922
.
7.
Isin
,
O.
, and
Uzunsoy
,
E.
,
2013
, “
Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System
,”
Arab. J. Sci. Eng.
,
38
(
12
), pp.
3485
3493
.
8.
Dhande
,
D. Y.
,
Choudhari
,
C. S.
,
Gaikwad
,
D. P.
,
Sinaga
,
N.
, and
Dahe
,
K. B.
,
2022
, “
Prediction of Spark Ignition Engine Performance With Bioethanol-Gasoline Mixes Using a Multilayer Perception Model
,”
Pet. Sci. Technol.
,
40
(
12
), pp.
1
25
.
9.
Fu
,
J.
,
Yang
,
R.
,
Li
,
X.
,
Sun
,
X.
,
Li
,
Y.
,
Liu
,
Z.
,
Zhang
,
Y.
, and
Sunden
,
B.
,
2022
, “
Application of Artificial Neural Network to Forecast Engine Performance and Emissions of a Spark Ignition Engine
,”
Appl. Therm. Eng.
,
201
, p. 117749.
10.
Panda
,
J. K.
,
Sastry
,
G. R. K.
, and
Rai
,
R. N.
,
2017
, “
A Taguchi-Fuzzy-Based Multi-Objective Optimization of a Direct Injection Diesel Engine Fueled With Different Blends of Leucas Zeylanica Methyl Ester and 2-Ethylhexyl Nitrate Diesel Additive With Diesel
,”
ASME J. Energy Resour. Technol.
,
139
(
4
), p. 042209.
11.
Bhowmik
,
S.
,
Panua
,
R.
,
Debroy
,
D.
, and
Paul
,
A.
,
2017
, “
Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel–Kerosene–Ethanol Blends: A Fuzzy-Based Optimization
,”
ASME J. Energy Resour. Technol.
,
139
(
4
), p. 042201.
12.
Sakthivel
,
G.
,
2016
, “
Prediction of CI Engine Performance, Emission and Combustion Characteristics Using Fish Oil as a Biodiesel at Different Injection Timing Using Fuzzy Logic
,”
Fuel
,
183
, pp.
214
229
.
13.
Sayın Kul
,
B.
, and
Ciniviz
,
M.
,
2021
, “
An Evaluation Based on Energy and Exergy Analyses in SI Engine Fueled with Waste Bread Bioethanol-Gasoline Blends
,”
Fuel
,
286
(
Part 2
).
14.
Sayin Kul
,
B.
, and
Ciniviz
,
M.
,
2020
, “
Assessment of Waste Bread Bioethanol-Gasoline Blends in Respect to Combustion Analysis, Engine Performance and Exhaust Emissions of a SI Engine
,”
Fuel
,
277
, pp.
118237
.
15.
Zadeh
,
L. A.
,
1997
, “
Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic
,”
Fuzzy Sets Syst.
,
90
(
2
), pp.
111
127
.
16.
Bellman
,
R. E.
, and
Zadeh
,
L. A.
,
1970
, “
Decision-Making in a Fuzzy Environment
,”
Manage. Sci.
,
17
(
4
), pp.
B-141
B-164
.
17.
Kiran
,
T. R.
, and
Rajput
,
S.
,
2011
, “
An Effectiveness Model for an Indirect Evaporative Cooling (IEC) System: Comparison of Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Inference System (FIS) Approach
,”
Appl. Soft Comput.
,
11
(
4
), pp.
3525
3533
.
18.
Tabar
,
M. E.
, and
Şişman
,
Y.
, “
Bulanık Mantık ile Arsa Değerleme Modelinin Oluşturulması
,”
Türkiye Arazi Yönetimi Dergisi
,
2
(
1
), pp.
18
24
.
19.
Rizvi
,
S.
,
Mitchell
,
J.
,
Razaque
,
A.
,
Rizvi
,
M. R.
, and
Williams
,
I.
,
2020
, “
A Fuzzy Inference System (FIS) to Evaluate the Security Readiness of Cloud Service Providers
,”
J. Cloud Comput.
,
9
(
1
), pp.
1
17
.
20.
Park
,
S. H.
,
Kim
,
D. S.
,
Kim
,
J. H.
, and
Na
,
M. G.
,
2014
, “
Prediction of the Reactor Vessel Water Level Using Fuzzy Neural Networks in Severe Accident Circumstances of NPPs
,”
Nucl. Eng. Technol.
,
46
(
3
), pp.
373
380
.
21.
Volna
,
E.
,
Jarusek
,
R.
,
Kotyrba
,
M.
, and
Zacek
,
J.
,
2021
, “
Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network
,”
Appl. Math. Comput.
,
398
, pp.
125994
.
22.
Thaker
,
S.
, and
Nagori
,
V.
,
2018
, “
Analysis of Fuzzification Process in Fuzzy Expert System
,”
Procedia Comput. Sci.
,
132
, pp.
1308
1316
.
23.
Li
,
Y.
,
He
,
Y.
,
Su
,
Y.
, and
Shu
,
L.
,
2016
, “
Forecasting the Daily Power Output of a Grid-Connected Photovoltaic System Based on Multivariate Adaptive Regression Splines
,”
Appl. Energy
,
180
, pp.
392
401
.
24.
Mamdani
,
E.
, and
Assilian
,
S.
,
1999
, “
An Experiment in Linguistic Synthesis With a Fuzzy Logic Controller
,”
Int. J. Hum-Comput. Stud.
,
51
(
2
), pp.
135
147
.
25.
Mali
,
G. U.
, and
Gautam
,
D.
,
2018
, “
Shortest Path Evaluation in Wireless Network Using Fuzzy Logic
,”
Wirel. Pers. Commun.
,
100
(
4
), pp.
1393
1404
.
26.
Shaban
,
W. M.
,
Rabie
,
A. H.
,
Saleh
,
A. I.
, and
Abo-Elsoud
,
M. A.
,
2021
, “
Detecting COVID-19 Patients Based on Fuzzy Inference Engine and Deep Neural Network
,”
Appl. Soft Comput.
,
99
, pp.
106906
.
27.
Rabie
,
A. H.
,
Ali
,
S. H.
,
Ali
,
H. A.
, and
Saleh
,
A. I.
,
2019
, “
A Fog Based Load Forecasting Strategy for Smart Grids Using Big Electrical Data
,”
Clust. Comput.
,
22
(
1
), pp.
241
270
.
28.
Užga-Rebrovs
,
O.
, and
Kuļešova
,
G.
,
2017
, “
Comparative Analysis of Fuzzy Set Defuzzification Methods in the Context of Ecological Risk Assessment
,”
Inf. Technol. Manage. Sci.
,
20
(
1
), pp.
25
29
.
29.
İnel
,
M. N.
, and
Armutlulu
,
İH
,
2016
, “
BELİRSİZLİK ORTAMINDA FUZZY FİNANSAL ORANLARLA KARAR VERME
,”
Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi
,
38
(
2
), pp.
129
145
.
30.
Verbruggen
,
H. B.
,
Zimmermann
,
H.-J.
, and
Babuška
,
R.
,
2013
,
Fuzzy Algorithms for Control
, Vol.
14
,
Springer Science & Business Media
,
New York
.
31.
Gaya
,
M. S.
,
Zango
,
M. U.
,
Yusuf
,
L. A.
,
Mustapha
,
M.
,
Muhammad
,
B.
,
Sani
,
A.
,
Tijjani
,
A.
,
Wahab
,
N. A.
, and
Khairi
,
M. T. M.
,
2017
, “
Estimation of Turbidity in Water Treatment Plant Using Hammerstein-Wiener and Neural Network Technique
,”
Indones. J. Electr. Eng. Comput. Sci.
,
5
(
3
), pp.
666
672
.
32.
Hota
,
H.
,
Handa
,
R.
, and
Shrivas
,
A.
,
2017
, “
Time Series Data Prediction Using Sliding Window Based RBF Neural Network
,”
Int. J. Comput. Intell. Syst.
,
13
(
5
), pp.
1145
1156
.
You do not currently have access to this content.