comparative study of ethanol and isobutanol as...
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COMPARATIVE STUDY OF ETHANOL AND ISOBUTANOL AS GASOLINE BLEND FOR INTERNAL
COMBUSTION ENGINE
MOHD NUR ASHRAF BIN MOHD YUSOFF
FACULTY OF ENGINEERING
UNIVERSITY OF MALAYA KUALA LUMPUR
2018
COMPARATIVE STUDY OF ETHANOL AND
ISOBUTANOL AS GASOLINE BLEND FOR INTERNAL
COMBUSTION ENGINE
MOHD NUR ASHRAF BIN MOHD YUSOFF
DISSERTATION SUBMITTED IN FULFILMENT OF
THE REQUIREMENTS FOR THE DEGREE OF MASTER
OF ENGINEERING SCIENCE
FACULTY OF ENGINEERING
UNIVERSITY OF MALAYA
KUALA LUMPUR
2018
iii
UNIVERSITY OF MALAYA
ORIGINAL LITERARY WORK DECLARATION
Name of Candidate: Mohd Nur Ashraf bin Mohd Yusoff (I.C. No: )
Matric No: KGA150025
Name of Degree: Master of Engineering Science
Title of Project Paper/Research Report/Dissertation/Thesis (“this Work”):
Comparative Study of Ethanol and Isobutanol as Gasoline Blend for Internal
Combustion Engine
Field of Study: Energy
I do solemnly and sincerely declare that:
(1) I am the sole author/writer of this Work;
(2) This Work is original;
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sufficiently and the title of the Work and its authorship have been
acknowledged in this Work;
(4) I do not have any actual knowledge nor do I ought reasonably to know that the
making of this work constitutes an infringement of any copyright work;
(5) I hereby assign all and every rights in the copyright to this Work to the
University of Malaya (“UM”), who henceforth shall be owner of the copyright
in this Work and that any reproduction or use in any form or by any means
whatsoever is prohibited without the written consent of UM having been first
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(6) I am fully aware that if in the course of making this Work I have infringed any
copyright whether intentionally or otherwise, I may be subject to legal action
or any other action as may be determined by UM.
Candidate’s Signature Date:
Subscribed and solemnly declared before,
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iv
COMPARATIVE STUDY OF ETHANOL AND ISOBUTANOL AS GASOLINE
BLEND FOR INTERNAL COMBUSTION ENGINE
ABSTRACT
The heavy reliance on petroleum-derived fuels such as gasoline in the transportation
sector is one of the major causes of environmental pollution. For this reason, there is a
critical need to develop cleaner alternative fuels. The alcohols such as ethanol and
isobutanol can be blended with gasoline to produce cleaner alternative fuels because of
their favorable physicochemical properties. This study examined the effect of single and
dual alcohol of ethanol and isobutanol in gasoline blends on the engine performance and
emission characteristics of a spark ignition engine. Six types of fuel blends consist of both
alcohols were mixed with unleaded gasoline at different volume rates (E10, E20, iB10,
iB20, E5iB5 and E10iB10) and the physicochemical properties of these blends are
measured and compared with the pure gasoline. Tests are conducted on a SI engine at
wide open throttle position by varying the engine speed ranging from 1000 to 5000 rpm
with step of 1000 rpm. Then, a constant- speed test was conducted at 4000 rpm by varying
engine torque ranging from 20 to 100 Nm with step of 20 Nm The results show that the
fuel blends have a significant increase in the density, viscosity and heat of vaporization,
but they are lower in heating value and Reid vapor pressure as compared with pure
gasoline. In addition, the E20 blend gives the most significant enhancement for torque,
brake power and brake thermal efficiency with an average enhancement of 3.88, 2.60 and
13.61%, respectively, while E10iB10 blend gives the largest improvement of brake
specific fuel consumption with an average enhancement of 5.77% than that of pure
gasoline. There are no significant changes of exhaust gas temperature between the fuel
blends and pure gasoline for both operating conditions. In terms of exhaust emissions,
E10iB10 blend results in the lowest CO and HC emissions by varying the engine speeds
with an average reduction of 11.21 and 17.13%, respectively, relative to pure gasoline.
v
By varying the engine torque, E20 and E10iB10 blends result in the lowest CO and HC
emissions with an average reduction of 44.02 and 46.32%, respectively, with respect to
pure gasoline. However, all the tested fuel blends show higher CO2 emissions as
compared to pure gasoline for both operating conditions. In terms of NOX emission, there
is no significant differences for all fuel blends with pure gasoline for both conditions.
Keywords: ethanol, isobutanol, alternative fuel, spark ignition engine
vi
KAJIAN PERBANDINGAN ETANOL DAN ISOBUTANOL SEBAGAI
CAMPURAN GASOLIN UNTUK ENJIN PEMBAKARAN DALAM
ABSTRAK
Kebergantungan tinggi terhadap sumber bahan api petroleum seperti petrol dalam
sektor pengangkutan adalah salah satu punca utama berlakunya pencemaran alam sekitar.
Oleh sebab ini, terdapat keperluan kritikal untuk membangunkan bahan api alternatif
yang lebih bersih. Alkohol seperti etanol dan isobutanol boleh dicampur bersama petrol
untuk menghasilkan bahan api alternatif yang lebih bersih kerana sifat-sifat fizikokimia
yang baik. Kajian ini menyiasat kesan penggunaan alkohol tunggal dan dual; etanol dan
isobutanol dalam campuran petrol terhadap prestasi enjin dan ciri-ciri pelepasan ekzos
pada enjin penyalaan cucuh. Enam jenis campuran bahan api terdiri daripada kedua-dua
alkohol dicampurkan bersama petrol tanpa plumbum pada kadar isipadu yang berbeza
(E10, E20, iB10, iB20, E5iB5 dan E10iB10) dan sifat-sifat fizikokimia campuran tersebut
telah diuji dan dibandingkan dengan petrol tulen. Ujian dijalankan pada enjin penyalaan
pencucuh dengan mengubah kelajuaan enjin daripada 1000 kepada 5000 putaran per
minit (rpm) dengan peningkatan sebanyak 1000 rpm. Kemudian, ujian enjin pada
kelajuan malar dijalankan pada kelajuan 4000 rpm dengan mengubah tork enjin daripada
20 kepada 100 Newton meter (Nm) dengan peningkatan sebanyak 20 Nm. Keputusan
kajian menunjukkan campuran bahan api menunjukkan peningkatan yang ketara pada
nilai ketumpatan, kelikatan dan haba pengewapan, tetapi nilai pemanasan dan tekanan
wap Reid adalah lebih rendah berbanding petrol tulen. Tambahan pula, campuran E20
menunjukkan peningkatan yang paling ketara pada keputusan tork enjin, kuasa brek dan
kecekapan haba brek dengan peningkatan purata 3.88, 2.60 dan 13.61%, manakala
campuran E10iB10 menunjukkan peningkatan paling tinggi pada keputusan penggunaan
bahan api tertentu brek dengan peningkatan purata 5.77% berbanding petrol tulen. Tiada
perubahan ketara pada keputusan suhu gas ekzos antara campuran bahan api dan petrol
vii
tulen yang diuji untuk kedua-dua keadaan kendalian. Dari segi pelepasan ekzos,
campuran E10iB10 menunjukkan keputusan pelepasan karbon monoksida (CO) dan
hidrokarbon (HC) yang paling rendah pada kelajuan enjin yang berbeza dengan
pengurangan purata 9.67 and 16.06% berbanding petrol tulen. Pada perbezaan tork enjin,
campuran E20 dan E10iB10 masing-masing menunjukkan keputusan pelepasan karbon
monoksida (CO) dan hidrokarbon (HC) yang paling rendah dengan pengurangan purata
44.02 dan 46.32% berbanding petrol tulen. Walaubaimanapun, semua campuran bahan
api yang diuji menunjukkan peningkatan karbon dioksida (CO2) pada kedua-dua keadaan
kendalian. Dari segi pelepasan nitrogen oksida (NOX) pula, tiada perbezaan yang
signifikan bagi semua campuran bahan api yang diuji berbanding petrol tulen untuk
kedua-dua keadaan kendalian.
Keywords: etanol, isobutanol, bahan api alternatif, enjin penyalaan cucuh
viii
ACKNOWLEDGEMENTS
Alhamdulillah and thank you to Almighty Allah SWT for giving the ability, courage
and strength to complete this research work to the best of my abilities.
I am deeply indebted to my supervisors Dr. Nurin Wahidah Mohd Zulkifli and Prof.
Ir. Dr. Masjuki Haji Hassan for giving me an opportunity to work with them as well as
their valuable guidance, invaluable assistance, scientific advices and immense support
throughout the entire duration in my research. This research work would not have been
possible without their generous support and persistent involvement in this work.
I would like to offer my greatest appreciation to Ministry of Higher Education of
Malaysia and University of Malaya for the financial support through MyBrain15
scholarship, High Impact Research Grant (HIR/MOHE/ENG/60), Fundamental Research
Grant Scheme (FP051-2015A) and Grand Challenge Grant Scheme (GC003D-17SBS).
A token of gratitude also goes to all members in Centre for Energy Sciences;
Associate Prof. Dr. Md. Abul Kalam, Muhammad Syahir Amzar, Muhammad Harith,
Leang So Khuong, Tengku Muhammad Ibrahim, Muhammad Zulfattah and Azham Alwi
for their assistance, valuable comments, encouragement and suggestions which helped
me to finish this research.
Last but not least, my gratitude to my lovely parents, family members and friends
for their numerous helps and constant support to complete this research work.
ix
TABLE OF CONTENTS
Abstract ........................................................................................................................... iv
Abstrak ............................................................................................................................ vi
Acknowledgements ....................................................................................................... viii
Table of Contents ........................................................................................................... ix
List of Figures ................................................................................................................ xii
List of Tables ................................................................................................................ xiv
List of Symbols and Abbreviations .............................................................................. xv
CHAPTER 1: INTRODUCTION .................................................................................. 1
1.1 Overview.................................................................................................................. 1
1.2 Background .............................................................................................................. 3
1.3 Problem statement ................................................................................................... 7
1.4 Objectives of study .................................................................................................. 8
1.5 Scope of work .......................................................................................................... 9
1.6 Organization of dissertation ..................................................................................... 9
CHAPTER 2: LITERATURE REVIEW .................................................................... 11
2.1 Introduction............................................................................................................ 11
2.2 Ethanol and butanol as alternative transportation fuel .......................................... 12
2.3 Production of ethanol and butanol ......................................................................... 13
2.3.1 Ethanol ...................................................................................................... 13
2.3.2 Butanol ..................................................................................................... 15
2.4 Physicochemical properties of ethanol and butanol .............................................. 18
2.4.1 Ethanol ...................................................................................................... 19
2.4.2 Butanol ..................................................................................................... 21
x
2.5 Effect of ethanol and butanol addition in gasoline on engine performances ......... 22
2.6 Effect of ethanol and butanol addition in gasoline on exhaust emissions ............. 24
2.7 Summary and research gap .................................................................................... 28
CHAPTER 3: METHODOLOGY ............................................................................... 30
3.1 Introduction............................................................................................................ 30
3.2 Selection of alcohol fuels....................................................................................... 31
3.3 Fuel blending preparation ...................................................................................... 31
3.4 Measurement of physicochemical properties ........................................................ 32
3.4.1 Heat of vaporization ................................................................................. 33
3.4.2 Higher heating value ................................................................................ 34
3.4.3 Reid vapor pressure .................................................................................. 34
3.4.4 Density ...................................................................................................... 35
3.4.5 Kinetic and dynamic viscosity.................................................................. 35
3.4.6 Research octane number ........................................................................... 35
3.5 Engine test setup and engine performance analysis .............................................. 36
3.6 Exhaust emission analysis ..................................................................................... 39
3.7 Uncertainty analysis............................................................................................... 41
CHAPTER 4: RESULTS AND DISCUSSION .......................................................... 42
4.1 Introduction............................................................................................................ 42
4.2 Physicochemical properties of ethanol-butanol-gasoline fuel blends ................... 42
4.3 Engine performance ............................................................................................... 45
4.3.1 Engine torque ........................................................................................... 45
4.3.2 Brake power ............................................................................................. 47
4.3.3 Brake thermal efficiency .......................................................................... 49
4.3.4 Brake specific fuel consumption .............................................................. 52
xi
4.3.5 Exhaust gas temperature ........................................................................... 55
4.4 Exhaust emissions .................................................................................................. 58
4.4.1 Carbon monoxide emissions .................................................................... 58
4.4.2 Carbon dioxide emissions ........................................................................ 60
4.4.3 Hydrocarbon emissions ............................................................................ 63
4.4.4 Nitrogen oxides emissions ........................................................................ 65
4.5 Summary ................................................................................................................ 68
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ............................. 70
5.1 Conclusions ........................................................................................................... 70
5.2 Recommendations.................................................................................................. 71
References ...................................................................................................................... 72
List of Publications and Papers Presented ................................................................. 79
Appendix A .................................................................................................................... 80
xii
LIST OF FIGURES
Figure 1.1: World energy consumption, year 1990-2040 (EIA, 2016) ............................. 1
Figure 1.2: Chart for world ethanol fuel production by country/region and year (RFA,
2016) ................................................................................................................................. 4
Figure 2.1: First, second and third generation of biofuels feedstocks ............................ 13
Figure 2.2: Production process for bioethanol from lignocellulosic biomass (Hahn-
Hägerdal et al., 2006) ...................................................................................................... 15
Figure 2.3: Production process for biobutanol from lignocellulosic feedstocks (Ezeji &
Blaschek, 2008; H. Liu et al., 2013) ............................................................................... 17
Figure 2.4: Structural formula of ethanol ........................................................................ 20
Figure 2.5: Structural formulae of butanol isomers ........................................................ 21
Figure 2.6: The proportion data of exhaust emissions produced by SI engine (Soruşbay,
2015) ............................................................................................................................... 25
Figure 3.1: Flowchart of the research methodology ....................................................... 30
Figure 3.2: IKA KS130 basic shaker machine ................................................................ 32
Figure 3.3: 1.6L Proton CamPro engine ......................................................................... 36
Figure 3.4: Schematic diagram of engine test set-up ...................................................... 38
Figure 3.5: AVL DICOM 4000 exhaust emission analyzer ............................................ 40
Figure 4.1: Variation of engine torque as a function of engine speed for all tested fuel
blends .............................................................................................................................. 47
Figure 4.2: Variation of brake power as a function of engine speed for all tested fuel
blends .............................................................................................................................. 48
Figure 4.3: Variation of brake thermal efficiency as a function of (a) engine speed and (b)
engine torque for all tested fuel blends ........................................................................... 51
Figure 4.4: Variation of brake specific fuel consumption as a function of (a) engine speed
and (b) engine torque for all tested fuel blends ............................................................... 54
Figure 4.5: Variation of exhaust gas temperature as a function of (a) engine speed and (b)
engine torque for all tested fuel blends ........................................................................... 57
xiii
Figure 4.6: Variation of CO emissions as a function of (a) engine speed and (b) engine
torque for all tested fuel blends ....................................................................................... 60
Figure 4.7: Variation of CO2 emissions as a function of (a) engine speed and (b) engine
torque for all tested fuel blend ........................................................................................ 62
Figure 4.8: Variation of HC emissions as a function of (a) engine speed and (b) engine
torque for all tested fuel blends ....................................................................................... 65
Figure 4.9: Variation of NOX emissions as a function of (a) engine speed and (b) engine
torque for all tested fuel blends ....................................................................................... 67
xiv
LIST OF TABLES
Table 1.1: Statistical data for world fuel ethanol production by country/region .............. 4
Table 1.2: Ethanol-gasoline fuel blends used in different countries ................................. 5
Table 2.1: Bioethanol routes from different raw material of feedstocks ........................ 14
Table 2.2: Comparison of biomass feedstocks and their potential biobutanol yield/
productivity via different fermentation process .............................................................. 18
Table 2.3: Comparison of physicochemical properties of gasoline, ethanol and butanol
isomers ............................................................................................................................ 19
Table 2.4: Research study on alcohol fuel for past few years ......................................... 29
Table 3.1: Composition of ethanol, butanol and gasoline for blending .......................... 31
Table 3.2: Equipment and standard method used for testing fuel properties .................. 33
Table 3.3: Characteristics of the tested engine................................................................ 37
Table 3.4: Specifications of the exhaust emission analyzer ............................................ 40
Table 4.1: Physicochemical properties of tested ethanol-butanol-gasoline fuel blends . 42
xv
LIST OF SYMBOLS AND ABBREVIATIONS
EIA : United States Energy Information Administration
OECD : Organization for Economic Co-operation and Development
GHG : Greenhouse Gases
SI : Spark Ignition or Gasoline Engine
FFV : Flex Fuel Vehicle
RON : Research Octane Number
RVP : Reid Vapor Pressure
HHV : High Heating Value
LHV : Lower Heating Value
HOV : Heat of Vaporization
ASTM : American Society for Testing and Materials
MPFI : Multi-Point Fuel Injection
WOT : Wide Open Throttle
DOHC : Dual Overhead Camshaft
T : Torque
BP : Brake Power
BTE : Brake Thermal Efficiency
BSFC : Brake Specific Fuel Consumption
EGT : Exhaust Gas Temperature
VE : Volumetric Efficiency
CO : Carbon Monoxide
CO2 : Carbon Dioxide
HC : Hydrocarbon
NOX : Nitrogen Oxides
xvi
NO : Nitrogen Monoxide
NO2 : Nitrogen Dioxide
PM : Particulate Matters
E : Ethanol
iB : Isobutanol
E10 : Blend containing 10 vol.% of ethanol in gasoline
E20 : Blend containing 20 vol.% of ethanol in gasoline
iB10 : Blend containing 10 vol.% of isobutanol in gasoline
iB20 : Blend containing 20 vol.% of isobutanol in gasoline
E5iB5 : Blend containing 5 vol.% of ethanol and 5 vol.% of isobutanol in
gasoline
E10iB10 : Blend containing 10 vol.% of ethanol and 10 vol.% of isobutanol in
gasoline
PG : Pure Gasoline
Btu : British thermal unit
rpm : revolution per minute
1
CHAPTER 1: INTRODUCTION
1.1 Overview
Most recently, with the significant growth of populations and economic activities all
over the worlds, energy plays an important role for a sustainable development. The
demand for energy resources is increasing day by day, and it has been invaluable to
human activities, domestic life, transportation, manufacturing process, industrial
facilities, lighting, etc. According to United States Energy Information Administration
(EIA), the world energy demand increases from 549 quadrillion Btu in 2012 to 629
quadrillion Btu in year 2020 and to 815 quadrillion Btu in 2040 as it presented in Figure
1.1 (EIA, 2016). Most terrifyingly, the existing amount of fossil fuels i.e. petroleum, coal
and natural gas as the primary source of energy is decreasing and it is assumed to be
completely diminished for the next 40-50 years (Saidur et al., 2011).
Note: Organization for Economic Co-operation and Development (OECD) is an intergovernmental
economic organization with 35 member countries.
Figure 1.1: World energy consumption, year 1990-2040 (EIA, 2016)
2
Over time, the extensive use fossil fuels also adversely affect environmental pollution
as it tends to contribute greenhouse gases i.e. carbon dioxide (CO2) hence aggravates
global warming. The rise of global temperature caused by global warming could leads to
extinction of millions natural species and also brings harm to the ecosystem. It is shown
that the CO2 emissions has increased approximately 1.6 times in the last three decades by
the anthropogenic activities (Hosseini & Wahid, 2013). The emissions such as CO2, NOX,
CO and SO2 are emitted and they are extremely harmful for humans too.
In spite of that, the government seeks into new policies to empower renewable energy
to solve environmental issues e.g. Kyoto Protocol (KP) 1997 has mandates any countries
that involved in industrial activities must reduce by at least 5% pollutants below 1990
levels (Hosseini & Wahid, 2013). Scientists are looking forward to discover new
sustainable and renewable energy sources of energy or known as bio-energy. This bio-
energy is created from various natural resources to produce biofuels, which helps to
sustain the economic growth and living society for the next generation. Berndes et al.
(2003) believes that the bio-energy is able to contribute about 100 EJ/year to 400 EJ/year
for the future global energy supply in 2050. Besides that, the government aims new
policies to empower renewable energy to solve the environmental issues. For example,
Malaysia’s National Energy Policy of 1979 targets to have an efficient, safe, clean and
environmental friendly of energy supply in the future (Ashnani et al., 2014; Sulaiman et
al., 2011). Besides that, Malaysia’s Fuel Diversification Policy was set out by the
Malaysia Government to develop biomass as the ‘fifth fuel’ resource of renewable energy,
and consequently to reduce dependency on fossil fuels (Mohamed & Lee, 2006).
3
1.2 Background
The use of bio based alcohol like ethanol as the alternative fuel is not a new concept
as Samuel Morely has developed an engine that ran with ethanol in 1826 and Henry
Ford’s Model T was built up to run on ethanol in 1908. However, the demand on ethanol
was drove up after World War I and then gasoline was dominating the market in 1920s.
In 1974, Solar Energy Research, Development, and Demonstration Act of 1974 promoted
ethanol as gasoline alternate due to energy crisis. At that time, the ethanol is the most
widely used biofuel in transportation due to rising oil price, tremendous risk of climate
change, increasing on fuel vehicle demand and also security of energy supply. Therefore,
the governments authorize new policies to do research, develop and deploy more
sustainable and renewable energy sources. Energy Policy Act of 2005 in United States is
most significant steps by mandate the use of ethanol through the Renewable Fuel Standard
(RFS) (Demirbas & Balat, 2006). Besides that, the initiation of National Alcohol Fuel
Program (Pró-Álcool) in Brazil targets to increase the production of bioethanol in order
to substitute the high cost and inadequate standard petroleum-based products
(Rasskazchikova et al., 2004).
Figure 1.2 and Table 1.1 shows the chart and the statistical data for world ethanol fuel
production by country or region from year 2007-2015, respectively (RFA, 2016). The
global ethanol fuel production reached from 13 billion gallons in year 2007 to more than
25 billion gallons in year 2015 which is the highest ethanol fuel production since 2007.
The production is increasing steadily across the nations with regard to reduce oil imports,
improve air quality and boost rural economies. In addition, there are 31 countries in
international level and 29 provinces which mandate the use of ethanol-gasoline blend in
year 2011 (J.L. Sawin et al., 2011). Table 1.2 listed the usage of ethanol-gasoline fuel
blends in different countries (Bajpai, 2013; Janet L Sawin, 2008).
4
Figure 1.2: Chart for world ethanol fuel production by country/region and year
(RFA, 2016)
Table 1.1: Statistical data for world fuel ethanol production by country/region
Country 2007 2008 2009 2010 2011 2012 2013 2014 2015
USA 6,521 9,309 10,938 13,298 13,948 13,300 13,300 14,300 14,806
Brazil 5,019 6,472 6,578 6,922 5,573 5,577 6,267 6,190 7,093
Europe 570 734 1,040 1,209 1,168 1,179 1,371 1,445 1,387
China 486 502 542 542 555 555 696 635 813
Canada 211 238 291 357 462 449 523 510 436
Rest of World 315 389 914 985 698 752 1,272 1,490 1,147
WORLD 13,123 17,644 20,303 23,311 22,404 21,812 23,429 24,570 25,682
Source: RFA (2016)
Note: The unit in million gallons
-
5
10
15
20
25
30
2007 2008 2009 2010 2011 2012 2013 2014 2015
Eth
an
ol
fuel
pro
du
ctio
n b
y c
ou
ntr
y/r
egio
n
(Bil
lio
n G
all
on
s)
Year
Rest of World
Canada
China
Europe
Brazil
USA
5
Table 1.2: Ethanol-gasoline fuel blends used in different countries
Country Bioethanol-gasoline fuel blend
Angola E10
Argentina E5
Australia E4: The blends are used in South wales
E5: The blends are used in Queensland
Brazil E25-E75: Higher blends are used for flex fuel vehicles
E100
Canada E5: The blends are used in National; British Columbia, Alberta
& Ontario provinces
E7.5: The blends are used in Saskatchewan province
E8.5: The blends are used in Manitoba province
Colombia E8
Costa Rica E7
Ethiopia E5
Guatemala E5
India E5
Indonesia E3
Jamaica E10
Malawi E10
Malaysia Not available
Mozambique E10: The blends are used in 2012-2012
E15: The blends are expected to be used in year 2016-2020
E20: The blends are expected to be used in year 2021 onwards
Paraguay E24
Peru E7.8
Philippines E10
South Africa E10
South Korea Not available
Sudan E5
Thailand E5
Turkey E2
United States E10 (gasohol): The blends used in Missouri, Montana, Florida,
Hawaii, New Mexico, Oregon states
E70-E85: Blends varies with states
Uruguay E5: The blends are expected to be used in 2015
Vietnam E5
Zambia E10
Source: Bajpai (2013) & J.L. Sawin et al. (2011)
Note:
E2: 2 vol% ethanol-98 vol% gasoline; E3: 3 vol% ethanol-97 vol% gasoline; E4: 4 vol% ethanol-
96 vol% gasoline; E5: 5 vol% ethanol-95 vol% gasoline; E7: 7 vol% ethanol-93 vol% gasoline;
E7.5: 7.5 vol% ethanol-92.5 vol% gasoline; E7.8: 7.8 vol% ethanol-92.2 vol% gasoline; E8: 8 vol%
ethanol-92 vol% gasoline; E8.5: 8.5 vol% ethanol-91.5 vol% gasoline; E10: 10 vol% ethanol-90
vol% gasoline; E15: 15 vol% ethanol-85 vol% gasoline; E20: 20 vol% ethanol-80 vol% gasoline;
E25: 25 vol% ethanol-75 vol% gasoline; E75: 75 vol% ethanol-25 vol% gasoline; E85: 85 vol%
ethanol-15 vol% gasoline; E100: 100 vol% ethanol or pure ethanol
6
The investigation on higher carbon chain of alcohols such as butanol as the option for
liquid fuel have received much interest recently. Butanol production has long history
since it was discovered by Wirtz in 1852 as fusel oil and then Louise Pasteur was clarified
the synthesis of butanol at laboratory scale 10 years later in 1861 (Costa & Sodré, 2010;
Jin et al., 2011; Ranjan & Moholkar, 2009). Then, the production of acetone-butanol-
ethanol (ABE) fermentation of molasses and cereal grains using Clostridium
acetobutylicum was achieved in 1912-1916 by the chemist Chaim Weizmann
(Shapovalov & Ashkinazi, 2008; Weizmann, 1919). However, the ABE fermentation
continuously declined since 1950s, and almost the butanol was produced via
petrochemical process due to lower price of petrochemicals and increased food demand
of sugar and starchy grains (Jin et al., 2011). The high cost, low-yield and slow
fermentations process results the bio based butanol could not compete on a commercial
scale thus it is produced synthetically. However, many countries and big oil companies
look forward on the bio based butanol again during oil crisis in 1970s as the rising price
of petroleum oil and the increase of GHGs in the atmosphere.
Butanol becomes an alternative to ethanol and gasoline as transportation fuels in spark
ignition engine due to its advantages in terms of physicochemical properties. Currently,
n-butanol and isobutanol are considered as gasoline components to be blended with in
higher concentrations without any modification on conventional gasoline engine
(Niemistö et al., 2013). Meanwhile, new automobiles FFVs that use 85 vol% ethanol
blend (E85) cost a lot of money and quite unaffordable for most car buyers. Therefore,
butanol fuel blends are able to replace conventional gasoline in existing cars without
modifying the engine’s specifications. Szulczyk (2010) explained that butanol can be
blended with gasoline in any percentage up to 100 vol% of butanol in conventional SI
engine.
7
1.3 Problem statement
Ethanol has much higher oxygen content with a value of 34.7 wt.%, which promotes
higher complete combustion and lower exhaust emissions. It also has higher research
octane number (RON) than gasoline and butanol, which prevents premature ignition that
cause knocking which can damage the engine. This higher octane rating also gives
advantages in improving the thermal efficiency. However, the combustion of ethanol in
gasoline engine gives some inherent problems due to its higher heat of vaporization that
leads problems when engine start-up including when running cold engine (Larsen et al.,
2009; Patakova et al., 2011) and also promotes higher emissions of organic gases (Chiba
et al., 2010). In addition, the low carbon chain alcohol like ethanol gives lower amount
of heating value, therefore it provides higher fuel consumption as compared to gasoline.
Moreover, ethanol also miscible with water, thus it creates water contamination to the
fuel system.
Recent studies focus on the production of higher carbon number alcohols from various
renewable sources as the alternative energy. Butanol which is a four carbon alcohol can
be used as an alternative fuel to ethanol and gasoline in a spark ignition engine due to its
stunning properties. Butanol has slightly lower heating value than gasoline, but it is much
higher than ethanol, which can improve fuel consumption. Besides that, butanol has
higher auto ignition temperature than ethanol and gasoline, which affect the thermal
efficiency and exhaust emissions characteristics. The lower water solubility of butanol
can resolve the water contamination problems.
In respect from previous studies, there are numerous valuable studies on ethanol and
butanol which can improve engine performances and emissions characteristics. However,
there are still lack of research on fuel optimization of single and dual alcohol of ethanol
8
and butanol in gasoline blends on physicochemical properties, engine performances and
exhaust emissions since both alcohols have their own advantages.
1.4 Objectives of study
This study is done to compare the single and dual alcohols of ethanol and isobutanol
as gasoline blend for an unmodified SI engine. The considered objectives of study are as
follows:
1. To characterize the physicochemical properties of single and dual alcohols of
ethanol and isobutanol fuel blends and compare with pure gasoline.
2. To investigate the effect of the tested fuel blends on engine performance, i.e. the
torque (T), brake power (BP), brake thermal efficiency (BTE), brake specific fuel
consumption (BSFC) and exhaust gas temperature (EGT).
3. To evaluate the effect of the tested fuel blends on the exhaust emission
characteristics, i.e. carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon
(HC) and nitrogen oxides (NOX).
9
1.5 Scope of work
This study aims to compare the single and dual alcohols as gasoline blend for a spark
ignition engine. The ethanol and isobutanol were used as the blending component with
gasoline fuel blends at different volume ratio. The physicochemical properties of tested
fuel blends such as oxygen content, higher heating value (HHV), research octane number
(RON), density, viscosity, Reid vapor pressure (RVP) and latent heat of vaporization
(HOV) were determined and then compared with pure gasoline. Then, the tested fuel
blends were examined in a 4-cylinder spark ignition engine to determine their engine
performances and exhaust emissions characteristic, then the tested results were
comprehensively compared with the pure gasoline.
1.6 Organization of dissertation
This dissertation consists of five chapters. The organization of each chapter is listed as
follows:
Chapter 1 presents an overview on energy security, economic developments and
environmental needs. This section also discusses on the problems that associated with
fossil fuel and enlighten on alcohol as an alternative fuel to gasoline. This is followed by
the problems which is related with physicochemical properties of ethanol and butanol fuel
blend. Finally, objectives and scopes of this research work are discussed.
Chapter 2 explains in detail the importance of alcohols as the gasoline alternative.
The potential sources and the production of ethanol and butanol are discussed. This
section also compares the physicochemical properties of gasoline, ethanol and butanol.
Then, the effect of ethanol and butanol in gasoline blend on engine performances and
10
exhaust emissions are evaluated. Subsequently, a summary and the research gap of this
study are discussed.
Chapter 3 describes the research methodology and experimental technique to achieve
the research objectives. These includes the selection of alcohol fuels, measurement of
physicochemical properties of selected alcohols and their blends, fuel blending
preparation for engine testing, engine performance and emissions analysis.
Chapter 4 presents the results that have been obtained from the experimental work.
This is followed by providing analysis and detailed discussion. These findings are then
compared with the previous studies.
Chapter 5 concludes the research findings and puts forward some recommendations
for the future study.
11
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
Although the gasoline is predominantly used in spark engine vehicles, the world has
proposed the use biofuels as the alternative and additive source in liquid transportation
fuel. Nowadays, the ethanol is extensively used in many countries as the blend component
for spark ignition vehicles. In United States, 10 vol% ethanol in gasoline (E10 or gasohol)
has been commercially used as fuel for vehicles. Besides that, the higher concentration of
ethanol-gasoline blend such as E85 (85 vol% ethanol, 15 vol% gasoline) have been
developed design for new flexible fuel vehicle (FFV) engine. As recorded, almost 90%
new cars in Brazil which are sold with FFV engine and the fuel sold contains 20-25 vol%
of anhydrous ethanol (Masum et al., 2013; Tavares et al., 2011). Meanwhile, there are
nearly 8 million of FFVs on the road in United States with various range of vehicles
models (Transport and Air Quality, 2010).
Interestingly, an immense investigation on higher carbon chain of alcohols such as
butanol as an option for liquid fuel have received much interest recently. Butanol is
believed to be a promising and competitive renewable biofuel in spark ignition vehicles
besides the ethanol. There are a few attempts to commercialize the butanol as an
alternative transportation fuel by David E Ramey since 1998-2003, however it was not
clarified by Department of Energy (DOE) or National Renewable Energy Laboratory.
Nonetheless, Ramey et al. (2007) successfully demonstrated pure butanol (Bu100) with
unmodified SI engine by moving across America in 2005 and South Dakota in 2007.
Besides that, International Clostridia Group has strived to acknowledge butanol
fermentation for 25 years but this has been ignored by the fuel producers as they are only
focus on ethanol fuel production. Although the biotechnological production of butanol is
much complicated than ethanol, it gives far more advantages over ethanol due to its
12
superior physicochemical properties. It can be mixed well with gasoline, higher of its
energy content, flash point, boiling point as compared with that ethanol.
The objective of this chapter is to provide a thorough literature review on the current
state of oxygenated alcohols in SI engine. The section first describes the physicochemical
properties of ethanol, butanol and gasoline. Then a large number of selective literatures
are reviewed in order to critically compare the effect of ethanol and butanol in gasoline
engine.
2.2 Ethanol and butanol as alternative transportation fuel
The great thing about biofuel is that it can be produced from various sources of raw
materials e.g. biomass. Biomass is the oldest source of fuel energy derived from living or
organism which can be substituted the commercial fossil derived fuels. Basically, the
biofuels production for bio-based ethanol and butanol can be categorized in two main
groups; (i) conventional biofuels (first generation) and (ii) advanced biofuels (second and
third generation) as illustrated in Figure 2.1.
The first generation of biofuels are basically produced from food crops such as sugar
rich crops (sugar cane, sugar beet, sweet sorghum) and starch rich crops (corn, milo,
wheat, rice, cassava, barley). The production of first generation biofuels can be achieved
50 billion liters per year (Naik et al., 2010). Bioethanol from agricultural food crops e.g.
corns are used commercially for the blend component in transportation fuel. However,
the feedstock of the first generation appears unsustainable due to the increasing demands
of biofuel production. Thus this event causes the rising of food prices and shortage of
these edible materials.
13
Figure 2.1: First, second and third generation of biofuels feedstocks
2.3 Production of ethanol and butanol
2.3.1 Ethanol
The production routes of bio-based ethanol vary for each feedstock materials as shown
in Table 2.1. The first generation bioethanol feedstocks e.g. sucrose and starch rich
materials can be produced via alcoholic fermentation (Masum et al., 2013; Sims et al.,
2008). The starch rich crops contain long chain polymers of glucose have an additional
process by mixing and grinding with water to break down into simpler glucose before it
is fermented into the ethanol as shown in Equation (2.1) and Equation (2.2). The
microorganism e.g. yeast (Saccharomyces sp.), bacteria (Zymomonas sp.) and mold
(mycelium) have been widely used for fermentation of ethanol (Naik et al., 2010).
Meanwhile, the lignocellulosic biomass consists of cellulose, hemicellulose and lignin
as its main components undergo several steps e.g. pre-treatment, enzymatic and acid
hydrolysis, fermentation, distillation and evaporation to produce bio-based ethanol as
shown in Figure 2.2 (Aakko-Saksa et al., 2011; Hahn-Hägerdal et al., 2006). The pre-
treatment process of hemicellulose is needed to increase the hydrolysis yield before it is
hydrolyzed and fermented into bioethanol. Hamelinck et al. (2005) reported that the
Bio
fuel
s
First Generation
Sugar rich crops
Starch rich crops
Second GenerationLignocellulosic
biomass and agricultural waste
Third Generation Algae
14
hydrolysis with pre-treatment yields over 90% while the hydrolysis without pre-treatment
yield less than 20%. The common method of diluted or concentrated acid hydrolysis is
used to convert lignocellulose into fermentable sugars, and then the hydrolysate is
fermented into bioethanol.
Table 2.1: Bioethanol routes from different raw material of feedstocks
Raw materials Process
Wood Acid hydrolysis and fermentation
Wood Enzymatic hydrolysis and fermentation
Straw Acid hydrolysis and fermentation
Straw Enzymatic hydrolysis and fermentation
Wheat Malting and fermentation
Sugar cane Fermentation
Sugar beet Fermentation
Corn grain Fermentation
Corn stalk Acid hydrolysis and fermentation
Sweet sorghum Fermentation
Source: Balat & Balat (2009)
n (C6 H10 O5) + nH2O nC6H12O6 (2.1)
Starch Water Glucose
C6H12O6 yeast 2C2H5OH + 2CO2 (2.2)
Glucose Bioethanol Carbon dioxide
15
Figure 2.2: Production process for bioethanol from lignocellulosic biomass
(Hahn-Hägerdal et al., 2006)
2.3.2 Butanol
Similar to bioethanol, bio based butanol can be produced from the same feedstocks,
e.g. sugar crops, starch crops, lignocellulosic biomass and agricultural waste as well. The
biological production of biobutanol has been invented decades ago but the process is quite
expensive than petrochemical hydration process e.g. oxo-synthesis and aldol
concentration. Therefore, almost all modern butanol is produced from petroleum known
as petrobutanol. However, due to the depletion of fossil-fuel reserves and environmental
issues, the interest on sustainable transportation fuels, especially from non-edible
materials, has encourages the technological development in biobutanol fermentation.
Biobutanol can be produced via ABE fermentation where the acetone, butanol and
ethanol are the main products of the process. Previously, the cereal grains and sugar
feedstocks were utilized for industrial scale by the ABE fermentation process. This
16
production process is the second largest industrial fermentation process after bioethanol
by yeast fermentation (Kumar & Gayen, 2011). The difference from ethanol production
is primarily in the feedstocks fermentation and also minor changes on distillation process.
The process uses C. Acetobutylicum as the substrate to utilize the fermentation process.
However, the utilization of these food crops into biobutanol was condemned because of
food shortage. Therefore, the researchers focus on the secondary generation biobutanol
due to abundant cheaper raw materials as the feedstocks. The process to produce
biobutanol from the lignocellulosic feedstocks is illustrated in Figure 2.3.
The lignocellulosic biomass undergoes several steps to produce biobutanol i.e. pre-
treatment, detoxification, fermentation and recovery. Fermentation process of the
biomass feedstocks can be done via different modes i.e. batch fermentation, fed-batch
fermentation and continuous fermentation processes, free cell continuous fermentation,
immobilized cells continuous fermentation, and cells recycling and bleeding. Table 2.2
shows the comparison of the biomass feedstocks via different fermentation process and
their potential butanol yield/ productivity.
Interestingly, biobutanol also can be derived from algae which is the third generation
biofuel. Algae provides several advantages, as it capable to produce an outstanding yields
with lower resource inputs than other feedstocks. In fact, algae that has high amount of
starch can yield as high as 20,000 gallons of biofuel per acre and the yields are ten times
higher than the second generation biofuels, according to US Department of Energy.
Presently, the developers i.e. Butamax Advanced Biofuels LLC, Swiss Butalco GmbH,
American Gevo Inc., ButylFuel LLC and Advanced Biofuels LLC are developing their
own fermentation process towards an economical synthesis of biobutanol (Aakko-Saksa
et al., 2011).
17
Figure 2.3: Production process for biobutanol from lignocellulosic feedstocks
(Ezeji & Blaschek, 2008; H. Liu et al., 2013)
Lignocellulosic feedstocks
•Bagasse, barley straw, wheat straw, corn stover, switchgrass, corn core, etc.
Pretreatment
•Dilute sulphuric acid, Alkaline peroxide, steam explosion pretreatment, hydrothermal pretreatment, organic acid pretreatment
Detoxification
•Activated charcoal, overliming, electrodialysis, membrane extraction, etc.
Fermentation
•Batch fermentation, Fed-batch fermentation, continuous fermentation, etc.
Recovery
•Distillation, Gas stripping, Pervaporisation, Liquid-liquid extraction, Adsorption, etc.
Butanol
18
Table 2.2: Comparison of biomass feedstocks and their potential biobutanol
yield/ productivity via different fermentation process
Feedstocks or
substrates
Fermentation process Strain used Yield (g/g)/
productivity (g/
L.h)
Maximum
titer of ABE
(g/L)
Barley straw Batch fermentation C. beijerinkii
P260
0.43/ 0.39 26.64
Wheat straw Batch fermentation C. beijerinkii
P260
0.41/ 0.31 21.42
Fed-batch fermentation C. beijerinkii
P260
-/ 0.36 16.59
Corn fibres Batch fermentation C. beijerinkii
BA101
0.36-0.39/ 0.10 9.3
Corn stover
& switchgrass
(1:1)
Batch fermentation C. beijerinkii
P260
0.43/ 0.21 21.06
Switchgrass Batch fermentation C. beijerinkii
P260
0.37/ 0.09 14.61
Sago starch Free cell continuous
fermentation
C.
saccharobutylicum
DSM13864
0.29/ 0.85 9.1
Degermed
corn
Free cell continuous
fermentation
C. beijerinkii
BA101
-/ 0.29-0.30 14.28
Whey
permeate
Immobilized cells
continue fermentation
C. acetobutylicum
P262
3.5-3.6/ 0.36-1.10 8.6
Corn Immobilized cells
continue fermentation
C. acetobutylicum
ATCC 55025
0.42/ 4.6 12.50 (butanol)
Sugar beet
juice
C. beijerinkii
CCM 6182
0.37/ 0.40 -
Sources: Kumar & Gayen (2011) and (Patakova et al., 2011)
2.4 Physicochemical properties of ethanol and butanol
Table 2.3 summarizes the comparison of physical and chemical properties of
respective gasoline, ethanol and butanol. The significant fuel properties are discussed
below.
19
Table 2.3: Comparison of physicochemical properties of gasoline, ethanol and
butanol isomers
Properties Gasoline Ethanol n-
butanol
sec-
butanol
tert-
butanol
Isobutan
ol
Chemical formula ~C8H15.6 C2H5OH C4H9OH C4H9OH C4H9OH C4H9OH
Oxygen content (wt. %) 0 34.7 21.6 21.6 21.6 21.6
LHV (MJ/kg) 43.46 26.8 33.2 32.9 32.7 33.1
RON 88-98 109 98 105 105 105
MON 80-88 90 85 93 89 90
Density at 15 ºC
(kg/m3)
750.8 794.6 812.6 810.5 787.3 805.6
Kinematic viscosity at
20 ºC (mm2/s)
0.51698 1.50663 3.5528 4.5797 - 4.9751
Dynamic viscosity at 20
ºC (MPa.s)
0.3860 1.1936 2.8823 3.6935 - 3.9898
RVP at 37.8 oC (kPa) 63.9 17 2.2 5.3 12.2 3.3
Latent HoV (kJ/kg) 352 920 707.9 671.1 527.2 686.4
Flash point (ºC) -43 13 29 24 11 28
Boiling point (ºC) 27-225 78 117.7 99.6 82.4 107.9
Auto-ignition
temperature (ºC)
257 363 343 405 478 415
Specific gravity at 20 ºC 0.69-
0.79
0.794 0.810 0.808 0.791 0.802
Solubility of compound
in water at 20 ºC
(wt. %)
neg
lig
ible
mis
cib
le
7.7 12.5
mis
cib
le
8.7
Source: Yanowitz et al. (2011)
2.4.1 Ethanol
Ethanol with formula C2H5OH as illustrated in Figure 2.4 is an alcohol which is
colourless liquid, transparent, neutral, volatile, flammable, miscible in both water and
non-polar solvents and oxygenated liquid hydrocarbons, which has a pungent odour and
sharp burning taste (Ganguly et al., 2012; Masum et al., 2013). Ethanol has much higher
oxygen content with value of 34.7 wt.%, which promotes higher complete combustion
and lower exhaust emissions. It also has higher octane number than gasoline and butanol
20
that can prevent premature ignition that cause knocking which can damage the engine.
This higher octane rating also gives an advantage in improving the thermal efficiency.
Figure 2.4: Structural formula of ethanol
However, ethanol has lower Reid vapor pressure (RVP) than gasoline, thus it brings
problems when starting a cold engine especially during cold weather (Bajpai, 2013;
Szulczyk et al., 2010). However, the low carbon chain alcohol likes ethanol gives lower
amount of lower heating value (LHV). The energy content of ethanol is approximately
65% of gasoline energy, therefore it has a higher fuel consumption as compared to
gasoline. Ethanol also has higher density than the gasoline which results in enhancing the
volumetric fuel economy. The viscosity of ethanol is higher than gasoline, which can
adversely affect the fuel injection system due to higher flow resistance especially at lower
temperature (Patakova et al., 2011).
Moreover, ethanol has higher heat of vaporization (HOV) than gasoline, thus reducing
the air-fuel mixture temperature during intake stroke. Higher HOV improves knock
resistance and achieves better volumetric efficiency of the engine. However, higher HOV
of ethanol leads to problems during engine start-up including when running cold engine
(Larsen et al., 2009; Patakova et al., 2011) and also promotes higher emissions of organic
gases (Chiba et al., 2010). The flash point, boiling point and auto-ignition temperature of
ethanol are 13 ºC, 78 ºC and 363 ºC, respectively.
21
2.4.2 Butanol
Butanol or butyl alcohol exists with four different isomers with respect to the location
of –OH and carbon chain structure as shown in Figure 2.5. It has 21.6% of oxygen content
which can enhance complete combustion. The isomers have the similar chemical
properties but can be distinguished by their structures that affect their properties as shown
in Table 2.3.
n-butanol sec-butanol Isobutanol tert-butanol
Figure 2.5: Structural formulae of butanol isomers
Butanol isomers have different solubility despite it has similar molecular weight and
same functional group. N-butanol and isobutanol have less water solubility; sec-butanol
has significantly greater water solubility while tert-butanol is fully miscible with water
but less soluble than ethanol. The high hydrophobic nature of n-butanol and isobutanol
can reduce water contamination, which can damage the engine’s fuel system.
Besides that, the isomers have lower LHV and RVP than gasoline but much higher
than ethanol. The significantly lower RVP of butanol as compared to gasoline, results in
cold engine start-up problem especially during winter season. The isomers also have
higher RON and HOV than gasoline, but lower than ethanol. The auto-ignition of butanol
isomers are higher than ethanol and gasoline which can affect the thermal efficiency and
exhaust emissions.
22
2.5 Effect of ethanol and butanol addition in gasoline on engine performances
Gasoline engine is an internal combustion engine with spark-ignition (known as Spark
Ignition engine), designed to run on gasoline and other similar volatile fuels. The engine
was invented by Nikolaus August Otto in 1876 in Germany. Though gasoline engine is
developed for gasoline, alcohols have been used intensively as a fuel blend or additives
since its invention. Many researchers have been carried out experimental studies on SI
engine fueled with various concentration of alcohols in gasoline blends to determine
engine performances i.e. torque (T), brake power (BP), brake specific fuel consumption
(BSFC), volumetric efficiency (VE), brake thermal efficiency (BTE) and exhaust gas
temperature (EGT) (Yusoff et al., 2015).
Masum, Kalam, Masjuki, Palash, et al. (2014) examined different alcohols fuel blends
i.e. 20 vol% of methanol, ethanol, propanol and butanol in gasoline (denoted as M20,
E20, Pr20 and Bu20, respectively) on a MPFI- SI engine at wide open throttle by varying
engine speed. The results showed that the alcohol fuel blends gave better performances
on torque and brake thermal efficiency (BTE) than the pure gasoline. However, the brake
specific fuel consumption (BSFC) of alcohols much higher than the gasoline due to their
lower energy content, resulting more fuel blended were needed to yield same power
output. The BSFC of Bu20 and E20 are higher than the gasoline with 1.95% and 5.17%
respectively.
Besides that, Ozsezen & Canakci (2011) studied the effect of ethanol and methanol
with 5 vol% and 10 vol% in gasoline (denoted as E5, E10, M5 and M10) at wide open
throttle and different vehicle speeds on a chassis dynamometer. It was showed that the
vehicle fueled with the alcohol blends increased the peak wheel power, fuel consumption
and combustion efficiency. The E10 blend provided the highest combustion efficiency at
60 km/h and 100 km/h. Further investigation was done by Canakci et al. (2013) on a
23
vehicle equipped with 4 cylinders SI engine fueled with the same alcohol-gasoline blends
on a chassis dynamometer at different vehicle speeds and wheel powers. The result
showed that E10 gave the highest BSFC among the blends at 80 km/h and 100 km/h with
3.6% and 1.5% respectively as compared to pure gasoline. The alcohol fuel blends also
showed reduction in EGT due to their higher latent HOV than the gasoline.
Moreover, Koç et al. (2009) experimentally studied the ethanol-gasoline blends i.e. 0
vol%, 50 vol% and 85 vol% (denoted as E0, E50 and E85) on a single cylinder 4-stroke
SI engine at wide open throttle and varying engine speed. The addition of oxygenated
fuel likes ethanol in gasoline blend produced higher T, BP and BSFC. Furthermore, Najafi
et al. (2009) also experimentally examined the ethanol-gasoline fuel blends of 0 vol%, 5
vol%, 10 vol%. 15 vol% and 20 vol% (denoted as E0, E5, E10, E15 and E20 respectively)
on 4 cylinder KIA 1.3L engine with the aid of artificial neural network. It was reported
that the gasoline blended with ethanol (derived from potato waste) increased the T, BP,
BTE, VE and BSFC as compared to pure gasoline. Substantial experimental studies have
been done by other researchers (Celik, 2008; Dhaundiyal, 2014; Elfasakhany, 2014;
Hsieh et al., 2002; Saridemir, 2012) to evaluate the effect of ethanol- gasoline blends on
SI engine performances.
Rigorous studies have been done by researchers to study the effect of butanol in
gasoline blend on engine performances. S. B. Singh et al. (2015) examined the effect of
various concentration rate of butanol in gasoline blend i.e. 5 vol%, 10 vol%, 20 vol%, 50
vol% and 75 vol% on a 4 cylinder MPFI medium duty SI engine. It showed that butanol-
gasoline blends produced lower BTE and EGT but slightly higher BSFC than the pure
gasoline. Furthermore, Elfasakhany (2015) studied the effect of hybrid isobutanol in
gasoline blend i.e. 3 vol%, 7 vol% and 10 vol% isobutanol in gasoline blends (denoted as
iBu3, iBu7, iBu10 respectively) on a SI engine. The addition of butanol in gasoline blend
24
reduced the torque T, BP, VE, EGT and in-cylinder pressure than pure gasoline without
engine optimization.
Besides that, Elfasakhany (2016b) also compared the effect of dual butanol blend (n-
butanol and isobutanol) with single butanol (iso-butanol or n-butanol) and baseline
gasoline on SI engine performances at different volume rates (3-10 vol%) and engine
conditions. The 10 vol% of n-butanol/isobutanol in gasoline (niBu10) gives the best
performances among the tested dual and single butanol blends. However, the dual butanol
blends give reduction on VE, BP, T and EGT compared to the pure gasoline. Other
researchers (Dernotte et al., 2009; Pukalskas et al., 2009; Xialong et al., 2009) also
observed the effect of butanol addition in gasoline blends on SI engine performances.
2.6 Effect of ethanol and butanol addition in gasoline on exhaust emissions
Exhaust emission is an undesirable element i.e. flue gas that is emitted and discharged
into the air as a result of fuel combustion in the internal combustion engine. Excessive
release of the undesirable foreign substances into the air will aggravate the air quality,
which can cause acid rain, health problems and also cause damage to the ecosystem.
Caiazzo et al. (2013) observed that road transportation contributes up to 53000 premature
deaths per year in United States due to exhaust emissions. In United Kingdom, the
pollution experts from MIT, Massachusetts have observed almost 5000 premature deaths
per year are caused by exhaust emission from vehicles, which is more than twice that
from traffic accidents (Pease, 2012).
The combustion gases consist of non-toxic gases, i.e. nitrogen (N2), water vapor (H20)
and also carbon dioxide (CO2) that contributes to global warming. The other little parts
of unpleasant gases which are toxic and very harmful such as carbon monoxide (CO)
25
discharged from incomplete combustion, hydrocarbon (HC) exhibits from unburned fuel,
nitrogen oxides, NOX reveals from extra combustion temperatures, ozone (O3) and also
particulate matters (PMs), i.e. soot. Figure 2.6 shows the proportion data of exhaust
emissions produced by SI engine (Soruşbay, 2015). In spite of that, the amounts of these
emissions varies depending on the engine design including its operating condition.
Figure 2.6: The proportion data of exhaust emissions produced by SI engine
(Soruşbay, 2015)
An experimental study was done by E. Singh et al. (2014) to examine the exhaust
emissions of ethanol and n-butanol in gasoline blends at volume concentration of 10 vol%
(denoted as E10 and nBu10, respectively) on a single cylinder SI engine by varying load
at a constant speed of 3000 rpm. The results showed that nBu10 gives the lowest CO2
emission than E10 and pure gasoline. The nBu10 also produces slightly comparable NO
emission to gasoline but much lower than E10 due to its identical properties to gasoline.
However, E10 emits the lowest CO and HC emissions than nBu10 and gasoline at all
loads. Besides that, Farkade & Panthre (2012) investigated the effect of different volume
N2
72%
O2 and
innert gases
1%
H2O
14%
CO2
12%
Pollutants
1%
Pollutants (%)
PM 0.0008
NOX 0.13
HC 0.09
CO 0.90
26
concentration of methanol, ethanol and butanol in gasoline blends on exhaust emission
characteristics of a Greaves MK-25 engine. These oxygenate alcohols in the gasoline
blends give a significant reduction in HC and CO emissions especially the 30 vol%
methanol in gasoline (M30) that emits the lowest HC and CO emissions at all operating
conditions.
Moreover, Lin et al. (2010) studied different ratios of ethanol in gasoline blended fuels
(E0, E3, E6 and E9) on a small engine generator under different loadings. Addition of
ethanol increases the oxygen content in fuel, results in lower CO, HC and NOX at each
engine loading. F. Liu et al. (2012) studied 10 vol% and 20 vol% of ethanol in gasoline
blends (denoted as E10 and E20) in a three cylinders PFI gasoline engine. The increasing
ethanol fraction in fuel blends showed reduction in HC, CO2 and NOX than pure gasoline.
Yücesu et al. (2006) studied different volume concentration of ethanol in gasoline
blends i.e. E10, E20, E40 and E60 compared with gasoline in a single cylinder SI engine
at different engine speed and compression ratio. It showed that the higher ratio of ethanol-
gasoline blends i.e. E40 and E60 reduced more HC and CO than the gasoline. In other
experiment, Koç et al. (2009) investigated the effect of ethanol in gasoline blends at
different ratios of E0, E50 and E85 on a single cylinder SI engine at wide open throttle
and varying engine speed on emissions characteristic. The addition of ethanol as
oxygenates reduces HC emissions as a result of the leaning effect, but HC emission
increased at higher compression ratio due to higher surface to volume ratio. Also, adding
ethanol in gasoline blends emitted lower NOX due to high latent HOV of ethanol.
Furthermore, there are also several studies that observed the emissions characteristics
of butanol in gasoline blends. Feng et al. (2015) studied the effect of n-butanol–gasoline
blend on a single cylinder SI motorcycle engine at 6500 rpm and 8500 rpm of speed under
full load and partial load conditions. The results showed that 35 vol% of n-butanol in
27
gasoline (nBu35) with optimized ignition timing released lower HC, CO and O2
emissions amount compared to 30 vol% n-butanol in gasoline (nBu30). However, NOX
and CO2 emissions are much higher than pure gasoline. Typical results were observed by
Deng et al. (2013) as 35 vol% in gasoline (Bu35) with optimum ignition timing produced
lower HC and CO emissions with decrease of 22% and 49.5% on average respectively,
but the NOx emission largely increases with 190% on average relative to that pure
gasoline. Similar investigation was achieved by Feng et al. (2013) as the 35 vol% of n-
butanol in gasoline (nBu35) with 1 vol% H2O addition, in combine with optimized
ignition timing is performed on the similar engine parameters. The tested blend gives
lower amount of CO and HC emissions nevertheless NOX and CO2 emissions are higher
than gasoline.
In addition, Wallner & Frazee (2010) studied the effect of three different alcohol
isomers; ethanol, n-butanol and isobutanol on the regulated and unregulated emissions on
a SIDI engine with disable EGR. The results showed that both n-butanol and isobutanol
increase the formaldehyde and acetaldehyde emissions compared to ethanol; butanol
increased propene, 1, 3-butadiene and acetylene emissions, while HC and NOX emissions
are lower with higher alcohol contents. Wallner et al. (2010) also examined the
unregulated emissions characteristics on a SIDI engine at constant load, speed and power.
The amount of formaldehyde emission is higher for isobutanol fuel blends as compared
to ethanol fuel blends for most operating conditions. But, the acetaldehyde emissions
increase for both ethanol and isobutanol blends.
28
2.7 Summary and research gap
The heavy reliance on petroleum-derived fuels such as gasoline in the transportation
sector is one of the major causes of environmental pollution. For this reason, there is a
critical need to develop cleaner alternative fuels. Numerous researchers have conducted
the use of ethanol in gasoline blends. To date, 10 vol% ethanol in gasoline (E10 or
gasohol) has been commercially used as fuel for vehicles in many countries. The higher
concentration of ethanol-gasoline blend such as E85 (85 vol% ethanol, 15 vol% gasoline)
have been developed design for new FFV engine. Interestingly, investigation on higher
alcohol such as butanol as the option for liquid fuel have received much interest recently.
Butanol is believed to be a promising and competitive renewable biofuel which can be
blended with gasoline in spark ignition vehicles to produce cleaner alternative fuels
because of their favourable physicochemical properties compared to ethanol. In respect
from the previous experimental studies, there are numerous valuable studies on ethanol
and butanol in gasoline blend which can affect the engine performances and exhaust
emissions as summarized in Table 2.4.
However, there are still limited information to acknowledge the effect of single
alcohol and dual alcohols of ethanol and isobutanol in gasoline blends since both alcohols
have their own advantages and disadvantages. Therefore, this research will focus on the
effect of dual ethanol-isobutanol and single ethanol and isobutanol in gasoline blends on
physicochemical properties, engine performances and exhaust emissions in a spark
ignition engine.
29
Table 2.4: Research study on alcohol fuel for past few years
No
Au
tho
rs
Type of
engine
Operating
condition
Alcohol used Engine performances Exhaust
emissions
Sin
gle
cy
lin
der
Mu
lti-
cyli
nd
er
Va
ryin
g s
pee
d
Va
ryin
g t
orq
ue
Met
ha
no
l
Eth
an
ol
Bu
tan
ol
Du
al
alc
oh
ols
To
rqu
e
BP
BT
E
BS
FC
EG
T
CO
CO
2
HC
NO
X
1 a X X X X X X X X X X X X X
2 b X X X X X X X X X
3 c X X X X X X X X X X X
4 d X X X X X X X X X X
5 e X X X X X X X X X X
6 f X X X X X X X X X
7 g X X X X X X X X X X
8 h X X X X X X X X X
9 i X X X X X X X X X X X
10 j X X X X X X X X
11 k ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷ ̷
Ref.
a. Masum, Kalam, Masjuki, Palash, et al. (2014)
b. Koç et al. (2009)
c. Najafi et al. (2009)
d. Ozsezen & Canakci (2011)
e. Canakci et al. (2013)
f. Elfasakhany (2014)
g. Elfasakhany (2015)
h. Elfasakhany (2016b)
i. Elfasakhany (2016c)
j. Elfasakhany (2016a)
k. This study
30
CHAPTER 3: METHODOLOGY
3.1 Introduction
In this chapter, the research methodology and approaches of the entire study for
achieving objectives have been discussed. These includes the selection of alcohol fuels,
measurement of physicochemical properties of selected alcohols and their blends, fuel
blending preparation for engine testing, engine performance and emissions analysis.
Figure 3.1 presents a summary of the work practice for this research.
Figure 3.1: Flowchart of the research methodology
No
Yes
Literature review
Selection of alcohol fuels
Fuel blending preparation
Measurement of physicochemical
properties
Experimental engine testing
• Data collection and analysis:
• Engine performances - T, BP,
BTE, BSFC and EGT
• Exhaust emissions - CO,
CO2, HC, NOX
Data
validation
Discussion & conclusion
31
3.2 Selection of alcohol fuels
There are two types of alcohols to be used in this study, i.e. ethanol and isobutanol.
The ethanol (purity 99.8%) was procured from Chemical Industries (Malaya) Sdn. Bhd.,
Malaysia and the isobutanol (purity ≥ 99%) was obtained from Merck Sdn. Bhd.,
Malaysia. Meanwhile, Primax95 unleaded gasoline with Research Octane Number 95
was acquired from Petronas, Malaysia.
3.3 Fuel blending preparation
Six types of fuel blends consist of single and dual alcohol fuel blends of ethanol and
butanol were mixed with unleaded gasoline at different volume rates as tabulated in Table
3.1. All the fuel mixtures were blended on a shaker machine (IKA KS130 basic) for 30
minutes at 400 rpm prior to the engine testing for every run as illustrated in Figure 3.2.
The mixtures were blended in closed bottle as the alcohols and gasoline easily evaporate
at ambient temperature.
Table 3.1: Composition of ethanol, butanol and gasoline for blending
Sample Name Volume of ethanol
(% vol.)
Volume of isobutanol
(% vol.)
Volume of gasoline
(% vol.)
E10 10 - 90
E20 20 - 80
iB10 - 10 90
iB20 - 20 80
E5iB5 5 5 90
E10iB10 10 10 80
PG - - 100
Note: E= ethanol, iB= isobutanol, PG= pure gasoline (baseline)
32
Figure 3.2: IKA KS130 basic shaker machine
3.4 Measurement of physicochemical properties
The physicochemical properties indicates the quality of fuel to be combusted in SI
engine (Masum et al., 2013). These includes heat of vaporization (HOV), higher heating
value (HHV), Reid vapor pressure (RVP), density, viscosity and research octane number
(RON). In this section, the properties of all the tested fuel blends were observed using
different equipment and recommended standard method as listed in Table 3.2.
33
Table 3.2: Equipment and standard method used for testing fuel properties
Property Equipment Manufacturer Standard
method
Accuracy
Heat of vaporization Heat Flux DSC 4000 Perkin Elmer, US - -
Higher heating value C2000 basic
Calorimeter- automatic
IKA, UK ASTM D240 ±0.1%
Reid vapour pressure at
37.8oC
Setavap 2 Automatic
Vapour Pressure Tester
Paragon Scientific
Ltd., UK
ASTM D5191 ±0.1 KPa
Density at 15oC DM40 LiquiPhysics™
Density Meter
Mettler Toledo,
US
ASTM D4052 0.0001
g/cm3
Kinematic and
dynamic viscosity at
20oC
SVM3000 Stabinger
Viscometer
Anton Paar, UK ASTM D445 ±0.35%
3.4.1 Heat of vaporization
Latent heat of vaporization also known as enthalpy of vaporization is the energy
required by a substance to change the state from liquid to vapor at a constant –
temperature. Heat flux DSC 4000 is used to measure the energy necessary at nearly zero
temperature difference between a pan containing a sample and an empty reference pan in
a single furnace, as both pans are subjected to an identical temperature regimes in an
environment heated or cooled at a controlled rate. At first, the computer, Pyris software,
DSC system, purge gas regulator and sub ambient cooling device system is turned on
accordingly. The purge gas flow rate of nitrogen is applied at 20 – 25 psi. Then, a fuel
sample typically 5-15 mg is weighed in a specific pan. Once the empty reference pan and
sample pan are put inside the furnace, the method editor is used to set up method for
sample run.
34
3.4.2 Higher heating value
The heating value (or calorific value or energy value) is the quantity of heat produced
by the complete combustion of a specific amount of fuel at constant pressure and under
standard conditions. The higher heating value (HHV) or gross energy of the tested fuels
are measured using constant-volume type of IKA C2000 bomb calorimeter according to
ASTM D240. At first, a fuel sample of 0.5g is weighted on a micro balance and it is then
poured into crucible. The crucible which is carrying the fuel sample is located in the bomb
and the ignition wire is tied to the terminal electric. Afterwards, the top of the bomb is
screwed down tightly and closed into the calorimeter. Then, 30 bar of oxygen is admitted
slowly before the ignition is occurred. The change in water temperature surrounding the
calorimeter is used to calculate the energy content of the sample fuel. The heating value
comes to the digital display automatically.
3.4.3 Reid vapor pressure
Reid vapor pressure (RVP) is defined as the absolute vapor pressure exerted by the
liquid at 37.8 ºC (100 ºF) temperature, determined by Setavap 2 automatic vapor pressure
tester according to the test method of ASTM D5191. This convenient alternative mini
measurement of vapor pressure is used with proven correlation to ASTM D323. The
analysis is carried out at a vapor to liquid ratio is 4:1 using 3 mL of fuel sample which is
injected through a septum into a fixed vacuum chamber. The vacuum chamber is
maintained at 37.8 ± 0.1 °C and its internal pressure is monitored by a low volumetric
displacement transducer. The test results are displayed and the instrument automatically
drains the sample and then shifted the instrument ready for the next test. The results
shown in total pressure (Ptot), dry vapor pressure equivalent (DVPE), EPA, or Reid using
pre-programmed correlation equations, and may be printed via the integral RS232 port
and optional 81000-2 printer. The value of each sample was recorded.
35
3.4.4 Density
Density is the mass per unit volume of fuel. The density of the fuel blends are measured
using DM40 LiquiPhysics™ Density Meter, US according to ASTM D4052. To measure
the density, the temperature is set at 15oC. Then, approximately 3mL of fuel sample is
injected to the equipment before the measurement is started. The sample fuel is injected
3 times for subsequent measurement until the result is valid.
3.4.5 Kinetic and dynamic viscosity
Viscosity is the measure of the flow resistance of a fluid fuel. Both kinematic viscosity
and dynamic viscosity are measured using SVM3000 Stabinger Viscometer, UK
according to ASTM D445. To measure the viscosity, the equipment is set at mode of M0:
PRECISE and the temperature of 20oC. Then, 3mL of fuel sample is injected to the
equipment before the measurement is started. The sample fuel is injected 2-3 times for
subsequent measurement until the results of dynamic and kinematic viscosity comes out
on the display.
3.4.6 Research octane number
Research octane number (RON) is a rating used to measure a fuel’s knocking
resistance in gasoline engines. RON for the tested fuels were computed on a molar basis
using the following equation (Anderson et al., 2010; Masum et al., 2015).
ONblend = (1-Xalc) ONbase + (Xalc) bONmol, alc (3.1)
Where,
ONblend : ON of the alcohol-gasoline blend
ONbase : ON of the base gasoline
bONmol, alc : Blending ON of the alcohol in the base gasoline
Xalc : Molar alcohol fraction
36
3.5 Engine test setup and engine performance analysis
The experiment was conducted on a 1.6L four cylinder PROTON CamPro engine at
Faculty of Engineering, University of Malaya as illustrated in Figure 3.3. The details of
the engine are presented in Table 3.3.
Figure 3.3: 1.6L Proton CamPro engine
37
Table 3.3: Characteristics of the tested engine
Engine parameter Value
Model PROTON CamPro
Number of cylinders 4
Valve mechanism 16-valve DOHC
Displacement volume 1597 cc
Bore 76 mm
Stroke 88 mm
Compression ratio 10:1
Fuel system Multi-point fuel injection (MPFI)
system
Max output 82 kW at 6000 rpm
Max torque 148 N/m at 4500 rpm
Figure 3.4 presents the schematic diagram of engine test bed. The tested engine was
directly coupled with an eddy current dynamometer (Froude Hofmann AG150, UK) at
maximum power of 150 kW. It was connected to a data acquisition system, which
automatically collects and processes the signals throughout the experiment. The data
acquisition system was connected to the computer, where the data was monitored,
controlled and analyzed using CADET V12 software.
38
Figure 3.4: Schematic diagram of engine test set-up
At first, the engine was warm up for 15 minutes with pure gasoline to ensure the engine
operating condition is perfectly stable. The fuel was then switched to different fuel blends,
where sufficient amount of blends were consumed to ensure the removal of residual
gasoline from the fuel lines. In this study, a constant-throttle test was operated at full
throttle position by varying the engine speed ranging from 1000 to 5000 rpm with an
increment of 1000 rpm. Then, a constant speed test was conducted at 4000 rpm by varying
engine torque ranging from 20 to 100 Nm with an increment of 20 Nm.
Both test conditions were performed to establish engine performance and emissions
characteristics and to determine the best mixture blends. The engine performances
parameters such as torque (T), brake power (BP), brake thermal efficiency (BTE), brake
specific fuel consumption (BSFC) and exhaust gas temperature (EGT) were determined.
The test on performances were repeated for three times for each fuel blending to get an
average data of the experiment.
39
In addition, several important procedure were taken into consideration before
operating with engine test bed.
i. The water supply from the cooling tower was opened.
ii. The water level inside the water tank was sufficient during engine testing.
iii. The engine oil level was checked using the dipstick indicator.
3.6 Exhaust emission analysis
The exhaust emissions such as carbon monoxide (CO), carbon dioxide (CO2), nitrogen
oxides (NOX), oxygen (O2) and hydrocarbon (HC) were measured using AVL DICOM
4000 exhaust emission analyzer as shown in Figure 3.5. The specifications for the
emission analyser is presented in Table 3.4. The sample line hose of the emission analyser
was put to the exhaust tailpipe 1.5 meter away from exhaust port to ensure sufficient
mixing of the exhaust emissions. The emissions results were then recorded for every
increment of engine speed ranging from 1000 to 5000 rpm at full throttle position and
also for every increment of engine torque ranging from 20 to 100Nm at constant 4000
rpm engine speed.
40
Figure 3.5: AVL DICOM 4000 exhaust emission analyzer
Table 3.4: Specifications of the exhaust emission analyzer
Model Measuring Element Measurement range Resolution
AVL DICOM
4000
CO 0 – 10 vol.% ±0.01 vol.%
CO2 0 – 20 vol.% ±0.1 vol.%
HC 0 – 20000 ppm vol. 1 ppm
NOX 0 – 5000 ppm vol. 1 ppm
O2 0 – 25 vol.% ±0.01 vol.%
41
3.7 Uncertainty analysis
The experimental uncertainties are related to various factors such as the selection,
condition and calibration of the instruments, test conditions, observation and data
collection techniques, as well as experimental planning and design. The uncertainties
calculation for the engine performance and exhaust emission parameters are presented in
Appendix A. The overall uncertainty was determined using the following equation (How
et al., 2014):
Overall experimental uncertainty = square root of [(uncertainty of Tspeed)2 +
(uncertainty of BPspeed)2 + (uncertainty of BTEspeed)
2 +(uncertainty of BTEtorque)2 +
(uncertainty of BSFCspeed)2 + (uncertainty of BSFCtorque)
2 + (uncertainty of EGTspeed)2 +
(uncertainty of EGTtorque)2 + (uncertainty of COspeed)
2 + (uncertainty of COtorque)2 +
(uncertainty of CO2_speed)2 + (uncertainty of CO2_torque)
2 + (uncertainty of HCspeed)2 +
(uncertainty of HCtorque)2 + (uncertainty of NOX_speed)
2 + (uncertainty of NOX_torque)2]
= square root of [(0.79)2 + (0.99)2 + (1.09)2 + (1.01)2 + (1.10)2 + (1.01)2 + (1.40)2 +
(0.65)2 + (0.68)2 + (1.22)2 + (0.46)2 + (0.22)2 + (0.92)2 + (1.72)2 + (1.80)2 + (0.75)2]
= ±4.27%
42
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Introduction
The results from all analysis is presented and discussed in this chapter. The first part
of this chapter presents the physicochemical properties of the of respective fuel blends of
ethanol, isobutanol and gasoline. The engine performance and exhaust emission
characteristics of spark ignition engine fueled with those blends were then presented and
compared with pure gasoline.
4.2 Physicochemical properties of ethanol-butanol-gasoline fuel blends
Measurement of physical and chemical properties is the only way to assess the quality
of fuel. In this study, the physicochemical properties were measured for each fuel blend
using various instruments, as listed in Table 3.2. The physicochemical properties of the
fuel blends tested are summarized in Table 4.1 below.
Table 4.1: Physicochemical properties of tested ethanol-butanol-gasoline fuel
blends
Properties Gasoline E10 E20 iB10 iB20 E5iB5 E10iB10
HHV (MJ/kg) 43.46 42.00 40.55 42.68 41.90 42.34 41.22
Latent HoV
(kJ/kg)
352 409 466 385 419 397 442
RVP (kPa) 63.9 59.7 55.5 58.9 53.7 59.3 54.6
Density (kg/m3) 750.8 757.2 760.9 759.3 763.2 754.4 761.3
Kinematic
viscosity (mm2/s)
0.52633 0.57351 0.64849 0.59485 0.70333 0.56405 0.64512
Dynamic
viscosity (MPa.s)
0.39262 0.43131 0.49096 0.44875 0.53549 0.42446 0.48661
RON 95 99.5 104.0 97.7 100.4 98.9 102.7
43
As can be seen clearly in Table 4.1, the fuel blends have significantly lower HHV than
pure gasoline. The increasing of oxygen content of the alcohols linearly reduces the
heating value of the tested fuel blends. It can be seen that the blends of E10, E20, iB10,
iB20, E5iB5 and E10iB10 showed the reduction of heating value with 3.4, 6.7, 1.8, 3.6,
2.6 and 5.1% respectively as compared with pure gasoline. In comparison with ethanol,
isobutanol which has more carbon-carbon bonds increases the change in enthalpy during
combustion, which results in higher energy content (Smith, 2013). The lower of heating
value gives higher fuel consumption and also effects on engine performances and
emissions (Masum et al., 2015).
Latent heat of vaporization (HOV) of the tested fuels containing ethanol and butanol
are significantly higher than pure gasoline. For comparison, the HOV of ethanol,
isobutanol and gasoline are 920, 686.4 and 352 kJ/kg respectively. The fuel blends of
E10, E20, iB10, iB20, E5iB5 and E10iB10 showed 16.1, 32.3, 9.5, 19, 12.8 and 25.6%
higher HOV respectively as compared with pure gasoline. The significantly higher HOV
results in a temperature reduction inside the engine intake system of port fuel injection,
as the energy taken from the intake air is needed to evaporate the fuel.
Reid vapor pressure (RVP) is determined to evaluate the volatility of gasoline and
other fuel blends. The results showed that the addition of ethanol and isobutanol in
gasoline blends reduce the RVP values, where ethanol and butanol have 17 and 3.3 kPa
respectively. Indeed, the E10, E20, iB10, iB20, E5iB5 and E10iB10 blend reduce the
RVP with 6.5, 13.2, 7.9, 15.9, 7.2 and 14.5% respectively. A lower RVP is desirable for
evaporative emissions, however too low of a RVP can cause cold start problem and an
increase in hydrocarbon emission (Kito-Borsa et al., 1998).
44
The addition of ethanol and butanol increase the fuel density values to the tested fuel
blends, where the density of ethanol and isobutanol are 791.61 and 802.1 kg/m3
respectively. The E10, E20, iB10, iB20, E5iB5 and E10iB10 blend increase the density
with 0.9, 1.3, 0.7, 1.1, 1.7, 0.5 and 1.4% respectively as compared with pure gasoline.
Therefore, the higher fuel density value, the higher mass of fuel that can be stored in the
tank and the higher the mass of the fuel that can be pumped to the engine via a pump.
The viscosity of the tested fuels containing the alcohols are significantly higher than
pure gasoline. The kinematic viscosity of E10, E20, iB10, iB20, E5iB5 and E10iB10 are
higher than pure gasoline with 9.0, 23.2, 13.0, 33.6, 7.2 and 22.6% respectively. The fuel
viscosity has not technically been an issue in their gasoline applications, as it is not
operating at much higher pressure as compared to diesel fuel pumps, therefore the
viscosity requirements for the gasoline are much lower as compare to diesel fuel (Agudelo
et al., 2011).
RON of the tested fuels containing ethanol and isobutanol are significantly higher than
pure gasoline. For comparison, the RON for gasoline, ethanol and isobutanol are 95,
107.4 and 105.1 respectively. The fuel blends of E10, E20, iB10, iB20, E5iB5 and
E10iB10 increase the RON with 4.7, 9.0, 2.9, 5.7, 4.2 and 8.1% respectively as compared
with pure gasoline. The significantly higher value of RON can prevent premature ignition
that causes engine knocking. This higher octane rating also gives an advantage in
improving the thermal efficiency.
45
4.3 Engine performance
Engine performance of a SI engine is usually described in terms of engine torque (T),
brake power (BP), brake thermal efficiency (BTE), brake specific fuel consumption
(BSFC) and exhaust gas temperature (EGT) under different engine operating conditions,
i.e. varying speed and engine torque. In this study, three readings were recorded for each
experiment test point for all tested fuel blends. The uncertainty value for each parameter
were determined and presented in the graph.
4.3.1 Engine torque
Figure 4.1 shows the variation of engine torque for the tested fuel blends as a function
of the engine speed. It can be observed that the engine torque increases steadily up to
4000 rpm and decreases thereafter for all fuel blends. The maximum engine torque
recorded at 4000 rpm is 111.8, 113.3, 112.3, 112.5, 111.4 and 112.6 and 111.0 Nm for
the E10, E20, iB10, iB20, E5iB5, E10iB10 blend and pure gasoline (PG), respectively.
As the engine speed increases to 4000 rpm, the amount of air intake into the engine
cylinders increases the engine torque and volumetric efficiency simultaneously.
Therefore, the increase in the engine torque is attributed to the volumetric efficiency and
engine speed (Al-Hasan, 2003). However, the engine torque decreases at an engine speed
higher than 4000 rpm due to fuel choking as a consequence of reduced air intake. Fuel
choking occurs when insufficient charge being supplied into the combustion chamber due
to short air intake time at high engine speed. Furthermore, there is only a slight increase
in the engine torque at 3000 rpm, followed by an abrupt increase up to an engine speed
of 4000 rpm. This phenomenon is known as torque dip or torque loss which naturally
occurs in DOHC CamPro engines manufactured by PROTON, Malaysia, or any other SI
engines that caused by designed intake manifold geometry and valve timing to
46
compromise the maximum engine torque and achieve the desired emission levels
(Mohiuddin et al., 2008). The low-end engine torque is not smooth and it typically occurs
within an engine speed range of 2500–3500 rpm.
In general, blending ethanol and butanol with gasoline improves the engine torque.
Indeed, the E10, E20, iB10, iB20, E5iB5, E10iB10 blend has higher engine torque, with
an average enhancement of 2.33, 3.88, 2.56, 3.06, 1.06 and 3.77%, respectively, relative
to PG. At a constant engine speed, the engine torque varies from one fuel blend to another
because of the fuel properties. The engine torque enhancement is due to the high HOV of
the fuel blends, whereby the fuel vaporizes in the intake manifold and combustion
chamber (Masum et al., 2015). This higher HOV is due to the decrease in the charge
temperature as the alcohols evaporate. Moreover, alcohols provide more oxygen to the
gasoline, which results in a leaner mixture as compared to PG. This increases the fuel
burning efficiency while concurrently improves the engine torque (Koç et al., 2009;
Masum, Kalam, Masjuki, Rahman, et al., 2014).
47
Figure 4.1: Variation of engine torque as a function of engine speed for all tested
fuel blends
4.3.2 Brake power
Figure 4.2 shows the variation of brake power for the tested fuel blends as a function
of the engine speed. It can be seen that the brake power increases in an almost linear
fashion when the engine speed is increased from 1000 to 5000 rpm. The higher volume
ratio of the alcohol blends of E20, iB20, E10iB10 and blend has higher brake power
enhancement with respect to PG, with average increase of 2.60, 2.57 and 2.22%
respectively. This is followed by the lower volume ratio of alcohol blends of iB10, E10
and E5iB5, with an average brake power enhancement of 1.92, 1.64 and 0.95%,
60
70
80
90
100
110
120
0 1000 2000 3000 4000 5000 6000
En
gin
e to
rqu
e (N
m)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.79%
48
respectively. Since the ethanol and isobutanol are essentially oxygenates, blending these
alcohols with gasoline increases the octane rating of the blends, making them less prone
to auto-ignition and eventually increasing the brake power. Moreover, both ethanol and
isobutanol have higher latent HOV than gasoline, which increases the fuel-air charge
cooling. This in turn, increases the intake air density, which boosts the engine power
(Najafi et al., 2009).
Figure 4.2: Variation of brake power as a function of engine speed for all tested
fuel blends
0
10
20
30
40
50
60
70
0 1000 2000 3000 4000 5000 6000
Bre
ak p
ow
er, B
P (
kW
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.99%
49
4.3.3 Brake thermal efficiency
Brake thermal efficiency (BTE) is a measure of the efficiency or completeness of the
engine to produce brake power from the thermal input over the fuel amount supplied.
Figure 4.3 shows the variation of BTE for all tested fuel blends as a function of (a) the
engine speed at constant full throttle condition and (b) the engine torque at constant
engine speed condition. At the constant full throttle condition as shown in Figure 4.3(a),
it can also be observed that the BTE is lower at low engine speed and BP and subsequently
increase with higher engine speed and BP. There is a slight increase in the BTE for the
E10, E20, iB10, iB20, E5iB5 and E10iB10 blend relative to that of PG, with an average
enhancement of 8.01, 13.61, 5.54, 6.45, 7.97 and 8.35%, respectively. It can be seen that
the BTE value for the lower carbon alcohol, i.e. ethanol is greater than higher carbon
alcohol, isobutanol. In fact, ethanol has greater oxygen content than isobutanol, thereby
enhances combustion efficiency and eventually produces higher BTE than butanol
(Campos-Fernandez et al., 2013). The maximum BTE was 22.9% at 5000 rpm when E20
was used in the fuel blend. Similar trends has been proven by Masum, Kalam, Masjuki,
Palash, et al. (2014), who determine the BTE of the ethanol-gasoline blend is greater than
butanol-gasoline blend due to better combustion efficiency.
At a constant engine speed condition in Figure 4.3(b), the E10, E20, iB10, iB20,
E5iB5 and E10iB10 blend have higher BTE, with an average enhancement of 3.06, 6.71,
6.02, 7.16, 3.92 and 2.80%, respectively, relative to PG. It can be observed that the BTE
is lower for all the alcohol-gasoline blends compared to the PG at lower engine torque of
20 Nm. This is due to the higher auto-ignition temperature and HOV of the ethanol and
isobutanol which inhibit mixing between these alcohols and air and consequently reduces
combustion efficiency and thermal efficiency. However, there is improvement in the BTE
at middle and higher engine torque from 40 to 100 Nm for all the alcohol-gasoline blends.
In fact, the throttle position increase proportionally with the engine torque at a constant
50
speed. The larger throttle opening at higher engine torque has shifted air fuel mixture to
become slightly leaner, enabling complete combustion of the fuel and eventually increase
the BTE. As the evidence, the alcohol-gasoline blend produces more CO2 emissions than
PG due to improve complete combustion.
(a)
10
12
14
16
18
20
22
24
0 1000 2000 3000 4000 5000 6000
Bra
ke
Ther
mal
Eff
icie
ncy
, B
TE
(%
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.09%
51
(b)
Figure 4.3: Variation of brake thermal efficiency as a function of (a) engine
speed and (b) engine torque for all tested fuel blends
10
15
20
25
30
35
0 20 40 60 80 100 120
Bra
ke
Th
erm
alE
ffic
iency
, B
TE
(%
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.01%
52
4.3.4 Brake specific fuel consumption
Brake specific fuel consumption (BSFC) is simply a measure for the fuel efficiency of
the engine which indicates the usage of fuel during operation. In other words, BSFC is
the ratio of the rate of fuel consumption to the brake power (g kW-1 h-1). Obviously, lower
amount of BSFC is desirable. Figure 4.4 presents the variation of the BSFC for all tested
fuel blends as a function of (a) the engine speed at constant full throttle condition and (b)
the engine torque at constant engine speed condition. As shown in the Figure 4.4(a), the
BSFC of the E10, E20, iB10, iB20, E5iB5 and E10iB10 blend is lower relative to that of
pure gasoline with average values of 4.00, 4.83, 3.19, 3.17, 3.48 and 5.77% respectively.
The reduction of the BSFC on the addition of ethanol and isobutanol was caused by the
normal consequences of BTE behaviour as shown in Figure 4.3(a), where the BTE for
the blends were higher than PG. The typical findings were founded by Al-Hasan (2003)
and Najafi et al. (2009) who determined the BSFC decreases with the addition of ethanol
percentage in fuel blend.
At a constant engine speed in Figure 4.4(b), it can be observed that the BSFC of the
PG is the lowest among the tested fuel blends at lower engine torque (10 Nm). This is due
to higher value of BTE of PG with 17.9% as presented in Figure 4.3(b). However, the
BSFC of the alcohol-gasoline blends were lower than PG at higher engine torque (80 to
100 Nm) due to improve combustion efficiency, thereby increase the thermal efficiency
and concurrently reducing the value of BSFC.
53
(a)
350
400
450
500
550
600
650
700
0 1000 2000 3000 4000 5000 6000
Bra
ke
Sp
ecif
ic F
uel
Co
nsu
mp
tio
n, B
SF
C (
g/k
WH
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.10%
54
(b)
Figure 4.4: Variation of brake specific fuel consumption as a function of (a)
engine speed and (b) engine torque for all tested fuel blends
250
300
350
400
450
500
550
0 20 40 60 80 100 120
Bra
ke
Sp
ecif
ic F
uel
Co
nsu
mp
tio
n, B
SF
C (
g/k
WH
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.01%
55
4.3.5 Exhaust gas temperature
Exhaust gas temperature (EGT) is a significant indicator of the cylinder temperature
as a function of combustion temperature. Figure 4.5 presents the variation of EGT for all
tested fuel blends as a function of (a) the engine speed at constant full throttle condition
and (b) the engine torque at constant engine speed. It can be observed that the EGT of the
alcohol-gasoline blends were lower relative to that of PG at lower engine speed. The
addition of alcohol in fuel blend may reduce the exhaust temperature due to more efficient
conversion process of heat to work (Topgül et al., 2006). At lower engine speeds, the
higher latent HOV of the ethanol and isobutanol in comparison to gasoline produces
higher temperature drop in cylinder charge at the end of intake valve stroke, resulting in
lower temperature at the end of combustion stroke and proportionally lower in EGT at
the end of combustion. Besides that, the decrease in EGT is also due to lower heating
value of ethanol and isobutanol as compared to gasoline (Ansari & Verma, 2012).
However, interestingly, the EGT of the alcohol-gasoline fuel blend is slightly higher than
that for PG at higher engine speeds (4000–5000 rpm). The E20 blend has the highest EGT
at 5000 rpm (with a percentage difference of 4.5% relative to PG) due to improve fuel
vaporization and combustible mixture formation during compression stroke (S. B. Singh
et al., 2015).
In Figure 4.5(b), the EGT of all the tested blends increases with increasing engine
torque. It reaches maxima at engine torque of 80Nm, and then it starts decreasing with
further increase in engine torque. In addition, the variation in EGT for all the fuel blends
is observed to be statistically insignificant in comparison to PG. The EGT of E10, iB10,
iB20, E5iB5 and E10iB10 blend is higher relative to that of PG with average values of
0.89, 2.01, 2.08, 0.11 and 1.65%. Meanwhile the EGT for E20 blend is lower than PG
with average value of 0.93%. It can be seen that the EGT of the E20 blend is lower than
56
PG at lower engine torque (20-60 Nm). This is due to very high amount of latent HOV of
the blend (by nearly 32%) with respect to PG, which is reflected by drop in EGT.
(a)
400
450
500
550
600
650
700
750
800
850
0 1000 2000 3000 4000 5000 6000
Exhau
st G
as T
emp
erat
ure
, E
GT
(oC
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.40%
57
(b)
Figure 4.5: Variation of exhaust gas temperature as a function of (a) engine
speed and (b) engine torque for all tested fuel blends
550
600
650
700
750
800
850
0 20 40 60 80 100 120
Ex
hau
st G
as T
emp
erat
ure
, E
GT
(oC
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.65%
58
4.4 Exhaust emissions
The measurements for all regulated emissions i.e. carbon monoxide (CO), carbon
dioxide (CO2), hydrocarbon (HC) and nitrogen oxides (NOX) was observed and discussed
in details.
4.4.1 Carbon monoxide emissions
CO emission is a product of incomplete combustion of hydrocarbon fuel due to lack
of air in the air-fuel mixture as well as time delay during the combustion cycle (Bayindir
et al., 2010; Masum, Kalam, Masjuki, Rahman, et al., 2014). Figure 4.6 shows the
concentration of CO emissions of all the fuel blends as a function of (a) the engine speed
at constant full throttle condition and (b) the engine torque at constant engine speed. In
overall engine speed range, it is evident that the CO concentration of E10, E20, iB10,
iB20, E5iB5 and E10iB10 were lower, with an average reduction of 10.29, 12.20, 6.82,
9.21, 6.35 and 11.21%, respectively, relative to PG. The E20 blend gives the lowest CO
emission among all the tested fuel blends, especially at an engine speed of 4000 and 5000
rpm. The fuel blends which contain alcohols are oxygenated fuels, therefore, they may
enhance the oxygen content in gasoline blends and improve the leaning effect and
combustion effectively, which in turns, reduce the CO emissions (Canakci et al., 2013;
Mittal et al., 2013). In addition, the higher flame speed of ethanol and isobutanol also
facilitates in completing the combustion process earlier, which reduces CO emissions
(Feng et al., 2013; Masum et al., 2015).
At a constant engine speed of 4000 rpm in Figure 4.6(b), the concentration of CO
emissions increases with increasing engine torque. At lower engine torque, less fuel
quantity is needed while higher engine torque needs more fuel quantity injection, leading
to higher amount of CO emissions. On average, the CO emissions of the E10, E20, iB10,
iB20, E5iB5 and E10iB10 blend decreased by 19.17, 44.02, 12.77, 27.29, 12.10 and
59
34.36%, respectively, relative to PG. The CO emissions of the alcohols-gasoline blends
is comparable to PG at lower engine torque, however marginally higher than PG at higher
engine torque. The significant reduction of CO emissions is due to presence of fuel
oxygen in the alcohols, which enhances the combustion efficiency, which in turns
reducing CO emissions.
(a)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
1000 2000 3000 4000 5000
Car
bon m
onoxid
e, C
O (
%vo
l)
Speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.68%
60
(b)
Figure 4.6: Variation of CO emissions as a function of (a) engine speed and (b)
engine torque for all tested fuel blends
4.4.2 Carbon dioxide emissions
CO2 emission is a primary greenhouse gas (GHG) that is formed from the complete
combustion of the air-fuel mixture in an internal combustion engine. Figure 4.7 shows
the concentration of CO2 emissions of all the fuel blends as a function of (a) the engine
speed at constant full throttle condition and (b) the engine torque at constant engine speed.
In overall engine speed range, it is apparent that the amount of CO2 concentrations is
higher for all the fuel blends compared to that PG. The E10iB10 blend produces the
highest concentration of CO2 emissions with an average increase of 10.67% with respect
to PG. This is followed by the E20, iB20, iB10, E10 and E5iB5 blend, with an average
increase of 8.41, 7.27, 6.96, 4.25 and 3.44%, respectively with respect to PG. The
variation of CO2 emissions is a stark contrast to CO emissions in Figure 4.6. The results
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
20 40 60 80 100
Car
bo
n m
on
ox
ide,
CO
(%
vo
l)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.22%
61
indicate that the higher amount of CO2 concentrations is released upon fuel combustion.
This is perhaps because of the present of oxygen content in the alcohols, which leads to
lean burning and promotes complete combustion. This in turn, decreases the amount non-
complete combustion products i.e. CO emissions.
In Figure 4.7(b), the concentration of CO2 emissions increases with increasing engine
torque. It reaches maxima at engine torque of 60Nm, and it starts dropping with further
increase of engine torque. At higher engine torque, excess fuel quantity is injected to
maintain the engine speed, resulting in insufficient amount of oxygen and lower thermal
efficiency, therefore lower CO2 emissions is produced. In addition, the CO2 emissions of
all the fuel blends is similar or slightly higher than pure gasoline, except for E5iB5 blend
which produces 0.66% lower CO2 emission with respect to PG. Meanwhile, the E10,
E20, iB10, iB20 and E10iB10 blend releases higher concentration of CO2 emissions
compared with PG, with an average increase of 1.23, 0.97, 0.42, 2.82 and 1.00%,
respectively.
62
(a)
(b)
Figure 4.7: Variation of CO2 emissions as a function of (a) engine speed and (b)
engine torque for all tested fuel blend
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1000 2000 3000 4000 5000
Car
bo
n d
iox
ide,
CO
2(%
vo
l.)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
10.00
10.50
11.00
11.50
12.00
12.50
13.00
13.50
14.00
14.50
15.00
20 40 60 80 100
Car
bon d
ioxid
e, C
O2 (
%vol.
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.22%
Uncertainty ±0.46%
63
4.4.3 Hydrocarbon emissions
HC emissions are found in unburned mixtures of fuel molecules as a consequence of
improper mixing and incomplete fuel combustion in an internal combustion engine. The
mechanisms of HC emissions formation of gasoline engine were explained in the previous
studies (Alasfour, 1999; Blint & Bechtel, 1982; G. Lavoie et al., 1980; G. A. Lavoie &
Blumberg, 1980). Figure 4.8 shows the concentration of HC emissions of all the fuel
blends as a function of (a) the engine speed at constant full throttle condition and (b) the
engine torque at constant engine speed. As shown in Figure 4.8(a), it can be observed
that the concentration of HC emissions decreases with increasing engine speeds except
for the abrupt increase at engine speed of 3000 rpm. The incomplete combustion is due
to the inhomogeneous charge of air and fuel which then leads to higher HC emissions.
Besides that, the E10, E20, iB10, iB20, E5iB5 and E10iB10 blend releases lower
concentration of HC emissions compared with PG, with an average reduction of 13.57,
13.71, 15.11, 14.50, 13.40 and 17.13%, respectively. It is also can be seen that the addition
of alcohols in gasoline slightly reduces HC emissions level especially at high engine
speed of 5000 rpm compared to the lower engine speed of 1000 and 2000 rpm. This is
due to that the air-fuel mixture that homogenises at high engine speed tends to raise the
in-cylinder temperature and enhances combustion efficiency. In addition, the reduction
of HC emissions is caused by the leaning effect which is associated with the higher
oxygen content in the alcohols, which enhances fuel combustion efficiency (Feng et al.,
2015; Koç et al., 2009). The reduced HC emissions also attributed to the faster laminar
flame speed of the alcohols, thus producing the lowest amount of HC emissions (Khuong
et al., 2016).
64
It can be observed in Figure 4.8(b) that the concentration of HC emissions increases
with increasing engine torque for all the tested fuel blends. The higher engine torque
needs more fuel quantity injection per engine cycle to maintain the engine speed,
therefore more HC emissions is produced. Besides that, the E10iB10 blend releases the
lowest concentration of HC emissions with an average reduction of 46.32% with respect
to PG. This is followed by the E20, iB20, E5iB5, iB10 and E10 blend, with an average
reduction of 44.39, 35.06, 31.49, 25.24 and 21.31%, respectively with respect to PG. This
is due to the presence of oxygen in the alcohols, which assist complete combustion,
resulting lower HC formation. The faster laminar flame speed of the alcohols compare to
gasoline may enhance combustion efficiency, therefore reducing amount of HC emissions
(Sayin, 2010).
(a)
0
100
200
300
400
500
600
700
1000 2000 3000 4000 5000
Hydro
carb
on, H
C (
ppm
vol.
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.92%
65
(b)
Figure 4.8: Variation of HC emissions as a function of (a) engine speed and (b)
engine torque for all tested fuel blends
4.4.4 Nitrogen oxides emissions
NOX emission is a group of gases formed when nitrogen combines with oxygen. The
endothermic reaction of nitrogen and oxygen takes place inside the engine cylinder during
the combustion cycle at high temperature, producing NOX. The two most common NOX
are nitric oxide (NO) and nitrogen dioxide (NO2). The mechanism of NOX formation in
gasoline engine were briefly explained by Masum et al. (2013). Figure 4.9 depicts the
concentration of NOX emissions of all the fuel blends as a function of (a) the engine speed
at constant full throttle condition and (b) the engine torque at constant engine speed. It
can be seen that in Figure 4.9(a), the variation of NOX emissions increases with
increasing engine speeds. In overall engine speed range, the concentration of NOX
emissions of E10, E20, iB10, iB20, E5iB5 and E10iB10 were higher, with an average
0
50
100
150
200
250
20 40 60 80 100
Hyd
roca
rbo
n, H
C (
pp
m v
ol.
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±1.72%
66
increase of 19.56, 34.56, 19.04, 20.30, 22.07 and 16.37%, respectively, relative to PG.
The significant increase of NOX emissions is indicates to higher combustion temperature
and EGT, which is associated with the higher oxygen concentration of the fuel containing
ethanol and isobutanol (Feng et al., 2015; Gravalos et al., 2013).
It can be observed in Figure 4.9(b) that the concentration of NOX emissions increases
with increasing engine torque. The NOX emissions reaches maxima at engine torque of
60Nm, and it starts deescalating with further increase of engine torque. At lower engine
torque of 20Nm, the lower combustion temperature results in lower formation of NOX
emissions. The high combustion temperature with additional oxygen availability during
higher engine torque results to more reaction between nitrogen and oxygen to form NOX.
However, when an excessive amount of fuel is injected to the combustion chamber to
maintain engine speed, it leads to lower charge temperature which causes lower NOx
emissions. The NOX emissions of all the fuel blends is similar or slightly higher than pure
gasoline, except for E5iB5 blend which produces 2.80% lower NOX emissions with
respect to PG. The E10, E20, iB10, iB20 and E10iB10 blend releases higher concentration
of NOX emissions compared with PG, with an average increment of 1.58, 0.64, 1.27, 0.49
and 2.26%, respectively, with respect to PG.
67
(a)
(b)
Figure 4.9: Variation of NOX emissions as a function of (a) engine speed and (b)
engine torque for all tested fuel blends
0
50
100
150
200
250
1000 2000 3000 4000 5000
Nit
rogen
ox
ides
, N
OX
(pp
m v
ol.
)
Engine speed (rpm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
0
500
1000
1500
2000
2500
3000
3500
20 40 60 80 100
Nit
rogen
oxid
es, N
Ox (
ppm
vol.
)
Engine torque (Nm)
PG
E10
E20
iB10
iB20
E5iB5
E10iB10
Uncertainty ±0.75%
Uncertainty ±1.80%
68
4.5 Summary
This chapter discussed the physicochemical properties, engine performances and
exhaust emissions of various fuel blends i.e. E10, E20, iB10, iB20, E5iB5, E10iB10 and
PG. The results can be summarized as follows:
1. The alcohol-gasoline fuel blends have a significant increase in the density,
viscosity, HOV and RON, but they are lower in heating value and Reid vapor
pressure as compared with pure gasoline.
2. The alcohol-gasoline blends result in significant enhancement of the engine torque,
BP and BTE compared to pure gasoline. The E20 blend gives the most significant
enhancement for these parameters, with an average enhancement of 3.88, 2.60 and
13.61%, respectively, with respect to PG.
3. The fuel blends also result in lower BSFC than PG caused by normal consequence
behaviour to BTE. The E10iB10 blend gives the largest improvement of BSFC
with an average enhancement of 5.77% than that of pure gasoline.
4. However, there are no significant changes of EGT between the fuel blends and
pure gasoline for both operating conditions. Interestingly, the EGT of the alcohol-
gasoline fuel blends is higher than PG at higher engine speeds and engine torque.
5. The E10iB10 blend results in the lowest CO and HC emissions at various engine
speeds with an average reduction of 11.21 and 17.13%, respectively, relative to
pure gasoline.
6. At various engine torque, E20 and E10iB10 blends result in the lowest CO and HC
emissions with an average reduction of 44.02 and 46.32%, respectively, with
respect to pure gasoline.
69
7. However, all the tested fuel blends show higher CO2 emissions as compared to
pure gasoline for both operating conditions.
8. In terms of NOX emission, there is no significant differences for all fuel blends
with pure gasoline for both conditions. However, the alcohol-gasoline fuel blends
show a higher NOX emissions than pure gasoline at higher engine speed and engine
torque.
70
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
In this study, six types of fuel blends consist of ethanol and isobutanol were mixed
with unleaded gasoline at different volume rates (E10, E20, iB10, iB20, E5iB5 and
E10iB10) and the physicochemical properties of these blends are measured and compared
with the pure gasoline. The fuel blends were tested on a four cylinder SI engine at various
engine speeds (1000–5000 rpm) and engine torque (20–100 Nm) and the results are
compared with those obtained for pure gasoline. The following conclusions are drawn
based on the results of this study:
1. A significant increase in the density, viscosity, HOV and RON of all the alcohol-
gasoline blends, but they are lower in heating value and RVP as compared with
pure gasoline.
2. The E20 blend gives the most significant enhancement for engine torque and brake
power, meanwhile the E10iB10 blend gives the largest improvement of BSFC
among the tested blends. However, there are no significant changes of EGT
between the fuel blends and pure gasoline for both operating conditions. The EGT
of the alcohol-gasoline fuel blends is higher than PG at higher engine speeds and
engine torque.
3. In terms of exhaust emissions, the E10iB10 blend results in the lowest CO and HC
emissions, respectively at various engine speeds. The E20 and E10iB10 blends
result in the lowest CO and HC emissions, respectively at various engine torque.
However, all the tested fuel blends show higher CO2 emissions as compared to
pure gasoline for both operating conditions. There is no significant differences of
NOX emissions for all fuel blends and pure gasoline for both conditions. However,
the alcohol-gasoline fuel blends show a higher NOX emissions at higher engine
speed and engine torque.
71
In general, it can be concluded that all of the alcohol-gasoline fuel blends in this study
gives better engine performance and reduces exhaust emissions. In particular, the E20
blend improves the engine performances meanwhile E10iB10 improves the exhaust
emissions of a four cylinder SI engine without engine modifications. This may be
attributed to the structure of the alcohols and physicochemical properties of the fuel blend,
which improves reactivity of the fuel blend.
5.2 Recommendations
Based on the conclusions have been made, the following recommendations can be
drawn for the future study:
1. This study only focusses on the effect of engine performance and emissions
characteristics of ethanol-isobutanol-gasoline fuel blends. Other engine tests are
also important, i.e. effect on combustion performance, engine durability,
lubricating oil, etc.
2. Further research is required to study these potential alcohols in gasoline fuel blend
with optimum operating parameters, i.e. varying compression ratio, ignition
timing, etc., therefore giving the best engine performances and exhaust emissions.
72
REFERENCES
Aakko-Saksa, P., Koponen, P., Kihlman, J., Reinikainen, M., Skyttä, E., Rantanen-
Kolehmainen, L., & Engman, A. (2011). Biogasoline options for conventional
spark-ignition cars. Finland: VTT Technical Research Centre of Finland.
Agudelo, J., Delgado, Á., & Benjumea, P. (2011). The lubricity of ethanol-gasoline fuel
blends. Revista Facultad de Ingeniería Universidad de Antioquia(58), 9-16.
Al-Hasan, M. (2003). Effect of ethanol-unleaded gasoline blends on engine performance
and exhaust emission. Energy Conversion and Management, 44, 1547-1561.
Alasfour, F. (1999). The effect of using 30% iso-butanol-gasoline blend on hydrocarbon
emissions from a spark-ignition engine. Energy Sources, 21(5), 379-394.
Anderson, J., Kramer, U., Mueller, S., & Wallington, T. (2010). Octane numbers of
ethanol− and methanol− gasoline blends estimated from molar concentrations.
Energy & Fuels, 24(12), 6576-6585.
Ansari, F. T., & Verma, A. P. (2012). Experimental determination of suitable ethanol–
gasoline blend for Spark ignition engine. International Journal of Engineering,
1(5).
Ashnani, M. H. M., Johari, A., Hashim, H., & Hasani, E. (2014). A source of renewable
energy in Malaysia, why biodiesel? Renewable and Sustainable Energy Reviews,
35(0), 244-257.
Bajpai, P. (2013). Advances in Bioethanol. Patial, India: Springer.
Balat, M., & Balat, H. (2009). Recent trends in global production and utilization of bio-
ethanol fuel. Applied Energy, 86(11), 2273-2282.
Bayindir, H., Yücesu, H., & Aydin, H. (2010). The Effects of λ and∊ on Engine
Performance and Exhaust Emissions Using Ethanol–unleaded Gasoline Blends in
an SI Engine. Energy Sources, Part A: Recovery, Utilization, and Environmental
Effects, 33(1), 49-56.
Berndes, G., Hoogwijk, M., & van den Broek, R. (2003). The contribution of biomass in
the future global energy supply: a review of 17 studies. Biomass and Bioenergy,
25(1), 1-28.
Blint, R., & Bechtel, J. (1982). Hydrocarbon combustion near a cooled wall: SAE
Technical Paper.
Caiazzo, F., Ashok, A., Waitz, I. A., Yim, S. H. L., & Barrett, S. R. H. (2013). Air
pollution and early deaths in the United States. Part I: Quantifying the impact of
major sectors in 2005. Atmospheric Environment, 79(0), 198-208.
Campos-Fernandez, J., Arnal, J. M., Gomez, J., Lacalle, N., & Dorado, M. P. (2013).
Performance tests of a diesel engine fueled with pentanol/diesel fuel blends. Fuel,
107(0), 866-872.
73
Canakci, M., Ozsezen, A. N., Alptekin, E., & Eyidogan, M. (2013). Impact of alcohol–
gasoline fuel blends on the exhaust emission of an SI engine. Renewable Energy,
52(0), 111-117.
Celik, M. B. (2008). Experimental determination of suitable ethanol–gasoline blend rate
at high compression ratio for gasoline engine. Applied Thermal Engineering,
28(5–6), 396-404.
Chiba, F., Ichinose, H., Morita, K., Yoshioka, M., Noguchi, Y., & Tsukagoshi, T. (2010).
High Concentration Ethanol Effect on SI Engine Emission. SAE International
Journal of Engines, 3(1), 1033-1041.
Costa, R. C., & Sodré, J. R. (2010). Hydrous ethanol vs. gasoline-ethanol blend: Engine
performance and emissions. Fuel, 89(2), 287-293.
Demirbas, M. F., & Balat, M. (2006). Recent advances on the production and utilization
trends of bio-fuels: A global perspective. Energy Conversion and Management,
47(15–16), 2371-2381.
Deng, B., Fu, J., Zhang, D., Yang, J., Feng, R., Liu, J., . . . Liu, X. (2013). The heat release
analysis of bio-butanol/gasoline blends on a high speed SI (spark ignition) engine.
Energy, 60(0), 230-241.
Dernotte, J., Mounaim-Rousselle, C., Halter, F., & Seers, P. (2009). Evaluation of
Butanol–Gasoline Blends in a Port Fuel-injection, Spark-Ignition Engine. Oil &
Gas Science and Technology – Revue de l’Institut Français du Pétrole, 65(2),
345-351.
Dhaundiyal, A. (2014). Influence of blending on the engine parameters and the Reynolds
number. International Journal of Applied Science and Engineering Research,
3(1), 129-152.
EIA. (2016). International Energy Outlook 2016: U.S. Energy Information
Administration.
Elfasakhany. (2014). The effect of ethanol -gasoline blends on performance and exhaust
emission characteristics of spark ignition engines. International Journal of
Automotive Engineering, 4(1), 609-620.
Elfasakhany. (2015). Experimental investigation on SI engine using gasoline and a hybrid
iso-butanol/gasoline fuel. Energy Conversion and Management, 95, 398-405.
Elfasakhany. (2016a). Engine performance evaluation and pollutant emissions analysis
using ternary bio-ethanol–iso-butanol–gasoline blends in gasoline engines.
Journal of Cleaner Production, 139, 1057-1067.
Elfasakhany. (2016b). Experimental study of dual n-butanol and iso-butanol additives on
spark-ignition engine performance and emissions. Fuel, 163, 166-174.
Elfasakhany. (2016c). Performance and emissions of spark-ignition engine using
ethanol–methanol–gasoline, n-butanol–iso-butanol–gasoline and iso-butanol–
74
ethanol–gasoline blends: A comparative study. Engineering Science and
Technology, an International Journal, 19(4), 2053-2059.
Ezeji, T., & Blaschek, H. P. (2008). Fermentation of dried distillers’ grains and solubles
(DDGS) hydrolysates to solvents and value-added products by solventogenic
clostridia. Bioresource Technology, 99(12), 5232-5242.
Farkade, H. S., & Panthre, A. P. (2012). Experimental investigation of methanol, ethanol
and butanol blends with gasoline on SI engine. International Journal of Emerging
Technology and Advanced Engineering, 2(4), 205-215.
Feng, R., Fu, J., Yang, J., Wang, Y., Li, Y., Deng, B., . . . Zhang, D. (2015). Combustion
and emissions study on motorcycle engine fueled with butanol-gasoline blend.
Renewable Energy, 81(0), 113-122.
Feng, R., Yang, J., Zhang, D., Deng, B., Fu, J., Liu, J., & Liu, X. (2013). Experimental
study on SI engine fuelled with butanol–gasoline blend and H2O addition. Energy
Conversion and Management, 74(0), 192-200.
Ganguly, A., Chatterjee, P. K., & Dey, A. (2012). Studies on ethanol production from
water hyacinth—A review. Renewable and Sustainable Energy Reviews, 16(1),
966-972.
Gravalos, I., Moshou, D., Gialamas, T., Xyradakis, P., Kateris, D., & Tsiropoulos, Z.
(2013). Emissions characteristics of spark ignition engine operating on lower–
higher molecular mass alcohol blended gasoline fuels. Renewable Energy, 50, 27-
32.
Hahn-Hägerdal, B., Galbe, M., Gorwa-Grauslund, M. F., Lidén, G., & Zacchi, G. (2006).
Bio-ethanol – the fuel of tomorrow from the residues of today. Trends in
Biotechnology, 24(12), 549-556.
Hamelinck, C. N., Van Hooijdonk, G., & Faaij, A. P. (2005). Ethanol from lignocellulosic
biomass: techno-economic performance in short-, middle-and long-term. Biomass
and Bioenergy, 28(4), 384-410.
Hosseini, S. E., & Wahid, M. A. (2013). Feasibility study of biogas production and
utilization as a source of renewable energy in Malaysia. Renewable and
Sustainable Energy Reviews, 19(0), 454-462.
How, H. G., Masjuki, H. H., Kalam, M. A., & Teoh, Y. H. (2014). An investigation of
the engine performance, emissions and combustion characteristics of coconut
biodiesel in a high-pressure common-rail diesel engine. Energy, 69, 749-759.
Hsieh, W.-D., Chen, R.-H., Wu, T.-L., & Lin, T.-H. (2002). Engine performance and
pollutant emission of an SI engine using ethanol–gasoline blended fuels.
Atmospheric Environment, 36(3), 403-410.
Jin, C., Yao, M., Liu, H., Lee, C.-f. F., & Ji, J. (2011). Progress in the production and
application of n-butanol as a biofuel. Renewable and Sustainable Energy Reviews,
15(8), 4080-4106.
75
Khuong, L. S., Zulkifli, N. W. M., Masjuki, H. H., Mohamad, E. N., Arslan, A., Mosarof,
M. H., & Azham, A. (2016). A review on the effect of bioethanol dilution on the
properties and performance of automotive lubricants in gasoline engines. RSC
Advances, 6(71), 66847-66869.
Kito-Borsa, T., Pacas, D. A., Selim, S., & Cowley, S. W. (1998). Properties of an
Ethanol− Diethyl Ether− Water Fuel Mixture for Cold-Start Assistance of an
Ethanol-Fueled Vehicle. Industrial & engineering chemistry research, 37(8),
3366-3374.
Koç, M., Sekmen, Y., Topgül, T., & Yücesu, H. S. (2009). The effects of ethanol–
unleaded gasoline blends on engine performance and exhaust emissions in a
spark-ignition engine. Renewable Energy, 34(10), 2101-2106.
Kumar, M., & Gayen, K. (2011). Developments in biobutanol production: new insights.
Applied Energy, 88(6), 1999-2012.
Larsen, U., Johansen, T., & Schramm, J. (2009). Ethanol as a Future Fuel for Road
Transportation: Main report: DTU Mekanik.
Lavoie, G., LoRusso, J., & Adamczyk, A. (1980). Hydrocarbon emissions modeling for
spark ignition engines Combustion Modeling in Reciprocating Engines (pp. 409):
Plenum Press.
Lavoie, G. A., & Blumberg, P. N. (1980). A Fundamental Model for Predicting Fuel
Consumption, NOx and HC Emissions of the Conventional Spark-Ignited Engine.
Combustion Science and Technology, 21(5-6), 225-258.
Lin, W. Y., Chang, Y. Y., & Hsieh, Y. R. (2010). Effect of ethanol-gasoline blends on
small engine generator energy efficiency and exhaust emission. J Air Waste
Manag Assoc, 60(2), 142-148.
Liu, F., Liu, P., Zhu, Z., Wei, Y., & Liu, S. (2012). Regulated and unregulated emissions
from a spark-ignition engine fuelled with low-blend ethanol–gasoline mixtures.
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of
Automobile Engineering, 226(4), 517-528.
Liu, H., Wang, G., & Zhang, J. (2013). The Promising Fuel-Biobutanol. In Z. Fang (Ed.),
Liquid, Gaseous and Solid Biofuels - Conversion Techniques (pp. 175-198):
Intech.
Masum, B. M., Kalam, M. A., Masjuki, H. H., Palash, S. M., & Fattah, I. M. R. (2014).
Performance and emission analysis of a multi cylinder gasoline engine operating
at different alcohol-gasoline blends. RSC Advances, 4(53), 27898-27904.
Masum, B. M., Kalam, M. A., Masjuki, H. H., Rahman, S. M. A., & Daggig, E. E. (2014).
Impact of denatured anhydrous ethanol-gasoline fuel blends on a spark-ignition
engine. RSC Advances, 4(93), 51220-51227.
Masum, B. M., Masjuki, H. H., Kalam, M. A., Palash, S. M., & Habibullah, M. (2015).
Effect of alcohol-gasoline blends optimization on fuel properties, performance
and emissions of a SI engine. Journal of Cleaner Production, 86, 230-237.
76
Masum, B. M., Masjuki, H. H., Kalam, M. A., Rizwanul Fattah, I. M., Palash, S. M., &
Abedin, M. J. (2013). Effect of ethanol–gasoline blend on NOx emission in SI
engine. Renewable and Sustainable Energy Reviews, 24(0), 209-222.
Mittal, N., Athony, R. L., Bansal, R., & Ramesh Kumar, C. (2013). Study of performance
and emission characteristics of a partially coated LHR SI engine blended with n-
butanol and gasoline. Alexandria Engineering Journal, 52(3), 285-293.
Mohamed, A. R., & Lee, K. T. (2006). Energy for sustainable development in Malaysia:
Energy policy and alternative energy. Energy Policy, 34(15), 2388-2397.
Mohiuddin, A., Rahman, A., Arfah, A., & Farraen, M. (2008). Investigation And Probable
Solution Of Low End Torque Dip Problem Of Campro 1.6 L Engine. International
Journal on Mechanical and Materials Engineering, 176.
Naik, S. N., Goud, V. V., Rout, P. K., & Dalai, A. K. (2010). Production of first and
second generation biofuels: A comprehensive review. Renewable and Sustainable
Energy Reviews, 14(2), 578-597.
Najafi, G., Ghobadian, B., Tavakoli, T., Buttsworth, D. R., Yusaf, T. F., & Faizollahnejad,
M. (2009). Performance and exhaust emissions of a gasoline engine with ethanol
blended gasoline fuels using artificial neural network. Applied Energy, 86(5), 630-
639.
Niemistö, J., Saavalainen, P., Isomäki, R., Kolli, T., Huuhtanen, M., & Keiski, R. L.
(2013). Biobutanol Production from Biomass. In V. K. Gupta & M. G. Tuohy
(Eds.), Biofuel Technologies (pp. 443-470). Oulu Finland: Springer.
Ozsezen, A. N., & Canakci, M. (2011). Performance and combustion characteristics of
alcohol-gasoline blends at wide-open throttle. Energy, 36(5), 2747-2752.
Patakova, P., Maxa, D., Rychtera, M., Linhova, M., Fribert, P., Muzikova, Z., . . .
Melzoch, K. (2011). Perspectives of Biobutanol Production and Use.
Pease, R. (2012). Traffic pollution kills 5,000 a year in UK, says study, BBC News Science
& Environment.
Pukalskas, S., Bogdanovicius, Z., Sendzikiene, E., Makareviciene, V., & Janulis, P.
(2009). The mixture of biobutanol and petrol for Otto engines. Transport, 24(4),
301-307.
Ramey, D. E., Eaglesham, A., & Hardy, R. (2007). Butanol: the other alternative fuel.
NABC Report(19), 137-147.
Ranjan, A., & Moholkar, V. (2009). Biobutanol: a Viable Gasoline Substitute through
ABE Fermentation. Paper presented at the Proceeding of international conference
on energy and environment.
Rasskazchikova, T. V., Kapustin, V. M., & Karpov, S. A. (2004). Ethanol as High–Octane
Additive to Automotive Gasolines. Production and Use in Russia and Abroad.
Chemistry and Technology of Fuels and Oils, 40(4), 203-210.
77
RFA. (2016). Global ethanol production by country/region and year Retrieved 9 July,
2017, from
http://www.ethanolrfa.org/resources/industry/statistics/#1454098996479-
8715d404-e546
Saidur, R., Abdelaziz, E. A., Demirbas, A., Hossain, M. S., & Mekhilef, S. (2011). A
review on biomass as a fuel for boilers. Renewable and Sustainable Energy
Reviews, 15(5), 2262-2289.
Saridemir, S. (2012). The effects of ethanol-unleaded gasoline blends in a single cylinder
SI engine performance and exhaust emission. Energy Education Science and
Technology Part A: Energy Science and Research, 30(1), 727-736.
Sawin, J. L. (2008). Renewables 2007 Global Status Report. Washington: Worldwatch
Institute.
Sawin, J. L., Barnes, D., Martinot, E., McCrone, A., Roussell, J., Sims, R., & O'Brien, V.
S. (2011). Renewables 2011 Global Status Report A Dynamic Policy Landscape
(pp. 13-14). France: REN21
Sayin, C. (2010). Engine performance and exhaust gas emissions of methanol and
ethanol–diesel blends. Fuel, 89(11), 3410-3415.
Shapovalov, O., & Ashkinazi, L. (2008). Biobutanol: Biofuel of second generation.
Russian Journal of Applied Chemistry, 81(12), 2232-2236.
Sims, R., Taylor, M., Saddler, J., & Mabee, W. (2008). From 1st-to 2nd-generation
biofuel technologies: an overview of current industry and RD&D activities.
International Energy Agency, 16-20.
Singh, E., Shukla, M. K., Pathak, S., Sood, V., & Singh, N. (2014). Performance Emission
& Noise Characteristics Evaluation of n-Butanol/Gasoline Blend in Constant
Speed SI Engine. International Journal of Engineering Research & Technology
(IJERT), 3(1), 993-999.
Singh, S. B., Dhar, A., & Agarwal, A. K. (2015). Technical feasibility study of butanol–
gasoline blends for powering medium-duty transportation spark ignition engine.
Renewable Energy, 76(0), 706-716.
Smith, M. B. (2013). March's advanced organic chemistry: reactions, mechanisms, and
structure (7th ed.): John Wiley & Sons.
Soruşbay, C. (2015). Emissions From SI Engines. Istanbul Technical University.
Instanbul. Retrieved from http://web.itu.edu.tr/~sorusbay/SI/LN09.pdf
Sulaiman, F., Abdullah, N., Gerhauser, H., & Shariff, A. (2011). An outlook of Malaysian
energy, oil palm industry and its utilization of wastes as useful resources. Biomass
and Bioenergy, 35(9), 3775-3786.
Szulczyk, K. R. (2010). Which is a better transportation fuel-butanol or ethanol?
International Journal of Energy and Environment, 1(1).
78
Szulczyk, K. R., McCarl, B. A., & Cornforth, G. (2010). Market penetration of ethanol.
Renewable and Sustainable Energy Reviews, 14(1), 394-403.
Tavares, J. R., Sthel, M. S., Campos, L. S., Rocha, M. V., Lima, G. R., da Silva, M. G.,
& Vargas, H. (2011). Evaluation of Pollutant Gases Emitted by Ethanol and
Gasoline Powered Vehicles. Procedia Environmental Sciences, 4(0), 51-60.
Topgül, T., Yücesu, H. S., Çinar, C., & Koca, A. (2006). The effects of ethanol–unleaded
gasoline blends and ignition timing on engine performance and exhaust emissions.
Renewable Energy, 31(15), 2534-2542.
Transport and Air Quality, O. (2010). E85 and flex fuel vehicles (pp. 1-3). United States:
United States Environmental Protection Agency.
Wallner, T., & Frazee, R. (2010). Study of regulated and non-regulated emissions from
combustion of gasoline, alcohol fuels and their blends in a DI-SI engine.
Wallner, T., Shidore, N., & Ickes, A. (2010). Impact of ethanol and butanol as oxygenates
on SIDI engine efficiency and emissions using steady-state and transient test
procedures. Paper presented at the 16th Directions in Engine Efficiency and
Emissions Research (DEER) Conference.
Weizmann, C. (1919). United States Patent No. US1315585.
Xialong, Y., Jing, Y., & Tieping, L. (2009). The Effect of an SI Engine using Butanol–
Gasoline Blended Fuel on Performance and Enviroment. Paper presented at the
International Conference on Energy and Environment Technology.
Yanowitz, J., Christensen, E., & McCormick, R. L. (2011). Utilization of renewable
oxygenates as gasoline blending components. Contract, 303, 275-3000.
Yücesu, H. S., Topgül, T., Çinar, C., & Okur, M. (2006). Effect of ethanol–gasoline
blends on engine performance and exhaust emissions in different compression
ratios. Applied Thermal Engineering, 26(17–18), 2272-2278.
Yusoff, M. N. A. M., Zulkifli, N. W. M., Masum, B. M., & Masjuki, H. H. (2015).
Feasibility of Bioethanol and Biobutanol as transportation fuel in Spark-Ignition
Engine: A Review. RSC Advances.
79
LIST OF PUBLICATIONS AND PAPERS PRESENTED
Journal papers
1. Yusoff, M.N.A.M., Zulkifli, N.W.M., Masjuki, H.H., Harith, M.H., Syahir, A.Z.,
Kalam, M.A., Mansor, M.F., Azham, A., Khuong, L.S., 2017. Performance and
emission characteristics of a spark ignition engine fuelled with butanol isomer-
gasoline blends. Transportation Research Part D: Transport and Environment 57,
23-38. (ISI Indexed, Tier 2)
2. Yusoff, M.N.A.M., Zulkifli, N.W.M., Masum, B.M., Masjuki, H.H., 2015.
Feasibility of bioethanol and biobutanol as transportation fuel in spark-ignition
engine: a review. RSC Advances 5(121), 100184-100211. (ISI indexed, Tier 1)
3. Yusoff, M.N.A.M., Zulkifli, N.W.M., Masjuki, H.H., Harith, M.H., Syahir, L.S.
Khuong, M.S.M. Zaharin, Abdullah Alabdulkarem. Comparative assessment of
ethanol and isobutanol addition in gasoline on engine performance and exhaust
emissions (Under Revision – Journal of Cleaner Production, Tier 1)
Conference proceedings
1. Yusoff, M. N. A. M., Khuong, L. S., Syahir, A. Z., Harith, M. H., Masjuki, H. H.,
Zulkifli, N.W.M. Comparative Study of Ethanol and Isobutanol as Gasoline Blend
for Internal Combustion Engine. International Technical Postgraduate
Conference, 2017, Kuala Lumpur, Malaysia.
80
APPENDIX A
Uncertainty analysis for engine torque (T)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m) T 1 T 2 T 3 Min. Max. Accuracy Avg. Uncertainty%
(Nm) (Nm) (Nm) (Nm) (Nm) (±0.1 Nm) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.1
Nm
G=
D-0.1
Nm
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 71.1 71.6 71.8 71.1 71.8 71.9 71.0 71.41 0.50 -0.50
2000 92.7 91.7 92.2 91.7 92.7 92.8 91.6 92.20 0.50 -0.50
3000 94.6 94.4 94.5 94.4 94.6 94.7 94.3 94.53 0.11 -0.11
4000 111.0 111.6 110.5 110.5 111.6 111.7 110.4 111.05 0.50 -0.50
5000 109.6 107.3 107.0 107.0 109.6 109.7 106.9 108.29 1.20 -1.20
E10
1000 74.1 75.2 75.8 74.1 75.8 75.9 74.0 74.95 1.19 -1.19
2000 93.9 95.2 95.0 93.9 95.2 95.3 93.8 94.58 0.68 -0.68
3000 95.5 97.3 97.5 95.5 97.5 97.6 95.4 96.52 1.05 -1.05
4000 109.1 113.1 113.3 109.1 113.3 113.4 109.0 111.21 1.89 -1.89
5000 107.9 111.1 110.8 107.9 111.1 111.2 107.8 109.48 1.48 -1.48
E20
1000 76.7 80.2 79.2 76.7 80.2 80.3 76.6 78.48 2.22 -2.22
2000 95.3 95.9 94.9 94.9 95.9 96.0 94.8 95.39 0.52 -0.52
3000 96.1 98.1 97.8 96.1 98.1 98.2 96.0 97.12 1.03 -1.03
4000 113.3 113.7 112.9 112.9 113.7 113.8 112.8 113.31 0.35 -0.35
5000 110.0 111.8 111.2 110.0 111.8 111.9 109.9 110.87 0.81 -0.81
iB1
0
1000 78.9 75.8 75.6 75.6 78.9 79.0 75.5 77.26 2.18 -2.18
2000 94.4 94.7 94.5 94.4 94.7 94.8 94.3 94.54 0.17 -0.17
3000 96.2 95.9 95.5 95.5 96.2 96.3 95.4 95.82 0.38 -0.38
4000 111.7 112.7 112.5 111.7 112.7 112.8 111.6 112.22 0.42 -0.42
5000 108.9 110.8 110.3 108.9 110.8 110.9 108.8 109.83 0.85 -0.85
iB2
0
1000 80.0 79.3 79.7 79.3 80.0 80.1 79.2 79.63 0.41 -0.41
2000 93.8 93.1 93.6 93.1 93.8 93.9 93.0 93.44 0.36 -0.36
3000 96.0 95.7 95.3 95.3 96.0 96.1 95.2 95.63 0.36 -0.36
4000 112.6 112.5 112.3 112.3 112.6 112.7 112.2 112.44 0.13 -0.13
5000 111.0 110.1 110.7 110.1 111.0 111.1 110.0 110.56 0.42 -0.42
E5
iB5
1000 70.1 73.3 74.5 70.1 74.5 74.6 70.0 72.32 3.01 -3.01
2000 92.0 93.7 94.5 92.0 94.5 94.6 91.9 93.23 1.36 -1.36
3000 95.4 95.4 95.7 95.4 95.7 95.8 95.3 95.55 0.16 -0.16
4000 110.8 111.6 111.8 110.8 111.8 111.9 110.7 111.30 0.45 -0.45
5000 109.2 109.3 109.5 109.2 109.5 109.6 109.1 109.37 0.12 -0.12
E10
iB10
1000 79.5 79.7 80.2 79.5 80.2 80.3 79.4 79.85 0.47 -0.47
2000 94.6 94.9 94.9 94.6 94.9 95.0 94.5 94.74 0.17 -0.17
3000 96.9 97.2 96.8 96.8 97.2 97.3 96.7 97.00 0.21 -0.21
4000 112.4 113.0 112.5 112.4 113.0 113.1 112.3 112.69 0.28 -0.28
5000 110.4 111.8 110.9 110.4 111.8 111.9 110.3 111.11 0.60 -0.60
Uncertainty level 0.79 -0.79
81
APPENDIX A
Uncertainty analysis for brake power (BP)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m) BP 1 BP 2 BP 3 Min. Max. Accuracy Avg. Uncertainty%
(kW) (kW) (kW) (kW) (kW) (±0.1 kW) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.1
kW
G=
D-0.1
kW
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 7.4 7.5 7.5 7.4 7.5 7.6 7.3 7.48 0.54 -0.54
2000 19.4 19.7 19.3 19.3 19.7 19.8 19.2 19.51 1.03 -1.03
3000 29.7 29.7 29.7 29.7 29.7 29.8 29.6 29.71 0.10 -0.10
4000 46.5 46.6 46.3 46.3 46.6 46.7 46.2 46.41 0.33 -0.33
5000 57.4 57.2 56.0 56.0 57.4 57.5 55.9 56.69 1.19 -1.19
E10
1000 7.8 7.9 7.8 7.8 7.9 8.0 7.7 7.82 0.77 -0.77
2000 19.7 19.9 19.5 19.5 19.9 20.0 19.4 19.73 1.01 -1.01
3000 30.0 30.6 31.2 30.0 31.2 31.3 29.9 30.58 1.92 -1.92
4000 45.7 47.4 47.3 45.7 47.4 47.5 45.6 46.54 1.79 -1.79
5000 56.5 58.2 58.5 56.5 58.5 58.6 56.4 57.47 1.76 -1.76
E20
1000 8.0 8.4 8.3 8.0 8.4 8.5 7.9 8.22 2.21 -2.21
2000 20.0 20.1 19.8 19.8 20.1 20.2 19.7 19.91 0.77 -0.77
3000 30.2 30.8 30.4 30.2 30.8 30.9 30.1 30.51 1.00 -1.00
4000 47.5 47.6 47.2 47.2 47.6 47.7 47.1 47.41 0.44 -0.44
5000 57.6 58.5 58.0 57.6 58.5 58.6 57.5 58.04 0.82 -0.82
iB1
0
1000 8.3 7.9 8.0 7.9 8.3 8.4 7.8 8.09 2.14 -2.14
2000 19.8 19.8 19.6 19.6 19.8 19.9 19.5 19.72 0.62 -0.62
3000 30.3 30.1 30.9 30.1 30.9 31.0 30.0 30.50 1.20 -1.20
4000 46.8 47.2 47.2 46.8 47.2 47.3 46.7 47.00 0.42 -0.42
5000 57.0 58.0 58.2 57.0 58.2 58.3 56.9 57.60 1.04 -1.04
iB2
0
1000 8.4 8.2 8.3 8.2 8.4 8.5 8.1 8.29 1.05 -1.05
2000 19.6 19.5 19.8 19.5 19.8 19.9 19.4 19.63 0.89 -0.89
3000 30.2 30.1 30.9 30.1 30.9 31.0 30.0 30.50 1.31 -1.31
4000 47.2 47.0 47.2 47.0 47.2 47.3 46.9 47.06 0.23 -0.23
5000 58.1 58.1 58.0 58.0 58.1 58.2 57.9 58.06 0.10 -0.10
E5
iB5
1000 7.3 7.5 7.7 7.3 7.7 7.8 7.2 7.52 2.35 -2.35
2000 19.3 19.4 19.6 19.3 19.6 19.7 19.2 19.43 0.90 -0.90
3000 30.0 30.0 30.5 30.0 30.5 30.6 29.9 30.22 0.76 -0.76
4000 46.4 46.8 47.1 46.4 47.1 47.2 46.3 46.76 0.78 -0.78
5000 57.2 57.7 58.0 57.2 58.0 58.1 57.1 57.59 0.71 -0.71
E10
iB10
1000 8.3 8.0 8.4 8.0 8.4 8.5 7.9 8.21 2.15 -2.15
2000 19.8 20.0 20.1 19.8 20.1 20.2 19.7 19.97 0.67 -0.67
3000 30.4 30.2 31.7 30.2 31.7 31.8 30.1 30.95 2.41 -2.41
4000 47.1 47.5 47.1 47.1 47.5 47.6 47.0 47.26 0.43 -0.43
5000 57.8 57.6 58.2 57.6 58.2 58.3 57.5 57.90 0.57 -0.57
Uncertainty level 0.99 -0.99
82
APPENDIX A
Uncertainty analysis for brake thermal efficiency (BTE)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m)
BTE
1
BTE
2
BTE
3
Min. Max. Accuracy Avg. Uncertainty%
(%) (%) (%) (%) (%) (±0.2 %) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.2
%
G=
D-0.2
%
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 12.0 12.0 12.1 12.0 12.1 12.3 11.8 12.05 0.72 -0.72
2000 15.5 15.7 15.7 15.5 15.7 15.9 15.3 15.59 0.81 -0.81
3000 17.2 17.2 17.2 17.2 17.2 17.4 17.0 17.19 0.12 -0.12
4000 19.1 19.2 19.2 19.1 19.2 19.4 18.9 19.18 0.15 -0.15
5000 20.2 20.2 20.2 20.2 20.2 20.4 20.0 20.22 0.13 -0.13
E10
1000 13.1 13.3 13.1 13.1 13.3 13.5 12.9 13.18 0.77 -0.77
2000 16.7 16.9 16.6 16.6 16.9 17.1 16.4 16.73 1.01 -1.01
3000 18.2 18.6 19.0 18.2 19.0 19.2 18.0 18.60 1.92 -1.92
4000 20.6 21.3 20.7 20.6 21.3 21.5 20.4 20.93 1.79 -1.79
5000 21.5 22.1 21.5 21.5 22.1 22.3 21.3 21.80 1.55 -1.55
E20
1000 13.6 14.2 14.0 13.6 14.2 14.4 13.4 13.87 2.21 -2.21
2000 18.0 18.1 17.3 17.3 18.1 18.3 17.1 17.68 2.17 -2.17
3000 18.3 18.7 18.4 18.3 18.7 18.9 18.1 18.49 1.00 -1.00
4000 22.9 23.0 22.3 22.3 23.0 23.2 22.1 22.62 1.46 -1.46
5000 22.7 23.1 22.7 22.7 23.1 23.3 22.5 22.90 0.94 -0.94
iB1
0
1000 13.5 12.9 13.1 12.9 13.5 13.7 12.7 13.21 2.14 -2.14
2000 16.5 16.6 16.4 16.4 16.6 16.8 16.2 16.46 0.56 -0.56
3000 17.1 17.0 17.4 17.0 17.4 17.6 16.8 17.24 1.20 -1.20
4000 20.9 21.0 20.1 20.1 21.0 21.2 19.9 20.57 2.22 -2.22
5000 21.2 21.6 21.5 21.2 21.6 21.8 21.0 21.38 0.85 -0.85
iB2
0
1000 14.0 13.7 13.9 13.7 14.0 14.2 13.5 13.87 1.05 -1.05
2000 16.3 16.2 16.5 16.2 16.5 16.7 16.0 16.31 0.89 -0.89
3000 17.7 17.6 18.1 17.6 18.1 18.3 17.4 17.85 1.31 -1.31
4000 20.7 20.6 20.7 20.6 20.7 20.9 20.4 20.65 0.23 -0.23
5000 21.0 21.0 21.0 21.0 21.0 21.2 20.8 21.02 0.10 -0.10
E5
iB5
1000 12.5 12.7 13.1 12.5 13.1 13.3 12.3 12.79 2.35 -2.35
2000 16.8 16.9 17.1 16.8 17.1 17.3 16.6 16.94 0.90 -0.90
3000 18.3 18.3 18.6 18.3 18.6 18.8 18.1 18.43 0.76 -0.76
4000 21.0 21.2 21.3 21.0 21.3 21.5 20.8 21.18 0.78 -0.78
5000 21.5 21.7 21.8 21.5 21.8 22.0 21.3 21.65 0.71 -0.71
E10
iB10
1000 14.1 13.7 14.3 13.7 14.3 14.5 13.5 13.96 2.15 -2.15
2000 17.0 17.1 17.2 17.0 17.2 17.4 16.8 17.10 0.67 -0.67
3000 18.0 17.9 18.4 17.9 18.4 18.6 17.7 18.13 1.28 -1.28
4000 20.4 20.6 20.4 20.4 20.6 20.8 20.2 20.53 0.43 -0.43
5000 21.5 21.4 21.7 21.4 21.7 21.9 21.2 21.57 0.57 -0.57
Uncertainty level 1.09 -1.09
83
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
BTE
1
BTE
2
BTE
3
Min. Max. Accuracy Avg. Uncertainty%
(%) (%) (%) (%) (%) (±0.2 %) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.2
%
G=
D-0.2
%
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 18.0 17.8 17.8 17.8 18.0 18.2 17.6 17.90 0.58 -0.58
40 22.4 22.7 23.8 22.4 23.8 24.0 22.2 23.11 3.04 -3.04
60 27.4 27.4 27.7 27.4 27.7 27.9 27.2 27.56 0.67 -0.67
80 28.2 28.2 28.4 28.2 28.4 28.6 28.0 28.29 0.32 -0.32
100 25.9 25.8 26.6 25.8 26.6 26.8 25.6 26.18 1.52 -1.52
E10
20 17.6 17.8 17.4 17.4 17.8 18.0 17.2 17.61 1.37 -1.37
40 24.3 24.5 24.2 24.2 24.5 24.7 24.0 24.33 0.68 -0.68
60 28.3 28.4 28.2 28.2 28.4 28.6 28.0 28.30 0.45 -0.45
80 29.1 29.2 29.0 29.0 29.2 29.4 28.8 29.09 0.34 -0.34
100 27.2 27.3 27.1 27.1 27.3 27.5 26.9 27.19 0.27 -0.27
E20
20 17.0 17.5 16.8 16.8 17.5 17.7 16.6 17.14 1.92 -1.92
40 24.5 24.8 24.3 24.3 24.8 25.0 24.1 24.54 0.95 -0.95
60 29.0 29.3 28.9 28.9 29.3 29.5 28.7 29.10 0.63 -0.63
80 31.1 31.3 31.0 31.0 31.3 31.5 30.8 31.12 0.47 -0.47
100 29.1 29.3 29.1 29.1 29.3 29.5 28.9 29.19 0.38 -0.38
iB1
0
20 17.2 17.0 17.7 17.0 17.7 17.9 16.8 17.35 2.08 -2.08
40 25.0 24.9 25.4 24.9 25.4 25.6 24.7 25.12 1.03 -1.03
60 28.7 28.6 29.0 28.6 29.0 29.2 28.4 28.77 0.69 -0.69
80 30.9 30.8 31.1 30.8 31.1 31.3 30.6 30.92 0.52 -0.52
100 28.1 28.0 28.2 28.0 28.2 28.4 27.8 28.12 0.42 -0.42
iB2
0
20 17.2 17.8 16.8 16.8 17.8 18.0 16.6 17.32 3.00 -3.00
40 25.3 25.7 25.0 25.0 25.7 25.9 24.8 25.36 1.46 -1.46
60 30.4 30.8 30.2 30.2 30.8 31.0 30.0 30.45 0.99 -0.99
80 30.0 30.3 29.9 29.9 30.3 30.5 29.7 30.08 0.74 -0.74
100 28.4 28.6 28.2 28.2 28.6 28.8 28.0 28.42 0.59 -0.59
E5
iB5
20 16.8 16.6 17.3 16.6 17.3 17.5 16.4 16.96 1.92 -1.92
40 24.8 24.6 25.1 24.6 25.1 25.3 24.4 24.85 0.94 -0.94
60 28.1 27.9 28.3 27.9 28.3 28.5 27.7 28.13 0.63 -0.63
80 29.9 29.8 30.1 29.8 30.1 30.3 29.6 29.96 0.48 -0.48
100 27.7 27.7 27.9 27.7 27.9 28.1 27.5 27.77 0.38 -0.38
E10
iB10
20 15.8 16.3 15.5 15.5 16.3 16.5 15.3 15.87 2.60 -2.60
40 23.8 24.1 23.5 23.5 24.1 24.3 23.3 23.81 1.29 -1.29
60 28.5 28.8 28.3 28.3 28.8 29.0 28.1 28.53 0.85 -0.85
80 29.9 30.1 29.8 29.8 30.1 30.3 29.6 29.95 0.64 -0.64
100 28.1 28.2 27.9 27.9 28.2 28.4 27.7 28.09 0.51 -0.51
Uncertainty level 1.01 -1.01
84
APPENDIX A
Uncertainty analysis for brake specific fuel consumption (BSFC)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m)
BSFC 1 BTE 2 BTE 3 Min. Max. Accuracy Avg. Uncertainty%
(g/kWH) (g/kWH) (g/kWH) (g/kWH) (g/kWH) (±5 g/kWH) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+5
g/kWH
G=
D-5
g/kWH
H=
(F+G)/2
I=
((F-
H)/H)
*100
J=
((G-
H)/H)
*100
PG
1000 692.3 686.1 682.4 682.4 692.3 697.3 677.4 687.35 0.72 -0.72
2000 535.8 518.5 529.2 518.5 535.8 540.8 513.5 527.13 1.64 -1.64
3000 481.4 481.3 481.2 481.2 481.4 486.4 476.2 481.30 0.02 -0.02
4000 432.6 428.5 431.3 428.5 432.6 437.6 423.5 430.54 0.48 -0.48
5000 409.2 401.2 409.8 401.2 409.8 414.8 396.2 405.51 1.06 -1.06
E10
1000 655.5 645.5 653.8 645.5 655.5 660.5 640.5 650.52 0.77 -0.77
2000 513.8 507.2 517.6 507.2 517.6 522.6 502.2 512.40 1.01 -1.01
3000 470.0 461.1 452.3 452.3 470.0 475.0 447.3 461.11 1.92 -1.92
4000 417.1 402.4 414.9 402.4 417.1 422.1 397.4 409.74 1.79 -1.79
5000 399.0 387.2 399.4 387.2 399.4 404.4 382.2 393.34 1.55 -1.55
E20
1000 654.6 629.8 633.1 629.8 654.6 659.6 624.8 642.19 1.93 -1.93
2000 493.6 513.8 513.4 493.6 513.8 518.8 488.6 503.68 2.01 -2.01
3000 485.0 476.6 483.1 476.6 485.0 490.0 471.6 480.80 0.87 -0.87
4000 388.1 394.8 398.3 388.1 398.3 403.3 383.1 393.20 1.30 -1.30
5000 390.4 387.8 391.3 387.8 391.3 396.3 382.8 389.55 0.44 -0.44
iB1
0
1000 624.9 652.3 645.8 624.9 652.3 657.3 619.9 638.60 2.14 -2.14
2000 511.3 508.9 515.2 508.9 515.2 520.2 503.9 512.03 0.62 -0.62
3000 493.4 495.3 483.6 483.6 495.3 500.3 478.6 489.47 1.20 -1.20
4000 404.4 419.4 419.4 404.4 419.4 424.4 399.4 411.92 1.82 -1.82
5000 397.9 393.9 392.4 392.4 397.9 402.9 387.4 395.15 0.70 -0.70
iB2
0
1000 613.2 626.2 618.6 613.2 626.2 631.2 608.2 619.68 1.05 -1.05
2000 526.4 531.5 522.1 522.1 531.5 536.5 517.1 526.76 0.89 -0.89
3000 485.9 487.9 475.2 475.2 487.9 492.9 470.2 481.55 1.31 -1.31
4000 415.2 417.1 415.2 415.2 417.1 422.1 410.2 416.15 0.23 -0.23
5000 408.3 408.3 409.1 408.3 409.1 414.1 403.3 408.67 0.10 -0.10
E5
iB5
1000 688.0 673.9 656.4 656.4 688.0 693.0 651.4 672.22 2.35 -2.35
2000 511.7 507.8 502.6 502.6 511.7 516.7 497.6 507.13 0.90 -0.90
3000 469.7 469.7 462.6 462.6 469.7 474.7 457.6 466.14 0.76 -0.76
4000 408.8 405.2 402.5 402.5 408.8 413.8 397.5 405.65 0.78 -0.78
5000 395.5 391.9 389.9 389.9 395.5 400.5 384.9 392.68 0.71 -0.71
E10
iB10
1000 601.2 604.1 596.3 596.3 604.1 609.1 591.3 600.22 0.65 -0.65
2000 500.7 496.9 494.0 494.0 500.7 505.7 489.0 497.38 0.67 -0.67
3000 471.4 475.1 463.1 463.1 475.1 480.1 458.1 469.07 1.28 -1.28
4000 416.0 412.4 416.0 412.4 416.0 421.0 407.4 414.19 0.43 -0.43
5000 405.5 407.3 402.6 402.6 407.3 412.3 397.6 404.94 0.57 -0.57
Uncertainty level 1.10 -1.10
85
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
BSFC 1 BSFC 2 BSFC 3 Min. Max. Accuracy Avg. Uncertainty%
(g/kWH) (g/kWH) (g/kWH) (g/kWH) (g/kWH) (±5 g/kWH) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+5
g/kWH
G=
D-5
g/kWH
H=
(F+G)/2
I=
((F-
H)/H)
*100
J=
((G-
H)/H)
*100
PG
20 460.1 464.8 465.5 460.1 465.5 470.5 455.1 462.79 0.58 -0.58
40 369.6 364.8 347.9 347.9 369.6 374.6 342.9 358.75 3.04 -3.04
60 302.5 302.1 298.5 298.5 302.5 307.5 293.5 300.53 0.67 -0.67
80 293.5 293.7 291.8 291.8 293.7 298.7 286.8 292.78 0.32 -0.32
100 319.5 321.3 311.7 311.7 321.3 326.3 306.7 316.49 1.52 -1.52
E10
20 487.7 480.3 493.6 480.3 493.6 498.6 475.3 486.92 1.37 -1.37
40 352.5 349.8 354.7 349.8 354.7 359.7 344.8 352.25 0.68 -0.68
60 303.0 301.5 304.2 301.5 304.2 309.2 296.5 302.85 0.45 -0.45
80 294.8 293.7 295.7 293.7 295.7 300.7 288.7 294.69 0.34 -0.34
100 315.4 314.4 316.1 314.4 316.1 321.1 309.4 315.26 0.27 -0.27
E20
20 521.8 508.3 528.2 508.3 528.2 533.2 503.3 518.21 1.92 -1.92
40 363.0 358.3 365.2 358.3 365.2 370.2 353.3 361.78 0.95 -0.95
60 305.8 303.2 307.0 303.2 307.0 312.0 298.2 305.09 0.63 -0.63
80 285.8 283.9 286.6 283.9 286.6 291.6 278.9 285.25 0.47 -0.47
100 304.6 303.0 305.3 303.0 305.3 310.3 298.0 304.14 0.38 -0.38
iB1
0
20 490.6 496.6 476.3 476.3 496.6 501.6 471.3 486.45 2.08 -2.08
40 337.3 339.3 332.4 332.4 339.3 344.3 327.4 335.86 1.03 -1.03
60 294.1 295.2 291.2 291.2 295.2 300.2 286.2 293.22 0.69 -0.69
80 273.4 274.2 271.4 271.4 274.2 279.2 266.4 272.82 0.52 -0.52
100 300.5 301.2 298.7 298.7 301.2 306.2 293.7 299.92 0.42 -0.42
iB2
0
20 499.1 481.7 511.5 481.7 511.5 516.5 476.7 496.59 3.00 -3.00
40 339.8 333.9 343.8 333.9 343.8 348.8 328.9 338.88 1.46 -1.46
60 282.7 279.4 284.9 279.4 284.9 289.9 274.4 282.15 0.99 -0.99
80 286.1 283.5 287.8 283.5 287.8 292.8 278.5 285.66 0.74 -0.74
100 302.7 300.6 304.1 300.6 304.1 309.1 295.6 302.35 0.59 -0.59
E5
iB5
20 510.3 516.5 497.1 497.1 516.5 521.5 492.1 506.78 1.92 -1.92
40 346.9 349.0 342.5 342.5 349.0 354.0 337.5 345.72 0.94 -0.94
60 306.2 307.4 303.6 303.6 307.4 312.4 298.6 305.49 0.63 -0.63
80 287.3 288.2 285.4 285.4 288.2 293.2 280.4 286.79 0.48 -0.48
100 306.6 307.4 305.0 305.0 307.4 312.4 300.0 306.21 0.38 -0.38
E10
iB10
20 538.2 522.3 550.2 522.3 550.2 555.2 517.3 536.25 2.60 -2.60
40 357.9 352.6 361.8 352.6 361.8 366.8 347.6 357.22 1.29 -1.29
60 298.5 295.6 300.6 295.6 300.6 305.6 290.6 298.09 0.85 -0.85
80 284.2 282.1 285.7 282.1 285.7 290.7 277.1 283.91 0.64 -0.64
100 311.1 309.3 312.5 309.3 312.5 317.5 304.3 310.89 0.51 -0.51
Uncertainty level 1.01 -1.01
86
APPENDIX A
Uncertainty analysis for exhaust gas temperature (EGT)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m)
EGT
1
EGT
2
EGT
3
Min. Max. Accuracy Avg. Uncertainty%
(o C) (o C) (o C) (o C) (o C) (±0.3 o C) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.3 o
C
G=
D-0.3 o
C
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 486.4 500.5 471.9 471.9 500.5 500.8 471.6 486.23 2.94 -2.94
2000 569.7 572.9 552.5 552.5 572.9 573.2 552.2 562.72 1.82 -1.82
3000 619.4 626.0 602.4 602.4 626.0 626.3 602.1 614.20 1.92 -1.92
4000 704.1 710.1 694.6 694.6 710.1 710.4 694.3 702.36 1.11 -1.11
5000 755.9 760.6 745.4 745.4 760.6 760.9 745.1 753.02 1.01 -1.01
E10
1000 484.2 490.0 470.3 470.3 490.0 490.3 470.0 480.16 2.05 -2.05
2000 568.1 570.5 552.1 552.1 570.5 570.8 551.8 561.30 1.64 -1.64
3000 622.6 622.4 616.5 616.5 622.6 622.9 616.2 619.53 0.49 -0.49
4000 726.5 723.2 720.6 720.6 726.5 726.8 720.3 723.55 0.41 -0.41
5000 777.6 778.9 776.3 776.3 778.9 779.2 776.0 777.60 0.17 -0.17
E20
1000 462.7 458.7 455.3 455.3 462.7 463.0 455.0 458.99 0.81 -0.81
2000 552.5 563.3 550.3 550.3 563.3 563.6 550.0 556.82 1.17 -1.17
3000 605.2 624.9 610.8 605.2 624.9 625.2 604.9 615.07 1.60 -1.60
4000 713.9 728.7 717.3 713.9 728.7 729.0 713.6 721.29 1.03 -1.03
5000 786.2 791.0 786.0 786.0 791.0 791.3 785.7 788.48 0.32 -0.32
iB1
0
1000 455.8 487.7 462.6 455.8 487.7 488.0 455.5 471.71 3.38 -3.38
2000 542.0 570.6 562.8 542.0 570.6 570.9 541.7 556.29 2.57 -2.57
3000 601.6 618.4 610.4 601.6 618.4 618.7 601.3 610.02 1.38 -1.38
4000 704.1 713.9 707.5 704.1 713.9 714.2 703.8 709.03 0.69 -0.69
5000 764.9 772.7 770.4 764.9 772.7 773.0 764.6 768.83 0.51 -0.51
iB2
0
1000 497.8 462.7 452.1 452.1 497.8 452.4 497.5 474.97 4.81 -4.81
2000 565.7 555.2 550.1 550.1 565.7 550.4 565.4 557.87 1.40 -1.40
3000 612.6 616.1 606.9 606.9 616.1 607.2 615.8 611.52 0.75 -0.75
4000 709.7 710.1 714.0 709.7 714.0 710.0 713.7 711.83 0.30 -0.30
5000 767.0 770.1 779.6 767.0 779.6 767.3 779.3 773.31 0.81 -0.81
E5
iB5
1000 503.9 475.6 467.8 467.8 503.9 468.1 503.6 485.86 3.72 -3.72
2000 569.4 561.0 549.3 549.3 569.4 549.6 569.1 559.34 1.79 -1.79
3000 616.5 615.2 615.5 615.2 616.5 615.5 616.2 615.83 0.10 -0.10
4000 715.2 715.3 716.2 715.2 716.2 715.5 715.9 715.72 0.07 -0.07
5000 773.3 769.0 769.3 769.0 773.3 769.3 773.0 771.13 0.28 -0.28
E10
iB10
1000 453.6 456.6 455.4 453.6 456.6 453.9 456.3 455.05 0.33 -0.33
2000 545.6 546.6 550.7 545.6 550.7 545.9 550.4 548.13 0.47 -0.47
3000 609.2 615.2 612.7 609.2 615.2 609.5 614.9 612.18 0.49 -0.49
4000 716.8 720.8 720.8 716.8 720.8 717.1 720.5 718.78 0.28 -0.28
5000 778.1 772.1 779.5 772.1 779.5 772.4 779.2 775.82 0.47 -0.47
Uncertainty level 1.40 -1.40
87
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
EGT
1
EGT
2
EGT
3
Min. Max. Accuracy Avg. Uncertainty%
(o C) (o C) (o C) (o C) (o C) (±0.3 o C) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.3 o
C
G=
D-0.3 o
C
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 620.6 644.3 649.4 620.6 649.4 649.7 620.3 635.03 2.27 -2.27
40 709.0 704.3 720.6 704.3 720.6 720.9 704.0 712.46 1.15 -1.15
60 755.5 748.2 759.1 748.2 759.1 759.4 747.9 753.68 0.73 -0.73
80 777.5 772.3 775.3 772.3 777.5 777.8 772.0 774.87 0.34 -0.34
100 767.5 763.0 761.7 761.7 767.5 767.8 761.4 764.57 0.38 -0.38
E10
20 639.4 642.5 636.3 636.3 642.5 642.8 636.0 639.41 0.48 -0.48
40 712.7 715.8 709.6 709.6 715.8 716.1 709.3 712.73 0.43 -0.43
60 759.6 762.7 756.5 756.5 762.7 763.0 756.2 759.60 0.41 -0.41
80 784.6 787.7 781.5 781.5 787.7 788.0 781.2 784.60 0.40 -0.40
100 778.9 782.0 775.8 775.8 782.0 782.3 775.5 778.94 0.40 -0.40
E20
20 608.2 605.1 612.3 605.1 612.3 612.6 604.8 608.68 0.59 -0.59
40 691.8 688.7 695.9 688.7 695.9 696.2 688.4 692.30 0.52 -0.52
60 747.8 744.7 751.9 744.7 751.9 752.2 744.4 748.27 0.48 -0.48
80 780.2 777.1 784.3 777.1 784.3 784.6 776.8 780.69 0.46 -0.46
100 779.3 776.2 783.4 776.2 783.4 783.7 775.9 779.81 0.46 -0.46
iB1
0
20 669.2 673.3 666.4 666.4 673.3 673.6 666.1 669.85 0.52 -0.52
40 727.5 731.6 724.6 724.6 731.6 731.9 724.3 728.07 0.48 -0.48
60 764.1 768.2 761.2 761.2 768.2 768.5 760.9 764.74 0.46 -0.46
80 783.8 787.9 780.9 780.9 787.9 788.2 780.6 784.38 0.45 -0.45
100 769.5 773.6 766.6 766.6 773.6 773.9 766.3 770.12 0.45 -0.45
iB2
0
20 667.1 664.2 670.4 664.2 670.4 664.5 670.1 667.32 0.46 -0.46
40 724.1 721.2 727.4 721.2 727.4 721.5 727.1 724.29 0.43 -0.43
60 762.6 759.7 765.9 759.7 765.9 760.0 765.6 762.82 0.41 -0.41
80 785.7 782.9 789.0 782.9 789.0 783.2 788.7 785.95 0.39 -0.39
100 778.3 775.4 781.6 775.4 781.6 775.7 781.3 778.48 0.40 -0.40
E5
iB5
20 639.5 644.9 632.2 632.2 644.9 632.5 644.6 638.53 1.00 -1.00
40 708.2 713.6 700.9 700.9 713.6 701.2 713.3 707.27 0.90 -0.90
60 755.7 761.1 748.4 748.4 761.1 748.7 760.8 754.77 0.84 -0.84
80 775.8 781.2 768.5 768.5 781.2 768.8 780.9 774.84 0.82 -0.82
100 770.8 776.2 763.5 763.5 776.2 763.8 775.9 769.87 0.83 -0.83
E10
iB10
20 654.7 647.4 660.1 647.4 660.1 647.7 659.8 653.77 0.98 -0.98
40 716.6 709.3 722.0 709.3 722.0 709.6 721.7 715.64 0.89 -0.89
60 762.0 754.7 767.4 754.7 767.4 755.0 767.1 761.06 0.84 -0.84
80 792.2 784.9 797.7 784.9 797.7 785.2 797.4 791.32 0.81 -0.81
100 780.5 773.2 786.0 773.2 786.0 773.5 785.7 779.61 0.82 -0.82
Uncertainty level 0.65 -0.65
88
APPENDIX A
Uncertainty analysis for carbon monoxide (CO)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m) CO 1 CO 2 CO 3 Min. Max. Accuracy Avg. Uncertainty%
(%vol) (%vol) (%vol) (%vol) (%vol) (±0.01 %vol) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.01
%vol
G=
D-0.01
%vol
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 7.362 7.359 7.339 7.339 7.362 7.372 7.329 7.35 0.16 -0.16
2000 6.302 6.546 6.574 6.302 6.574 6.584 6.292 6.44 2.11 -2.11
3000 12.300 11.970 11.550 11.550 12.300 12.310 11.540 11.93 3.14 -3.14
4000 9.800 9.940 9.937 9.800 9.940 9.950 9.790 9.87 0.71 -0.71
5000 9.118 9.072 9.100 9.072 9.118 9.128 9.062 9.10 0.25 -0.25
E10
1000 6.832 6.819 6.952 6.819 6.952 6.962 6.809 6.89 0.97 -0.97
2000 5.352 5.358 5.358 5.352 5.358 5.368 5.342 5.36 0.06 -0.06
3000 10.540 10.910 10.530 10.530 10.910 10.920 10.520 10.72 1.77 -1.77
4000 9.067 9.059 9.064 9.059 9.067 9.077 9.049 9.06 0.04 -0.04
5000 8.197 8.207 8.208 8.197 8.208 8.218 8.187 8.20 0.07 -0.07
E20
1000 7.512 7.546 7.512 7.512 7.546 7.556 7.502 7.53 0.23 -0.23
2000 5.167 5.203 5.286 5.167 5.286 5.296 5.157 5.23 1.14 -1.14
3000 11.310 11.060 11.030 11.030 11.310 11.320 11.020 11.17 1.25 -1.25
4000 8.240 8.457 8.353 8.240 8.457 8.467 8.230 8.35 1.30 -1.30
5000 7.081 7.024 7.112 7.024 7.112 7.122 7.014 7.07 0.62 -0.62
iB1
0
1000 7.323 7.355 7.399 7.323 7.399 7.409 7.313 7.36 0.52 -0.52
2000 6.244 6.250 6.620 6.244 6.620 6.630 6.234 6.43 2.92 -2.92
3000 10.340 10.320 10.300 10.300 10.340 10.350 10.290 10.32 0.19 -0.19
4000 9.287 9.302 9.243 9.243 9.302 9.312 9.233 9.27 0.32 -0.32
5000 8.218 8.456 8.453 8.218 8.456 8.466 8.208 8.34 1.43 -1.43
iB2
0
1000 6.811 6.848 6.894 6.811 6.894 6.904 6.801 6.85 0.61 -0.61
2000 6.170 6.159 6.165 6.159 6.170 6.180 6.149 6.16 0.09 -0.09
3000 10.540 10.550 10.610 10.540 10.610 10.620 10.530 10.58 0.33 -0.33
4000 8.993 9.012 9.034 8.993 9.034 9.044 8.983 9.01 0.23 -0.23
5000 8.048 8.036 8.030 8.030 8.048 8.058 8.020 8.04 0.11 -0.11
E5
iB5
1000 7.474 7.474 7.474 7.474 7.474 7.484 7.464 7.47 0.00 0.00
2000 6.164 6.164 6.164 6.164 6.164 6.174 6.154 6.16 0.00 0.00
3000 10.070 10.500 10.080 10.070 10.500 10.510 10.060 10.29 2.09 -2.09
4000 9.542 9.520 9.520 9.520 9.542 9.552 9.510 9.53 0.12 -0.12
5000 8.526 8.535 8.541 8.526 8.541 8.551 8.516 8.53 0.09 -0.09
E10
iB10
1000 7.228 7.245 7.260 7.228 7.260 7.270 7.218 7.24 0.22 -0.22
2000 6.081 6.076 6.058 6.058 6.081 6.091 6.048 6.07 0.19 -0.19
3000 10.140 10.120 10.090 10.090 10.140 10.150 10.080 10.12 0.25 -0.25
4000 8.590 8.607 8.590 8.590 8.607 8.617 8.580 8.60 0.10 -0.10
5000 7.700 7.712 7.720 7.700 7.720 7.730 7.690 7.71 0.13 -0.13
Uncertainty level 0.68 -0.68
89
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue
(rp
m)
CO 1 CO 2 CO 3 Min. Max. Accuracy Avg. Uncertainty%
(%vol) (%vol) (%vol) (%vol) (%vol) (±0.01 %vol) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.01
%vol
G=
D-0.01
%vol
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 0.237 0.256 0.249 0.237 0.256 0.266 0.227 0.25 3.85 -3.85
40 0.323 0.324 0.329 0.323 0.329 0.339 0.313 0.33 0.92 -0.92
60 1.243 1.306 1.365 1.243 1.365 1.375 1.233 1.30 4.68 -4.68
80 2.931 2.920 2.934 2.920 2.934 2.944 2.910 2.93 0.24 -0.24
100 6.797 6.882 6.918 6.797 6.918 6.928 6.787 6.86 0.88 -0.88
E10
20 0.204 0.204 0.204 0.204 0.204 0.214 0.194 0.20 0.00 0.00
40 0.269 0.269 0.269 0.269 0.269 0.279 0.259 0.27 0.00 0.00
60 0.803 0.811 0.822 0.803 0.822 0.832 0.793 0.81 1.17 -1.17
80 2.255 2.353 2.353 2.255 2.353 2.363 2.245 2.30 2.13 -2.13
100 5.824 5.834 5.827 5.824 5.834 5.844 5.814 5.83 0.09 -0.09
E20
20 0.181 0.178 0.182 0.178 0.182 0.192 0.168 0.18 1.05 -1.05
40 0.160 0.160 0.160 0.160 0.160 0.170 0.150 0.16 0.00 0.00
60 0.268 0.262 0.261 0.261 0.268 0.278 0.251 0.26 1.32 -1.32
80 1.368 1.408 1.408 1.368 1.408 1.418 1.358 1.39 1.44 -1.44
100 4.523 4.534 4.547 4.523 4.547 4.557 4.513 4.54 0.26 -0.26
iB1
0
20 0.219 0.220 0.224 0.219 0.224 0.234 0.209 0.22 1.13 -1.13
40 0.238 0.232 0.231 0.231 0.238 0.248 0.221 0.23 1.49 -1.49
60 0.991 0.991 1.002 0.991 1.002 1.012 0.981 1.00 0.55 -0.55
80 2.471 2.285 2.124 2.124 2.471 2.481 2.114 2.30 7.55 -7.55
100 6.435 6.435 6.443 6.435 6.443 6.453 6.425 6.44 0.06 -0.06
iB2
0
20 0.325 0.326 0.323 0.323 0.326 0.336 0.313 0.32 0.46 -0.46
40 0.268 0.268 0.269 0.268 0.269 0.279 0.258 0.27 0.19 -0.19
60 0.642 0.644 0.647 0.642 0.647 0.657 0.632 0.64 0.39 -0.39
80 1.929 1.935 1.929 1.929 1.935 1.945 1.919 1.93 0.16 -0.16
100 5.258 5.355 5.340 5.258 5.355 5.365 5.248 5.31 0.91 -0.91
E5
iB5
20 0.210 0.218 0.206 0.206 0.218 0.228 0.196 0.21 2.83 -2.83
40 0.319 0.315 0.312 0.312 0.319 0.329 0.302 0.32 1.11 -1.11
60 0.865 0.888 0.899 0.865 0.899 0.909 0.855 0.88 1.93 -1.93
80 2.774 2.786 2.786 2.774 2.786 2.796 2.764 2.78 0.22 -0.22
100 6.070 6.068 6.062 6.062 6.070 6.080 6.052 6.07 0.07 -0.07
E10
iB10
20 0.198 0.198 0.201 0.198 0.201 0.211 0.188 0.20 0.75 -0.75
40 0.184 0.184 0.189 0.184 0.189 0.199 0.174 0.19 1.34 -1.34
60 0.375 0.371 0.367 0.367 0.375 0.385 0.357 0.37 1.08 -1.08
80 1.695 1.666 1.623 1.623 1.695 1.705 1.613 1.66 2.17 -2.17
100 5.250 5.243 5.238 5.238 5.250 5.260 5.228 5.24 0.11 -0.11
Uncertainty level 1.22 -1.22
90
APPENDIX A
Uncertainty analysis for carbon dioxide (CO2)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m) CO2 1 CO2 2 CO2 3 Min. Max. Accuracy Avg. Uncertainty%
(%vol) (%vol) (%vol) (%vol) (%vol) (±0.1 %vol) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.1
%vol
G=
D-0.1
%vol
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 2.90 2.90 2.92 2.90 2.92 3.02 2.80 2.91 0.34 -0.34
2000 3.49 3.51 3.55 3.49 3.55 3.65 3.39 3.52 0.85 -0.85
3000 6.12 6.16 6.32 6.12 6.32 6.42 6.02 6.22 1.61 -1.61
4000 9.30 9.28 9.24 9.24 9.30 9.40 9.14 9.27 0.32 -0.32
5000 9.78 9.80 9.79 9.78 9.80 9.90 9.68 9.79 0.10 -0.10
E10
1000 2.92 2.89 2.87 2.87 2.92 3.02 2.77 2.90 0.86 -0.86
2000 3.17 3.17 3.17 3.17 3.17 3.27 3.07 3.17 0.00 0.00
3000 6.97 6.53 6.72 6.53 6.97 7.07 6.43 6.75 3.26 -3.26
4000 9.84 9.84 9.84 9.84 9.84 9.94 9.74 9.84 0.00 0.00
5000 10.37 10.40 10.40 10.37 10.40 10.50 10.27 10.39 0.14 -0.14
E20
1000 3.25 3.24 3.25 3.24 3.25 3.35 3.14 3.25 0.15 -0.15
2000 3.23 3.23 3.25 3.23 3.25 3.35 3.13 3.24 0.31 -0.31
3000 6.55 6.70 6.76 6.55 6.76 6.86 6.45 6.66 1.58 -1.58
4000 10.24 10.13 10.14 10.13 10.24 10.34 10.03 10.19 0.54 -0.54
5000 11.03 11.04 11.01 11.01 11.04 11.14 10.91 11.03 0.14 -0.14
iB1
0
1000 3.24 3.24 3.25 3.24 3.25 3.35 3.14 3.25 0.15 -0.15
2000 3.99 3.99 4.02 3.99 4.02 4.12 3.89 4.01 0.37 -0.37
3000 6.83 6.85 6.85 6.83 6.85 6.95 6.73 6.84 0.15 -0.15
4000 9.63 9.61 9.61 9.61 9.63 9.73 9.51 9.62 0.10 -0.10
5000 10.28 10.15 10.14 10.14 10.28 10.38 10.04 10.21 0.69 -0.69
iB2
0
1000 3.00 3.00 3.00 3.00 3.00 3.10 2.90 3.00 0.00 0.00
2000 3.67 3.70 3.71 3.67 3.71 3.81 3.57 3.69 0.54 -0.54
3000 6.99 7.01 7.02 6.99 7.02 7.12 6.89 7.01 0.21 -0.21
4000 9.87 9.87 9.86 9.86 9.87 9.97 9.76 9.87 0.05 -0.05
5000 10.41 10.43 10.43 10.41 10.43 10.53 10.31 10.42 0.08 -0.08
E5
iB5
1000 3.05 3.05 3.05 3.05 3.05 3.15 2.95 3.05 0.00 0.00
2000 3.94 3.93 3.95 3.93 3.95 4.05 3.83 3.94 0.25 -0.25
3000 6.45 6.29 6.40 6.29 6.45 6.55 6.19 6.37 1.26 -1.26
4000 9.38 9.38 9.38 9.38 9.38 9.48 9.28 9.38 0.00 0.00
5000 10.03 10.03 10.02 10.02 10.03 10.13 9.92 10.03 0.05 -0.05
E10
iB10
1000 3.26 3.26 3.26 3.26 3.26 3.36 3.16 3.26 0.00 0.00
2000 4.08 4.11 4.15 4.08 4.15 4.25 3.98 4.12 0.85 -0.85
3000 7.16 7.17 7.01 7.01 7.17 7.27 6.91 7.09 1.13 -1.13
4000 10.02 10.02 10.02 10.02 10.02 10.12 9.92 10.02 0.00 0.00
5000 10.56 10.56 10.56 10.56 10.56 10.66 10.46 10.56 0.00 0.00
Uncertainty level 0.46 -0.46
91
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
CO2 1 CO2 2 CO2 3 Min. Max. Accuracy Avg. Uncertainty%
(%vol) (%vol) (%vol) (%vol) (%vol) (±0.1 %vol) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+0.1
%vol
G=
D-0.1
%vol
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 14.03 14.11 14.07 14.03 14.11 14.21 13.93 14.07 0.28 -0.28
40 14.41 14.39 14.39 14.39 14.41 14.51 14.29 14.40 0.07 -0.07
60 14.21 14.30 14.28 14.21 14.30 14.40 14.11 14.26 0.32 -0.32
80 13.20 13.15 13.12 13.12 13.20 13.30 13.02 13.16 0.30 -0.30
100 11.12 11.12 11.08 11.08 11.12 11.22 10.98 11.10 0.18 -0.18
E10
20 13.71 13.71 13.71 13.71 13.71 13.81 13.61 13.71 0.00 0.00
40 14.30 14.30 14.33 14.30 14.33 14.43 14.20 14.32 0.10 -0.10
60 14.29 14.29 14.74 14.29 14.74 14.84 14.19 14.52 1.56 -1.56
80 13.46 13.56 13.56 13.46 13.56 13.66 13.36 13.51 0.37 -0.37
100 11.83 11.83 11.83 11.83 11.83 11.93 11.73 11.83 0.00 0.00
E20
20 13.27 13.27 13.27 13.27 13.27 13.37 13.17 13.27 0.00 0.00
40 13.77 13.77 13.78 13.77 13.78 13.88 13.67 13.78 0.04 -0.04
60 14.20 14.22 14.23 14.20 14.23 14.33 14.10 14.22 0.11 -0.11
80 13.96 13.96 13.97 13.96 13.97 14.07 13.86 13.97 0.04 -0.04
100 12.41 12.42 12.42 12.41 12.42 12.52 12.31 12.42 0.04 -0.04
iB1
0
20 13.89 13.92 13.90 13.89 13.92 14.02 13.79 13.91 0.12 -0.12
40 14.26 14.27 14.27 14.26 14.27 14.37 14.16 14.27 0.04 -0.04
60 14.31 14.31 14.30 14.30 14.31 14.41 14.20 14.31 0.03 -0.03
80 13.38 13.39 13.39 13.38 13.39 13.49 13.28 13.39 0.04 -0.04
100 11.41 11.41 11.41 11.41 11.41 11.51 11.31 11.41 0.00 0.00
iB2
0
20 13.95 13.95 13.96 13.95 13.96 14.06 13.85 13.96 0.04 -0.04
40 14.41 14.44 14.45 14.41 14.45 14.55 14.31 14.43 0.14 -0.14
60 14.39 14.40 14.40 14.39 14.40 14.50 14.29 14.40 0.03 -0.03
80 13.77 13.76 14.62 13.76 14.62 14.72 13.66 14.19 3.04 -3.04
100 12.08 12.03 12.04 12.03 12.08 12.18 11.93 12.06 0.21 -0.21
E5
iB5
20 13.65 13.60 13.63 13.60 13.65 13.75 13.50 13.63 0.18 -0.18
40 14.28 14.28 14.29 14.28 14.29 14.39 14.18 14.29 0.04 -0.04
60 14.09 14.09 14.10 14.09 14.10 14.20 13.99 14.10 0.04 -0.04
80 13.11 13.11 13.10 13.10 13.11 13.21 13.00 13.11 0.04 -0.04
100 11.44 11.44 11.44 11.44 11.44 11.54 11.34 11.44 0.00 0.00
E10
iB10
20 13.55 13.55 13.55 13.55 13.55 13.65 13.45 13.55 0.00 0.00
40 14.05 14.05 14.06 14.05 14.06 14.16 13.95 14.06 0.04 -0.04
60 14.14 14.15 14.16 14.14 14.16 14.26 14.04 14.15 0.07 -0.07
80 13.93 13.90 13.89 13.89 13.93 14.03 13.79 13.91 0.14 -0.14
100 11.99 12.01 12.01 11.99 12.01 12.11 11.89 12.00 0.08 -0.08
Uncertainty level 0.22 -0.22
92
APPENDIX A
Uncertainty analysis for hydrocarbon (HC)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m)
HC 1 HC 2 HC 3 Min. Max. Accuracy Avg. Uncertainty%
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(±1 ppm vol.) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+1
ppm
vol.
G=
D-1
ppm
vol.
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 526 523 521 521 526 527 520 523.50 0.48 -0.48
2000 466 466 467 466 467 468 465 466.50 0.11 -0.11
3000 646 652 658 646 658 659 645 652.00 0.92 -0.92
4000 452 427 421 421 452 453 420 436.50 3.55 -3.55
5000 289 288 288 288 289 290 287 288.50 0.17 -0.17
E10
1000 458 460 477 458 477 478 457 467.50 2.03 -2.03
2000 386 386 386 386 386 387 385 386.00 0.00 0.00
3000 580 597 596 580 597 598 579 588.50 1.44 -1.44
4000 342 341 341 341 342 343 340 341.50 0.15 -0.15
5000 235 224 221 221 235 236 220 228.00 3.07 -3.07
E20
1000 513 511 512 511 513 514 510 512.00 0.20 -0.20
2000 393 392 392 392 393 394 391 392.30 0.08 -0.08
3000 593 597 598 593 598 599 592 595.50 0.42 -0.42
4000 326 361 356 326 361 362 325 343.50 5.09 -5.09
5000 190 189 196 189 196 197 188 192.50 1.82 -1.82
iB1
0
1000 445 448 457 445 457 458 444 451.00 1.33 -1.33
2000 436 436 434 434 436 437 433 435.00 0.23 -0.23
3000 547 548 549 547 549 550 546 548.00 0.18 -0.18
4000 337 335 334 334 337 338 333 335.50 0.45 -0.45
5000 250 232 231 231 250 251 230 240.50 3.95 -3.95
iB2
0
1000 451 451 454 451 454 455 450 452.50 0.33 -0.33
2000 389 389 389 389 389 390 388 389.00 0.00 0.00
3000 559 558 558 558 559 560 557 558.50 0.09 -0.09
4000 374 373 370 370 374 375 369 372.00 0.54 -0.54
5000 250 249 248 248 250 251 247 249.20 0.48 -0.48
E5
iB5
1000 505 505 505 505 505 506 504 504.60 0.00 0.00
2000 450 453 444 444 453 454 443 448.50 1.00 -1.00
3000 561 568 561 561 568 569 560 564.50 0.62 -0.62
4000 325 325 325 325 325 326 324 325.00 0.00 0.00
5000 205 205 204 204 205 206 203 204.50 0.24 -0.24
E10
iB10
1000 491 491 491 491 491 492 490 491.00 0.00 0.00
2000 413 413 412 412 413 414 411 412.50 0.12 -0.12
3000 546 546 576 546 576 577 545 561.00 2.67 -2.67
4000 313 313 313 313 313 314 312 313.00 0.00 0.00
5000 187 186 185 185 187 188 184 186.00 0.54 -0.54
Uncertainty level 0.92 -0.92
93
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
HC 1 HC 2 HC 3 Min. Max. Accuracy Avg. Uncertainty%
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(±1 ppm vol.) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+1
ppm
vol.
G=
D-1
ppm
vol.
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 89 89 90 89 90 91 88 89.60 0.22 -0.22
40 113 113 113 113 113 114 112 113.20 0.18 -0.18
60 139 138 138 138 139 140 137 138.50 0.36 -0.36
80 157 156 156 156 157 158 155 156.60 0.13 -0.13
100 202 205 202 202 205 206 201 203.40 0.88 -0.88
E10
20 62 60 61 60 62 63 59 61.00 1.64 -1.64
40 89 87 89 87 89 90 86 88.20 1.36 -1.36
60 116 118 116 116 118 119 115 116.80 1.03 -1.03
80 124 128 123 123 128 129 122 125.50 1.99 -1.99
100 160 164 159 159 164 165 158 161.60 1.49 -1.49
E20
20 59 60 58 58 60 61 57 59.00 1.69 -1.69
40 50 48 49 48 50 51 47 48.80 2.05 -2.05
60 62 64 61 61 64 65 60 62.70 2.71 -2.71
80 89 90 88 88 90 91 87 89.00 1.12 -1.12
100 130 128 135 128 135 136 127 131.50 2.66 -2.66
iB1
0
20 49 51 48 48 51 52 47 49.50 3.03 -3.03
40 73 72 75 72 75 76 71 73.50 2.04 -2.04
60 103 104 102 102 104 105 101 103.00 0.97 -0.97
80 123 127 120 120 127 128 119 123.50 2.83 -2.83
100 176 174 177 174 177 178 173 175.60 0.68 -0.68
iB2
0
20 53 53 54 53 54 55 52 53.50 0.93 -0.93
40 73 74 75 73 75 76 72 74.00 1.35 -1.35
60 89 91 88 88 91 92 87 89.50 1.68 -1.68
80 102 100 103 100 103 104 99 101.50 1.48 -1.48
100 136 137 139 136 139 140 135 137.50 1.09 -1.09
E5
iB5
20 37 38 40 37 40 41 36 38.50 3.90 -3.90
40 75 74 72 72 75 76 71 73.60 1.90 -1.90
60 97 97 99 97 99 100 96 98.00 1.22 -1.22
80 116 118 113 113 118 119 112 115.60 2.08 -2.08
100 154 155 156 154 156 157 153 155.20 0.77 -0.77
E10
iB10
20 30 29 30 29 30 31 28 29.50 1.69 -1.69
40 52 50 55 50 55 56 49 52.50 4.76 -4.76
60 67 68 63 63 68 69 62 65.50 3.82 -3.82
80 85 88 85 85 88 89 84 86.50 1.73 -1.73
100 142 139 147 139 147 148 138 143.00 2.80 -2.80
Uncertainty level 1.72 -1.72
94
APPENDIX A
Uncertainty analysis for nitrogen oxides (NOX)
(a) Varying engine speed at constant throttle condition
Fu
el
Sp
eed
(rp
m)
NOX 1 NOX 2 NOX 3 Min. Max. Accuracy Avg. Uncertainty%
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(±1 ppm vol.) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+1
ppm
vol.
G=
D-1
ppm
vol.
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
1000 19 19 20 19 20 21 18 19.50 2.56 -2.56
2000 39 38 38 38 39 40 37 38.50 1.30 -1.30
3000 65 66 67 65 67 68 64 66.00 1.52 -1.52
4000 140 138 134 134 140 141 133 137.00 2.19 -2.19
5000 189 189 189 189 189 190 188 189.00 0.00 0.00
E10
1000 30 29 28 28 30 31 27 29.00 3.45 -3.45
2000 56 57 58 56 58 59 55 57.00 1.75 -1.75
3000 87 90 86 86 90 91 85 88.00 2.27 -2.27
4000 172 174 171 171 174 175 170 172.50 0.87 -0.87
5000 188 191 197 188 197 198 187 192.50 2.34 -2.34
E20
1000 28 28 29 28 29 30 27 28.30 1.06 -1.06
2000 72 72 73 72 73 74 71 72.50 0.69 -0.69
3000 87 86 88 86 88 89 85 87.00 1.15 -1.15
4000 185 190 192 185 192 193 184 188.50 1.86 -1.86
5000 229 228 230 228 230 231 227 229.00 0.44 -0.44
iB1
0
1000 31 33 34 31 34 35 30 32.50 4.62 -4.62
2000 58 58 59 58 59 60 57 58.50 0.85 -0.85
3000 87 83 86 83 87 88 82 85.00 2.35 -2.35
4000 160 167 167 160 167 168 159 163.50 2.14 -2.14
5000 191 195 198 191 198 199 190 194.50 1.80 -1.80
iB2
0
1000 24 26 27 24 27 28 23 25.50 5.88 -5.88
2000 61 60 58 58 61 62 57 59.50 2.52 -2.52
3000 85 86 84 84 86 87 83 85.00 1.18 -1.18
4000 169 172 170 169 172 173 168 170.50 0.88 -0.88
5000 202 201 199 199 202 203 198 200.50 0.75 -0.75
E5
iB5
1000 25 26 24 24 26 27 23 25.00 4.00 -4.00
2000 62 63 63 62 63 64 61 62.50 0.80 -0.80
3000 80 81 82 80 82 83 79 81.00 1.23 -1.23
4000 176 175 173 173 176 177 172 174.50 0.86 -0.86
5000 207 205 206 205 207 208 204 206.00 0.49 -0.49
E10
iB10
1000 27 26 28 26 28 29 25 27.00 3.70 -3.70
2000 57 56 55 55 57 58 54 56.00 1.79 -1.79
3000 68 69 67 67 69 70 66 68.00 1.47 -1.47
4000 170 169 167 167 170 171 166 168.50 0.89 -0.89
5000 207 203 202 202 207 208 201 204.50 1.22 -1.22
Uncertainty level 1.80 -1.80
95
APPENDIX A
(b) Varying engine torque at constant speed condition
Fu
el
Torq
ue (
rp
m)
NOX 1 NOX 2 NOX 3 Min. Max. Accuracy Avg. Uncertainty%
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(ppm
vol.)
(±1 ppm vol.) + -
A B C D=Min
(A to C)
E=Max
(A to C)
F=
E+1
ppm
vol.
G=
D-1
ppm
vol.
H=
(F+G)/2
I= ((F-
H)/H)
*100
J= ((G-
H)/H)
*100
PG
20 2087 2148 2125 2087 2148 2149 2086 2117.50 1.44 -1.44
40 3089 3100 3123 3089 3123 3124 3088 3106.00 0.55 -0.55
60 2936 2889 2932 2889 2936 2937 2888 2912.30 0.81 -0.81
80 1960 1948 1971 1948 1971 1972 1947 1959.50 0.59 -0.59
100 486 481 491 481 491 492 480 486.00 1.03 -1.03
E10
20 2109 2055 2069 2055 2109 2110 2054 2082.00 1.30 -1.30
40 2813 2788 2819 2788 2819 2820 2787 2803.50 0.55 -0.55
60 2966 2967 2966 2966 2967 2968 2965 2966.50 0.02 -0.02
80 2258 2250 2246 2246 2258 2259 2245 2252.00 0.27 -0.27
100 662 643 656 643 662 663 642 652.50 1.46 -1.46
E20
20 1576 1559 1581 1559 1581 1582 1558 1570.00 0.70 -0.70
40 2867 2847 2784 2784 2867 2868 2783 2825.50 1.47 -1.47
60 3109 3093 3085 3085 3109 3110 3084 3097.00 0.39 -0.39
80 2378 2347 2330 2330 2378 2379 2329 2354.00 1.02 -1.02
100 802 809 803 802 809 810 801 805.50 0.43 -0.43
iB1
0
20 1865 1863 1854 1854 1865 1866 1853 1859.50 0.30 -0.30
40 3232 3208 3227 3208 3232 3233 3207 3220.00 0.37 -0.37
60 3193 3186 3176 3176 3193 3194 3175 3184.50 0.27 -0.27
80 2032 2050 2129 2032 2129 2130 2031 2080.50 2.33 -2.33
100 386 384 383 383 386 387 382 384.50 0.39 -0.39
iB2
0
20 1770 1791 1778 1770 1791 1792 1769 1780.50 0.59 -0.59
40 2959 2995 2998 2959 2998 2999 2958 2978.50 0.65 -0.65
60 3069 3058 3065 3058 3069 3070 3057 3063.50 0.18 -0.18
80 2313 2312 2315 2312 2315 2316 2311 2313.50 0.06 -0.06
100 495 501 503 495 503 504 494 498.80 0.76 -0.76
E5
iB5
20 2156 2043 2075 2043 2156 2157 2042 2099.50 2.69 -2.69
40 2857 2831 2858 2831 2858 2859 2830 2844.50 0.47 -0.47
60 2993 2986 2996 2986 2996 2997 2985 2991.00 0.17 -0.17
80 1986 1999 1998 1986 1999 2000 1985 1992.50 0.33 -0.33
100 362 365 372 362 372 373 361 367.00 1.36 -1.36
E10
iB10
20 1966 1931 1923 1923 1966 1967 1922 1944.50 1.11 -1.11
40 2851 2834 2806 2806 2851 2852 2805 2828.50 0.80 -0.80
60 3235 3226 3233 3226 3235 3236 3225 3230.50 0.14 -0.14
80 2432 2440 2463 2432 2463 2464 2431 2447.50 0.63 -0.63
100 381 379 384 379 384 385 378 381.50 0.66 -0.66
Uncertainty level 0.75 -0.75