•
Time: The data used were published 2008, 2004 and 2001
Life cycle assessment of marine fuels
A comparative study of four fossil fuels for marine propulsion
SELMA BENGTSSON
KARIN ANDERSSON
ERIK FRIDELL
Department of Shipping and Marine Technology
Division of Sustainable Ship Propulsion
CHALMERS UNIVERSITY OF TECHNOLOGY
ISSN 1652-9189 Report No. 11:125
Gothenburg, Sweden, 2011
Report No. 11:125
Life cycle assessment of marine fuels
A comparative study of four fossil fuels for marine propulsion
SELMA BENGTSSON
KARIN ANDERSSON
ERIK FRIDELL
Department of Shipping and Marine Technology
CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden, 2011
Life cycle assessment of marine fuels
A comparative study of four fossil fuels for marine propulsion
SELMA BENGTSSON, KARIN ANDERSSON, ERIK FRIDELL
© SELMA BENGTSSON, KARIN ANDERSSON, ERIK FRIDELL 2011.
Technical report no 11:125
Department of Shipping and marine technology
Chalmers University of Technology
SE-412 96 Gothenburg
Sweden
Telephone + 46 (0)31-772 1000
Cover: Island from above in Gothenburg’s archipelago, photo Fredrik Brynolf.
Gothenburg, Sweden, 2011
Abstract
Maritime transportation is facing harder requirements on fuel quality and exhaust emissions in the
coming decades, especially in the Emission Control Areas (ECAs). To address these requirements the
shipping industry will either need to use alternative fuels or implement exhaust abatement
techniques. Consequently, the maritime sector within ECAs is on the verge of a fuel and/or
technology shift in the near future. At the same time, there is limited information regarding marine
fuels’ overall environmental impact during their life cycle.
The overall aim of this report is therefore to investigate the environmental performance of maritime
fuels from a life cycle perspective. This has been done through a life cycle assessment (LCA) of four
possible fossil marine fuels combined with two exhaust gas cleaning techniques. The geographical
location is set to the North Sea and the Baltic Sea and the time perspective is 2015 to 2020.
The following fuel alternatives are assessed:
•
•
•
•
•
•
•
Heavy fuel oil with a sulphur content of 1% (base scenario)
Heavy fuel oil with a sulphur content of 1% with a scrubber (fulfils the regulation within the
sulphur ECAs 2015)
Marine gas oil with a sulphur content of 0.1% (fulfils the regulation within the sulphur ECAs
2015)
Marine gas oil with a sulphur content of 0.1 % with a selective catalytic reduction unit (fulfils
the regulation within the sulphur ECAs 2015 and the Tier III regulation for nitrogen oxide
emissions)
Liquefied natural gas (fulfils the regulation within the sulphur ECAs 2015 and the Tier III
regulation for nitrogen oxide emissions)
Gas-to-liquid produced by the Fischer-Tropsch process (fulfils the regulation within the
sulphur ECAs 2015)
Gas-to-liquid diesel produced by the Fischer-Tropsch process with a selective catalytic
reduction unit (fulfils the regulation within the sulphur ECAs 2015 and the Tier III
regulation for nitrogen oxide emissions)
It is shown that the “use phase”, i.e. the combustion of marine fuels, is the dominant contributor to
the overall environmental impact. Two main results are robust during all the modelled scenarios.
Firstly, the global warming potential of the compared fuels are of the same order of magnitude.
Maritime transportation with LNG as fuel can be attributed to comparable or a somewhat lower
global warming potential than the other fuels depending on modelling choices. Secondly, the
potential contribution to acidification and eutrophication is significantly lower for fuel alternatives
that fulfil the Tier III requirement regarding nitrogen oxide emissions, i.e. the LNG fuel alternatives
and the fuel alternatives with selective catalytic reduction units.
A problematic issue related to LCA is how to allocate the impact from crude oil refining into marine
fuels. This is problematic since there is a wide diversity of refineries and since the choice of allocation
method could change the result. The problem is specific for marine fuels since they only contribute to
a small part of a refinery’s overall impact. It is therefore suggested to perform a separate study with
focus on how future changes in refinery production and different allocation methods will change the
environmental impact of crude oil based fuels. It is also recommended that a study with a longer time
perspective is carried out, in order to evaluate what fuels that are desirable in the future and what
fuel properties that are important.
I
Preface
This project is a part of Selma Bengtsson’s Ph.D. studies concerning environmental impact from sea
transports in a life cycle perspective and funded by Vinnova grants to Lighthouse. Lighthouse is a
multidisciplinary maritime competence and research centre initiated by Chalmers, the School of
Business, Economics and Law at Göteborg University and The Swedish Shipowners’ Association
(http://www.lighthouse.nu).
This project has also resulted in a publication in the Proceedings of the Institution of Mechanical
Engineers, Part M: Journal of Engineering for the Maritime Environment (Bengtsson et al., 2011).
The following persons have contributed to this report with valuable insights and knowledge:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Britt-Helene Kvittingen, Project manager, Gasnor AS
Christer André, Environmental technology, FKAB
Erik Krona, Project manager, Port planning, Port of Gothenburg
Filippa Fuhrman, Environmental Systems Analysis, Energy and Environment, Chalmers
University of Technology
Gisle Rong, Superintendent, Seatrans AS
Ingemar Nylund, General Manager, Wärtsilä Corporation
Johan Hammarberg, Marine, Preem AB
Johan Roos, Director of Sustainability, Stena Rederi AB
Kjell Sandaker, Project Development, Eidesvik AS
Lars Pennman, Chief engineer, Stena Rederi AB
Linda Sahlén, Project manager, Transportgas, Göteborg energi AB
Maelfeyt Stijn, LNG line manager, Fluxys NV
Monica Johansson, Ph.D. student, Applied Mechanics, Chalmers University of Technology
Øyvind Buhaug, Principal Engineer Marine Fuels and Engine Technology, Product
Technology and Customer Service, Statoil ASA
Sören Eriksson, Refinery Development, Preem AB
Thomas Stenhede, Senior Application Manager, Wärtsilä EcoTech
Trude Gullaksen, Gasnor AS
Ulf Bäckström, Managing Director, MAN Diesel Sverige AB
Gothenburg, April 2011
Selma Bengtsson, Karin Andersson and Erik Fridell
III
Abbreviations
BOG
CARB
CH4
CNG
CO
CO2
ECA
FCC
F-T
GTL
HFO
IMO
ISO
LCA
LCI
LHV
LNG
LPG
MARPOL
MGO
MEPC
MSD
NECA
NMVOC
NOX
Ro-Ro
SCR
SECA
SOX
SSD
VOC
boil-off gas
California Air Resources Board
methane
compressed natural gas
carbon monoxide
carbon dioxide
emission control area
fluid-bed catalytic cracking
Fischer-Tropsch
gas-to-liquid
heavy fuel oil
International Maritime Organisation
International Organization for Standards
life cycle assessment
life cycle inventory
lower heating value
liquefied natural gas
liquefied petroleum gas
International Convention for the Prevention of Pollution from Ships,
1973, as modified by the Protocol of 1978 relating thereto
marine gas oil
the Maritime Environmental Protection Committee
medium speed diesel
nitrogen oxides emission control area
non-methane volatile organic compound
nitrogen oxides
roll on roll off
selective catalytic reduction
sulphur emission control area
sulphur oxides
slow speed diesel
volatile organic compounds
V
Table of content
1
2
Introduction ........................................................................................................................ 1
1.1
Background .............................................................................................................................. 1
1.2
Aim .............................................................................................................................................. 4
1.3
Method ....................................................................................................................................... 4
Identified fuel alternatives ............................................................................................. 5
2.1
Fuels............................................................................................................................................ 5
2.2
Engines....................................................................................................................................... 8
2.3
Exhaust gas abatement techniques ............................................................................... 10
2.4
Selected fuel alternatives .................................................................................................. 12
3
LCA methodology ............................................................................................................. 15
4
Goal and scope definition .............................................................................................. 17
5
4.1
Goal of the study ................................................................................................................... 17
4.2
Scope of the study ................................................................................................................ 17
4.3
Choice of impact categories.............................................................................................. 19
4.4
Evaluation of robustness of the result.......................................................................... 21
Inventory analysis ........................................................................................................... 23
5.1
Base scenario......................................................................................................................... 23
5.2
Alternative scenarios ......................................................................................................... 34
6
Results from base scenario........................................................................................... 37
7
Results from alternative scenarios ............................................................................ 45
8
7.1
Uncertainty in the emissions from gas engines ........................................................ 45
7.2
Uncertainty in the emissions from diesel engines fuelled with GTL ................. 51
7.3
Changes in engine efficiency ............................................................................................ 53
7.4
Changes in liquefaction efficiency ................................................................................. 55
Discussion .......................................................................................................................... 57
8.1
Critical methodological choices ...................................................................................... 57
8.2
Data reliability and representativeness ...................................................................... 58
8.3
Reliability and robustness of the result....................................................................... 60
8.4
Comparison with other results ....................................................................................... 60
8.5
Prerequisites for the LNG fuel chain’s climate performance ............................... 61
8.6
Implications for further research .................................................................................. 61
VII
9
10
Conclusions ....................................................................................................................... 63
References ..................................................................................................................... 65
Appendix A – LCI results for marine transportation with HFO ..................................... i
Appendix B – LCI results for marine transportation with MGO ...............................xvii
Appendix C – LCI results for marine transportation with LNG................................ xxix
Appendix D – LCI results for marine transportation with GTL ................................xlix
Appendix E – Uncertainty in emission from gas engines ...........................................lxix
Appendix F – Uncertainty in the emissions from diesel engines fuelled with GTL
...................................................................................................................................................lxxix
Appendix G – Changes in engine efficiency................................................................ lxxxiv
Appendix H – Changes in liquefaction efficiency ........................................................ xciii
Appendix I – Specifications of the modelled vessels ................................................... xcix
VIII
1
Introduction
Shipping faces a number of challenges related to sustainability in the coming decades. Sea transport
has been regarded as energy efficient and environmentally friendly, however in contrast to other
sectors shipping has not decreased its energy demand or emissions to any large extent in recent
years.
1.1 Background
Marine transportation is facing harder requirements on fuel quality and exhaust emissions in the
coming years as striker regulations are enforced in different regions of the world. The introduction of
Sulphur Emission Control Areas (SECAs) by the International Maritime Organization (IMO) with a
maximum of 0.1 % sulphur allowed in marine fuel from 2015 as compared to 1% today will increase
the demand for low sulphur fuel. Nitrogen oxides (NOX) emission regulation is also introduced in a
stepwise manner until 2016 increasing the pressure on low NOX energy conversion.
1.1.1 Marine fuel quality and emissions regulation
Fuel sulphur conent, wt. %
The main international convention regulating the pollution from shipping is the “International
Convention on the Prevention of Pollution from Ships”, known as MARPOL 73/78. The convention
aims at reducing pollutant emissions from ships during accidents and routine operations. It includes
six technical Annexes, where the last Annex VI, entitled “Regulations for the Prevention of Air
Pollution from Ships” entered into force in May 2005. Annex VI prohibits deliberate emissions of
ozone depleting substances and sets limits on NOX and sulphur oxides (SOX) emissions from ship
exhausts (IMO, 2010). Annex VI includes a cap on sulphur content in the fuel oil used onboard ships,
one global cap and one cap for SECAs, successively reduced until 2020 (Figure 1-1).
5%
Global
4%
3%
2%
1%
0%
2010
SECA
2015
Year
2020
2025
Figure 1-1 MARPOL Annex VI fuel sulphur limits.
The use of sulphur dioxide abatement technologies, such as scrubbers, is recognised as an alternative
to low-sulphur fuel in Annex VI. It is stipulated that “The Administration of a Party may allow any
fitting, material, appliance or apparatus to be fitted in a ship or other procedures, alternative fuel oils,
or compliance methods used as an alternative to that required by this Annex if such fitting, material,
appliance or apparatus or other procedures, alternative fuel oils, or compliance methods are at least
as effective in terms of emissions reductions as that required by this Annex...” (MEPC, 2008).
The emissions of NOX are regulated based on engine speed as illustrated in Figure 1-2. These limits
apply to marine diesel engines with a power output of more than 130 kW. Tier I shall apply to ships
1
Limit of nitrogen oxides, g/kWh
constructed before 2000 while the regulation in force at the time the ship is constructed shall apply
to ships constructed after 2000. Tier II enters into force in 2011 and is expected to be met by
combustion process optimisation, that change parameters like fuel injection timing, pressure and
cylinder compression volume. More advanced control technologies, such as injection of various forms
of water into the chamber, exhaust gas recirculation or selective catalytic reduction will be required
in order to meet the Tier III standard that enter into force in 2016 in NOX ECAs.
18
16
14
12
Tier I (Global)
10
Tier II (Global)
8
6
4
2
0
Tier III (NOX ECA)
100
300
500
700 900 1100 1300 1500 1700 1900 2100
Rated engine speed, rpm
Figure 1-2 MARPOL Annex VI NOX emissions limits.
The first designated SECA in effect from 2006 was the Baltic Sea followed by the North Sea and the
English Channel in 2007. These areas are the only SECAs adopted to date. On March 26, 2010, the IMO
amended MARPOL Annex VI designating a zone up to 200 nautical miles from the coast of the United
States and Canada as an Emissions Control Area (ECA). The ECA is planned to enter into force in 2012
(U.S.-EPA, 2010).
There are also some regional regulations in force. One example is the EU Directive 2005/33/EC
limiting the sulphur content of marine fuels used by ships on inland waterways and at berth to 0.1%
from January 2010. The California Air Resources Board (CARB) announced in July 2008 the adoption
of a new regulation, requiring the use of low sulphur marine fuel within 24 miles of the California
coastline from 2009. The regulation require vessels to operate with either marine gas oil (MGO), with
a maximum of 1.5% sulphur content, or marine diesel oil (MDO), with a maximum sulphur content of
0.5%. The regulation will be even stricter in 2012 limiting the sulphur content to 0.1 % for both MDO
and MGO (CARB, 2010).
Emissions of greenhouse gases from the shipping industry are not regulated by the Kyoto Protocol.
The responsibility to develop the mechanisms needed to reduce ship emissions of greenhouse gases
have been delegated to IMO. No measures have so far been adopted. Buhaug et al. (2009) have
estimated that in the absence of global policies to control greenhouse gas emissions from
international shipping, the emissions may increase by between 220 and 310 percent (compared to
the emissions in 2007) by the year 2050 due to the expected continued growth in international
seaborne trade. The Maritime Environment Protection Committee (MEPC) within IMO adopted a
work plan to mitigate this development in 2006 which includes improvement in the carbon dioxide
(CO2) indexing methodology, establishment of a CO2 emissions baseline and consideration of
technical, operational and market-based mechanisms for reducing greenhouse gas emissions from
ships (T&E, 2009). Development of the Energy Efficiency Design Index (EEDI) for new vessels is one
effort striving to reduce emissions of greenhouse gases from shipping. It has been developed by a
series of submissions to MEPCs 57-59 and the 1st and 2nd Working Groups on Greenhouse Gases.
2
EEDI baselines for six different types of vessels were first suggested by Denmark in document MEPC
58/4/8 and are under development (Ozaki et al., 2010). The EEDI is intended to be used to set a
minimum requirement for energy efficiency of new vessels and enable comparison between similar
vessels of the same size.
1.1.2 Response from the shipping industry
To address the requirements described above, the shipping industry will either need to use new
types of fuels and/or implement exhaust gas cleaning techniques. The maritime sector within SECA
thus faces a fuel and/or technology shift within the near future.
The industry is striving to find ways to comply with the coming regulation. One possible solution, that
has been suggested, is to use liquefied natural gas (LNG) as fuel. LNG is a fossil fuel, but still viewed as
an alternative since the sulphur content is low and less NOX is formed during combustion. LNG is also
put forward as a bridging technology towards biogas and hydrogen. Gotlandsbolaget presents a
vision to use liquefied biogas produced on Gotland as fuel in 2020 and hydrogen in 2030
(Gotlandsbolaget, 2009). Wallenius Marine AB describes in the brochure “Zero – A roadmap to our
future emission free ships” (2009) that LNG will probably “...be the next coming logic substitute to
fossil oil”. They also describe a transition toward biogas and perhaps methanol in a longer time
perspective.
There has been a rising interest in LNG from the maritime sector during the last years. One example is
Göteborg Energi’s and Gasnor AS’s plans to build an LNG terminal in the Port of Gothenburg. The goal
is to start to deliver LNG to shipping companies in 2013 (Sahlén, 2009).This will create the need for a
way of distributing and bunkering LNG. Svenskt Marintekniskt Forum has together with Göteborg
Energi, Fartygskonstruktioner AB, Cryo AB, Gasnor AS, Det Norske Veritas AS and White Smoke AB
started a project “LNG Bunker Ship to Ship” to find ways to achieve this (Svenskt-marinteknisktforum, 2010). Other ways of complying with the coming regulations are to combine existing engines
and fuels with abatement technologies. Scrubbers, as previously mentioned, are one such example. A
scrubber has been installed on a DFDS Ro-Pax ferry in 2009.
These are examples of developments set in motions to reduce air pollution from shipping. At the
same time as a technology and/or fuel shift in the maritime sector is pushed forward by regulatory
demands and other initiatives, information regarding marine fuels’ overall environmental impact
from a life cycle perspective is limited. Much effort has been put into investigating fuels well-towheel performance for land based transportation modes, but according to the author’s knowledge
only two reports have been published in open literature regarding the life cycle emissions of marine
fuels (Winebrake et al., 2007, Corbett and Winebrake, 2008).
The present focus on climate change and the demand for a decrease in emissions of CO2 and other
green-house gases occur simultaneously with a global competition for limited fossil fuel resources.
Thus, the fuel market and the prices will be subject to large changes. The present use of oil will be
impossible in a long term perspective and the result will be a search for other energy sources as well
as higher energy efficiency. Emission constraints will increase the demand for low sulphur fuel from
all sectors. Fuels or technologies that give low emissions of NOX and particles are also requested. In
many cases there is a direct relation between the use of oil and emissions, which makes energy
efficiency a starting point for reduction of emissions related to combustion of fossil fuels.
Thus, the market for new fuels and emission abatement technology, as well as for new energy
conversion systems is huge and the questions about the efficiency and impact from these at a systems
level are growing. In light of this, the assessment of environmental impact and resource use within
shipping and the effect of changes in technology or energy sources is an important research area.
3
1.2 Aim
The overall aim of this report is to investigate the environmental performance of marine fuels from a
life cycle perspective.
The aim can be further specified into the following parts:
•
•
•
Increase learning and identify knowledge gaps of the life cycle performance of maritime
transportation.
Set a base-line for further studies.
Identify methodological problems and special requirements when using life cycle assessment
to evaluate maritime transportation.
1.3 Method
In order to achieve the aim specified above a case study with some possible marine fuels is
conducted. The study will first identify possible fuels and abatement technologies that can be used in
SECAs and ECAs when the most stringent requirements has entered into force in 2015 for SECAs and
2016 for NOX ECAs.
The second part of the report is a life cycle assessment (LCA) comparing the identified alternatives.
The LCA methodology is further described in chapter 3 and detailed specifications for the goal and
scope of the LCA study are presented in chapter 4.
4
2
Identified fuel alternatives
This chapter introduce available technologies for marine transportation that fulfil the regulatory
requirements in Sulphur and NOX ECAs. Emissions of sulphur dioxides depend on the sulphur content
in the fuels. Therefore, fuels with sulphur content equal to or lower than 0.1 % needs to be used in
SECAs after 2015. It is also possible to use exhaust abatement techniques to reduce the sulphur
dioxide released to the air, as discussed in the introduction. NOX can be formed in two ways, either
from a reaction between nitrogen and oxygen in the air (thermal NOX) or from nitrogen fixed in the
fuel (fuel NOX). The fuel NOX is usually a minor part of the emissions. Emissions of NOX depend on
engines characteristics, mainly combustion temperature and detention time at high temperature.
2.1 Fuels
Residual fuel or heavy fuel oil (HFO) is used in the majority of the marine engines (Figure 2-1). In
2007 almost 350 million tonnes of fuels were consumed by shipping of which about 250 tonnes were
residual fuels (Buhaug et al., 2009). Figure 2-1 also shows that diesel engines are dominant in
shipping, especially slow-speed diesel engines. Two different grades of distillate fuels are usually
used: MGO and MDO. MDO is a mix between HFO and MGO.
350
300
Boilers
250
Million tonnes
Medium-speed engines
200
Slow-speed engines
150
Destillate fuel
100
Residual fuel
50
0
Consumption of fuel Consumption of fuel
in 2007 by type
in 2007 by
combustion source
Figure 2-1 Fuel consumption from shipping in 2007 divided by type and combustion source (Buhaug et
al., 2009).
HFO, MDO and MGO are produced from refining of crude oil. The sulphur content of fuels used for
marine transportation depends on the sulphur content in the crude oil, which refinery streams used
and how they are blended. The sulphur content is highest in the heaviest fractions from the
distillation column. Low sulphur heavy fuel oil with a sulphur content of about 1% and marine gas oil
with a sulphur content lower than 0.5% is produced today in some refineries, e.g. Preemraff in
Gothenburg. There are number of possibilities to produce marine bunker fuels with even lower
sulphur content. The study Impacts on the EU refining industry & markets of IMO specification changes
& other measures to reduce the sulphur content of certain fuels by Avis and Birch (2009) investigates
the possibility to produce low sulphur marine bunker fuels (with 0.5 -0.1% sulphur) from catalytic
5
cracking 1 and hydroskimming 2. It is motivated with that European refineries are based on catalytic
cracking and have marginal hydroskimming capacity that can be used when margins are favourable.
Liquefied natural gas (LNG) has been suggested to be used as a marine fuel due to its low sulphur
content, but there is no existing infrastructure for LNG except for along parts of the Norwegian coast.
LNG is produced by liquefaction of natural gas. There are different liquefaction technologies that can
be used, e.g. cascading; the number of cooling stages and type of refrigerant used may also differ. A
plant in Qatar is, for example, based on three compression trains, each driven by a 125 MW gas
turbine (GE-Oil-&-Gas, 2010).
LNG is mostly methane and nitrogen, a small share is also ethane and propane. The composition of
LNG from Gasnor AS’s LNG-plant in Kollsnes 2006 is shown in Table 2-1 as an example, but the
composition differs between production sites.
Table 2-1 Chemical composition of LNG at Kollsnes 2006 (Kvittingen, 2009)
Composition vapour phase, % mol, at -162 °C
85.6806
methane
0.0061
ethane
propane
0.0000
14.3133
nitrogen (N2)
Natural gas could also be used to produce synthetic diesel and in this way make use of the existing
infrastructure. Synthetic diesel, or gas-to-liquid (GTL) as it also is called when produced from natural
gas, is produced by the Fischer-Tropsch (FT) process or similar processes. It is a three step process
consisting of syngas generation, hydrocarbon synthesis and upgrading. The process was invented by
two German scientists and first used in Germany during World War II to produce synthetic fuel, e.g.
diesel, from coal; it has also been used for example in South Africa and in the USA after that (Field).
Historically synthetic fuel production has been limited to times when the feedstock was cheap
compared to crude oil (van Vliet et al., 2009). The FT process can be used to produce diesel from
hydrocarbons such as coal, natural gas and biomass. Production with natural gas as raw material (i.e.
Gas-to-liquid) started first in the beginning of the 1990s.
Example of the properties of low sulphur HFO, MGO, GTL and LNG is showed in Table 2-2. The MGO
and GTL have much lower content of ash, sulphur and carbon residue. The heavy fuel oil contains
vanadium and used lubricating oil. This is not specified for the other fuels. The viscosity of the HFO
can be as high as 368 mm2/s at 50 °C while MGO and GTL have a viscosity of about 3 mm2/s at 40°C.
This illustrates that the properties of the fuels differ substantially. The properties of MGO and GTL are
the most similar. A lot of the properties in Table 2-2 have not been found for LNG, partly depending
on that LNG is a totally different fuel, as it is a cryogenic liquid.
The values in Table 2-2 can be compared with existing fuel standards for marine fuels. The ISO 8217
Fuel Standard (Third edition) specifies different qualities for marine distillate fuels and marine
residual fuels (see Table 2-3). DMA is pure gas oil and what is called marine gas oil. The dualities
DMB and DMC is gas oil mixed with residual fuel and called marine diesel oil (MDO). There are a
number of different qualities of residual fuels specified in the standard; two of them are presented in
the diagram. RMG 380 is a quality that is used a lot. The bunker fuel presented in Table 2-2 fulfils the
RMG 380 standard and the marine gas oil fulfils the DMA standard. The GTL does also fulfil the DMA
standard even if it is at the limit regarding the pour point.
Cracking means to break long hydrocarbon chains into lighter products. Catalytic cracking is a special type of
cracking that uses a catalyst to speed up and direct the process.
2 A hydroskimming refinery has a simple configuration with a distillation unit and treatment units for the lighter
products but not for the residue, thereby producing a high yield of heavy fuel oils.
1
6
An update of the fuel standard (ISO 8217:2010) is expected to be released in July 2010. A new
distillate grade (DMZ) is expected to be proposed with a minimum viscosity of 3 mm2/s in the coming
revision of the standard. The minimum viscosity for the DMA and DMB are expected to be changes to
2 mm2/s (Odland, 2010).
Table 2-2 Example of properties of low-sulphur HFO, MGO and GTL.
Bunker fuel oil,
1.5% sulphur
(MARINIUS)
(Preem, 2009a)
a
40- 42.5
989.1
88.0
-9
368.3
-
Bunker gasoil
(NORTHEN
OCEAN)
(Preem, 2009b)
b
41.5-43
836.2
65.0
-27
2.747
GTL(Norton et
al., 1998, Wang
et al., 2009)
LNG
Lower heating value
MJ/kg
43.709
49
3
c
Density at 15°C
kg/m
777.3 (784.5)
440
Flash point
°C
(72)
Pour point
°C
(0)
2
Viscosity at 50°C
mm /s
2
Viscosity at 40°C
mm /s
2.568 (3.57)
Carbon/hydrogen ratio
5.68 (5.67)
Ash content
% m/m
<0.100
<0,010
(<0.001)
Carbon residue
% m/m
13.5
<0,30
(0.02)
Nitrogen content
% m/m
(0.67)
Sulphur content
% m/m
1.49
<0.03
<0.01 (<0.05)
Total sediment
% m/m
<0.01
(Accelerated)
Aluminium + Silicon
mg/kg
<80
content
Hydrogen sulphide content mg/kg
1.00
Vanadium content
mg/kg
98
Aromatics
% m/m
<0.1
Water content
% V/V
<0.50
a Range of lower heating values for HFO from published measurements (Fridell et al., 2008, Winnes and Fridell, 2009,
Cooper, 2003, Cooper, 2005).
b Range of lower heating values for MGO from published measurements (Fridell et al., 2008, Winnes and Fridell, 2009,
Cooper, 2003, Cooper, 2005).
c At -162°C (Kvittingen, 2009).
Table 2-3 Marine fuel standards (ISO, 2005).
Density at 15°C (max)
Viscosity at 40°C (max)
Viscosity at 40°C (min)
Viscosity at 50°C (max)
Micro carbon residue at 10 % residue (max)
Micro carbon residue (max)
Water content (max)
Sulphur content (max)
Total sediment existent (max)
Ash content (max)
Vanadium content (max)
Aluminium + Silicon content (max)
Flash point (min)
Pour point, summer (max)
Pour point, winter (max)
Cloud point (max)
3
kg/m
2
mm /s
2
mm /s
2
mm /s
% m/m
% m/m
% V/V
% m/m
% m/m
% m/m
mg/kg
mg/kg
°C
°C
°C
°C
7
DMA
(MGO)
890.0
6.0
1.5
6.0
0.30
1.5
0.01
60
0
-6
-
DMB
(MDO)
900.0
11.0
0.30
0.3
2.0
0.10
0.01
60
6
0
-
DMC
(MDO)
920.0
14.0
2.50
0.3
2.0
0.10
0.05
100
25
60
6
0
-
RME 180
(HFO)
991.0
180.0
15
0.5
4.50
0.10
0.10
200
80
60
30
30
-
RMG 380
(HFO)
991.0
380.0
18
0.5
4.50
0.10
0.15
300
80
60
30
30
-
Storage of fuel onboard
Storage of LNG is more space consuming than HFO, MGO and GTL since LNG has lower density and
requires pressure containers of a certain design. There are suggestions to use un-pressurised
containers that can be shaped to fit the hull, but this has not been implemented so far. Experiences
from Norwegian LNG ferries suggests that about 2.5 to 4 times as much space is needed (Hellén,
2009, SWECO, 2009). Below is an illustration of how the extra volume is distributed (Figure 2-2).
3,5
3,0
Relative Volume
2,5
Tank room
2,0
Tank
1,5
1,0
Fuel
0,5
0,0
MGO
LNG (10 bar)
Figure 2-2 Example of storage volume for MGO and LNG (Hellén, 2009).
2.2 Engines
Most marine engines in operation today are two-stroke or four-stroke diesel engines. There are also
vessels with steam turbines and high speed ferries with gas turbines. Gas engines for marine
applications have been developed and it is possible to buy gas engines and dual-fuel engines on the
market.
Exhaust emissions
[g/MJ fuel input]
Diesel engines
Slow speed diesel engines are two-stroke engines with a typical shaft power between 1500 and
100 000 kW and operates at 50 to 250 revolutions per minute. A two-stroke engine can reach as high
thermal efficiency as 65% and has an exhaust gas temperature about 325-375°C (Kuiken, 2008a).
Medium speed diesel engines are four-stroke engines with a typical shaft power between 500 and
30 000 kW and operates at 400 to 1000 revolutions per minute. The thermal efficiency for a fourstroke engine is in the range 25% to 55% and the exhaust gas temperature is 400 to 500 °C (Kuiken,
2008a). Exhaust emissions are affected by fuel and combustion parameters, i.e. temperature, oxygen
concentration, residence time (see Figure 2-3 for typical emissions).
2,5
2
1,5
1
0,5
0
nitrogen oxides
carbon oxides
NMVOC
SSD - HFO
SSD - MGO
MSD - HFO
MSD - MGO
Figure 2-3 Typical exhaust emissions of nitrogen oxides, carbon oxide and non-methane volatile organic
carbons (NMVOC) from slow speed diesel engines (SSD) and medium speed diesel engines (MSD) with
heavy fuel oil (HFO) and marine gas oil (MGO) (Data are from Cooper and Gustafsson (2004)).
8
Marine diesel engines are today fuelled with heavy fuel oil or distilled fuels, but GTL is also a possible
fuel. GTL has not been tested in two-stroke diesel engines or in large four-stroke marine engines as
far as the author knows. There have however been emission tests with GTL in trucks. Emissions of
particles, NOX and carbon monoxide (CO) were reduced by 33.5%, 5.2% and 19.5% with GTL
compared to conventional diesel in a test with an intercooled and turbocharged Euro III diesel engine
(Wang et al., 2009). The emission of particles relates both to the properties of the fuel (e.g. sulphur
content) and combustion characteristics, while emissions of NOX and CO mainly depend on the
characteristics of the engine.
Gas and dual-fuel engines
The LNG propelled ships in operation in Norway are either equipped with lean burn single gas
engines or dual fuel engines. Single fuel engines are supplied from Rolls Royce and dual fuel engines
from Wärtsilä and MAN (SWECO, 2009). It is also possible to use boilers combined with steam
turbines for propulsion with LNG. This is the dominant in LNG Carrier propulsion today. Steam
turbines are less efficient than diesel engines and the propulsion trend for new LNG Carriers is
toward diesel or dual fuel engines.
The lean burn gas-fuelled engines run only on gas and lean refers to that the air-fuel ratio is high. The
extremely lean air-fuel mixtures lead to lower combustion temperatures and therefore lower NOX
formation. The engine operates according to the Otto cycle and combustion is trigged with a spark
plug ignition. The gas is injected at low pressure. Rolls-Royce (i.e. its Norwegian subsidiary Bergen
Diesel) started the development of lean burn gas-fuelled engines in 1980s for land power and
cogeneration. It is now also used for propulsion of some of the LNG fuelled ships in Norway. Their
lean burn combustion system is based on spark plug ignition in a pre-chamber where pure gas is
mixed with the lean mixture in the cylinder, thus forming a rich mixture which is easily ignited.
Combustion of the lean mixture in the cylinder is fostered by the ignition discharge from the prechamber (Doug, 2010). Wärtsilä has a similar lean burn spark ignited engine with a pre-chamber, but
have at the moment no intentions to use it for marine applications (Stenhede, 2010).
Dual-fuel engines can run in either gas mode or liquid-fuelled diesel mode. The engine works
according to the lean burn Otto principle in gas mode, but the lean air mixture is ignited by injection
of a small amount of diesel fuel into the combustion chamber instead of a spark plug (Figure 2-4). The
injected diesel fuel is normally less than 1 percent of total fuel. In diesel mode, the engines works
according to the normal diesel cycle with diesel fuel injected at high pressure just before top dead
centre. Gas admission is activated, but pilot diesel fuel is still injected (Doug, 2010).
Figure 2-4 Example of the operation of a dual-fuel engine in the gas mode (Doug, 2010).
MAN has developed a new series of two-stroke dual-fuel engines (ME-GI Dual Fuel MAN B&W
Engines). It is especially developed for LNG carriers, but can also be used for other segments such as
LPG, Ro-Ro, and container vessels. The working principle is similar to MANs traditional two-stroke
engines, but with the difference that the combustion process is based on higher air surplus and a
pressurised gas injection system, injecting pressurized gas at a maximum pressure of about 250 bar
9
(Doug, 2010). MAN expects it to fulfil Tier III NOX requirements in combination with an exhaust gas
recirculation system (Clausen, 2010).
Gas diesel engines runs on various gas and diesel mixtures or alternatively on diesel. The engines use
the diesel cycle, combusting a mixture of gas, diesel and air and the gas is injected at high pressure.
Conversion of existing engines to natural gas operation can be made with small modifications (Doug,
2010). The emissions of NOX are higher from this engine as compared to lean burn and dual-fuel
engines. Example of emissions from these engine types from Wärtsilä are shown in Table 2-4. The gas
diesel engine can thus not comply with Tier III regulations. The emissions from Wärtsilä’s lean burn
gas engine and dual-fuel engine show similar characteristics.
Table 2-4 Typical emissions from Wärtsliä’s gas engines at 100% load and nominal speed (Hattar,
2010).
CO2 (g/kWh)
Total hydrocarbon as methane (g/kWh)
NOX (g/kWh)
Lean burn gas engine
425
2.1
1.3
Dual-fuel engine, gas mode
425
3.6
1.3
Gas diesel engine
455
0.3
6
g/MJ fuel
Emissions from similar engines in stationary installations have been compiled by US EPA. The
emissions the ME-GI Dual Fuel MAN B&W Engine is compared with emissions from dual-fuel and lean
burn four stroke and two stroke engines from land based power installations compiled by US EPA in
Figure 2-5. The emissions of NOX are considerably different for the two four-stroke lean burn engine
measurements. The methane slip varies between all compared alternatives in Figure 2-5.
2,0
1,8
1,6
1,4
1,2
1,0
0,8
0,6
0,4
0,2
0,0
nitrogen oxides
carbon oxide
NMVOC
Four-stroke lean Tow-stroke lean Four-stroke leanME-GI Dual Fuel
burn (US EPA) burn (US EPA) burn (Wärtsilä) MAN B&W
Engine
methane
Figure 2-5 Example of emissions from different gas engines. The emissions from a four-stroke lean burn
gas engine from Wärtsilä are recalculated from the data in Table 2-4 above with the assumption that 425
g CO2/kWh corresponds to 47.3 g CO2/MJ fuel. Information about emissions of CO and NMVOC is missing
for Wärtsilä’s four-stroke engine and NMVOC are missing for the ME-GI Dual Fuel engine. Emissions of
methane from the ME-GI Dual Fuel engine represent total emissions of hydrocarbons.
2.3 Exhaust gas abatement techniques
The possible measures to reduce sulphur dioxide emissions are scrubbing and fuel switching to low
sulphur fuels. There are a number of different options to reduce NOX emissions from shipping (Table
2-5). Selective catalytic reduction (SCR) is the technology with the highest reduction potential and in
addition it will not increase the fuel consumption. The humid air motor abatement technology has the
second highest reduction potential in the table, but it is not expected to be able to reach the Tier III
reduction demands. Exhaust gas recirculation has varying potential depending on how much gas that
is recirculated. MAN is planning to use an exhaust gas recirculation system together with their ME-GI
10
engine in order to comply with Tier III (Clausen, 2010). They expect to reduce the emissions of
nitrogen oxides much more than suggested in Table 2-5 (but with gas as fuel).
Table 2-5 Measures to reduce emissions of NOX and their emission reduction efficiency (Adapted from
Winnes (2007)).
NOX abatement technique
Basic internal engine modifications
Advanced internal engine
modifications
Direct water injection
Humid air motor
Water-Oil Emulsion
Exhaust gas recirculation
Selective catalytic reduction (SCR)
Cleaning
potential
20%
~30%
Increase in fuel
consumption
No
Depends on
modification
35-50%
70%
20-25%
22-69%
90%
No
No
No
Yes
No
Reference
Entec (2005a)
Entec (2005a), Goldsworthy (2002),
Prior et al. (2005) and Sletnes et al.
(2005)
Entec (2005a) and Prior et al. (2005)
Entec (2005a)
Prior et al. (2005)
Goldsworthy (2002)
Entec (2005a)
Only two different exhaust gas cleaning techniques have been further studied in this report:
scrubbing and SCR.
Scrubber
Sulphur oxides are formed when sulphur in the fuel reacts with oxygen, over 90 % of the sulphur
oxides formed in marine engines are sulphur dioxide (SO2) (Karle and Turner, 2007). Gas scrubbing is
a technique where sulphur oxides react with water and form sulphates. There are two types of units
for on-board flue gas scrubbing: open (seawater) scrubbers and closed (freshwater) scrubbers. It is
also possible to use a combination of both, e.g. closed in harbours and sensitive areas like the Baltic
Sea and open at open ocean water (Bosch et al., 2009).
In an open system, seawater with natural alkalinity is used to capture the sulphur oxides. The amount
of sulphur oxides captured depends of the alkalinity of the water. In the Baltic Sea where the
alkalinity is low compared to open sea much more sea water is needed to capture the same amount of
sulphur oxides. An example of a sea water scrubber is seen in Figure 2-6.
Figure 2-6 Exhaust gas seawater scrubber (Clausen, 2010).
In a closed system the water is instead re-circulated with continuous addition of alkali, normally
caustic soda. The following reaction occurs in a closed system with addition of caustic soda.
11
NaOH + SO2(g)+ ½ O2(g) → Na+ + HSO4-+ H2O
The scrubber will also remove particles and NOX to some extent. In tests of scrubbers for marine
application the reduction of particles has been between 0 and 85 %. The seawater is filtered before it
is returned into the sea leaving sludge that should be treated on land (Bosch et al., 2009).
Selective catalytic reduction
SCR has been commercially installed on more than 300 vessels around the world and it is the most
common method to reduce NOX emissions (Lövblad and Fridell, 2006).
In the SCR process NOX and urea are converted to nitrogen and water in the presence of a solid
catalyst. A water solution with urea is injected to the flue gas after the combustion. The efficiency of
the SCR depends on the amount of injected urea, approximately 15 g of urea per kWh energy from the
engine is needed to achieve a 90 % reduction (Lövblad and Fridell, 2006).
SCR systems can be installed in any type of engine, but a minimum exhaust gas temperature is needed
for efficient operation, this is normally around 300 °C, but depends on the sulphur content of the fuel
(Bosch et al., 2009). Hence, SCRs are less effective at low loads and for two-stroke engines, when the
exhaust gas temperature is lower. There is also a period during start up and before the catalyst has
reached operational temperature where the SCR cannot be used at all (Fridell and Steen, 2007).
A slip of ammonia may occur when the reaction between NOX and urea are incomplete, which cause
release of ammonia to the air. The reasons for ammonia slip can for example be too low exhaust gas
temperature or not properly tuned urea dosage (Fridell and Steen, 2007). An oxidation catalyst after
the SCR can be used to reduce the ammonia slip (Bosch et al., 2009).
2.4 Selected fuel alternatives
There are a number of different types of marine vessels (Table 2-6). This report will compare
different fuel alternatives in Ro-Ro vessels, since it is one of the ship types that has been identified to
be suitable for LNG-propulsion in Sweden in a report by SWECO (2009). Ro-Ro vessels usually have
route based operation and it is therefore easier for them to use fuels, with limited infrastructure.
Most of the smaller Ro-Ro vessels have four-stroke engines, while it is more evenly distributed
between four-stroke and two-stroke engines for vessel longer than 150 m according to the port calls
investigated in the EX-TREMIS database.
12
Table 2-6 Segmentation and parameters of the European fleet by engine types from the EX-TREMIS
database (Chiffi and Fiorello, 2009).
There are a number of Ro-Ro vessels operating in the North Sea and Baltic Sea, e.g. DFDS Torlines and
Finnlines. There are for example routes from Gothenburg to Immingham, Gent, Tilbury and
Travemünde. Seatrans AS ordered two small Ro-Ro ships in 2008 with propulsion by LNG-fuelled gas
engines. The vessels are also equipped with two auxiliary diesel engines (Sea-Cargo, 2008).
The following fuel alternatives are assessed in this report:
•
•
•
•
•
•
•
HFO with a sulphur content of 1 % (base scenario)
HFO with a sulphur content of 1 % with a scrubber (fulfil SCEA 2015)
MGO with a sulphur content of 0.1 % (fulfil SCEA 2015)
MGO with a sulphur content of 0.1 % with a SCR (fulfil SECA 2015 and Tier III)
LNG (fulfil SECA 2015 and Tier III)
GTL produced by the Fischer-Tropsch process (fulfil SCEA 2015)
GTL produced by the Fischer-Tropsch process with a SCR (fulfil SECA 2015 and Tier III)
All these fuels except LNG are possible to use with the present engine technology and infrastructure
for fuel supply. HFO with a sulphur content of 1 % is selected as a baseline since it represents the
most used maritime transportation fuel in the SECA area at the moment. The other fuel alternatives
have been selected since they fulfil the requirement of a maximum of 0.1% sulphur content without
exhaust gas abatement techniques. HFO can fulfil the SECA requirements if a scrubber is used. This
option has been included in the study. In order to include alternatives that also fulfil the
requirements for NOX emissions for new productions in emission control areas (ECAs) after 2016, the
MGO and the Fischer-Tropsch fuel are also studied with a selective catalytic converter.
In order to meet the SECA requirement in 2015 with the existing fuels it is possible to use HFO or a
distilled marine fuel with 0.1% sulphur content. HFO with 0.1 % sulphur content have not been
chosen to be further assessed in this report even if it is technically possible to produce. It would also
be possible to use HFO with a scrubber and a SCR, but it is technically difficult and is therefore not
assessed in this report. The effect of a SCR is still captured in the case with MGO and GTL. Biofuels
are not evaluated in this report.
13
A lean burn gas engine will be analysed for the LNG fuel alternative while four-stroke diesel engines
will be assessed in all other fuel alternatives. Four stroke lean burn gas engines and dual-fuel engines
are the gas engines that are available on the market today and can fulfil the Tier III NOX for new
buildings in 2016. More alternatives will possibly be presented during the coming years, but leanburn gas engines will probably still give a god representation of the environmental performance of
LNG from a life cycle perspective. The lean burn gas engine is chosen instead of the dual-fuel engine,
since both have similar emission characteristics but the gas engine is only dependent on one fuel
chain which facilitates the analysis.
Four-stroke diesel engines are chosen instead of two-stroke since the lean burn gas engines are fourstroke which will make the engines a better match. A second reason is that it could be problematic to
use SCRs with two-stroke engines due to the lower exhaust gas temperature.
14
3
LCA methodology
Life cycle assessment (LCA) addresses the potential environmental impact of a product or service in a
cradle-to-grave perspective (ISO, 2006a). The cradle represents raw material acquisition, which is
followed by production, use, transportation, waste management and final disposal, the grave. LCA
aims to avoid problem-shifting from one environmental problem to another and from one phase in
the life cycle to another. The holistic perspective is the unique feature of LCA.
Two ISO standards (ISO 14040 and ISO 14044) with general requirements for how to conduct an LCA
have been developed, but there is no single method for this; it needs to be adapted from case to case.
There are however a lot of manuals and guidelines with more detailed requirements and practical
advices, e.g. Bauman and Tillman (2004) and Guinée (2002) have been used as support in this study.
The procedure for conducting an LCA consists of four phases (Figure 3-1) according to the ISO 14040
standard:
•
•
•
•
Goal and scope definition
Inventory analysis
Impact assessment
Interpretation
The phases are dependent on each other and conducting an LCA is therefore often an iterative
process. An example of this is that the goal and scope definition usually needs to be refined during the
process.
Figure 3-1 The life-cycle model and the LCA procedure (Adapded from Bauman and Tillman, 2004, p. 20).
The goal and scope definition describes the studied system and the purpose of the study. The goal
should for example include intended application and reasons for carrying out the study, according to
the ISO standard. An important modelling specification that should be stated in the goal and scope is
the choice of functional unit. It is a quantitative unit representing the function of the system. LCA
relates the environmental impact to the functional unit. One tonne cargo transported one km with a
Ro-Ro vessel, is chosen as the functional unit in this study.
15
The inventory analysis consists of three parts: construction of a flow model according to the system
boundaries, data collection and calculation of resource use and emissions of the system in relation to
the functional unit. The validity of the collected data shall be checked during the process according to
the ISO standard.
The environmental flows, quantified in the inventory analysis, are in the impact assessment classified
into different impact categories and characterised, e.g. the relative contribution of the emissions and
resource consumptions are calculated. Emissions of greenhouse gases are for example aggregated to
one indicator for global warming. This results in more aggregated information, which is easier to
interpret. The use of characterisation models might on the other hand increase the uncertainties of
the result since they are simplified. This step is compulsory; an LCA without impact assessment is
called a life cycle inventory analysis.
Interpretation is the final phase of the LCA in which the results from either or both the inventory
analysis and impact assessment are summarised and discussed. This can be used as a base for
conclusions and recommendations.
The LCA community recognises two fundamentally different types of LCA studies: attributional and
consequential. Attributional LCA strives to be as complete as possible, accounting for all
environmental impacts of a product, while a consequential LCA strives to describe the environmental
consequences of alternative courses of action. There has been and is still much debate in the LCA
community regarding when the different types should be used. It is argued that LCA study’s used for
decision making must be consequential (see Finnveden et al. (2009) for an overview of different
opinions). A consequential LCA addresses questions like what would be the environmental
consequence if Fuel A is used instead of Fuel B. An attributional LCA on the other hand addresses
questions like what is the overall environmental impact of marine transportation with Fuel A.
Allocation is another difficult question in LCA. Allocation problems occur when several products (or
functions) share the same processes and the environmental load of those processes is to be expressed
only to one function. One example of a process with multiple outputs is refining of crude oil, where
the refining results in a number of products (e.g. liquefied petroleum gas, petrol, diesel, asphalt)
which is used in a number of different applications. When assessing the life cycle impact of for
example truck transportation the environmental impacts between the outputs need to be distributed.
Another example is the leachate from a landfill. How much leachate should be associated with food
waste and how much with the other types of waste? This is an example of a problem connected to a
process with multiple inputs.
Refining
Landfill
Figure 3-2 Examples of multi-output and multi-input processes (Adapted from Bauman and Tillman,
2004, p. 84).
Allocation can for example be done by physical relationship or by monetary value of the products. It
is also possible to avoid the allocation problem by using system enlargement, incorporating
additional functions to the system. The ISO standard states that allocation shall if possible be avoided
either by refining the system or by expanding it (ISO, 2006b).
LCA addresses environmental impacts of a service or production system. Economical and social
impacts are typically not included. LCA needs to be combined with other tools for more extensive
assessments.
16
4
Goal and scope definition
The aim of this study is to evaluate some possible fuel choices for marine transportation and their
environmental performance in a life cycle perspective. In addition it should identify uncertainties,
complement earlier published studies, and give advises on further studies. Only fossil fuels have been
included at this stage.
4.1 Goal of the study
The goal of the life cycle assessment (LCA) is to investigate the environmental performance of marine
transportation related to the fuel used. Four fuel types are compared: heavy fuel oil (HFO), marine
gas oil (MGO), liquefied natural gas (LNG) and Fisher-Tropsch diesel produced from natural gas
combined with two abatement technologies: seawater scrubbing and SCR. Thus, seven alternatives
for marine transportation are analysed (Figure 14).
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
6
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
7
Figure 4-1 Overview of the studied alternatives for marine transportation.
The aim is specified by the following question:
•
What would be the effect of changing fuel and/or installing abatement techniques from HFO
to HFO with scrubber, MGO with and without SCR, LNG and GTL with and without SCR?
The fact that some of the studied fuels and technologies are in full scale use and some are only
suggested or at a pilot level, makes the study a learning process, with identification of knowledge
gaps and potential impacts. It can thus be seen as a starting point for further research. Another
application can be as a decision support for ship owners when changing fuels and/or technology in
order to adapt to the new requirements adopted by IMO.
4.2 Scope of the study
This study is a comparative LCA, comparing different fuels for marine transportation and is thus
consequential. A change oriented study apply that steps in the life cycle that are the same can be
omitted and the data used should reflect the change in the system.
17
Functional unit
The functional unit is one tonne cargo transported 1 km at normal cruise conditions with a Ro-Ro
vessel.
System boundaries
An overview of the studied system is shown in Figure 4-2. The studied system includes extraction of
raw materials, production and transportation, bunkering, storage and finally combustion of fuels for
the transportation of one tonne cargo 1 kilometre. The study will thus include all activities from raw
material extraction to the release of waste to the environment. Production of lubrication oil is not
included in the system nor is waste treatment of oil sludge and used lubrication oil.
Raw
material
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Extraction of raw
material
Fuel production
Transportation and
storage
Bunkering
Raw
material
Fuel
Fuel
Emissions to air
Transportation of 1
tonne cargo 1 km
Fuel
(functional
flow)
System boundaries
Figure 4-2 An overview of the studied system.
The differences in use of lubrication oil between the investigated fuel types are estimated to be small
(Nylund, 2009). The amount of sludge and solid waste could differ more between the alternatives, e.g.
since LNG and GTL is much cleaner fuels than HFO, but the sludge treatment has not been included.
The geographical boundary for marine transportation is set to the SECAs in northern Europe (the
English Channel, the Northern Sea and the Baltic Sea), see Figure 4-3.
Figure 4-3 The sulphur emission control areas (SECAs) in the northern part of Europe (Sjöfartsverket,
2009).
Extraction, production and processing of the fuels are in most cases taking place outside this area.
Crude oil is assumed to be imported mainly from the North Sea and Russia and refining of crude oil is
assumed to be performed in Europe. LNG is assumed to be imported from the North Sea or Qatar. The
18
Fischer-Tropsch diesel is assumed to be produced in Qatar and the natural gas used in the process is
assumed to be extracted there as well.
Production and maintenance of capital goods, such as ships, ports and terminals etc, should ideally be
included in an LCA study, since it could differ between the compared alternatives. However it is not
included in this study, nor is personnel-related environmental impacts.
The time perspective in this study is from today until 2020, in order to be relevant for the fuel
regulations that will be adopted in 2015. If possible, best available technologies in operation today
are studied. The change in production by refineries to adjust for change in demand will not be
covered in this study. The fractions from the refinery considered here are produced today and they
will be modelled in alignment with today’s production.
Part of the infrastructure for distribution and bunkering of LNG is not available today in the studied
region or elsewhere and it will therefore be modelled according to planned investments. Today’s
infrastructure for diesel is assumed to be available for the distribution of Fisher-Tropsch diesel.
Allocation
Many products are produced in the crude oil refinery process and an allocation of the impacts
between these products is needed. Natural gas is both produced as associated gas together with oil
and as non-associated gas. The ISO 14044 states that allocation should if possible be avoided either
by dividing the unit processes into sub-processes and collecting data for those sub-processes or by
system expansion. A system expansions means that more functions are included in the system. It is
commonly acknowledged in the LCA community that system expansion should be used in
consequential LCAs. System expansion of either of the processes described above would, however,
lead to new allocation problems, almost equal in magnitude, and has therefore not been used.
Allocation is instead made based on energy content (i.e. lower heating value (LHV) 3. Energy content
is commonly used as a base for allocation of fuels.
Data quality requirements
The collected data should be relevant for today’s conditions and a few years into the future if possible
as it is the time scale of this study. Most recent available data will therefore be used. Data
representative for the geographical area is preferred over generic data. Data concerning extraction
and production of oil and natural gas should preferably reflect the situation in the regions exporting
to the SECAs in northern Europe. Generic data is used where this is not possible. All data used in this
LCA is documented in the Appendix to this report, in order to make the analysis transparent.
4.3 Choice of impact categories
The flows of emissions and the resource use, described in the inventory analysis and in the appendix,
are sorted into different impact categories depending on their environmental impact.
The impact categories studied in this LCA are:
•
•
•
Total primary energy use
The total primary energy use in MJ is calculated from the different used energy sources.
Lower heating values are used for recalculation of mass into energy.
Global warming
IPPCs global warming potential for a 100 year time perspective is used.
Acidification
Characterisation into generic acidification equivalents relative to sulphur dioxide is used.
The heating value is the heat released per unit mass, when the fuel, initially at 25°C reacts completely with
oxygen and the products are returned to 25°C. LHV implies that the water is not condensed. The energy released
from condensation is not utilised in internal combustion engines.
3
19
•
•
•
Eutrophication
Characterisation into generic eutrophication equivalents for emissions to air, water and soil
expressed relative to phosphate is used.
Photo-oxidant formation
Characterisation into photochemical ozone creation potentials (POCPs) for high NOX and low
NOX background concentrations which is expressed relative to ethylene is used.
Human health
No characterisation of pollutants is made in this category. The different pollutants with
showed effects of human health are showed individually as an indication of potential effect
on human health.
The following primary pollutants are considered: carbon dioxide, carbon monoxide, nitrogen
monoxide, nitrogen dioxide, nitrous oxide, sulphur dioxide, methane, ammonia, particles and volatile
organic compounds. Table 4-1 shows to which impact categories the different emissions are related.
Table 4-1 Impacts categories and related flows.
Impact category
Energy use
Global warming
Global warming
Eutrophication
Photo-oxidant formation
Human health
Flows that are related
Crude oil, natural gas, coal, electricity
CO2, methane, nitrous oxide
Nitrogen oxides, sulphur dioxide
Nitrogen oxides
Volatile organic compound, nitrogen oxides
Particles, sulphur dioxide, nitrogen oxides, CO, volatile
organic compounds
The characterisation factors used for impact categorisation are shown in Table 4-2. The
characterisation factors are taken from a database compiled by Institute of Environmental Sciences
(CML) at University of Leiden. The original references are indicated in the table below.
The effect of CO2 emissions on the acidification of oceans has not been included. The oceans absorb
CO2 from the atmosphere and this decreases the pH of the oceans. This is an additional effect of CO2
emissions that has received more attention to in recent years. It has for example been described by
the Royal Society (2005).
Only primary gaseous pollutants effect on climate change has been considered. The effect of ozone
and particles has not been included and neither has emissions of sulphur dioxide. When sulphur
dioxide is solved in sea water this contributes to the formation of CO2 which leads to that less CO2 can
be absorbed by the oceans and thereby increasing the CO2 concentration in the atmosphere.
20
Table 4-2 Characterisation factors for the impact categories global warming, acidification,
eutrophication and photo-oxidant formation.
Compounds
Global
warming
potential (kg
CO2-eq.)
(IPPC, 2007)
Acidification
(kg SO2 eq.)
(Hauschild
and Wenzel,
1998)
Eutrophication
(kg
eq.)
(Heijungs et al.,
1992)
CO2
4
Methane
Nitrous oxide
Nitrogen monoxide
Nitrogen dioxide
Nitrogen oxides
Sulphur dioxide
Carbon monoxide
Ammonia
Non-methane volatile
4,5
organic compounds
•
ethane
•
propane
•
n-butane
•
n-pentane
•
ethylene
•
propylene
•
benzene
•
toluene
•
methanol
•
ethanol
•
acetone
•
formaldehyde
1
25
298
-
1.07
0.7
0.7
1
1.88
-
0.2
0.13
0.13
0.35
-
Photo-oxidant
formation (
kg ethylene eq.)
High NOX
background
(Jenkin and
Hayman, 1999,
Derwent et al.,
1998)
0.006
-0.427
0.028
-0.095
0.048
0.027
0.15
Photo-oxidant
formation (
kg ethylene eq.)
Low NOX
background
(Andersson-Sköld
et al., 1992)
0.007
0.04
6
0.195932127
0.123
0.176
0.352
0.395
1
1.12
0.218
0.637
0.14
0.399
0.094
0.519
0.126
0.503
0.467
0.298
1
0.599
0.402
0.47
0.213
0.225
0.124
0.261
4.4 Evaluation of robustness of the result
In order to evaluate the robustness of the results, i.e. to check whether the conclusions hold even if
critical and/or approximate data is changed, several alternative scenarios have been identified.
Critical and approximate data are changed in these scenarios and compared to the base results. The
different scenarios analysed are:
•
•
•
•
Uncertainty in the emissions from the gas engine
Uncertainty in the emissions from a diesel engine fuelled with GTL
Changes in engine efficiency
Changes in liquefaction efficiency
4Note
for VOC and NMVOC: In datasets where emissions of volatile organic compounds are presented as well as
emissions of methane, the volatile organic compounds are assumed to be non-methane volatile organic
compounds. For datasets were only VOC is stated and not methane, VOC is instead assumed to be methane,
except for a few exceptions.
5 Characterisation factors for specific volatile organic compounds have been used if information about specific
emissions where available.
6 This figure has been calculated from the characterisation factor for individual VOC and estimation factors for
converting NMVOC group emissions into individual compounds from GUINÉE, J. B. (ed.) 2002. Handbook on life
cycle assessment : operational guide to the ISO standards Boston: Kluwer Academic Publishers.
21
5
Inventory analysis
This chapter presents the processes and data used in the seven fuel alternatives presented in Figure
4-1. It is divided in two parts: one base scenario and four alternative scenarios. The base scenario
uses the data that is considered as most relevant regarding geographical, technical and time
representativeness. Four alternative scenarios are presented in the end of this chapter as a
complement. These scenarios explore parameters and processes that are uncertain and parts of the
data used in the base scenario have been replaced. All data used is extensively documented in the
Appendix to this report.
5.1 Base scenario
The first three sections describe the fuel chains from extraction of raw material to production and
distribution. These are followed by a section about the bunkering process and a section about two
possible exhaust gas cleaning techniques. The last section describes the use phase when cargo is
transported with a Ro-Ro vessel.
5.1.1 Production and distribution of heavy fuel oil and marine gas oil
Extraction and transportation of crude oil and the production of HFO and MGO and the data used for
these processes are described in this section (Figure 5-1). MGO and HFO are both outputs from
refining of crude oil and therefore presented in the same section.
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
6
7
Figure 5-1 Modelling of the marked processes, extraction of crude oil and the production and
distribution of marine gas oil (MGO) and heavy fuel oil (HFO), are further described in this section.
Reservoirs of oil and gas are searched for by different geophysical techniques (e.g. seismic,
gravimetric and magnetic methods). When a reservoir is discovered exploration and development
wells are drilled in order to receive more information (Hocking, 2005a, Sevenster and Croezen,
2006). It is not all test wells that are successful, but if large enough quantities of oil and/or natural
gas are found these wells will be used. This step is difficult to include in an LCA.
Extraction is the process of transporting crude oil from the reservoir to the surface of the well. The
formation pressure in most new reservoirs is enough for transporting the oil to the surface, but when
the formation pressure is insufficient oil is pumped to the surface instead. If gas is present in the oil,
23
it is separated before the oil reaches the pumping section (Hocking, 2005a). The extracted crude oil
contains water, dissolved gas and impurities which need to be separated (Hocking, 2005b).
Crude oil is extracted from onshore and offshore locations and the characteristics of the crude oil
vary extensively depending on the location of the oil field and age of the production site. Crude oil can
be transported either by pipeline or by a crude oil carrier from the extraction site to the refineries.
Typical crude oil contain 84,5% carbon, 13% hydrogen, 1-3% sulphur, and less than 1 % each of
nitrogen, oxygen, metals and salts, but the properties varies in a wide range (Aitani, 2004). The
refining process needs to be adapted depending on the characteristics of the crude oil. The first step
in the refining process is to remove contaminants by desalting using chemical or electrostatic
separation. The crude oil is distilled in atmospheric and vacuum towers in order to separate the
crude into fractions depending on boiling temperature. In order to gain a larger share of lighter
products the heavier products is usually chemically modified either by thermal or catalytic cracking
(Hocking, 2005b).
Many refinery processes may be used in the production of marine fuels. This includes:
•
•
•
•
•
•
Atmospheric distillation
Vacuum distillation
Thermal cracking
Catalytic cracking
Hydrocracking
Coking
The exact processes that are present in a refinery vary from refinery to refinery and the feedstock to
these processes also varies from refinery to refinery and with time. The outputs of most of these
processes is fractionated resulting in distillate and residual fractions such as hydrockracker gas oil,
hydrockracker residue, fluid-bed catalytic cracking (FCC) gas oil and FCC bottoms etc. Different
worlds may be used to describe similar refinery processes. For instance, “residues” from FCC may be
referred to as decant oil, heavy cycle oil or FCC bottoms. A simplified picture of different fractions
that is used for the production of marine fuels is shown in Figure 5-2.
Distillates from
catalytic cracking
Residues from
catalytic cracking
Residues from
thermal kracking
Distillates from
thermal kracking
Vacuum
residue
Vacuum
distillates
Atmospheric
distillates
Atmospheric
residue
Blending
Distillate fuels
Heavy fuel oils
Figure 5-2 Fractions used in marine fuels adapted from Fiskaa (1997).
24
Gas oils are light and heavy gas oil fractions, and blends thereof, from straight-run and cracked
origins, in a boiling range between 200 and 350 °C. They are predominantly used as automotive
diesel fuels and as domestic heating fuels. MGO are produced from this fraction (Alfke et al., 2007).
Heavy fuel oils consist of various mixtures of residual oils from distilling and conversion processes in
the refinery. These products are used as marine bunker fuels, in power stations and in industrial
furnaces. The heavy fuel oils can be blended with gas oils to adjust density, viscosity and sulphur
content (Alfke et al., 2007). For reasons of cost and stability light cycle oils are much used for these
type of blending (Buhaug, 2010).
Hydrotreating is for example used to remove sulphur components by reaction with hydrogen in the
presence of a suitable catalyst, to form hydrogen sulphide. It can also be used to remove other
contaminants such as nitrogen and to saturate molecules such as olefins and aromatics. Hydrogen
sulphide is removed from the process gas stream and is then usually converted into elemental
sulphur (Alfke et al., 2007).
Cracking means to break long hydrocarbon chains into lighter products. This can be achieved by
extreme temperature such as thermal cracking or coking or at lower temperature by the use of a
catalyst to speed up and direct the process. Atmospheric residue is the most important feedstock
after the introduction of metal-resistant catalysts and installation of feed pretreatment facilities in
the mid 1970s. Distillate fractions from crude oil are also used as cracking feedstock — mostly heavy
gas oils (vacuum gas oils) and to some extent de-asphalted oils. Fluid-bed catalytic cracking is the
most commonly used catalytic cracking process. The cracked gas oils are referred to as light and
heavy “cycle oils” since they have been widely used as recycle feed, that is blended back into the
cracker (Alfke et al., 2007).
The hydrocracking process was developed to produce high yields of distillates with better qualities
than can be obtained by fluid-bed catalytic cracking. Distillates from vacuum distillation, from
catalytic and thermal cracking, or de-asphalted oils are typical feedstock to the hydrockracking
process. Metal sulphide catalyses the reaction in the presence of hydrogen. Hydrogen is produced
inside the refinery from catalytic reforming of naphtha into gasoline (Alfke et al., 2007).
Distribution of oil products is done by pipelines or product tankers.
Data used
The European Commission Joint Research centre on LCA Tools, Services and Data has published a life
cycle inventory database, ELCD core database, where life cycle inventory data for European heavy
fuel oil and light fuel oil production are available (ELCD-core-database-version-II, 2009a, ELCD-coredatabase-version-II, 2009b). The allocation has been based on energy content (i.e. mass and LHV)
and considerations has been taken for that different refinery outputs passes different processes, i.e.
allocation has been made after each process in the refinery. The average sulphur content of the heavy
fuel oil is not specified in the database and the light fuel oil has a sulphur content of 2000 ppm. The
data for heavy fuel oil is assumed to be representative for heavy fuel oil with 1 % sulphur content and
the data for light fuel oil with 2000 ppm sulphur content is assumed to be representative for MGO
with sulphur content of 0.1 %. These data sets represents cradle-to-gate, which covers exploration,
processing, transportation and production of HFO and MGO.
The fuels are assumed to be transported by pipeline to the harbour. Distribution from refinery to
harbour is not included in this data set.
5.1.2 Production and distribution of liquefied natural gas
The data for extraction and transportation of natural gas and the liquefaction process is described in
this section (Figure 5-3).
25
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5a,5b
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
6
7
Figure 5-3 Modelling of the marked processes, extraction of natural gas and the production and
distribution of liquefied natural gas (LNG), are further described in this section.
Natural gas is extracted both offshore and onshore and both associated and non-associated to oil. The
energy requirement and pollution of course differ with location and method. After extraction, the gas
is cleaned from water, acid gases, nitrogen and hydrocarbons (Sevenster and Croezen, 2006). The
treatment processes for the gas depend on raw gas quality and required standard for the processed
gas. Most treatment processes requires electricity for valves and pumps. Electricity is usually
produced on site, but if the field is located near the grid this is also a possibility. Methanol is used in
the dehydration process, when water vapour that exists in solution with the gas is removed; it is
usually recovered and recycled. Other inputs are active carbon and glycol which are used in the
desulphurization and dehydration steps (Sevenster and Croezen, 2006). As fields mature the amount
of water produced with the oil and gas increases and this water may harm the environment then
dumped.
Natural gas is transported from the extraction place by pipeline or liquefied on site; it is also possible
to transport the gas by compressed natural gas (CNG) carrier (Beronich et al., 2009). The alternative
to transport natural gas with CNG vessels is not common at present, but it is suggested as a solution
for remote gas fields. Before the gas is transported by pipeline it is compressed to approximately 70
bar, but the initial pressure might be higher more than 200 bar with subsea pipelines. The gas is
compressed along the pipeline in order to maintain the pressure due losses caused by friction. The
compressors are generally driven by natural gas since this is always available (Sevenster and
Croezen, 2006). There is also leakage of methane during pipeline transportation which is hard to
quantify. The methane emissions from the long distance pipeline network from Russia are
approximately 0.6 % of the gas delivered (Lechtenböhmer et al., 2007).
The processing step for LNG is essentially the same as for natural gas. If LNG is produced at the
extraction site, the processing and liquefaction step can be integrated, and the undesired
hydrocarbons can be removed during the liquefaction process (Figure 19). The heavier hydrocarbons
condense at higher temperatures than methane and are then removed. Ethane is usually added to the
methane again after liquefaction. There are different technologies that can be used to liquefy the gas
(e.g. cascade cycle, throttling cycle etc. 7), the number of cooling stages and type of refrigerant used
For more information see for example: KIRILLOV, N. G. 2004. Analysis of Modern Natural Gas
Liquefaction Technologies. Chemical and Petroleum Engineering, 40, 401-406.
7
26
may also differ. Liquefied petroleum gas (LPG) and gasoline are by-products from the process
(Sevenster and Croezen, 2006). The liquefaction efficiency varies between different plants. Gasnor’s
LNG plant in Kollsnes uses approximately 12 % of the natural gas (Kvittingen, 2009).
C3
C2 C4+
Fractionation
Natural gas
Reception
Acid gas removal
CO2 removal
Dehydration and
Mercury removal
Liquefaction
Hg
CO2
Nitrogen removal
LNG storage and
loading
N2
LNG
Refrigeration
Sulphur recovery
Figure 5-4 Typical processing elements in a modern LNG production (Brendeng and Hetland, 2004).
○
LNG is stored and transported below -162 C and absorption of heat from the surrounding
continuously takes place as tanks cannot be perfectly insulated. The absorbed heat evaporates liquid
at the surface, called boil-off gas (BOG). BOG is generated during the whole LNG supply chain (Figure
5-5). LNG is stored in atmospheric storage tanks both at LNG plants and receiving terminals. BOG is
usually compressed and exported back to the fuel system at LNG production plants while it is either
flared or sent to regasification at receiving terminals. BOG is also produced during loading and
unloading of LNG, this BOG is usually also handled by the LNG plant or receiving terminal (Hasan et
al., 2009). The tubes, used for loading, are filled with nitrogen before and after loading and unloading
of LNG at Gasnor AS, in order to minimize the leakage of methane (Kvittingen, 2009). Gasnor AS
estimates the boil-off during loading and unloading to 0.2 %. This gas is then used for production of
electricity according to Kvittingen (2009)
Figure 5-5 Boil-off sources in the LNG supply chain (Hasan et al., 2009).
Large vessels with insulated tanks of membrane or moss type without external refrigeration, i.e. LNG
carriers, are used for transportation of LNG. A significant amount of the transported LNG evaporates
during voyage. The BOG can be used as fuel, re-liquefied or burnt (Dimopoulos and Frangopoulos,
2008). The handling of the BOG effects the emissions and energy requirements during the
transportation. A part of the LNG needs to be left for the return journey in order to cool the tank,
called heel. The heel directly influences the revenue of the trip and it also affects the boil-off rate
during loading, unloading and ballast voyage. A common practice is to use 5% of the total cargo
capacity as heel (Hasan et al., 2009).
Zeebrygge is one of the largest LNG terminals in Europe with four storage tanks at total volume of
380 000 m3 of LNG. The boil off rate is approximately 4.5 tonne per hour from the tanks and 2.5 tonne
per hour for the cold circulation. LNG is circulated in the transfer lines to keep them in cold condition.
The BOG is mixed with sufficient amount of LNG in a recondensor vessel, thereby cooled down and
27
reliquefied due to the large heat exchange surface and the intense contact with LNG. This LNG is then
pumped to the vaporizers and sent to the grid. Energy is required for the electric engine driven
compressors, these compressors discharge in the recondensor vessel. The average storage time in
Zeebrugge terminal is about 10 days (Maelfeyt, 2009).
Most of the LNG imported to Europe originates from Algeria, Nigeria, Qatar and Egypt (Figure 5-6).
Trinidad &
Tobago
9%
Belgium
0%
Norway
3%
Nigeria
26%
Libya
1%
Equatorial
Guinea
0%
Egypt
12%
Qatar
14%
Oman
0%
Algeria
35%
Figure 5-6 Trade movements of LNG to Europe (BP, 2009).
Data used
The data used for extraction, processing and pipeline transportation of natural gas is from the CPM
database (SPINE-LCI-dataset, 2008). The natural gas produced as associated gas and the allocation
for natural gas is based on mass and lower heating value. This data is representative for natural gas
and crude oil from the North Sea in 1991. This data is rather old, but still chosen since it is
representative for the region and extensive, including information about more flows than only
greenhouse gases.
The data for energy use and emissions of CO2, methane and nitrous oxide in the liquefaction process
is from the report “Well-to-Wheels analysis of future fuels and powertrains in the European context” by
CONCAWE, EUCAR and JCR (Edwards et al., 2007, JEC, 2008b, JEC, 2008a). This data is used since it is
representative for LNG used in Europe. Specific information about energy balances are taken from
JCE (2008a). Methane losses of 0.17% and flaring of off-gas of 0.25% of the produced LNG from the
liquefaction process is used (Edwards et al., 2007). Further it is assumed that all energy is supplied
by natural gas combustion in a combined cycle gas turbine. Emission factors for a combined cycle gas
turbine is from Global Emission Model for Integrated Systems (GEMIS) Version 4.5 (2010). The
liquefaction process also includes storage at LNG terminal before distribution, information about this
is also from JCE (2008a).
The overall liquefaction efficiency is 92% and approximately 8.5 % of the natural gas is expended in
the process including storage (see Figure 5-7). This data is representative for a large LNG plant.
28
0.06173684 MJ natural gas
1.02313 MJ natural gas
CCGT, 57%
efficiency
0.03434 MJ electricity
Liquefaction
1.01 MJ LNG
0.00085 MJ electricty
Storage at
terminal
1 MJ LNG
Figure 5-7 Modelling of the liquefaction process.
Two distribution scenarios for LNG are studied, either transportation from the North Sea or from
Qatar. This is done since there is no existing distribution network for LNG in the region at the
moment. The transportation from North Sea is chosen since Göteborg energi AB and Gasnor AS have
plans to establishing a LNG terminal in Gothenburg, which could be supplied with LNG from Kollsnes.
Qatar is chosen since they are a major exporter of LNG to Europe and is fairly representative for
transportation of LNG from North Africa to Europe. In the case of transportation from Qatar it is
assumed that the LNG is first transported from Qatar to Zeebrygge and then to Gothenburg.
Electricity consumption from the LNG terminal in Zeebrygge is not modelled. Transportation
distances have been calculated by the webpage www.distances.com (Distances.com, 2010) and
presented in Table 5-1. Gasnor AS plans to distribute LNG with the vessel Coral Methane. For the
distance between Qatar and Rotterdam an LNG carrier with a cargo capacity of 147000 cubic meters
and as steam turbine with an efficiency of 30% is assumed to be used. Coral Methane has a gas engine
and a diesel engine and it is assumed to only use the gas engine for both LNG and ballast transport
(Gullaksen, 2009). For more vessel details see Appendix I.
Table 5-1 Transportation distances for LNG distribution.
Distance
North Sea (Kollsnes) - Gothenburg
Qatar - Rotterdam
Rotterdam – Gothenburg
Vessel
Coral Methane
LNG carrier
Coral Methane
Distance
150 NM
6500 NM
500 NM
Days of journey
1
14
1.5
The boil-off rate is typically between 0.1-0.15 % of the full cargo content per day over a 21-day
voyage according to Hasan et al. (2009). The evaporation rate is assumed to be 0.15 % of the gas per
day and 5% of the LNG is assumed to be left in the tank to cool the hull.
5.1.3 Production and distribution of Fischer-Tropsch diesel
The data for extraction and transportation of natural gas and the Fischer-Tropsch process is
described in this section (Figure 5-8).
29
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5a,5b
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
6
7
Figure 5-8 Modelling of the marked processes, extraction of natural gas and the production and
distribution of gas-to-liquid (GTL), are further described in this section.
The life cycle starts with extraction and processing of natural gas as described above in section 5.1.2.
The gas is transported to a Fischer-Tropsch (FT) diesel production unit instead of a liquefaction
plant. It is most efficient to produce GTL near the natural gas reservoir to be able to use the same
infrastructure as for liquid fuels. The FT process mainly consists of three steps: syngas generation,
carbon synthesis and upgrading. Syngas is produced through methane reforming and non-catalytic
partial oxidation. The syngas is then converted to synthetic crude by carbon synthesis and then
further refined into the desired products, e.g. diesel (Jaramillo et al., 2008). Fuel combustion, vents
and fugitive emissions are sources of air emissions from a FT conversion plant. The primary fuel
combusted in FT plants is fuel gases generated during the FT conversion and upgrading (Marano and
Ciferno, 2001).
Data used
The same data as in the case of LNG production is used for extraction, processing and pipeline
transportation of natural gas. It is representative for natural gas from North Sea and not for natural
gas produced in Qatar. Data from the Well-to-Wheels study by JEC (2007) is used for energy use and
CO2 emissions for production of FT diesel. Since the report only includes data regarding energy use
and greenhouse gas emissions a life cycle inventory report by Marano and Ciferno (2001) is used for
the other emissions. This has not been used for all flows as it is not adapted to European context. In
the report by Marano and Ciferno different feedstock for FT production and a few different FT
processes are modelled. The option that was considered most relevant for this study was FT diesel
produced from associated gas with conventional product upgrading with 2000 years state of the art
FT production and cogeneration of electricity. The included emissions sources in the study are fuel
gas combustion, incineration, flaring, direct and indirect venting of CO2, and upstream emissions from
all ancillary feedstock to the processes (Marano and Ciferno, 2001). The data sources are compiled in
Table 5-2.
30
Table 5-2 Overview of the data sources used for GTL and upstream processes.
Covered processes
Covered flows
Allocation
Reference
Natural gas extraction, processing
Based on LHV and mass
(SPINE-LCI-dataset,
and pipeline transport
2008)
a
FT diesel production
Energy use,
Substitution
(Edwards et al.,
CO2, CH4
2007)
FT diesel production
Based on LHV and mass (due to co(Marano and
production of electricity)
Ciferno, 2001)
a The JEC Well-to-Wheels study uses the following allocation method: “All energy and emissions generated by the
process are allocated to the main or desired product of that process. The by-product generates an energy and
emission credit equal to the energy and emissions saved by not producing the material that the co-product is most
likely to displace” (Edwards et al., 2007).
The GTL is assumed to be transported from Qatar to Gothenburg with a product tanker of Panamax
size, see Table 5-3. The vessel is assumed to use HFO with a sulphur content of 2% outside SECAS and
MDO with a sulphur content of 0.1% inside SECAs. An average sulphur content of 2 % has also been
used in the study by Eyring et al. (2005) for the year 2020 in a business-as-usual scenario meeting
IMO emission regulations. For more details of the vessel see Appendix I. Emission factors for fuel
combustion are from NTM (2008) and Cooper and Gustafsson (2004).
Table 5-3 Transportation distances for GTL distribution.
Qatar – SECA border
SECA border – Gothenburg
Vessel
Product tanker
Product tanker
Distance
6000 NM
1000 NM
Fuel and sulphur content
HFO, 2 % sulphur
MGO, 0.1 % sulphur
5.1.4 Bunkering
The data used to model storage, transportation and bunkering of all the fuels is described in this
section (Figure 5-9).
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5a,5b
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
6
7
Figure 5-9 Modelling of the marked processes, storage, transportation and bunkering, are further
described in this section.
When the fuel is unloaded in the port of Gothenburg (or another port in the SECA area) it is stored
and loaded at bunker ships for distribution of the fuel to the Ro-Ro vessel. Emissions of VOC are
released during storage and unloading of the fuels. For HFO, MGO and GTL the dominant parts are
non-methane VOC while methane is the dominant part for LNG. The oil harbour in Gothenburg has a
31
vapour recovery unit, which recovers approximately 90 % of emitted VOCs (Port-of-Gothenburg,
2008).
VOC emissions will not occur during unloading of a vessels tank. Air or inert gas is drawn into the
tank as the liquid levels drops and the pressure in the tank will be slightly below atmospheric
pressure (Rudd and Hill, 2001). VOC emitted during loading of tanks result from vapour present in
the tank before loading commences and from evaporation of the loaded product. The rate of
emissions depends on a number of factors:
•
•
•
•
•
•
•
•
Nature of previous product
Nature of current product
Temperature
Loading rate
Turbulence in the vapour space
Sea conditions (for offshore loading)
Time since unloading of previous cargo
Design of ship (Rudd and Hill, 2001).
Since LNG continuously evaporates, it needs to be either re-liquefied or combusted during storage.
The average storage time for LNG at Zeebrugge is about 10 days (Maelfeyt, 2009). If it is combusted,
emissions from the combustion are released to the air. If it is re-liquefied instead, electricity is needed
either from the grid or from natural gas used to generate electricity. To Produce electricity from the
boil-off gas and feed it to the grid is also a possibility, as is done at Gasnor AS storage facility in
Kollsnes.
The pumps used to move the fuel during unloading and loading is usually driven by the auxiliary
engines; another possibility at port would be to drive the pumps with shore power from the grid, but
no such possibilities are available at the oil harbour in Gothenburg presently. The bunker ship
transports the fuel to the Ro-Ro vessel and the fuel is pumped to the vessel.
Data used
For all fuels except for LNG the environmental report for the Port of Gothenburg (2009) has been
used in order to quantify the emissions of VOCs. These emissions are assumed to not include
methane. Equal amount of VOCs are assumed to be emitted from all the products loaded at the Port of
Gothenburg, this is a simplification. Methane emissions from LNG during loading have not been
modelled. It is assumed that the bunker ships transport the bunker fuel 10 km and the emissions
during this transportation have been included. The bunker ship is assumed to be equipped with
medium speed diesel engines, fuelled with MGO with 0.1% sulphur content or in the case of LNG lean
burn gas engines, fuelled with LNG. The emissions from the bunker vessel during loading have not
been included. Table 5-4 gives an overview of the data sources used to model this process. The
emissions during the actual bunkering are not included.
32
Table 5-4 Overview of the data used to model the storage, loading, transportation and bunkering of the
fuels.
Covered processes
Loading of HFO, MGO
and GTL at port terminal
Covered flows
Fugitive emissions
of VOC (not from
combustion)
Allocation
Based on mass
of loaded and
unloaded
products
Electricity consumption
LNG terminal
Transportation 10 km
with a bunker ship ( HFO,
MGO and GTL)
Reference
Port of Gothenburg (2009)
JEC (2008a)
None
Transportation 10 km
with a bunker ship (LNG)
For emissions from combustion (NTM, 2008,
Cooper and Gustafsson, 2004). For
estimations of fuel use per tonne and km see
Appendix I
Emissions from Hattar (2010) and Stenhede
(2009)
None
5.1.5 Transportation of cargo
The data used to model the “use phase”, transportation of one tonne cargo one km with a Ro-Ro
vessel is described in this section (Figure 5-10).
Extraction and
transport of crude
oil
Extraction and
transport of crude
oil
Extraction and
transport of
natural gas
Extraction and
transport of
natural gas
Crude oil
Crude oil
Natural gas
Natural gas
Production and
transportation of
HFO
Production and
transportation of
MGO
Production and
transportation of
LNG
Production and
transportation of
GTL
HFO
MGO
LNG
GTL
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
Storage,
transportation and
bunkering
HFO
HFO
MGO
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
1
2
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
3
MGO
GTL
LNG
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
4
5a,5b
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
GTL
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel with SCR
6
7
Figure 5-10 Modelling of the marked processes, transportation of 1 tonne cargo 1 km with a Ro-Ro
vessel, the “use phase” are further described in this section.
Transportation with a Ro-Ro vessel has been chosen as a functional unit in this study. However it is
easy to use the cradle-to-gate life cycle emissions for the studied fuels and combine them with direct
emission for other types of marine transportation, e.g. passenger transportation and container
transportation.
Data used
The vessel is assumed to travel at normal cruising condition at design speed. Specifications for the
vessel are presented in Table 5-5. The characteristics of the Ro-Ro ships are intended to be as
representative as possible for the different fuels cases, since there are some differences between a
vessel propelled by oil and gas products. The transportation of one tonne of cargo one kilometre with
the Ro-Ro vessel is assumed to require 0.06 kWh of work at the crankshaft. (The work is slightly
higher for the LNG propelled vessel due to the lower cargo capacity: 0.060 as compared to 0.057 kWh
per tonne and kilometre.) See Appendix I for more details.
33
Table 5-5 The characteristics of the Ro-Ro vessel.
Ro-Ro vessel fuelled by HFO, MDO or GTL
14680 KW
Medium speed four-stroke diesel engine
85%
15000 tonne
50%
Ro-Ro vessel fuelled by LNG
14680 kW
Lean burn four-stroke gas engine
85%
15000 tonne
48%
Load factor
88%
88%
Average amount of loaded cargo
Speed
Vessel efficiency
6600 tonne
18 knots
0.057 kWh energy per tonne km
6336 tonne
18 knots
0.060 kWh energy per tonne km
Total engine capacity
Engine type
Engine load
Cargo capacity
Pay load
The specific fuel consumption (mass of fuel per engine work) differs between the studied vessels
depending on the fuel used. The reason is that the energy content of the different fuels varies. The
values used are shown in Table 5-6; these values are from literature or modelled. The efficiency of the
engines fuelled with HFO and MGO is approximately 41% (NTM, 2008). The specific fuel consumption
for natural gas has been calculated based on 41% efficiency and a lower heating value of 48 MJ/kg.
Table 5-6 Specific fuel consumption with different fuels.
Type of fuel
HFO
MGO
LNG
GTL
Specific fuel consumption
203 g per kWh
213g per kWh
183 g per kWh
203 g per kWh
Reference
NTM (2008)
NTM (2008)
Assumption based on NTM (2008)
Assumption based on NTM (2008)
The emissions factors used for HFO, MGO and GTL are from NTM (2008) and Cooper and Gustafsson
(2004). The same emissions factors from combustion of GTL as for MGO have been used, expect for
sulphur dioxide emissions which is set to zero. The emission factors from Wärtsilä’s four stroke lean
burn gas engine from Hattar (2010) and Stenhede (2009) have been used in the base scenario.
An open sea water scrubber is assumed to be used in fuel alternative two. It is modelled to reduce the
emissions of sulphur dioxide from HFO with 1 % sulphur content to the same levels as the sulphur
dioxide emissions from a fuel with 0.1 % sulphur content. The sulphur dioxide released from 1 tonne
of fuel with a sulphur content of 1% is calculated from the chemical reaction,
,
according to Endresen et al. (2005), to be 20 kg as compared to 2 kg for a fuel with 0.1% sulphur
content. The reduction required by the scrubber unit is thus 90 %. A scrubber unit is modelled to
remove 25 % of the particles according to Entec (2005b). The extra fuel consumption due to the
scrubber is 0.2-1.4 % of the engine fuel consumption (Linde, 2009). A 2% increase of energy
consumption is assumed in the calculations.
It is assumed that the SCR reduces the NOX emissions with 85%. To achieve 90% efficiency
approximately 15 g of urea per kWh energy from the engine is needed (Lövblad and Fridell, 2006). 15
g of urea is here assumed to be used for 85% reduction in NOX emissions. The same emission factors
as without SCR are used except for the 85 % reduction of NOX and for emissions of ammonia. The
specific emission of ammonia is assumed to be 0.025 g per kWh (Cooper, 2001). This was measured
on a medium speed diesel engine with a SCR unit installed. Data for the production and
transportation of urea are from as study Andersson and Winnes (2011).
5.2 Alternative scenarios
Four alternative scenarios are modelled where some uncertain data are changed. The data that have
been changed are described in this section.
34
5.2.1 Uncertainty in the emissions from gas engines
The emissions to air from a gas fuelled engine at real operating conditions are uncertain. Data from
emissions tests with Wärtsilä’s four-stroke lean burn gas engine has been used in the base scenario.
US EPA (2000) has published data for emission tests from stationary lean burn gas engines, gathered
from 70 emission reports containing over 400 source tests, which differ substantially compared to
the data from Wärtsilä. These emission factors do not fulfil the coming Tier III requirements for
emissions of NOX and the methane slip from the engines is much higher, approximately 0.6 g/MJ fuel
as compared to 0.3 g/MJ.
5.2.2 Uncertainty in the emissions from diesel engines fuelled with GTL
As was mentioned in chapter 2 GTL has not been tested in two-stroke diesel engines or in large fourstroke marine engines. Therefore the same emission factors as for combustion of MGO are assumed
in the base scenario. Some emissions were decreased in emission tests with GTL compared to diesel
in trucks. For example, emissions of particles, NOX and carbon monoxide (CO) were reduced by
33.5%, 5.2% and 19.5% with GTL compared to conventional diesel in a test with an intercooled and
turbocharged Euro III diesel engine by Wang et al. (2009). These reductions are used in this
alternative scenario.
5.2.3 Changes in engine efficiency
The efficiency of the lean burn gas engine is much more uncertain than for the four-stroke diesel
engine that has been used for marine applications much longer. The same efficiency of about 41 % of
the lean burn gas engine as for the four-stroke diesel engines has been assumed in the modelling. In
order to test how this assumption will affect the result an increase to 46% and a decrease to 36% of
engine efficiency are tested.
5.2.4 Changes in liquefaction efficiency
When Europe’s largest LNG plant on Melkøya island off Hammerfest was to be put into use in 2006 it
was expected to be the most efficient liquefaction plant as only 5% of the gas was assumed to be
needed for liquefaction (Brendeng and Hetland, 2004). An alternative scenario where only 5% of the
gas is consumed during liquefaction is studied in order to study the impact of improved liquefaction
efficiency.
Gasnor AS estimates that approximately 12% of the gas is used in the liquefaction process
(Kvittingen, 2009). This is also included here as an example of decreased liquefaction efficiency as
compared to the base scenario.
A 0.17 % leakage of methane and 0.25% flaring of natural gas are also included in both cases
(Edwards et al., 2007).
35
6
Results from base scenario
Results for the base scenario are presented as potential environmental impact, which uses the data
that are considered as most relevant regarding geographical and technical representativeness. The
impact categories, total primary energy use, global warming potential, acidification, eutrophication
and photo-oxidant formation are presented. Emissions of particles are also presented as an indication
of the impact category human health. The results will both present which emissions and which
processes in the life cycle that contribute to the respective impact category. All results are related to
the functional unit, i.e. one tonne cargo transported one km with a Ro-Ro vessel (in the unit tonne
km).
Total primary energy use and global warming potential
Energy use and global warming potential for the investigated fuel alternatives are presented in
Figure 6-1.
Life cycle GHG emissions [g CO2-eq./ tonne km]
60
7 - GTL with SCR
50
2 - HFO with scrubber
40
1 - HFO
6 - GTL
4 - MGO with SCR
3 - MGO
5b - LNG from Qatar
5a - LNG from North Sea
30
20
10
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Life cycle energy use [MJ/tonne km]
Figure 6-1 Energy use in MJ for goods transportation compared to global warming potential in g CO2
equivalents for heavy fuel oil (HFO), marine gas oil (MGO), liquefied natural gas (LNG) from the North
Sea and Qatar and Gas-to-liquid (GTL).
HFO is in this study found to be the most energy efficient fuel while LNG from North Sea is the fuel
with the lowest global warming potential. The transport distance for LNG from Qatar compared to
LNG from North Sea is about 45 times longer, which make both the energy use and the contribution
to global warming higher. GTL is the fuel with highest contribution to both categories. The use of
exhaust gas abatement techniques (scrubber and SCR) increases both the primary energy use and the
emissions of greenhouse gases.
The contribution to global warming is presented in more detail in Figure 6-2 and Figure 6-3. The
results are divided among the processes in the life cycle in Figure 6-2 while Figure 6-3 shows the
contributing emissions.
37
GHG emissions [g CO2-eq./ tonne km]
60
50
40
30
20
10
0
Transportation of cargo
HFO
HFO with
scrubber
MGO
MGO with LNG from LNG from
SCR
North Sea
Qatar
3,9E+01
4,0E+01
3,7E+01
Urea production
3,7E+01
3,3E+01
3,3E+01
GTL
GTL with
SCR
3,7E+01
3,7E+01
1,6E+00
1,8E+00
Fuel distribution
3,8E-03
3,6E-03
3,4E-03
3,4E-03
3,6E-01
2,3E+00
1,4E+00
1,4E+00
Fuel production
4,2E+00
4,3E+00
4,5E+00
4,5E+00
3,0E+00
3,2E+00
7,1E+00
7,1E+00
1,4E+00
1,4E+00
1,8E+00
1,8E+00
Raw material extraction
Figure 6-2 Global warming potential for all compared fuel alternatives in g CO2 equivalents for goods
transportation, divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
60
GHG emissions [g CO2-eq./ tonne km]
50
40
30
20
10
0
HFO
HFO with
scrubber
MGO
MGO with
SCR
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
methane
9,0E-01
9,1E-01
9,7E-01
1,0E+00
4,3E+00
4,3E+00
9,3E-02
1,4E-01
nitrous oxide
5,5E-01
5,6E-01
5,5E-01
5,5E-01
5,5E-01
6,0E-01
1,4E+00
1,4E+00
carbon dioxide
4,2E+01
4,3E+01
4,0E+01
4,2E+01
3,3E+01
3,5E+01
4,6E+01
4,8E+01
Figure 6-3 Global warming potential for all compared fuel alternatives in g CO2 equivalents for goods
transportation divided between the contributing emissions, CO2, methane and nitrous oxide.
The difference between the alternative with the smallest and the highest global warming potential is
31 %. The global warming potential is of the same order of magnitude for all the compared fuel
38
alternatives. LNG from North Sea and LNG from Qatar are the alternatives with the lowest global
warming potential. Figure 6-2 shows that the use phase contributes the most to the global warming
potential during the life cycle. The other parts of the life cycle stands for between 13 percent in the
case of LNG from North Sea to 25 percent for GTL with SCR.
Emissions of methane are substantially higher for the two LNG fuelled alternatives. This is a result of
higher emissions during the whole life cycle. The emissions of methane during combustion with a
lean burn four-stroke gas engine are uncertain, see section 5.2.1 Uncertainty in the emissions from gas
engine.
Methane emissions are related to the management in all stages of the life cycle and are therefore
difficult to quantify. The effect of increased fugitive emission of methane is evaluated in a cut-off
analysis (Figure 6-4). It can be seen that the LNG fuel alternatives have a higher global warming
potential than the crude oil based fuel alternatives, if more than 2.5 % of the LNG used for
transportation leaks during the life cycle for the lower estimate.
60
GHG emissions [g CO2-eq./ tonne km]
55
50
45
40
HFO
HFO with scrubber
MGO
35
MGO with SCR
GTL
GTL with SCR
30
LNG North Sea
LNG Qatar
25
0,0%
0,5%
0,9%
1,4%
1,8%
2,3%
2,8%
3,2%
3,7%
4,2%
4,6%
5,1%
5,5%
Share off fugitive emissions from LNG in order to break even with different fuel alternatives
Figure 6-4 Share of fugitive emissions from LNG released in order to break even with the other fuel
alternatives.
Acidification and eutrophication
The acidification potential is presented in Figure 6-5 and Figure 6-6 and the eutrophication potential
in Figure 6-7 and Figure 6-8. Emission of NOX contributes the most to the acidification and
eutrophication potential. Sulphur dioxide contributes to lower extent and only to acidification. The
use phase is the dominant contributor to the life cycle acidification and eutrophication potential
standing for between 53 -99% of the overall impact.
The LNG fuel alternatives and the alternatives with a SCR unit, which is assumed to reduce the direct
emissions of NOX with 85%, have the smallest acidification- and eutrophication potentials. These
alternatives have significantly lower impact than the other alternatives.
39
0,9
Acidifying emissions [g SO2-eq./tonne km]
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
Transportation of cargo
HFO
HFO with
scrubber
MGO
MGO with LNG from LNG from
SCR
North Sea
Qatar
8,0E-01
5,9E-01
5,5E-01
Urea production
1,0E-01
6,3E-02
6,3E-02
GTL
GTL with
SCR
5,3E-01
8,1E-02
8,6E-03
8,9E-03
Fuel distribution
7,7E-05
5,4E-05
5,0E-05
5,0E-05
6,9E-04
1,8E-03
3,6E-02
3,6E-02
Fuel production
2,6E-02
2,6E-02
2,7E-02
2,7E-02
1,4E-03
1,5E-03
8,0E-03
8,0E-03
1,5E-02
1,6E-02
2,1E-02
2,1E-02
Raw material extraction
Figure 6-5 Acidification potential for all compared fuel alternatives in g SO2 equivalents for goods
transportation divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
0,9
Acidifying emissions [g SO2-eq./tonne km]
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
HFO
HFO with
scrubber
MGO
MGO with
SCR
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
Nitrogen oxides
5,6E-01
5,7E-01
5,3E-01
8,8E-02
6,9E-02
7,0E-02
5,6E-01
1,1E-01
Sulphur oxides
2,6E-01
4,4E-02
4,3E-02
4,7E-02
1,2E-02
1,2E-02
3,2E-02
3,6E-02
Ammonia
3,9E-04
4,0E-04
3,9E-04
5,3E-03
0,0E+00
0,0E+00
3,3E-04
5,2E-03
Figure 6-6 Acidification potential for all compared fuel alternatives in g SO2 equivalents for goods
transpotation divided between the contributing emissions, sulphur dioxide, nitrogen oxides and
ammonia.
40
Eutrophicating emissions [g PO43--eq./tonne km]
0,12
0,10
0,08
0,06
0,04
0,02
0,00
Transportation of cargo
HFO
HFO with
scrubber
MGO
MGO
with SCR
1,0E-01
1,1E-01
9,8E-02
1,5E-02
Urea production
LNG from LNG from
North Sea
Qatar
1,2E-02
1,2E-02
GTL
GTL with
SCR
9,8E-02
1,5E-02
9,1E-04
9,2E-04
Fuel distribution
1,0E-05
9,6E-06
8,9E-06
8,9E-06
1,3E-04
3,3E-04
3,8E-03
3,8E-03
Fuel production
1,2E-03
1,3E-03
1,3E-03
1,3E-03
2,7E-04
2,8E-04
1,3E-03
1,3E-03
6,6E-04
7,0E-04
8,9E-04
8,9E-04
Raw material extraction
Figure 6-7 Eutrophication potential for all compared fuel alternatives in g
equivalents for goods
transportation divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
Eutrophicating emissions [g PO43--eq./tonne km]
0,12
0,10
0,08
0,06
0,04
0,02
0,00
HFO
HFO with
scrubber
MGO
MGO with
SCR
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
Nitrogen oxides
1,0E-01
1,1E-01
9,9E-02
1,6E-02
1,3E-02
1,3E-02
1,0E-01
2,1E-02
Ammonia
7,2E-05
7,4E-05
7,3E-05
9,9E-04
0,0E+00
0,0E+00
6,2E-05
9,8E-04
Figure 6-8 Eutrophication potential for all compared fuel alternatives in g
equivalents for goods
transportation divided between the contributing emissions, nitrogen oxides and ammonia.
Photo-oxidant formation
Ground level ozone is a secondary pollutant formed in the troposphere. Ozone formation is complex
and depends on a number of factors, e.g. NO, NO2, VOC and UV radiation. The effect of different
41
emissions depends on the background concentration of NOX as well as on the location. The photooxidant potential is presented both for high and low background concentrations of NOX below. During
the transport of cargo at sea, the levels of NOX are usually low while they may be high in ports. Figure
6-9 and Figure 6-10 present the photo-oxidant formation potential for high and low NOX background
concentration. The photochemical ozone formation potential for high and low concentration of NOX
showd similar tendency.
Photo-oxidant formation potential
for high NOx background
( g ethylene eq./tonne km)
Raw material extraction, fuel production and transportation of cargo are the processes that
contribute the most to the overall photo-oxidant formation potential. HFO without scrubber has
significantly higher photo-oxidant formation potential than the other alternative; it depends on the
sulphur dioxide emissions.
0,025
0,020
0,015
0,010
0,005
0,000
MGO
MGO
with SCR
LNG
from
North
Sea
LNG
from
Qatar
GTL
GTL
with SCR
7,2E-03
7,0E-03
6,8E-03
4,8E-03
4,8E-03
5,9E-03
5,7E-03
3,1E-04
3,2E-04
3,0E-04
3,0E-04
5,2E-05
1,2E-04
1,3E-03
1,3E-03
3,0E-03
3,1E-03
3,3E-03
3,3E-03
1,8E-04
2,0E-04
2,6E-04
2,6E-04
2,2E-03
2,3E-03
3,0E-03
3,0E-03
HFO
HFO
with
scrubber
1,8E-02
Fuel distribution
Fuel production
Transportation of cargo
Urea production
4,1E-04
Raw material extraction
4,2E-04
Figure 6-9 Photo-oxidant formation potential for high NOX background concentrations for all compared
fuel alternatives in g ethylene equivalents for goods transportation on the processes in the life cycle Raw
material extraction and part of the distribution is included in the process fuel production for the
alternatives with HFO and MGO.
42
Photo-oxidant formation potential
for low NOx background
( g ethylene eq./tonne km)
0,030
0,025
0,020
0,015
0,010
0,005
0,000
Transportation of cargo
HFO
HFO
with
scrubber
2,0E-02
9,4E-03
MGO
MGO
with SCR
LNG
from
North
Sea
LNG
from
Qatar
GTL
GTL
with SCR
9,1E-03
8,9E-03
6,8E-03
6,8E-03
8,0E-03
7,8E-03
Urea production
4,8E-04
4,9E-04
Fuel distribution
4,1E-04
4,1E-04
3,9E-04
3,9E-04
7,5E-05
1,7E-04
1,5E-03
1,5E-03
Fuel production
4,2E-03
4,3E-03
4,6E-03
4,6E-03
2,3E-04
2,4E-04
3,6E-04
3,6E-04
2,7E-03
2,9E-03
3,6E-03
3,6E-03
Raw material extraction
Figure 6-10 Photo-oxidant formation potential for low NOX background concentrations for all compared
fuel alternatives in g ethylene equivalents for goods transportation divided on the processes in the life
cycle. Raw material extraction and part of the distribution is included in the process fuel production for
the alternatives with HFO and MGO.
Emissions of particles
Emissions of primary particles smaller than 10 micrometer (PM10) are presented in Figure 6-11. It
should be emphasised that only part of the particles actually formed are included, since secondary
particles are formed when compounds such as NOX and sulphur dioxides react in the atmosphere.
The dominant process for particle emissions is the use phase (transportation of cargo), contributing
to 87-99.5% of the life cycle emissions. The LNG fuel alternatives are connected to the smallest mass
of particle emissions.
43
Emissions of particles (g PM10/tonne km)
0,050
0,045
0,040
0,035
0,030
0,025
0,020
0,015
0,010
0,005
0,000
Transportation of cargo
HFO
HFO with
scrubber
MGO
MGO
with SCR
LNG from
North Sea
LNG
from
Qatar
GTL
GTL with
SCR
4,5E-02
3,5E-02
1,7E-02
1,7E-02
4,8E-03
4,8E-03
1,7E-02
1,7E-02
Urea production
5,7E-04
5,7E-04
Fuel distribution
4,4E-06
1,7E-06
1,6E-06
1,6E-06
5,3E-05
1,8E-04
1,5E-03
1,5E-03
Fuel production
2,2E-04
2,2E-04
2,3E-04
2,3E-04
1,4E-05
1,4E-05
2,3E-04
2,3E-04
Raw material extraction
Figure 6-11 Emission of particles in g PM10 for goods transportation. Raw material extraction and part of
the distribution is included in the process fuel production for the alternatives with HFO and MGO.
44
7
Results from alternative scenarios
Results from the four alternative scenarios are presented in this chapter. All impact categories as
presented in the base scenario are not shown here for all alternatives. Instead the most relevant
impact categories for each alternative scenario are presented. These results give insight in how
robust the results are and how the results could change with different assumptions and input data.
7.1 Uncertainty in the emissions from gas engines
The emissions from the gas engines are uncertain since hard to find measurement results from at real
operating conditions. The effect of an alternative set of data (U.S.-EPA, 2000) is therefore tested here.
Total primary energy use and global warming potential
Energy use and global warming potential for the investigated fuel alternatives are presented in
Figure 7-1.
60
7 - GTL with SCR
GHG emissions [g CO2-eq./ tonne km]
50
2 - HFO with scrubber
5b - LNG from Qatar
1 - HFO
4 - MGO with SCR
3 - MGO
5a - LNG from North Sea
40
6 - GTL
30
20
10
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Energy use [MJ/tonne km]
Figure 7-1 Energy use in MJ per transported tonne and km compared to global warming potential in g
CO2 equivalents for goods transportation for heavy fuel oil (HFO), marine gas oil (MGO), liquefied
natural gas (LNG) from the North Sea and Qatar and Gas-to-liquid (GTL).
The global warming potential from the LNG fuelled alternatives are higher here as compared to the
base scenario. This makes the differences between the LNG fuelled alternatives and the crude oil
based alternatives much smaller. One example is that the global warming potential for MGO is smaller
than for LNG from Qatar.
The contribution to global warming potential for all the investigated alternatives is presented in
Figure 7-2 and Figure 7-3.
45
GHG emissions [g CO2-eq./ tonne km]
60
50
40
30
20
10
0
Transportation of cargo
HFO
HFO with
scrubber
MGO
MGO with LNG from LNG from
SCR
North Sea
Qatar
3,9E+01
4,0E+01
3,7E+01
Urea production
3,7E+01
3,7E+01
3,7E+01
GTL
3,7E+01
1,6E+00
GTL with
SCR
3,7E+01
1,8E+00
Fuel distribution
3,8E-03
3,6E-03
3,4E-03
3,4E-03
4,0E-01
2,3E+00
1,4E+00
1,4E+00
Fuel production
4,2E+00
4,3E+00
4,5E+00
4,5E+00
3,0E+00
3,2E+00
7,1E+00
7,1E+00
1,4E+00
1,4E+00
1,8E+00
1,8E+00
Raw material extraction
Figure 7-2 Global warming potential for all compared fuel alternatives in g CO2 equivalents for goods
transportation, divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
60
GHG emissions [g CO2-eq./ tonne km]
50
40
30
20
10
0
methane
HFO
HFO with
scrubber
MGO
MGO with
SCR
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
9,0E-01
9,1E-01
9,7E-01
1,0E+00
7,9E+00
8,0E+00
9,3E-02
1,4E-01
nitrous oxide
5,5E-01
5,6E-01
5,5E-01
5,5E-01
5,5E-01
6,0E-01
1,4E+00
1,4E+00
carbon dioxide
4,2E+01
4,3E+01
4,0E+01
4,2E+01
3,3E+01
3,5E+01
4,6E+01
4,8E+01
Figure 7-3 Global warming potential for all compared fuel alternatives in g CO2 equivalents for goods
transportation divided between the contributing emissions, CO2, methane and nitrous oxide.
Both figures show the same results; in Figure 7-2 the results are divided among the processes in the
life cycle while Figure 7-3 shows the contributing emissions. The difference between the alternative
with the least and the highest global warming potential is only 20 % in this case as compared to 31 %
46
in the base scenario. MGO and LNG from North Sea and from Qatar are the alternatives with the
lowest global warming potential.
The contribution of methane to the global warming potential is substantially higher for the two LNG
alternatives as compared to the base scenario. This is originating from an increase in the methane
slip from the engine with the data set used here from US EPA (2000).
Acidification and eutrophication
The acidification potential is presented in Figure 7-4 and Figure 7-5 and eutrophication potential in
Figure 7-6 and Figure 7-7.
The LNG fuel alternatives and the alternatives with a SCR unit which reduce the direct emissions of
NOX with about 85% have the least acidification and eutrophication potential even in this alternative
scenario. The two alternatives with LNG increase their acidification potential with about 85% and
their eutrophication potential is doubled with the data set from US EPA (which does not fulfil the Tier
III requirement).
0,9
Acidifying emissions [g SO2-eq./tonne km]
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
HFO
HFO with
scrubber
MGO
8,0E-01
5,9E-01
5,5E-01
1,0E-01
Fuel distribution
7,7E-05
5,4E-05
5,0E-05
Fuel production
2,6E-02
2,6E-02
2,7E-02
Transportation of cargo
Urea production
MGO with LNG from LNG from
SCR
North Sea
Qatar
GTL
GTL with
SCR
1,3E-01
1,3E-01
5,3E-01
8,1E-02
5,0E-05
1,4E-03
2,9E-03
3,6E-02
3,6E-02
2,7E-02
1,4E-03
1,5E-03
8,0E-03
8,0E-03
1,5E-02
1,6E-02
2,1E-02
2,1E-02
8,6E-03
Raw material extraction
8,9E-03
Figure 7-4 Acidification potential for all compared fuel alternatives in g SO2 equivalents for goods
transportation on the processes in the life cycle. Raw material extraction and part of the distribution is
included in the process fuel production for the alternatives with HFO and MGO.
47
0,9
Acidifying emissions [g SO2-eq./tonne km]
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
HFO
HFO with
scrubber
MGO with
SCR
MGO
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
Nitrogen oxides
5,6E-01
5,7E-01
5,3E-01
8,8E-02
1,4E-01
1,4E-01
5,6E-01
1,1E-01
Sulphur oxides
2,6E-01
4,4E-02
4,3E-02
4,7E-02
1,2E-02
1,2E-02
3,2E-02
3,6E-02
Ammonia
3,9E-04
4,0E-04
3,9E-04
5,3E-03
0,0E+00
0,0E+00
3,3E-04
5,2E-03
Figure 7-5 Acidification potential for all compared fuel alternatives in g SO2 equivalents for goods
transportation divided between the contributing emissions, sulphur dioxide, nitrogen oxides and
ammonia.
Eutrophicating emissions [g PO43--eq./tonne km]
0,12
0,10
0,08
0,06
0,04
0,02
0,00
HFO
HFO with
scrubber
MGO
MGO
with SCR
1,0E-01
1,1E-01
9,8E-02
1,5E-02
Fuel distribution
1,0E-05
9,6E-06
8,9E-06
8,9E-06
Fuel production
1,2E-03
1,3E-03
1,3E-03
1,3E-03
Transportation of cargo
Urea production
LNG from LNG from
North Sea
Qatar
GTL
GTL with
SCR
2,5E-02
2,5E-02
9,8E-02
1,5E-02
2,7E-04
5,3E-04
3,8E-03
3,8E-03
9,1E-04
Raw material extraction
9,2E-04
2,7E-04
2,8E-04
1,3E-03
1,3E-03
6,6E-04
7,0E-04
8,9E-04
8,9E-04
Figure 7-6 Eutrophication potential for all compared fuel alternatives in g
equivalents for goods
transportation divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
48
Eutrophicating emissions [g PO43--eq./tonne km]
0,12
0,10
0,08
0,06
0,04
0,02
0,00
HFO
HFO with
scrubber
MGO
MGO with
SCR
LNG from
North Sea
LNG from
Qatar
GTL
GTL with
SCR
Nitrogen oxides
1,0E-01
1,1E-01
9,9E-02
1,6E-02
2,6E-02
2,6E-02
1,0E-01
2,1E-02
Ammonia
7,2E-05
7,4E-05
7,3E-05
9,9E-04
0,0E+00
0,0E+00
6,2E-05
9,8E-04
Figure 7-7 Eutrophication potential for all compared fuel alternatives g
equivalents for goods
transportation divided between the contributing emissions, nitrogen oxides and ammonia.
Photo-oxidant formation potential
for high NOx background
( g ethylene eq./tonne km)
Photo-oxidant formation
Figure 7-8 and Figure 7-9 present the photo-oxidant formation potential for high and low NOX
background concentrations. The photo-oxidant formation potential for both LNG alternatives has
increased with about 4 % from the base scenario.
0,025
0,020
0,015
0,010
0,005
0,000
MGO
MGO
with SCR
LNG
from
North
Sea
LNG
from
Qatar
GTL
GTL
with SCR
7,2E-03
7,0E-03
6,8E-03
5,1E-03
5,1E-03
5,9E-03
5,7E-03
3,1E-04
3,2E-04
3,0E-04
3,0E-04
5,6E-05
1,2E-04
1,3E-03
1,3E-03
3,0E-03
3,1E-03
3,3E-03
3,3E-03
1,8E-04
2,0E-04
2,6E-04
2,6E-04
2,2E-03
2,3E-03
3,0E-03
3,0E-03
HFO
HFO
with
scrubber
1,8E-02
Fuel distribution
Fuel production
Transportation of cargo
Urea production
4,1E-04
Raw material extraction
4,2E-04
Figure 7-8 Photo-oxidant formation potential for high NOX background concentrations for all compared
fuel alternatives in g ethylene equivalents for goods transportation. Raw material extraction and part of
the distribution is included in the process fuel production for the alternatives with HFO and MGO.
49
7.1.1
Photo-oxidant formation potential
for low NOx background
( g ethylene eq./tonne km)
0,030
0,025
0,020
0,015
0,010
0,005
0,000
Transportation of cargo
HFO
HFO
with
scrubber
MGO
MGO
with SCR
LNG
from
North
Sea
LNG
from
Qatar
GTL
GTL
with SCR
2,0E-02
9,4E-03
9,1E-03
8,9E-03
7,0E-03
7,0E-03
8,0E-03
7,8E-03
Urea production
4,8E-04
4,9E-04
Fuel distribution
4,1E-04
4,1E-04
3,9E-04
3,9E-04
7,7E-05
1,7E-04
1,5E-03
1,5E-03
Fuel production
4,2E-03
4,3E-03
4,6E-03
4,6E-03
2,3E-04
2,4E-04
3,6E-04
3,6E-04
2,7E-03
2,9E-03
3,6E-03
3,6E-03
Raw material extraction
Figure 7-9 Photo-oxidant formation potential for low NOX background concentrations for all compared
fuel alternatives in g ethylene equivalents for goods transportation. Raw material extraction and part of
the distribution is included in the process fuel production for the alternatives with HFO and MGO.
Emissions of particles
Emissions of particles of size from 1 micrometer and smaller are presented in Figure 7-10. The
emissions of particles are reduced with almost 100% from the base scenario.
50
Emissions of particles (g PM10/tonne km)
0,050
0,045
0,040
0,035
0,030
0,025
0,020
0,015
0,010
0,005
0,000
HFO
HFO with
scrubber
MGO
MGO
with SCR
LNG from
North Sea
LNG
from
Qatar
GTL
GTL with
SCR
4,5E-02
3,5E-02
1,7E-02
1,7E-02
1,7E-05
1,7E-05
1,7E-02
1,7E-02
Fuel distribution
4,4E-06
1,7E-06
1,6E-06
1,6E-06
1,9E-07
1,1E-04
1,5E-03
1,5E-03
Fuel production
2,2E-04
2,2E-04
2,3E-04
2,3E-04
1,4E-05
1,4E-05
2,3E-04
2,3E-04
Transportation of cargo
Urea production
5,7E-04
5,7E-04
Raw material extraction
Figure 7-10 Emission of particles in g PM10 for goods transportation. Raw material extraction and part
of the distribution is included in the process fuel production for the alternatives with HFO and MGO.
7.2 Uncertainty in the emissions from diesel engines fuelled with GTL
The emissions from the GTL fuelled engine are assumed to be the same as with MGO as fuel (except
for emissions of sulphur dioxide) in the base scenario. Emissions of particles, NOX and CO have been
decreased in this scenario, in order to illustrate how this will change the result.
Acidification and eutrophication
The acidification potential is presented in Figure 7-11 and eutrophication potential in Figure 7-12.
The results are the same as in the base scenario even if the acidification and eutrophication potential
have decreased slightly for the GTL fuelled alternatives.
51
0,9
Acidifying emissions [g SO2-eq./tonne km]
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
Transportation of cargo
HFO
HFO with
scrubber
MGO
8,0E-01
5,9E-01
5,5E-01
Urea production
MGO with LNG from LNG from
SCR
North Sea
Qatar
1,0E-01
6,3E-02
6,3E-02
GTL
GTL with
SCR
5,0E-01
7,5E-02
8,6E-03
8,9E-03
Fuel distribution
7,7E-05
5,4E-05
5,0E-05
5,0E-05
6,9E-04
1,8E-03
3,6E-02
3,6E-02
Fuel production
2,6E-02
2,6E-02
2,7E-02
2,7E-02
1,4E-03
1,5E-03
8,0E-03
8,0E-03
1,5E-02
1,6E-02
2,1E-02
2,1E-02
Raw material extraction
Figure 7-11 Acidification potential for all compared fuel alternatives in g SO2 equivalents for
goodstransportation divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
Eutrophicating emissions [g PO43--eq./tonne km]
0,12
0,10
0,08
0,06
0,04
0,02
0,00
Transportation of cargo
HFO
HFO with
scrubber
MGO
1,0E-01
1,1E-01
9,8E-02
Urea production
MGO
with SCR
1,5E-02
LNG from LNG from
North Sea
Qatar
1,2E-02
1,2E-02
GTL
9,2E-02
9,1E-04
GTL with
SCR
1,4E-02
9,2E-04
Fuel distribution
1,0E-05
9,6E-06
8,9E-06
8,9E-06
1,3E-04
3,3E-04
3,8E-03
3,8E-03
Fuel production
1,2E-03
1,3E-03
1,3E-03
1,3E-03
2,7E-04
2,8E-04
1,3E-03
1,3E-03
6,6E-04
7,0E-04
8,9E-04
8,9E-04
Raw material extraction
Figure 7-12 Eutrophication potential for all compared fuel alternatives in g
equivalents for goods
transportation divided on the processes in the life cycle. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
Emissions of particles
52
Particle emissions from the GTL fuelled alternatives have been reduced to such extent that they are
lower than the emissions from the MGO fuelled alternatives (Figure 7-13).
Emissions of particles (g PM10/tonne km)
0,050
0,045
0,040
0,035
0,030
0,025
0,020
0,015
0,010
0,005
0,000
HFO
HFO with
scrubber
MGO
MGO
with SCR
LNG from
North Sea
LNG
from
Qatar
GTL
GTL with
SCR
4,5E-02
3,5E-02
1,7E-02
1,7E-02
4,8E-03
4,8E-03
1,1E-02
1,1E-02
Fuel distribution
4,4E-06
1,7E-06
1,6E-06
1,6E-06
5,3E-05
1,8E-04
1,5E-03
1,5E-03
Fuel production
2,2E-04
2,2E-04
2,3E-04
2,3E-04
1,4E-05
1,4E-05
2,3E-04
2,3E-04
Transportation of cargo
Urea production
5,7E-04
5,7E-04
Raw material extraction
Figure 7-13 Emission of particles in g PM10 for goods transportation. Raw material extraction and part
of the distribution is included in the process fuel production for the alternatives with HFO and MGO.
7.3 Changes in engine efficiency
The efficiency of the lean burn gas engine is much more uncertain than for the four-stroke diesel
engine that has been used for marine applications much longer. The same efficiency of about 41 % of
the lean burn gas engine as for the four-stroke diesel engines has been assumed in the modelling in
the base scenario. In order to test how this assumption will affect the result both an increase and a
decrease of engine efficiency with 5 % have been tested. The increase and the decrease are
represented by the ranges in Figure 7-14.
53
GHG emissions [g CO2-eq./ tonne km]
60
50
40
30
20
10
0
Transportation of cargo
HFO
HFO with
scrubber
MGO
3,9E+01
4,0E+01
3,7E+01
Urea production
MGO with LNG from LNG from
SCR
North Sea
Qatar
3,7E+01
3,3E+01
3,3E+01
GTL
GTL with
SCR
3,7E+01
3,7E+01
1,6E+00
1,8E+00
Fuel distribution
3,8E-03
3,6E-03
3,4E-03
3,4E-03
3,6E-01
2,3E+00
1,4E+00
1,4E+00
Fuel production
4,2E+00
4,3E+00
4,5E+00
4,5E+00
3,0E+00
3,2E+00
7,1E+00
7,1E+00
1,4E+00
1,4E+00
1,8E+00
1,8E+00
Raw material extraction
Figure 7-14 Global warming potential for all compared fuel alternatives in g CO2 equivalents for goods
transportation. The ranges for the transportation alternatives with LNG as fuel represent a 5 % increase
and decrease in engine efficiency for the lean burn gas engine. Raw material extraction and part of the
distribution is included in the process fuel production for the alternatives with HFO and MGO.
A five percent lower engine efficiency than for the crude oil based fuel alternatives will increase the
global warming potential for the LNG fuel alternatives to the same levels as the crude oil based fuels.
GTL with and without SCR are still the alternatives with the highest global warming potential.
54
7.4 Changes in liquefaction efficiency
Changes in liquefaction efficiency will only change the result slightly (Figure 7-15). The case with low
liquefaction efficiency has 6 % higher global warming potential than the case with high liquefaction
efficiency.
GHG emissions [g CO2-eq./ tonne km]
60
50
40
30
20
10
0
Transportation of cargo
HFO
HFO with
scrubber
MGO
3,9E+01
4,0E+01
3,7E+01
Urea production
MGO
with SCR
LNG
from
North
Sea
LNG
from
North
Sea 5%
liq. eff.
LNG
from
North
Sea 12%
liq. eff.
GTL
3,7E+01
3,3E+01
3,3E+01
3,3E+01
3,7E+01
1,6E+00
GTL
with SCR
3,7E+01
1,8E+00
Fuel distribution
3,8E-03
3,6E-03
3,4E-03
3,4E-03
3,6E-01
3,6E-01
3,6E-01
1,4E+00
1,4E+00
Fuel production
4,2E+00
4,3E+00
4,5E+00
4,5E+00
3,0E+00
1,3E+00
3,3E+00
7,1E+00
7,1E+00
1,4E+00
1,3E+00
1,4E+00
1,8E+00
1,8E+00
Raw material extraction
Figure 7-15 Global warming potential for all compared fuel alternatives with improved efficiency in the
liquefaction process in g CO2 equivalents for goods transportation. Raw material extraction and part of
the distribution is included in the process fuel production for the alternatives with HFO and MGO.
55
8
Discussion
There are number of critical choices both methodological and of data that affect the result of an LCA.
It is therefore important to evaluate the robustness of the results and compare with other studies.
One way to deal with this is to use alternative scenarios. The result from the base scenario and the
alternative scenarios will be discussed and analysed in this chapter. In addition critical choices and
data that have not been tested will be discussed. This chapter also describe some prerequisites for
the LNG fuel chains climate performance and implications for further research.
8.1 Critical methodological choices
The methodological choices made in an LCA study should be in accordance with the goal of the study.
Typical choices that have great impact are functional unit, system boundaries, allocation procedures
and choices of data.
The functional unit was in this study chosen to be one tonne of cargo transported one km with a RoRo vessel at normal operating conditions. All operations included in marine transportation are thus
not included, i.g. manoeuvring, berth and loading of cargo. An inclusion of these modes is not likely to
change the relative impact of the fuel alternatives to a large extent. Another possibility would have
been to choose the sailing of a Ro-Ro vessel between two ports as the functional unit. The result
would then be more representative for an average journey. This was done in the study by Corbett
and Winebrake (2008). This approach was not chosen here since there is a lack of emission data for
manoeuvring with most of the studied fuels.
The only allocation method used for heavy fuel oil and marine gas oil in this study is based on the
energy content of the fuel. A detailed allocation of energy use in relation to petroleum products is
performed in a study by Wang et al. (2004). Three different allocation methods at the refining
process level 8 are compared with each other and with an allocation on the refinery level 9. HFO is
associated to between 0.8-2.9% and gas oil between 4.6-5.1% of the total energy use at the refinery in
the three process level allocation methods (Wang et al., 2004), most if mass is used as base for
allocation and least if the economic value is used. The allocation method used in the data for HFO and
MGO in the ELCD core database (according to the description) is what Wang refers to as an energy
content based approach at a process level. Wang’s study implies that if a market value based process
level allocation method had been used for HFO and MGO the energy use and emissions per tonne of
product would decrease slightly compared to the data from the ELCD core database. No marketbased study has been found for European conditions. The difference in allocated energy use between
HFO and gas oil is larger in the study by Wang et al. (2004) than in the ELCD core database.
Approximately the double amount is allocated to gas oil compared to HFO in the study by Wang et al.
while only about 14% more energy is allocated to light fuel oil in the ELCD core database.
A more problematic issue related to the emissions from the refinery process is how it will change in
2020 when the maximum sulphur content allowed in marine fuels worldwide will be 0.5%. This will
imply changes in the refinery process and the data used in this report are not applicable for that. The
changes are for example investigated in a study by Avis and Birch (2009). They estimate that the
total CO2 emissions from European refineries will increase with approximately 3% in 2020 compared
to a baseline scenario without any regulation of the sulphur content in marine fuels. The bunker fuels
with ultra low sulphur content (0.5, 0.1% sulphur) will then be associated to an increase by
approximately 91 kg CO2 per tonne bunker fuel for the production process (compared to 296 kg CO2
emissions per tonne HFO that is used in this study for cradle to refinery gate) if all the increase in CO2
Allocation is done after each process in the refinery.
Allocation is made between the products after they have passes through the whole refinery, e.g. the
refinery is viewed as a black box.
8
9
57
emissions are allocated to the ultra low sulphur marine bunker fuel. A smaller change in refinery
production will probably also occur in the SECA area already 2015 when the limit of 0.1% sulphur is
introduced.
Liquid fuels are assumed to have a distribution system similar to today’s condition, as this
infrastructure already exists, and in line with the coming regulations. The HFO alternative without
scrubber, which is included in order to represents today’s conditions, is assumed to be distributed
with HFO with 1% sulphur content. The other alternatives, except for the LNG alternatives, are
assumed to be distributed with a product tanker with medium speed diesel engines fuelled by HFO
with 2% sulphur content outside SECA and MGO with a sulphur content of 0.1% inside SECA. The
distribution part of the life cycle stands for less than 1% of the total acidification potential for the
HFO and MGO alternatives and is therefore not considered to affect the result. When it comes to GTL,
which is transported much longer distances, this is of course different. The distribution phase stands
for about 6 % without SCR and almost 25 % with SCR, of the total acidification potential. That the
relative contribution of the distribution phase increases with a SCR depends on that the SCR
significantly reduces the NOX emissions in the use phase. The transportation of GTL takes place
mostly outside the SECAs, where 3.5% sulphur content will be allowed until 2020.
The distribution chain for LNG is assumed to use LNG as fuel, but is this likely? Why is not the MGO
and GTL fuel chains then fuelled by MGO and GTL respectively? If LNG should be distributed with
diesel engines fuelled with HFO and MGO the vessels must be equipped with reliquefaction units. This
will increase both the energy use and the emissions from the LNG chain slightly. The distribution is
only a small part of the environmental impact caused by marine transportation with LNG as fuel. It
does not contribute to more than 6 % of the total impact for any impact category.
Production and maintenance of capital goods, such as ships, ports and terminals etc, should ideally be
included in an LCA study, since it could differ between the compared alternatives. However these
factors are not included in this study, nor are personnel-related environmental impacts. To get a full
picture of the environmental performance of marine transportation, and to be able to compare it with
other transport modes the infrastructure should be included. It has been shown in other studies that
this could affect the results (Chester and Horvath, 2009). There are, for example, documented
differences between maintenance and service intervals for vessels propelled by HFO and MGO
(Kuiken, 2008b). This would however be difficult to include in the LCA and would only change the
result slightly.
The analysis in this report has a short time perspective and does therefore not evaluate the marine
fuels environmental impact in a longer time horizon, for example to 2050. All fuels analysed in this
study originate from fossil fuels, i.e. natural gas and crude oil, which are limited. The natural gas and
the crude oil of the best quality and at the most accessible locations will be used first, but a transition
towards more costly and lower quality fossil fuels is necessary. This will have an effect of the life
cycle performance in the longer perspective due to more difficult distribution and processing. It is an
example of one factor that cannot be included in a study with a short time perspective. At the same
time, if a shipping company chose to investigate in new technology for marine transportation toady,
they would like to know that the technology will be a sustainable solution in a longer time
perspective. There is thus also a need for studies with a longer perspective.
8.2 Data reliability and representativeness
In order for data to be reliable it should be representative geographically, technically and in a certain
time. One problem with LCA is how to deal with old and new technologies in the same study. It is for
example difficult to know which data that are representative for the LNG and GTL fuel chains. The
newest and most comprehensive available information has been used for the existing technologies
58
while data for new technologies have been modelled based on information from articles and personal
communication. There is thus a difference in data quality for existing and new technologies.
The Ro-Ro vessel fuelled with LNG was modelled with a four-stroke lean burn gas diesel engine.
Another choice would have been to model it with a dual-fuel engine which is more flexible and thus
an easier step for the ship owners to take. The dual-fuel and the lean burn engine have similar
characteristics when it comes to emissions and efficiency, the result differ more between different
data sets than between engines as long as the dual fuel engine is operated in gas mode. The dual fuel
engine can also be seen as an intermediate technology used to gain acceptance of gas as a marine fuel,
where the pure gas engine is the goal. Neither of these choices are necessarily the right one for this
study. The lean burn gas engine has been chosen since this choice only includes the natural gas fuel
chain and is therefore easier to model.
The Ro-Ro vessels fuelled with liquid fuel are modelled to be equipped with four-stroke diesel
engines. Two-stroke engine are more energy efficient than four-stroke engines and the characteristic
of the exhaust gas differs to some extent. The difference in efficiency is the most important factor
even thought the emissions of NOX also differ significantly. All engines have therefore been assumed
to have approximately the same efficiency (41%). Energy efficiency and energy management may
change the condition, but the potential for all the fuel alternatives are similar in that aspect.
The data for the energy consumption per tonne and km transported cargo of the Ro-Ro vessel are the
most uncertain data in the study, but this uncertainty has no effect on the relation between the fuel
alternatives. These data are much dependent on the characteristics of the vessel and weather
conditions. A wide range of vessels with different energy consumption profiles exists. The modelled
hypothetical Ro-Ro vessel has been used as a case study, in order to convert the emission from
mechanical energy from the engine to propulsion energy connected to the functional unit. The
relative contribution of the different fuel alternative will not change if the result is recalculated for
another type of vessel.
Two different data sets for the emissions from the lean burn gas engine have been used. One from
Wärtsilä (Hattar, 2010, Stenhede, 2009), which is from engine tests at optimal condition and the
other is from U.S. EPA (2000) for stationary lean burn gas engines and much more extensive testing.
This data set differs when it comes to methane, nitrogen oxide and particle emissions. The emission
of methane (methane slip) and the emission of NOX are much higher in the data set from U.S. EPA
while the data set from Wärtsilä has much higher emissions of particles. The alternative scenario
with U.S. EPA data show that LNG’s positive greenhouse gas contribution, compared to HFO and MGO,
will dissapere if the methane silp is as high as 0.6 g/MJ fuel. Which of these data sets that is most
representative is difficult to know. The data from Wärtsilä represents data from the engines that so
far only have been used for land based applications but that are considered for marine transportation
and the data from U.S. EPA represents data from the same type of engines, but this data is older and
more extensive. The emissions of NOX from the U.S. EPA’s dataset is higher than the Tier III
requirement, but it will not change the overall conclusions since it is still considerably lower than for
the other fuel alternatives.
There are not data for emissions from GTL in marine engines and the emissions have been assumed
to be similar as if MGO with lower sulphur content was used. A decrease in emissions has been
modelled in an alternative scenario, but this showed to have only a marginal effect on the overall
results.
There are more data that are considered as uncertain. This includes:
•
Emissions for other gases than greenhouse gases from the GTL process as this is from a
combination of two datasets for GTL production
59
•
•
•
•
•
•
Data for natural gas extraction as it is from 1991
No resource use or emissions have been connected to the LNG storage terminal
Emissions from the actual bunkering are not included
Reduction of particle emissions with a scrubber are uncertain
Methane emissions and NMVOCs during the whole supply chain
Pay load for the Ro-Ro vessel fuelled with LNG as the fuel requires larger space for storage.
8.3 Reliability and robustness of the result
Two main results are robust during all the modelled scenarios. Firstly, the global warming potential
of the compared fuels are of the same order of magnitude. Marine transportation with LNG as the fuel
can be attributed to similar or a little lower global warming potential than the other fuels depending
on modelling choices. The use phase is the major contributor to the global warming potential but the
upstream parts of the lifecycle are also of importance.
Secondly, the potential contribution to acidification and eutrophication are significantly lower for the
LNG fuelled alternatives and the fuel alternatives with SCR units, thus for the fuels complying with
the Tier III requirements. The “use” phase is the absolute dominant contributor to acidificating and
eutrophicating emissions. Upstream emissions have much lower contribution.
The effect on photo-chemical ozone formation is relatively uncertain. This is partly due to that it is
more complex to model and that the actual effect is very much dependent on factors other than
release of emissions, and partly due to that the emissions of methane and VOCs are more uncertain
than for example emissions of greenhouse gases. No fuel alternative can be said to be the best when it
comes to this alternative, but it is indicated that HFO without scrubber is the worst alternative.
Particles is just one factor affecting human health, but the only one looked at here. NOX and sulphur
dioxide are example of emissions with health risks, but they have not been presented separately in
this study. The LNG fuelled alternatives show much lower emissions of PM10 during its life cycle. The
use phase is the dominant contributor to particle emissions.
8.4 Comparison with other results
Winebrake and Corbett have developed a model, the Total Energy & Emissions Analysis for Marine
Systems (TEAMS), to calculate the life-cycle performance of six fuel pathways: residual fuel oil,
conventional diesel, low-sulphur diesel, compressed natural gas, Fischer-Tropsch diesel from natural
gas and biodiesel from soybeans. Winebrake et al. (2007) present results from the model in three
case studies of which a case with the container vessel is examined here in more detail. The lowest life
cycle emissions of greenhouse gases are from the residual fuel, closely followed by conventional
diesel while the greatest fuel-cycle emissions are from the Fischer-Tropsch diesel. The fact that the
global warming potential is highest for the Fischer-Tropsch diesel is supported by this study. The
compressed natural gas fuel chain gives rise to slightly more greenhouse gas emissions than
conventional diesel. It is not possible to compare the CNG fuel chain directly with the liquefied fuel
chain studied here since the processes differ. CNG is more space consuming than LNG, but more
energy efficient to produce. In the study by Winebrake et al., the life cycle emissions of NOX are by far
the lowest for CNG and the total life cycle emissions of sulphur dioxide are lowest for CNG, lowsulphur diesel and GTL. This is also in accordance with the results from this study.
The TEAMS model has also been used in another study by Winebrake and Corbett (2008) comparing
residual fuel with marine gas oil and marine diesel oil. They yield similar life cycle CO2 emissions for
all cases but with slightly higher emissions for the distilled alternatives. The result is the opposite in
this study, but the differences between the crude oil based alternative are not large in either case.
There reason that the distilled fuels is connected to more CO2 emissions in the study by Winebrake
and Corbett than in this study is that a larger proportion of the emissions from the refinery process
60
have been allocated to the distilled fuels. The contribution of the different parts of the life cycle is of
similar magnitude in both studies.
The study by Winebrake and Corbett (2008) has an accounting approach (even if this is not specified
in the text) and is based on a typical U.S. refinery, as reported in Wang et al. (2004). The results are
not applicable if the refineries change their production due to coming fuel requirements nor are the
results in this study.
8.5 Prerequisites for the LNG fuel chain’s climate performance
LNG as marine fuel has a global warming potential from a life cycle perspective of the same order of
magnitude as today’s marine fuels. But LNG has the potential to reduce the global warming
contribution from marine transportation, since approximate 25% less carbon is emitted from
combustion of natural gas compared to oil for the same amount of fuel energy. Methane is however a
greenhouse gas with 25 times the global warming potential of CO2 and small leakages of methane can
cancel out the effect (about 2.5% is enough as shown in the cut off analysis in the base scenario). The
amount of methane slip from the engine is uncertain and has a large effect on the LNG-chains global
warming potential. This slip can probably be decreased in the future by improvements in engine
technology.
In order for the LNG alternative to have a lower global warming potential, the engine efficiency
should be at least the same as for the corresponding diesel engines. Leakage of methane during the
life cycle needs to be managed and kept at minimal level. Even small diffusive leakages during the life
cycle can make LNG a worse alternative when it comes to climate change potential. The liquefaction
efficiency can be increased in order to decrease the emissions of greenhouse gases, but this potential
is limited.
8.6 Implications for further research
This study is in many aspects a first screening of the life cycle environmental impact of marine fuels.
Therefore, important and interesting aspects have been left for future studies. It is the intent of the
author that this study shall be complemented with data for those process that are missing and with
more representative data when such data become available. Example of processes for which data
needs to be refined are found in section 8.2.
One important future research area is to investigate the impact from refining of marine fuels based
on crude oil. For example questions like: What impact will the changes in refinery production have?
What is the relation between the sulphur content of crude oil based fuels and their environmental
impact? How will different allocation methods change the result? Are the differences between
refineries bigger than between different methodologies?
Another limitation with this study is that only fossil fuels have been investigated and that the time
perspective has been short. It could be interesting to investigate the potential of biofuels for marine
transportation. One argument for LNG has been that it can act as a bridging fuel towards biogas. What
are the potential of biogas as a marine fuel? And what is the environmental impact? What marine
fuels could be used in 2050 and what are their respective environmental impacts?
61
9
Conclusions
The overall aim of this report is to investigate the environmental performance of maritime fuels from
a life cycle perspective. It is shown that the “use phase”, the combustion of marine fuels, is the major
contributor to the overall environmental impact. The aim is specified into three separate parts:
•
•
•
Increase knowledge and to identify knowledge gaps of the life cycle performance of maritime
transportation.
Set a base-line for further studies.
Identify methodological problems and special requirements when using life cycle assessment
to evaluate maritime transportation.
This study has been a learning process where the knowledge of the maritime transportations life
cycle has increased. However, there are still areas where information is limited or even non existing.
Two important knowledge gasps have been identified: fugitive emissions of methane and volatile
organic compounds during distribution and the performance of gas engines during operating
conditions. In addition, several parameters have been identified as uncertain including: the emissions
from the lean burn four-stroke engine and emissions from a diesel engine fuelled with GTL during
normal operations, NOX reduction potential by SCRs and reduction in particle emissions from
scrubbers.
A methodological problem with LCA in this study is how to deal with old and new technologies in
order to make the data representative for a future technological system. Old technologies are in this
report been assumed to be the same from 2015 to 2020 as they are now, while the new technologies
are modelled. Another problem is how to allocate the impact from refining of crude oil into marine
fuels. This is problematic since there is a wide diversity of refineries and since the choice of allocation
method could change the results.
Two main results are robust during all the modelled scenarios. Firstly, the global warming potential
of the compared fuel are of the same order of magnitude. Marine transportation with LNG as fuel can
be attributed to comparable or a little lower global warming potential than the other fuels depending
on modelling choices. Secondly, the potential contribution to acidification and eutrophication is
significantly lower for the alternatives that fulfil Tier III, i.e. the LNG alternatives and the fuel
alternatives with SCR units.
63
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69
Appendix A – LCI results for marine transportation with HFO
The two alternatives with HFO are described in this appendix. An overview of the system is described
in Figure A-1. The data used for each process in the flowchart are described below.
532.7 kJ
primary energy /tonne km
Extraction and
transport of crude
oil, production of
HFO (ELCD core
database verision
II, 2009a)
a1 = 12.1016 g HFO / tonne km
Unloading,
storage and
loading (Port of
Gothenburg,
2009)
b1 = 12.0996 g HFO / tonne km
0.057 kJ
primary energy /tonne km
Extraction and
transport of
crude oil,
production of
MGO (ELCD
core database
verision II,
2009b)
543.3 kJ
primary energy /tonne km
Extraction and
transport of crude
oil, production of
HFO (ELCD core
database verision
II, 2009a)
a2 = 12.3425 g HFO / tonne km
Unloading,
storage and
loading (Port of
Gothenburg,
2009)
c4 = 0.0012 g MGO
/tonne km
Unloading,
storage and
loading (Port of
Gothenburg,
2009)
Bunkering
b2 = 12.3404 g HFO / tonne km
d4 = 0.0012 g MGO
/tonne km
Bunkering
f1 = 12.0984 g HFO / tonne km
f2 = 12.3404 g HFO / tonne km
Transportation of
1 tonne cargo 1
km in a Ro-Ro
vessel
Transportation of 1
tonne cargo 1 km in
a Ro-Ro vessel with
scrubber
2
1
Figure A-1 Overview of how the two alternatives HFO with and without scrubber have been modelled.
The total primary energy consumption is:
•
•
Fuel alternative 1: 532.7 kJ ≈ 530 kJ
Fuel alternative 2: 543.3 kJ + 0.057 kJ ≈540 kJ
i
Extraction, transportation and refining of crude oil to HFO (A1, A2)
Process type: Cradle-to-gate
Reference flow: 1 g HFO at refinery gate
Data source: ELCD core database version II (2009a)
Representativeness:
•
•
•
Geographical: Average data for EU-15 countries
Technical: “HFO from refinery for combustion in power plants, vessels and combustion
engines... The data set covers all relevant process steps / technologies over the supply chain of
the represented cradle to gate inventory with a good overall data quality” (ELCD-coredatabase-version-II, 2009a)
Time: Average data for the year 2003
Other important information:
•
Allocation made by the data provider: “For the combined crude oil, natural gas and natural
gas liquids production allocation by net calorific value is applied. For all products of the
refinery, allocation by mass and net calorific value is applied. The manufacturing route of every
refinery product is modelled and so the effort of the production of these products is calculated
specifically. Two allocation rules are applied: The raw-material (crude oil) consumption of the
respective stages, which is necessary for the production of a product or an intermediate
product, is allocated by energy (mass of the product * calorific value of the product). In these
way products with high caloric values, e.g. gasoline or gases are assigned to higher raw
material consumption and so higher environmental impacts compared with low caloric value
products (e.g. asphalt, residual oil). The energy consumption (thermal energy, steam,
electricity) of a process, e.g. atmospheric distillation, being required by a product or a
intermediate product, are charged on the product according to the share of the throughput of
the stage (mass allocation). The products, which are more complex to produce and therefore
pass a lot of refinery facilities e.g. gasoline, are assigned with a higher energy consumption
(and so higher emissions) compared with e.g. straight run products” (ELCD-core-databaseversion-II, 2009a)
Assumptions for modelling:
•
•
•
The data set are representative for HFO produced in Europe with a sulphur content of 1%
The lower heating value of HFO is 40.40 MJ/kg
Only selected flows are modelled. Inputs considered is energy carriers and outputs
considered are emissions to air from the substances specified in the goal and scope
definition
Data used: see LCI data set, column three, below
Calculations of primary energy consumption:
•
•
•
0.03861kJ/g HFO + 41.44kJ/g HFO + 0.1059 kJ/g HFO +2.381 kJ/g HFO + 0.04679 kJ/g HFO +
0.003832 MJ/kg HFO + 0.004298 kJ/g HFO + 0.005201 kJ/g HFO =44.02 kJ/g HFO
A1: 44.02 kJ/g HFO × a1 kg HFO/tonne km = 530 kJ/tonne km
A2: 44.02 kJ/g HFO × a2 kg HFO/tonne km = 540 kJ/tonne km
Calculations of emissions:
ii
•
•
A1: 0.002944 kg CH4/kg HFO × a1 kg HFO/tonne cargo km =0.04 g CH4/tonne cargo km
A2: 0.002944 kg CH4/kg HFO × a2 kg HFO/tonne cargo km = 0.04 g CH4/tonne cargo km
LCI data set:
Extraction, transportation and refining of
crude oil to HFO (A1, A2)
Type of flow
Unit process
[g (kJa)/g HFO]
Inputs:
brown coal; 11 MJ/kg
resource
crude oil; 42.3 MJ/kg
resource
hard coal; 26.3 MJ/kg
resource
natural gas; 44.1 MJ/kg
resource
primary energy from hydro power
resource
primary energy from solar energy
resource
primary energy from wind power
resource
Outputs:
HFO
reference flow
ammonia
emissions to air
CO2
emissions to air
CO
emissions to air
CH4
emissions to air
nitrogen dioxide
emissions to air
nitrogen monoxide
emissions to air
nitrous oxide
emissions to air
NMVOC
emissions to air
emissions to air
•
acetone
emissions to air
•
benzene
emissions to air
•
ethane
emissions to air
•
ethanol
emissions to air
•
ethylene
emissions to air
•
formaldehyde
emissions to air
•
methanol
emissions to air
•
n-butane
emissions to air
•
propane
emissions to air
•
toluene
particles (PM10)
emissions to air
sulphur dioxide
emissions to air
a The inputs are expressed in kJ instead of g per g fuel and per tonne km.
iii
A1
[g (kJa)/tonne km]
A2
[g (kJa)/tonne km]
3,861E-02
4,144E+01
1,059E-01
2,381E+00
4,679E-02
3,832E-03
4,298E-03
4,672E-01
5,015E+02
1,281E+00
2,882E+01
5,662E-01
4,637E-02
5,201E-02
4,765E-01
5,114E+02
1,307E+00
2,939E+01
5,775E-01
4,729E-02
5,304E-02
1,000E+00
2,999E-06
2,693E-01
3,687E-04
2,944E-03
7,739E-04
6,616E-12
6,223E-06
2,373E-04
6,598E-08
1,213E-06
1,600E-04
2,245E-08
1,763E-08
2,275E-07
2,073E-08
5,999E-05
2,892E-04
5,696E-08
1,798E-05
1,560E-03
12,1016
3,629E-05
3,259E+00
4,462E-03
3,563E-02
9,365E-03
8,007E-11
7,531E-05
2,871E-03
7,985E-07
1,467E-05
1,936E-03
2,716E-07
2,133E-07
2,754E-06
2,509E-07
7,260E-04
3,500E-03
6,893E-07
2,176E-04
1,888E-02
12,3425
3,701E-05
3,324E+00
4,551E-03
3,634E-02
9,552E-03
8,166E-11
7,681E-05
2,928E-03
8,144E-07
1,497E-05
1,975E-03
2,770E-07
2,176E-07
2,808E-06
2,559E-07
7,405E-04
3,570E-03
7,030E-07
2,219E-04
1,925E-02
Extraction, transportation and refining of crude oil to MGO (C2)
Process type: Cradle-to-gate
Reference flow: 1 g MGO at refinery gate
Data source: ELCD core database version II (2009b)
Representativeness:
•
•
•
Geographical: Average data for EU-15 countries
Technical: “The data set describes a mass-weighted average refinery for the respective country
/ region.” (ELCD-core-database-version-II, 2009b)
Time: Average data for the year 2003
Other important information:
•
Allocation made by the data provider: “For the combined crude oil, natural gas and natural
gas liquids production allocation by net calorific value is applied. For all products of the
refinery, allocation by mass and net calorific value is applied. The manufacturing route of every
refinery product is modelled and so the effort of the production of these products is calculated
specifically. Two allocation rules are applied: The raw-material (crude oil) consumption of the
respective stages, which is necessary for the production of a product or an intermediate
product, is allocated by energy (mass of the product * calorific value of the product). In these
way products with high caloric values, e.g. gasoline or gases are assigned to higher raw
material consumption and so higher environmental impacts compared with low caloric value
products (e.g. asphalt, residual oil). The energy consumption (thermal energy, steam,
electricity) of a process, e.g. atmospheric distillation, being required by a product or a
intermediate product, are charged on the product according to the share of the throughput of
the stage (mass allocation). The products, which are more complex to produce and therefore
pass a lot of refinery facilities e.g. gasoline, are assigned with a higher energy consumption
(and so higher emissions) compared with e.g. straight run products” (ELCD-core-databaseversion-II, 2009b).
Assumptions for modelling:
•
•
•
The data set are representative for MGO produced in Europe with a sulphur content of 0.1%
The lower heating value of MGO is 43 MJ/kg
Only selected flows are modelled. Inputs considered is energy carriers and outputs
considered are emissions to air from the substances selected in the goal and scope definition
Data used: see LCI data set, column three, below
Calculations for primary energy consumption:
•
•
0.04746 kJ/g MGO + 47.13 kJ/g MGO + 0.1285 kJ/g MGO +2.695 kJ/kg MGO + 0.05568 kJ/g
MGO + 0.004795 kJ/g MGO + 0.005421 kJ/g MGO = 50.06 kJ/g MGO
C2: 50.06 kJ/g MGO KJ/kg MGO × c2 g MGO/tonne km = 0.06 kJ/tonne km
Calculations for emissions:
•
C2: 0.3013 g CO2 /g MGO × c2 = 0.0004 g CO2 / tonne cargo km
iv
LCI data set:
Extraction, transportation and refining of crude oil to MGO (C2)
Type of flow
Inputs:
brown coal; 11 MJ/kg
resource
crude oil; 42.3 MJ/kg
resource
hard coal; 26.3 MJ/kg
resource
natural gas; 44.1 MJ/kg
resource
primary energy from hydro power
resource
primary energy from solar energy
resource
primary energy from wind power
resource
Outputs:
MGO
reference flow
ammonia
emissions to air
ammonia
emissions to air
CO2
emissions to air
CO
emissions to air
CH4
emissions to air
nitrogen dioxide
emissions to air
nitrogen monoxide
emissions to air
nitrous oxide
emissions to air
NMVOC
emissions to air
emissions to air
•
acetone
emissions to air
•
benzene
emissions to air
•
ethane
emissions to air
•
ethanol
emissions to air
•
ethylene
emissions to air
•
formaldehyde
emissions to air
•
methanol
emissions to air
•
n-butane
emissions to air
•
propane
emissions to air
•
toluene
particles (PM10)
emissions to air
a The inputs are expressed in kJ instead of kg per kg fuel and tonne km.
v
Unit process
[g (kJa)/g MGO]
C2
[g (kJa)/tonne km]
4,746E-02
4,713E+01
1,285E-01
2,695E+00
5,568E-02
4,795E-03
5,421E-03
5,375E-05
5,337E-02
1,456E-04
3,052E-03
6,305E-05
5,430E-06
6,139E-06
1,000E+00
3,265E-06
3,013E-01
4,146E-04
3,348E-03
8,756E-04
7,529E-12
6,958E-06
2,601E-04
7,541E-08
1,319E-06
1,818E-04
2,647E-08
2,005E-08
2,662E-07
2,434E-08
6,821E-05
3,289E-04
6,685E-08
1,965E-05
1,752E-03
0,0011
3,698E-09
3,413E-04
4,695E-07
3,791E-06
9,916E-07
8,527E-15
7,880E-09
2,945E-07
8,541E-11
1,494E-09
2,059E-07
2,997E-11
2,271E-11
3,014E-10
2,756E-11
7,724E-08
3,724E-07
7,571E-11
2,226E-08
1,984E-06
Storage and loading of oil products (B1, B2, D2)
Process type: Unit process
Reference flow: 1 kg of oil product
Data source: Port of Gothenburg (2009)
Representativeness:
•
•
•
Geographical: Oil harbour, Gothenburg
Technical: The oil harbour in Gothenburg has a vapour recovery unit used during loading of
oil products.
Time: Average data for the year 2008
Assumptions for modelling:
•
•
•
All fuels handled at the port emit the same proportions of volatile organic compounds
Emission of volatile organic compound are assumed to be NMVOC
The energy consumption at the oil harbour and the emissions to water is not included
Data used:
•
•
Products loaded to vessels:
Emissions of NMVOC:
Calculations of primary energy consumption:
•
•
•
•
•
•
•
12996468 tonne
2220.620 tonne
2220.620 tonne evaporated fuel/ 12996468 tonne loaded fuel = 0.0001709 g evaporated fuel
/ g loaded fuel
0.0001709 g evaporated fuel / g loaded fuel × b1 = 0.002057 g evaporated fuel / tonne cargo
km
0.0001709 g evaporated fuel / g loaded fuel × b2 = 0.002098 g evaporated fuel / tonne cargo
km
0.0001709 g evaporated fuel / g loaded fuel × d2 = 0.0000002 g evaporated fuel / tonne
cargo km
a1 = b1 + 0.002057 g fuel / tonne cargo = 12.1016 g fuel /tonne cargo km
a2 = b2 + 0.002098 g fuel / tonne cargo = 12.3425 g fuel /tonne cargo km
c2 = d2 + 0.0000002 g fuel / tonne cargo = 0.0011 g fuel /tonne cargo km
Calculations of emissions:
•
•
•
2220.620 tonne evaporated fuel/ 12996468 tonne loaded fuel = 0.0001709 g NMVOC / g
loaded fuel
B1: 0.0001709 g NMVOC / g loaded fuel × b1 = 0.002 g NMVOC /tonne cargo km
B2 + D2: 0.0001709 g NMVOC / g loaded fuel × (b2 + d2) = 0.002g NMVOC /tonne cargo km
vi
LCI data set:
Storage and loading of oil products (B1, B2, D2)
Inputs:
fuel
Outputs:
fuel
NMVOC
Type of flow
Unit process
[kg /kg fuel ]
B1
[g /tonne km]
B2+D2
[g /tonne km]
product
1,000E+03
12,1016
12,3436
reference flow
emissions to air
1,000E+03
1,709E-01
12,0996
2,067E-03
12,3415
2,109E-03
vii
Transportation and bunkering (F1)
Process type: Unit process
Reference flow: 1 kg of fuel delivered to the Ro-Ro vessel
Data sources: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the bunker ship
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the bunker ship is estimated to 0.0452 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The bunker ship is assumed to have medium speed engines
The bunker ship is assumed to be fuelled by HFO with a sulphur content of 1 %.
The distribution of HFO from the port to the Ro-Ro vessel with the bunker vessel is assumed
to be 10 km.
The emissions during the actual bunkering are not included
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
213 g/kWh
677g/kWh
14 g/kWh
0.496 g/kWh
0.004 g/kWh
0.8 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy consumption:
•
•
•
213 g fuel/kWh ×0.0452 kWh/tonne HFO/ km × 10 km = 96.276 g fuel/tonne HFO
96.276 g fuel/tonne HFO × 12.0984 g HFO /tonne cargo km = 0.001165 g fuel /tonne cargo
km
b1= f1 + 0.001165 g fuel /tonne cargo km = 12.0996 g fuel /tonne cargo km
viii
Calculations of emissions of sulphur dioxide:
•
•
•
•
1% sulphur × 1 tonne HFO = 10 000 g sulphur /tonne fuel
10 000 g sulphur /tonne fuel × 2 g sulphur dioxide / g sulphur = 20 000 g sulphur
dioxide/tonne fuel
20 000 g sulphur dioxide/tonne fuel × 96.276 g fuel/tonne HFO = 1.92552 g sulphur dioxide
/tonne HFO
F1: 1.92552 g sulphur dioxide /tonne HFO × f1 = 2.3E-05 g sulphur /tonne cargo km dioxide
Calculations of the other emissions:
•
•
677g CO2 /kWh × 0.0452 kWh /tonne HFO/ km × 10 km = 306.004 g CO2 /tonne HFO
F1: 306.004 g CO2 /tonne HFO × f1 = 0.0037 g CO2 /tonne cargo km
LCI data set:
Transportation and bunkering (F1)
Inputs:
HFO
HFO
Outputs:
HFO
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a tonne/ tonne HFO
Type of flow
Unit process
[g/tonne HFO]
F1
[g/tonne km]
fuel
product
9,628E+01
1,000E+00a
0,0012
12,0984
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00a
1,356E-03
3,060E+02
4,972E-01
1,808E-03
6,328E+00
1,401E-02
2,246E-01
3,616E-01
1,926E+00
12,0984
1,641E-08
3,702E-03
6,015E-06
2,187E-08
7,656E-05
1,695E-07
2,718E-06
4,375E-06
2,330E-05
ix
Transportation and bunkering (F2)
Process type: Unit process
Reference flow: 1 kg of fuel delivered to the Ro-Ro vessel
Data sources: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the bunker ship
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the bunker ship is estimated to 0.0452 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The bunker ship is assumed to have medium speed engines
The bunker ship is assumed to be fuelled by MGO with a sulphur content of 0.1%.
The distribution of MGO from the port to the Ro-Ro vessel with the bunker vessel is assumed
to be 10 km.
The emissions during the actual bunkering are not included
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy consumption:
•
•
203 g fuel/kWh × 0.0452 kWh/tonne HFO/ km × 10 km = 91.756 g fuel/tonne HFO
d2 = 91.756 g fuel/tonne HFO × b2 = 0.001133 g fuel /tonne cargo km
x
Calculations of emissions of sulphur dioxide:
•
•
•
•
0.1 % sulphur × 1 tonne MGO = 1 000 g sulphur /tonne MGO
1 000 g sulphur /tonne MGO × 2 g sulphur dioxide / g sulphur = 2 000 g sulphur
dioxide/tonne MGO
2000 g sulphur dioxide/tonne fuel × 91.756 g fuel/tonne HFO= 1.835 g sulphur dioxide
/tonne HFO
F2: 1.835 g sulphur dioxide /tonne HFO × f2 = 2 E-06 g sulphur /tonne cargo km dioxide
Calculations of the other emissions:
•
•
645g CO2 /kWh × 0.0452 kWh /tonne HFO/ km × 10 km = 291.54 g CO2 /tonne HFO
F2: 291.54 g CO2 /tonne HFO × f2 = 0.0036 g CO2 /tonne cargo km
LCI data set:
Transportation and bunkering (F2)
Inputs:
MGO
HFO
Outputs:
HFO
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a tonne/ tonne HFO
Type of flow
Unit process
[g/tonne HFO]
F2
[g/tonne km]
fuel
product
9,176E+01
1,000E+00a
0,0011
12,3404
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00a
1,356E-03
2,915E+02
4,972E-01
1,808E-03
5,966E+00
1,401E-02
2,246E-01
1,356E-01
1,835E-01
12,3404
1,673E-08
3,598E-03
6,136E-06
2,231E-08
7,363E-05
1,729E-07
2,772E-06
1,673E-06
2,265E-06
xi
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U1)
Process type: Unit process
Reference flow: One tonne cargo transported one km
Data source: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0568 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The Ro-Ro vessel is assumed to have medium speed engines
The HFO has a sulphur content of 1 %.
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
213 g/kWh
677g/kWh
14 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.8 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy consumption:
•
f1 = 213 g fuel /kWh × 0.0568 kWh /tonne cargo km =12.0984 g fuel/tonne cargo km
Calculations of emissions of sulphur dioxide:
•
•
1 % sulphur × 1 tonne HFO = 10 000 g sulphur /tonne HFO
10 000 g sulphur/tonne HFO × 2 g sulphur dioxide / g sulphur = 20 000 g sulphur
dioxide/tonne HFO
xii
•
U2: 20 000 g sulphur dioxide/tonne HFO × 12.0984 g fuel/tonne cargo km = 0.24 g sulphur
dioxide / tonne cargo km
Calculations of other emissions:
•
U1: 677g CO2 /kWh × 0.0568 kWh /tonne cargo km = 38 g CO2 /tonne cargo km
LCI data set:
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U1)
Inputs:
HFO
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a Propulsion energy is expressed in kWh instead of g.
Type of flow
fuel
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
xiii
U1
[g (kWha)/tonne km]
12,0984
5,680E-02
1,704E-04
3,845E+01
6,248E-02
2,272E-04
7,952E-01
1,761E-03
2,817E-02
4,544E-02
2,420E-01
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel with an open
scrubber (U2)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data sources:
•
•
•
Lövblad and Fridell (2006)
Linde (2009)
The same as for the process above: Transportation of 1 tonne of cargo 1 km with a Ro-Ro
vessel (U1).
Representativeness:
•
•
•
Geographical: Technical: Medium speed diesel engines, state-of-the-art scrubber
Time: The data used for emission reduction of particles were published 2006 and in 2009
for scrubber fuel consumption
Other important information:
Assumptions for modelling:
•
•
•
•
Assumptions for modelling: The energy consumption for the Ro-Ro vessel is estimated to
0.0568 kWh per transported tonne and km see Appendix I Specifications of the modelled
vessels for more details.
The Ro-Ro vessel is assumed to have medium speed engines
The HFO has a sulphur content of 1 %.
The scrubber can reduce the emissions of sulphur dioxide to the extent that the emissions
are the same as if the sulphur content would have been 0.1%. This represents a reduction of
sulphur dioxide emissions of approximately 90 %.
Data used:
•
•
The reduction of particles are 25 % with a scrubber (Lövblad and Fridell, 2006).
The energy consumption of the scrubber is between 0.2-1.4 % of the engine power
dependent on solution (Linde, 2009). 2% increase of energy consumption is assumed.
Calculations of primary energy consumption:
•
•
213 g fuel /kWh × 1.02 = 217.26 g fuel/kWh
f2 = 217.26 g fuel /kWh × 0.0568 kWh/tonne cargo km =12.3404 g fuel/tonne cargo km
Calculations of emissions of sulphur dioxide:
•
•
•
10 000 g sulphur/tonne HFO × 2 g sulphur dioxide / g sulphur = 20 000 g sulphur
dioxide/tonne HFO
20 000 g sulphur dioxide/tonne HFO × 12.3404 g fuel/tonne cargo km = 0.2468 g sulphur
dioxide/ tonne cargo km
U2: 0.2468 g sulphur dioxide/ tonne cargo km × 0.10 = 0.025 g sulphur dioxide/ tonne cargo
km
xiv
Calculations of other emissions:
•
•
U2: 677g CO2 /kWh × 1.02 × 0.0568 kWh /tonne cargo km = 39 g CO2 /tonne cargo km
U2: 0.8 PM10 g/kWh × 1.02 × 0.0568 kWh /tonne cargo km× 0.75 = 0.35 g PM10/tonne
cargo km
LCI data set:
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel with an open
scrubber (U2)
Inputs:
HFO
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a Propulsion energy is expressed in kWh instead of g.
xv
Type of flow
fuel
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
U2
[g (kWha)/tonne km]
12,3404
5,680E-02
1,738E-04
3,922E+01
6,373E-02
2,317E-04
8,111E-01
1,796E-03
2,874E-02
3,476E-02
2,468E-02
Appendix B – LCI results for marine transportation with MGO
The two alternatives with MGO (MGO) are described in this appendix. An overview of the system is
described in Figure B-1. All processes for the two alternatives are the same except for the last one
since no extra energy consumption has been related to the SCR unit. The data used for each process in
the flowchart are described below.
577.4 kJ
primary energy /tonne km
577.4 kJ
primary energy /tonne km
Extraction and
transport of
crude oil,
production of
MGO (A3)
Extraction and
transport of
crude oil,
production of
MGO (A4)
a3 = 11.5334 g MGO / tonne km
a4 = 11.5334 g MGO / tonne km
Unloading,
storage and
loading (B3)
Unloading,
storage and
loading (B4)
b3 = 11.5315 g MGO / tonne km
Bunkering (F3)
20.45 kJ
energy /tonne km
Production and
transportation of
urea (P4)
Bunkering (F4)
f4 = 11.5304 g MGO / tonne km
f3 = 11.5304 g MGO / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U3)
b4 = 11.5315 g MGO / tonne km
0.852 g urea
/ tonne km
3
Transportation of 1
tonne cargo 1 km
in a RO-Ro vessel
with SCR (U4)
4
Figure B-1 Overview of how the two MGO alternatives with and without SCR have been modelled.
The total primary energy consumption is:
•
•
Fuel alternative 3: 577.4 kJ ≈ 580 kJ
Fuel alternative 4: 577.4 kJ + 20.45 kJ ≈ 600 kJ
xvii
Extraction, transportation and refining of crude oil to MGO (A3, A4)
Process type: Cradle-to-gate
Reference flow: 1 g of MGO at refinery gate
Data source: ELCD core database version II (2009b)
Representativeness:
•
•
•
Geographical: Average data for EU-15 countries
Technical: “The data set describes a mass-weighted average refinery for the respective country
/ region.” (ELCD-core-database-version-II, 2009a)
Time: Average data for the year 2003
Other important information:
•
Allocation made by the data provider: “For the combined crude oil, natural gas and natural
gas liquids production allocation by net calorific value is applied. For all products of the
refinery, allocation by mass and net calorific value is applied. The manufacturing route of every
refinery product is modelled and so the effort of the production of these products is calculated
specifically. Two allocation rules are applied: The raw-material (crude oil) consumption of the
respective stages, which is necessary for the production of a product or an intermediate
product, is allocated by energy (mass of the product * calorific value of the product). In these
way products with high caloric values, e.g. gasoline or gases are assigned to higher raw
material consumption and so higher environmental impacts compared with low caloric value
products (e.g. asphalt, residual oil). The energy consumption (thermal energy, steam,
electricity) of a process, e.g. atmospheric distillation, being required by a product or a
intermediate product, are charged on the product according to the share of the throughput of
the stage (mass allocation). The products, which are more complex to produce and therefore
pass a lot of refinery facilities e.g. gasoline, are assigned with a higher energy consumption
(and so higher emissions) compared with e.g. straight run products” (ELCD-core-databaseversion-II, 2009b).
Assumptions for modelling:
•
•
•
The data set are representative for MGO produced in Europe with a sulphur content of 0.1%
The lower heating value of MGO is 43 MJ/kg
Only selected flows are modelled. Inputs considered is energy carriers and outputs
considered are emissions to air from the substances selected in the goal and scope definition
Data used: see LCI data set, column three, below
Calculations for primary energy consumption:
•
•
0.04746 kJ/g MGO + 47.13 KJ/g MGO + 0.1285 kJ/g MGO +2.695 kJ/kg MGO + 0.05568 kJ/g
MGO + 0.004795 kJ/g MGO + 0.005421 kJ/g MGO = 50.06 kJ/g MGO
A3, A4: 50.06 kJ/g MGO × a3 = 577.4 kJ/tonne km
Calculations for emissions:
•
A3, A4: 0.3013 kg CO2 /kg MGO × a3 = 3.5 g CO2 / tonne cargo km
xviii
LCI data set:
Extraction, transportation and refining of crude oil to
Type of flow
MGO (A3, A4)
Inputs:
brown coal; 11 MJ/kg
resource
crude oil; 42.3 MJ/kg
resource
hard coal; 26.3 MJ/kg
resource
natural gas; 44.1 MJ/kg
resource
primary energy from hydro power
resource
primary energy from solar energy
resource
primary energy from wind power
resource
Outputs:
MGO
reference flow
ammonia
emissions to air
CO2
emissions to air
CO
emissions to air
CH4
emissions to air
nitrogen dioxide
emissions to air
nitrogen monoxide
emissions to air
nitrous oxide
emissions to air
NMVOC
emissions to air
emissions to air
•
acetone
emissions to air
•
benzene
emissions to air
•
ethane
emissions to air
•
ethanol
emissions to air
•
ethylene
emissions to air
•
formaldehyde
emissions to air
•
methanol
emissions to air
•
n-butane
emissions to air
•
propane
emissions to air
•
toluene
particles (PM10)
emissions to air
sulphur dioxide
emissions to air
a The inputs are expressed in kJ instead of g per g fuel and tonne km.
xix
Unit process
[g (kJa)/g MGO]
A3, A4
[g (kJa)//tonne km]
4,746E-02
4,713E+01
1,285E-01
2,695E+00
5,568E-02
4,795E-03
5,421E-03
5,474E-01
5,435E+02
1,483E+00
3,108E+01
6,421E-01
5,530E-02
6,252E-02
1,000E+00
3,265E-06
3,013E-01
4,146E-04
3,348E-03
8,756E-04
7,529E-12
6,958E-06
2,601E-04
7,541E-08
1,319E-06
1,818E-04
2,647E-08
2,005E-08
2,662E-07
2,434E-08
6,821E-05
3,289E-04
6,685E-08
1,965E-05
1,752E-03
11,5334
3,766E-05
3,475E+00
4,782E-03
3,861E-02
1,010E-02
8,684E-11
8,025E-05
2,999E-03
8,698E-07
1,521E-05
2,097E-03
3,052E-07
2,312E-07
3,070E-06
2,807E-07
7,867E-04
3,793E-03
7,710E-07
2,267E-04
2,021E-02
Storage and loading of oil products (B3, B4)
Process type: Unit process
Reference flow: 1 kg of oil product
Data source: Port of Gothenburg (2009)
Representativeness:
•
•
•
Geographical: Oil harbour, Gothenburg
Technical: The oil harbour in Gothenburg has a vapour recovery unit used during loading of
oil products.
Time: Average data for the year 2008
Assumptions for modelling:
•
•
•
All fuels handled at the port emit the same proportions of volatile organic compounds
Emission of volatile organic compound are assumed to be NMVOC
The energy consumption at the oil harbour and the emissions to water is not included
Data used:
•
•
Products loaded to vessels:
Emissions of NMVOC:
Calculations of energy consumption:
•
•
•
12996468 tonne
2220.620 tonne
2220.620 tonne evaporated fuel/ 12996468 tonne loaded fuel = 0.0001709 g evaporated fuel
/ g loaded fuel
0.0001709 g evaporated fuel / g loaded fuel × b3 = 0.00197 g evaporated fuel / tonne cargo
km
a3 = a4 = b3 + 0.00197 g HFO/ tonne cargo km =11.5334
Calculations for emissions:
•
•
2220.620 tonne NMVOC/ 12996468 tonne loaded products = 0.1709 kg NMVOC / tonne
loaded product
B3, B4: 0.1709 g NMVOC / kg loaded product × b3 = 0.0020 g NMVOC /tonne cargo km
LCI data set:
Storage and loading of oil products (B3, B4)
Inputs:
Fuel
Outputs:
Fuel
NMVOC
Type of flow
Unit process
[kg /kg fuel]
B3, B4
[g /tonne km]
product
1,000E+03
11,5334
reference flow
emissions to air
1,000E+03
1,709E-01
11,5315
1,970E-03
xx
Transportation and bunkering (F3, F4)
Process type: Unit process
Reference flow: 1 kg of fuel delivered to the Ro-Ro vessel
Data sources: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the bunker ship
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the bunker ship is estimated 0.0452 kWh per transported tonne
and km see Appendix I Specifications of the modelled vessels for more details.
The bunker ship is assumed to have medium speed engines
The bunker ship is assumed to be fuelled by MGO with a sulphur content of 0.1%.
The distribution of MGO from the port to the Ro-Ro vessel with the bunker vessel is assumed
to be 10 km.
The emissions during the actual bunkering are not included
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy consumption:
•
•
•
203 g fuel/kWh ×0.0452 kWh/tonne MGO/ km × 10 km =91.756 g fuel/tonne MGO
91.756 g fuel/tonne MGO × f3 =0.0011 g fuel /tonne cargo km
b3 =b4= f3 + 0.0011 g fuel /tonne cargo km = 12.5315 g fuel /tonne cargo km
xxi
Calculations of emissions of sulphur dioxide:
•
•
•
•
0.1 % sulphur × 1 tonne MGO = 1 000 g sulphur /tonne MGO
1 000 g sulphur /tonne MGO × 2 g sulphur dioxide / g sulphur = 2 000 g sulphur
dioxide/tonne MGO
2000 g sulphur dioxide/tonne fuel × 91.756 g fuel/tonne MGO= 1.835 g sulphur dioxide
/tonne MGO
F3, F4: 1.835 g sulphur dioxide /tonne MGO × f3 = 2.116 E-06 g sulphur /tonne cargo km
dioxide
Calculations of the other emissions:
•
•
645g CO2 /kWh × 0.0452 kWh /tonne MGO/ km × 10 km = 291.54 g CO2 /tonne MGO
F3, F4: 291.54 g CO2 /tonne MGO × f3= 0.0034 g CO2 /tonne cargo km
LCI data set:
Transportation and bunkering (F3, F4)
Inputs:
MGO
MGO
Outputs:
HFO
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
Type of flow
Unit process
[g/tonne MGO]
F3, F4
[g/tonne km]
product
fuel
9,176E+01
1,000E+00
0,0011
11,5304
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00
1,356E-03
2,915E+02
4,972E-01
1,808E-03
5,966E+00
1,401E-02
2,246E-01
1,356E-01
1,835E-01
11,5304
1,564E-08
3,362E-03
5,733E-06
2,085E-08
6,879E-05
1,616E-07
2,590E-06
1,564E-06
2,116E-06
xxii
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U3)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data source: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0568 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The Ro-Ro vessel is assumed to have medium speed engines
MGO with a sulphur content of 0.1% is used.
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy use:
•
f3 = 203 g fuel /kWh × 0.0568 kWh /tonne cargo km = 11.5304 g fuel/tonne cargo km
Calculations of emissions of sulphur dioxide:
•
•
1 000 g sulphur/tonne MGO × 2 g sulphur dioxide / g sulphur = 2 000 g sulphur
dioxide/tonne MGO
U3: 2 000 g sulphur dioxide/tonne HFO × f3 = 0.023 g sulphur dioxide / tonne cargo km
xxiii
Calculations of other emissions:
•
U3: 645 g CO2 /kWh × 0.0568 kWh /tonne cargo km = 37g CO2 /tonne cargo km
LCI data set:
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U3)
Inputs:
MGO
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a Propulsion energy is expressed in kWh instead of g.
Type of flow
fuel
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
xxiv
U3
[g (kWha)/tonne km]
11,5304
0,0568
1,704E-04
3,664E+01
6,248E-02
2,272E-04
7,498E-01
1,761E-03
2,823E-02
1,704E-02
2,306E-02
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U4)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data sources:
•
•
Cooper (2001) for emissions of ammonia from a medium speed diesel engine with SCR
Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission except of
ammonia and fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines with SCR
Time: The data used were published 2008, 2004 and 2001
Other important information:
Assumptions for modelling:
•
•
•
As process above: Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U3)
Only emissions of ammonia and NOX are affected by the SCR unit
The SCR unit is assumed to reduce the emissions of NOX with 85 %. It is possible to reach
95% reduction according to Lövblad and Fridell (2006), but 85% has been used here as this
will be enough to fulfil Tier III
Data used:
From Cooper (2001)
•
Emissions of ammonia:
0.025 g /kWh
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of nitrous oxide:
0.031 g/kWh
Data from NTM (2008)
•
•
•
•
•
•
•
Data from Cooper and Gustafson (2004)
•
Calculations of energy consumption:
•
f4 = 203 g fuel/kWh × 0.0568 kWh/tonne cargo km = 11.5304 g fuel/tonne cargo km
Calculations of emissions:
•
U4: 13.2 g NOX/kWh × 0.15 × 0.0568 kWh /tonne cargo km = 0.1125 g NOX /tonne cargo km
xxv
•
U4: 0.025 g ammonia/kWh × 0.0568 kWh/tonne cargo km = 0.00142 g ammonia/tonne
cargo km
LCI data set:
Transportation of 1 tonne of cargo 1 km with a Ro-Ro vessel (U4)
Inputs:
MGO
urea
Outputs:
propulsion energya
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a Propulsion energy is expressed in kWh / tonne km instead of g /tonne km.
xxvi
Type of flow
fuel
product
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
U4
[g /tonne km]
11,5304
0,8520
0,0568
1,420E-03
3,664E+01
6,248E-02
2,272E-04
1,125E-01
1,761E-03
2,698E-02
1,704E-02
2,306E-02
Production and transportation of urea (P4)
Process type: Cradle-to-gate
Reference flow: 1 g urea delivered to the Ro-Ro vessel
Data source: Andersson and Winnes (2011)
Representativeness:
•
•
•
Geographical: Production of urea in Europe
Technical: Demineralised water is used for preparation of the urea solution.
Time: The study uses data for urea production published in 1999 to 2008.
Other important information:
•
•
Andersson and Winnes (2011) has assumed that the average transport distance for urea is
1000 km and 500 km each direction and empty return trip with truck.
The production of the fuel used to transport urea is not included
Assumptions for modelling:
•
15 g/kWh urea is assumed to be used by the Ro-Ro vessel
Data used:
From Andersson and Winnes (2011)
The production of 15 g of urea results in the following resource use and emissions:
•
•
•
•
•
•
•
•
•
Fuel:
Emissions of ammonia:
Emissions of CO2:
Emissions of CO:
Emissions of CH4:
Emissions of NOX:
Emissions of NMVOC:
Emissions of particles:
Emissions of sulphur dioxide:
Calculations of primary energy use:
•
0.36 MJ
0.24 g
28.169 g
0.011 g
0.03 g
0.058 g
0.024 g
0.01 g
0.066 g
0.36 MJ/kWh × 0.0568 kWh /tonne cargo km =0.020 MJ/ tonne cargo km = 20 kJ/ tonne
cargo km
Calculations of emissions:
•
P4: 0.24 g ammonia/kWh × 0.0568 kWh /tonne cargo km = 0.14 g ammonia / tonne km
xxvii
LCI data set:
Production and transportation of urea (P4)
Type of flow
Inputs:
Fuela
Outputs:
urea
ammonia
CO2
CO
CH4
NOX
NMVOC
particles (PM10)
sulphur dioxide
a Primary energy is expressed in kJ / tonne km instead of g / tonne km.
xxviii
P4
[g /tonne km]
fuel
2,045E+01
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
0,8520
1,363E-03
1,600E+00
6,248E-04
1,704E-03
3,294E-03
1,363E-03
5,680E-04
3,749E-03
Appendix C – LCI results for marine transportation with LNG
The two alternatives with LNG are described in this appendix. The system is illustrated in Figure C-1.
The two alternatives represent different distribution routes for LNG. The data used for each process
in the flowchart are described below.
The total primary energy consumption is:
•
•
Fuel alternative 5a: 7.433 kJ + 11.8567 g × 48 kJ/g = 576.5546 ≈ 580 kJ
Fuel alternative 5b: 7.903 kJ + 12.6068 g × 48 kJ/g = 613.0294 ≈ 610 kJ
xxix
7.433 kJ
primary energy / tonne km
7.903 kJ
primary energy / tonne km
Extraction and
transport of
natural gas (A5a)
Extraction and
transport of
natural gas (A5b)
a5a = 11.8567 g
natural gas / tonne km
a5b = 12.6068 g
natural gas / tonne km
Liquefaction (B5a)
Liquefaction (B5b)
b5a = 10.9291 g LNG / tonne km
b5b = 11.6206 g LNG / tonne km
Transportation
350 NM with
product tanker
(C5a)
Transportation
6500 NM with
LNG Carrier (C5b)
c5b = 10.9792 g LNG / tonne km
Unloading,
storage and
loading
c5b = 10.9792 g LNG / tonne km
c5a = 10.8127 g LNG / tonne km
Transportation
500 NM with Coral
Methane (D5b)
d5b = 10.8127 g LNG / tonne km
Unloading,
storage and
loading
Unloading,
storage and
loading
c5a = 10.8127 g LNG / tonne km
d5b = 10.8127 g LNG / tonne km
Bunkering (F5a)
Bunkering (F5b)
f5a = 10.8110 g LNG / tonne km
f5b = 10.8110 g LNG / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5a)
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5b)
5a
5b
Figure C-1 Overview of the energy consumption for the two alternatives with LNG as fuel. The uncoloured
processes these have not been included.
xxx
Extraction and transportation of natural gas (A5a, A5b)
Process type: Cradle-to-gate
Reference flow: 1 tonne natural gas delivered to the liquefaction plant
Data source: SPINE LCI dataset (2008)
Representativeness:
•
•
•
Geographical: Norway
Technical: Technology for oil and gas extraction used in Norway. Comment from the data
provider: “Extraction of oil in Norway is internationally considered to be one of the most
environmental friendly, because the oil companies have to follow strict environmental laws and
therefore use the most advanced technology and equipment available on the market” (SPINELCI-dataset, 2008)
Time: Average data from 1991
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
The data is still representative even if it is from 1991
Allocation between crude oil and natural gas is based on energy content (lower heating
valued times mass)
The lower heating value of crude oil is 42.3 MJ/kg and the lower heating value of natural gas
is 48 MJ/kg
Lower heating value of diesel is 43 MJ/kg
Lower heating value of fuel gas is 36.8 MJ/m3
Lower heating value of jet fuel is 44.1 MJ/kg
VOC is assumed to be NMVOC in the SPINE LCI dataset (2008)
Data used:
From SPINE LCI dataset (2008)
Inflows
Diesel
Fuel gas
Jet fuel
Outflows
3663
11000008
123
tonnes
m3
tonnes
CO2
CH4
nitrous oxide
CO
NOX
61248
86
136
83
388
tonnes
tonnes
tonnes
tonnes
tonnes
sulphur dioxide
NMVOC
Crude oil
Gas
896
817
786000
213000
tonnes
tonnes
tonnes
tonnes
xxxi
Calculation of allocation factor:
•
213 000 tonnes × 48 000 MJ/tonne/(213 000 tonnes × 48 000 MJ/tonne + 786000 × 42 300
MJ/tonne) = 0.23518695
Calculations of primary energy use:
•
•
•
(3663 tonne × 43 000 MJ/tonne +11000008 m3 × 36.8 MJ/m3 +123 tonne × 44 100 MJ/tonne)
/ 213 000 tonnes natural gas × 0.23518695 = 626.871 MJ / tonnes natural gas
5a: 626.871 MJ / tonnes natural gas × a5a = 7.4 kJ /tonne km
5b: 626.871 MJ / tonnes natural gas × a5b = 7.9 kJ /tonne km
Calculations of emissions:
•
•
•
61248 tonnes CO2 / 213000 tonnes natural gas × 0.23518695 = 67627.8 g CO2 / tonnes
natural gas
A5a: 67627.8 g CO2 / tonnes natural gas × a5a g natural gas /tonne km = 0.80 g CO2 /tonne
cargo km
A5b: 67627.8 g CO2 / tonnes natural gas × a5b g natural gas /tonne km = 0.85 g CO2 /tonne
cargo km
LCI data set:
Extraction and transportation of natural gas
Type of flow
Unit process
(A5a, A5b)
[ga/tonne natural gas]
Inputs:
Diesel
resource
4,045E+03
Fuel gas
resource
1,215E+01
Jet fuel
resource
1,358E+02
Outputs:
natural gas
Reference flow
1,000E+00b
Emissions to air
6,763E+04
CO2
Emissions to air
9,496E+01
CH4
Emissions to air
1,502E+02
nitrous oxide
Emissions to air
9,165E+01
CO
Emissions to air
4,284E+02
NOX
Emissions to air
9,893E+02
sulphur dioxide
Emissions to air
9,021E+02
NMVOC
a Fuel gas is expressed in Nm3 / tonne natural gas instead of g / tonne natural gas
b In tonne/tonne natural gas
xxxii
A5a
[g /tonne km]
A5b
[g /tonne km]
4,795E-02
1,440E-04
1,610E-03
5,099E-02
1,531E-04
1,712E-03
11,8567
8,018E-01
1,126E-03
1,780E-03
1,087E-03
5,080E-03
1,173E-02
1,070E-02
12,6068
8,526E-01
1,197E-03
1,893E-03
1,155E-03
5,401E-03
1,247E-02
1,137E-02
Liquefaction (B5a, B5b)
Process type: Unit process
Reference flow: 1 MJ LNG
Data source:
•
•
Data for energy use and greenhouse gas emissions from JEC (2008b, 2008a)
Data for other emissions from natural gas combustion in combined cycle gas turbine from
GEMIS version 4.5 (2010).
Representativeness:
•
•
•
Geographical: Europe
Technical: Consider technologies that are expected to be commercial available in 2010-2020
Time: 2010-2020
Other important information:
Assumptions for modelling:
•
The lower heating value of natural gas is 48 MJ/kg.
Data used:
From JEC (2008b)
•
•
Emissions of CO2:
Emissions of CH4:
From JEC (2008a)
0.06173684 MJ natural gas
4.7 g/MJ LNG
0.04 g MJ/LNG
1.02313 MJ natural gas
CCGT, 57%
efficiency
0.03434 MJ electricity
Liquefaction
1.01 MJ LNG
0.00085 MJ electricty
Storage at
terminal
1 MJ LNG
xxxiii
From GEMIS version 4.5
Data as collected
Inflows:
Natural gas
fuel
Outflows:
Electricity
CO
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
Energy carrier
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Type of flow
Unit
1,754386
TJ
1
73,65728
110,48592
4,4194368
7,365728
0,7365728
0,750576
TJ
kg
kg
kg
kg
kg
kg
Comment: Emissions of CO2 and CH4 from electricity production is included in the data from JEC (2008b) and therefore
not shown here. There are of cause emissions of CO2 and CH4 from combustion of natural gas.
Calculations of primary energy consumption:
•
•
•
(0.06173684 MJ natural gas + 1.02313 MJ natural gas) / MJ LNG = 1.08486684 MJ natural gas
/MJ LNG = 1.08486684 tonne natural gas / tonne LNG
B5a: 1.08486684 tonne natural gas / tonne LNG × b5a = 11.8567 g natural gas /tonne km
B5b: 1.08486684 tonne natural gas / tonne LNG × b5b = 12.6068 g natural gas /tonne km
Calculations of emissions:
•
•
•
•
•
•
•
•
4.7 g CO2 /MJ LNG × 48 MJ/kg = 225.6 g CO2 / kg LNG
B5a: 225.6 g CO2 / kg LNG × b5a = 2.5 g CO2 / tonne cargo km
B5b: 225.6 g CO2 / kg LNG × b5a = 2.6 g CO2 / tonne cargo km
Electricity use: 0.00085 MJ + 0.03434 MJ = 0.03519 MJ electricity/ MJ LNG shipped
73.65728 kg CO / 1 TJ electricity × 0.03519 MJ electricity = 0.025919997 g CO / MJ LNG
combusted
0.025919997 g CO / MJ LNG combusted × 48 000 MJ /tonne LNG = 0.00012441598 tonne CO
/tonne LNG
B5a: 0.00012441598 tonne CO /tonne LNG × b5a = 0.0014 g CO / tonne cargo km
B5b: 0.00012441598 tonne CO /tonne LNG × b5b = 0.0014 g CO / tonne cargo km
LCI data set:
Liquefaction (B5a, B5b)
Type of flow
Inflows:
Natural gas
resource
1,085E+00
11,8567
12,6068
Outflows:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
reference flow
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
1,000E+00
2,279E-01
1,244E-04
1,939E-03
1,866E-04
7,465E-06
1,244E-05
1,244E-06
1,268E-06
10,9291
2,490E+00
1,360E-03
2,119E-02
2,040E-03
8,159E-05
1,360E-04
1,360E-05
1,386E-05
11,6206
2,648E+00
1,446E-03
2,253E-02
2,169E-03
8,675E-05
1,446E-04
1,446E-05
1,473E-05
Unit process
[tonne/tonne LNG]
xxxiv
B5a
[g/tonne km]
B5b
[g/tonne km]
Transportation of LNG 350 NM with product tanker (C5a)
Process type: Unit process
Reference flow: 1 tonne LNG
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from North
Sea to Gothenburg
Data from Wärtsilä (Hattar, 2010, Stenhede, 2009) has been used for emissions from natural
gas combustion in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Product tanker with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
•
•
•
The energy consumption for the product tanker is estimated to 156.38 kWh per km and the
cargo capacity is 7500 m3, see Appendix I Specifications of the modelled vessels for more
details. The fillrate is set to 0.55, i.e. the vessels travels without cargo almost all the way back.
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
“THC” in the data from Hattar (2010) is assumed to be CH4.
If more BOG is generated than needed for propulsion this is also assumed to be combusted in
the engine. If less BOG then needed for propulsion is generated this is disregarded in the
calculations as long as the fuel needed is less than the BOG and the heel together.
The data from Hattar is from an engine at 100 % load and nominal speed this is not
representative for real operating conditions, but it is assumed that it is approximately the
same.
Emissions from the gas engine are approximately proportional to the engine efficiency in a
small efficiency range
The lower heating value for natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
The engine efficiency is 41%.
1 Ib = 453.5924 g
1 MMBtu =1055.06 MJ
Data used:
From World Port Distances Calculator (Distances.com, 2010)
•
Bergen – Gothenburg: 350 NM, 1 day at 15 knots
From Hasan et al. (2009)
•
Heel: 5% of total cargo capacity
xxxv
•
Boil-off rate:
0. 15% per day
Emissions of CO2:
Emissions of NOX:
Emissions of CH4:
425 g/kWh
1.3 g/kWh
2.1 g/kWh
From Hattar (2010)
•
•
•
From Stenhede (2009)
•
•
•
Efficiency:
Emissions of CO:
Emissions of particles:
Calculation of transported cargo:
•
•
•
•
•
•
•
48%
2.1 g/kWh
0.07 g/kWh
Cargo capacity: 7500 m3 × 440 kg/m3 × 0.55= 1815 tonne
Boil-off-gas: 1815 tonne × 0.0015 /day × 1 day = 2.7225 tonne
Heel: 1850 tonne × 0.05 = 90.75tonne
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ )/ 0.41 = 0.1829268... kg
LNG/kWh ≈ 0.183 kg LNG/kWh
0.183 kg LNG/kWh × 156.38 kWh/km × 350 NM ×1.852 km/NM= 18542.47244 kg LNG ≈
19 tonne LNG > 2.7225 tonne boil-off-gas, 19 tonne LNG < 90.75 tonne
Transported cargo = 1815 tonne – 90.75 tonne – 2.7225 tonne = 1721.5275 tonne
156.38 kWh/km / 1721.5275 tonne LNG × 350 NM ×1.852 km/NM = 58.881148 kWh/
tonne transported LNG
Calculations of primary energy use:
•
•
•
58.881148 kWh/ tonne transported LNG × 0.182927 kg LNG/kWh = 10.770952 kg LNG /
tonne transported LNG
10.770952 kg LNG / tonne transported LNG × c5a = 0.116463 g LNG/ tonne km
b5a = c5a + 0.116463 g LNG/ tonne km = 10.9291 g LNG/ tonne km
Calculations of CO2 emissions:
•
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG × 10.770952 kg LNG / tonne transported LNG = 29472 g CO2 / tonne
transported LNG
C5a: 29472g CO2 / tonne transported LNG × c5a = 0.32 g CO2 /tonne km
Calculations of emissions from Hattar (2010):
•
•
•
•
•
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG / 0.048 MJ /g LNG = 57.0052 g CO2 / MJ fuel
425 g CO2/ kWh / 3.6 MJ /kWh = 118.056 g CO2 / MJ energy from engine
(57.0052 g CO2 / MJ fuel) / (118.056 g CO2 / MJ energy from engine) = 0.4828676471 MJ
energy from engine / MJ fuel ≈ 48 % efficiency.
Emissions factor at 41% efficiency: 2.1 g CH4 /kWh × 0.4828676471 / 0.41 = 2.47322 g CH4
/kWh
2.47322 g CH4 /kWh × 58.881148 kWh/ tonne transported LNG = 145.626 g CH4 /tonne
transported LNG
C5a: 145.626 g CH4 /tonne transported LNG × c5a = 0.0016 g CH4/ tonne cargo km
xxxvi
Calculations of emissions from Stenhede (2009):
•
•
•
•
•
2.1 g CO/kWh from engine × 0.48 = 1.008 g CO/kWh fuel input
1.008 g CO/kWh fuel input / 3.6 MJ/kWh = 0.28 g CO /MJ fuel
0.28 g CO /MJ fuel × 48 MJ /kg fuel = 13.44 g CO /kg fuel
13.44 g CO / kg fuel × 10.770952 kg LNG / tonne transported LNG = 144.762 g CO / tonne
transported LNG
C5a: 144.762 g CO / tonne transported LNG × c5a = 0.0016 g CO / tonne cargo km
LCI data set:
Transportation of LNG 350 NM with product tanker (C5a)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne per tonne LNG
xxxvii
Unit process
[g/tonne LNG]
D5a
[g /tonne km]
fuel
product
1,077E+04
1,000E+00a
0,1165
10,8127
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
2,947E+04
1,448E+02
1,456E+02
9,015E+01
4,825E+00
10,8127
3,187E-01
1,565E-03
1,575E-03
9,747E-04
5,217E-05
Transportation of LNG 6500 NM with LNG carrier (C5b)
Process type: Unit process
Reference flow: 1 tonne LNG
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from Qatar to
Rotterdam
Data for other emissions from natural gas combustion from the GREET-model (ArgonneNational-Laboratory, 1999)
Representativeness:
•
•
•
Geographical: Technical: LNG carrier with a steam turbine with 30% efficiency
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
•
The energy consumption for the LNG Carrier is estimated to 642.55 kWh per km and the
cargo capacity is 147237 m3 see Appendix I Specifications of the modelled vessels for more
details. The fillrate is set to 0.55, i.e. the vessels travels without cargo almost all the way back.
The LNG carrier is propelled by a steam turbine fired with boil-off gas. The efficiency of the
steam turbine is 30%. The steam turbine uses only natural gas.
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion.
The energy used for liquefaction is generated from combustion of natural gas in a future
natural gas industrial boiler. Since the data are from 1999 a future industrial boiler is
assumed more representative than a current. The only difference between the emission
factors is the emission of NOX 39.1 g/10^6 Btu in the future boiler and 92.9 g/10^6 Btu in the
current.
Emissions of sulphur dioxide are not included since LNG is assumed to be cleaned of sulphur
during liquefaction.
If more BOG is generated than needed for propulsion this is also assumed to be combusted in
the engine. If less BOG then needed for propulsion is generated this is disregarded in the
calculations as long as the fuel needed is less than the BOG and the heel together.
10^6 Btu = 1055.06 MJ
The lower heating value of natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
Data used:
From World Port Distances Calculator (Distances.com, 2010)
•
Qatar – Rotterdam: 6500 NM. 14 days at 19.5 knots
From Hasan et al. (2009)
•
•
Boil-off rate:
Heel:
0. 15% per day
5% of total cargo capacity
xxxviii
From Argonne National Laboratory (1999)
Emissions to air from utility/industrial boiler (current)
CO
CH4
nitrous oxide
particles (PM10)
NOX
NMVOC
41.1
1.1
1.1
3.7
39.1
2.7
Unit
g/10^6 Btu
g/10^6 Btu
g/10^6 Btu
g/10^6 Btu
g/10^6 Btu
g/10^6 Btu
Calculation of transported cargo:
•
•
•
•
•
•
•
Cargo capacity: 147237 m3 × 440 kg/m3 × 0.55 = 35 631.354 tonne
Boil-off-gas: 35 631.354 tonne × 0.0015 /day × 14 days = 748.258434 tonne
Heel: 35 631.354 tonne × 0.05 = 1781.5677 tonne
Specific fuel consumption: 1 / (48 MJ/kg ×1/3.6 kWh /MJ )/ 0.30 = 0.250 kg LNG/kWh
0.250 kg LNG/kWh × 642.55 kWh/km × 6500 NM ×1.852 km/NM= 1933.754225 tonne LNG
< 748.258 tonne BOG + 1781.5677 tonne heel
Transported cargo = 35 631.354 tonne – (748.258 tonne BOG + 1781.5677 tonne heel) =
33101.5 tonne
0.250 kg LNG/kWh × 642.55 kWh/km × 6500 NM ×1.852 km/NM / 33101.5 tonne =
58.418880 LNG/ tonne transported LNG
Calculations of primary energy use:
•
•
58.418880 kg LNG/ tonne transported LNG × c5b = 0.6414 g LNG/ tonne cargo km
b5b = c5b + 0.6414 g LNG/ tonne cargo km = 11.6202 g LNG/ tonne cargo km
Calculations of CO2 emissions:
•
•
•
0.995×0.75×44/11 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG × 58.418880 kg LNG/ tonne transported LNG = 159.849 kg CO2 / tonne
transported LNG
C5b: 159.849 kg CO2 / tonne transported LNG × c5b = 1.8 g CO2 /tonne km
Calculations of the other emissions:
•
•
•
•
41.1 g CO /10^6 Btu LNG combusted × 10^6 Btu /1055.06 MJ = 0.038955 g CO / MJ LNG
combusted
0.038955 g CO / MJ LNG combusted × 48 MJ/kg = 1.86984 g CO / kg LNG combusted
1.86984 g CO / kg LNG combusted × 58.418880 kg LNG/ tonne transported LNG = 109.234 g
CO /tonne transported LNG
C5b: 109.234 g CO /tonne transported LNG × c5b g LNG /tonne cargo km = 0.0012 g CO
/tonne cargo km
xxxix
LCI data set:
Transportation of LNG 6500 NM with LNG carrier (C5b)
Type of flow
Inputs:
LNG
LNG
Fuel
Product
5,842E-02a
1,000E+00a
0,6414
10,9792
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
1,598E+05
1,092E+02
2,924E+00
1,039E+02
2,924E+00
7,176E+00
9,834E+00
10,9792
1,755E+00
1,199E-03
3,210E-05
1,141E-03
3,210E-05
7,879E-05
1,080E-04
Outputs:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a In tonne per tonne LNG
xl
Unit process
[g /tonne LNG]
C5b
[g /tonne km]
Transportation of LNG 500 NM with product tanker (D5b)
Process type: Unit process
Reference flow: 1 tonne LNG
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from
Rotterdam to Gothenburg
Data from Wärtsilä (Hattar, 2010, Stenhede, 2009) has been used other emissions from
natural gas combustion in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Product tanker with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
•
•
•
The energy consumption for the product tanker is estimated to 156.38 kWh per km and the
cargo capacity is 7500 m3, see Appendix I Specifications of the modelled vessels for more
details. The fillrate is set to 0.55, i.e. the vessels travels without cargo almost all the way back.
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
“THC” in the data from Hattar (2010) is assumed to be CH4.
If more BOG is generated than needed for propulsion this is also assumed to be combusted in
the engine. If less BOG then needed for propulsion is generated this is disregarded in the
calculations as long as the fuel needed is less than the BOG and the heel together.
The data from Hattar is from an engine at 100 % load and nominal speed this is not
representative for real operating conditions, but it is assumed that it is approximately the
same.
Emissions from the gas engine are approximately proportional to the engine efficiency in a
small efficiency range
The lower heating value for natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
The engine efficiency is 41%.
1 Ib = 453.5924 g
1 MMBtu =1055.06 MJ
Data used:
From World Port Distances Calculator (Distances.com, 2010)
•
Rotterdam – Gothenburg: 500 NM, 1.5 days at 14 knots
From Hasan et al. (2009)
•
Boil-off rate:
0. 15% per day
xli
•
Heel: 5% of total cargo capacity
From Hattar (2010)
•
•
•
Emissions of CO2:
Emissions of NOX:
Emissions of CH4:
425 g/kWh
1.3 g/kWh
2.1 g/kWh
Efficiency:
Emissions of CO:
Emissions of particles:
48%
2.1 g/kWh
0.07 g/kWh
From Stenhede (2009)
•
•
•
Calculation of transported cargo:
•
•
•
•
•
•
•
Cargo capacity: 7500 m3 × 440 kg/m3 × 0.55= 1815 tonne
Boil-off-gas: 1815 tonne × 0.0015 /day × 1.5 day = 4.08375 tonne
Heel: 1850 tonne × 0.05 = 90.75 tonne
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ )/ 0.41 = 0.18292683 kg
LNG/kWh ≈ 0.183 kg LNG/kWh
0.18292683 kg LNG/kWh × 156.38 kWh/km × 500 NM ×1.852 km/NM= 26489.214634 kg
LNG ≈ 26 tonne LNG > 4.08375 tonne boil-off-gas, 26 tonne LNG < 90.75 tonne
Transported cargo = 1815 tonne – 90.75 tonne – 4.08375 tonne = 1720.16625 tonne
156.38 kWh/km / 1720.16625 tonne LNG × 500 NM ×1.852 km/NM = 84.18249108 kWh/
tonne transported LNG
Calculations of primary energy use:
•
•
•
84.18249108 kWh/ tonne transported LNG × 0.18292683 kg LNG/kWh = 15.399250 kg LNG
/ tonne transported LNG
15.399250 kg LNG / tonne transported LNG × d5a = 0.166507 g LNG/ tonne km
c5b = d5b + 0.166507 g LNG/ tonne km = 10.9792
Calculations of CO2 emissions:
•
•
•
0.995×0.75×44/11 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG × 15.399250 kg LNG / tonne transported LNG = 42136.2 g CO2 / tonne
transported LNG ≈ 42 000 g CO2 / tonne transported LNG
D5b: 42136.2 g CO2 / tonne transported LNG × d5b = 0.46 g CO2 /tonne km
Calculations of emissions from Hattar (2010):
•
•
•
•
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG / 0.048 MJ /g LNG = 57.0052 g CO2 / MJ fuel
425 g CO2/ kWh / 3.6 MJ /kWh = 118.056 g CO2 / MJ energy from engine
(57.0052 g CO2 / MJ fuel) / (118.056 g CO2 / MJ energy from engine) = 0.4828676471 MJ
energy from engine / MJ fuel ≈ 48 % efficiency.
Emissions factor at 41% efficiency: 2.1 g CH4 /kWh × 0.4828676471 / 0.41 = 2.47322 g CH4
/kWh
Emissions factor per fuel input: 2.1 g CH4 /kWh × 0.4828676471 / 3.6 MJ/kWh × 48 MJ LNG
/kg = 0.013520 g CH4 /g LNG
xlii
•
•
0.013520 g CH4 /g LNG × 15.399250 kg LNG combusted / tonne LNG transported = 210 g CH4
/ tonne LNG transported
D5b: 0.013520 g CH4 /g LNG × 0.166507 g LNG combusted/tonne km = 0.0023 g CH4 /tonne
transported LNG
Calculations of emissions from Stenhede (2009):
•
•
•
•
•
•
2.1 g CO/kWh from engine × 0.48 = 1.008 g CO/kWh fuel input
1.008 g CO/kWh fuel input / 3.6 MJ/kWh = 0.28 g CO /MJ fuel
0.28 g CO /MJ fuel × 48 MJ /kg fuel = 13.44 g CO /kg fuel
13.44 g CO / kg fuel × 15.399250kg LNG / tonne transported LNG = 206.966 g CO / tonne
transported LNG
D5b: 206.966 g CO / tonne transported LNG × d5b = 0.002 g CO / tonne cargo km
D5b: 13.44 g CO / kg fuel × 0.166507 g LNG combusted/tonne km = 0.002 g CO/ tonne km
LCI data set:
Transportation of LNG 500 NM with product tanker (D5b)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne per tonne LNG
xliii
Unit process
[g/tonne LNG]
D5b
[g /tonne km]
fuel
product
1,540E+04
1,000E+00a
0,1665
10,8127
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
4,213E+04
2,070E+02
2,082E+02
1,289E+02
6,899E+00
10,8127
4,556E-01
2,238E-03
2,251E-03
1,394E-03
7,459E-05
Transportation and bunkering (F5a, F5b)
Process type: Unit process
Reference flow: 1 tonne LNG delivered to the Ro-Ro vessel
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from North
Sea to Gothenburg
Data from Wärtsilä (Hattar, 2010, Stenhede, 2009) has been used other emissions from
natural gas combustion in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Bunker ship with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
•
•
•
•
The energy consumption for the bunker vessel is assumed to be 0.0858 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The bunker vessel transports the fuel 10 km
Boil-off gas is not considered
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
“THC” in the data from Hattar (2010) is assumed to be CH4.
The data from Hattar is from an engine at 100 % load and nominal speed this is not
representative for real operating conditions, but it is assumed that it is approximately the
same.
Emissions from the gas engine are approximately proportional to the engine efficiency in a
small efficiency range
The lower heating value for natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
The engine efficiency is 41%.
1 Ib = 453.5924 g
1 MMBtu =1055.06 MJ
Data used:
From Hattar (2010)
•
•
•
Emissions of CO2:
Emissions of NOX:
Emissions of CH4:
425 g/kWh
1.3 g/kWh
2.1 g/kWh
Efficiency:
48%
From Stenhede (2009)
•
xliv
•
•
Emissions of CO:
Emissions of particles:
Calculations of primary energy use:
•
•
•
•
2.1 g/kWh
0.07 g/kWh
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ)/ 0.41 = 0.1829268... kg LNG/kWh
≈ 0.183 kg LNG/kWh
0.1829268... kg LNG/kWh × 0.0858 kWh /tonne LNG km × 10 km= 156.951 g LNG
combusted / tonne LNG transported
156.951 g LNG / tonne × f5a = 0.00169679... g LNG combusted/tonne km
c5a = c5b = f5a + 0.0017 g LNG /tonne transported LNG = 10.8127 g / tonne km
Calculations of CO2 emissions:
•
•
•
0.995×0.75×44/11 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG × 156.951 g LNG combusted / tonne LNG transported = 429.457 g CO2
/ tonne LNG transported
F5a, F5b: 2.73625 g CO2 /g LNG × 0.0016968 g LNG combusted /tonne km = 0.0046 g CO2 /
tonne km
Calculations of emissions from Hattar (2010):
•
•
•
•
•
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG / 0.048 MJ /g LNG = 57.0052 g CO2 / MJ fuel
425 g CO2/ kWh / 3.6 MJ /kWh = 118.056 g CO2 / MJ energy from engine
(57.0052 g CO2 / MJ fuel) / (118.056 g CO2 / MJ energy from engine) = 0.4828676471 MJ
energy from engine / MJ fuel ≈ 48 % efficiency.
Emissions factor at 41% efficiency: 2.1 g CH4 /kWh × 0.4828676471 / 0.41 = 2.47322 g CH4
/kWh
Emissions factor per fuel input: 2.1 g CH4 /kWh × 0.4828676471 / 3.6 MJ/kWh × 48 MJ LNG
/kg = 0.013520 g CH4 /g LNG
F5a, F5b: 0.013520 g CH4 /g LNG × 0.0016968 g LNG combusted/tonne km = 2.3 E-05 g CH4
/tonne transported LNG
Calculations of emissions from Stenhede (2009):
•
•
•
•
•
2.1 g CO/kWh from engine × 0.48 = 1.008 g CO/kWh fuel input
1.008 g CO/kWh fuel input / 3.6 MJ/kWh = 0.28 g CO /MJ fuel
0.28 g CO /MJ fuel × 48 MJ /kg fuel = 13.44 g CO /kg fuel
13.44 g CO / kg fuel × 156.951 g LNG / tonne transported LNG = 2.10942 g CO / tonne
transported LNG
F5a, F5b: 13.44 g CO / kg fuel × 0.0016968 g LNG combusted/tonne km = 2.3 E-05 g CO/
tonne km
xlv
LCI data set:
Transportation and bunkering (F5a, F5b)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne/tonne LNG
xlvi
Unit process
[g/tonne LNG]
F5a, F5b
[g /tonne km]
fuel
product
1,569E+02
1,000E+00a
0,0017
10,8110
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
4,293E+02
2,109E+00
2,121E+00
1,313E+00
7,028E-02
10,8110
4,641E-03
2,280E-05
2,293E-05
1,420E-05
7,598E-07
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
Process type: Unit process
Reference flow: 1 tonne cargo transported 1 km
Data sources: Data from Wärtsilä (Hattar, 2010, Stenhede, 2009) has been used other emissions from
natural gas combustion in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Ro-Ro vessel with lean burn four stroke engines (main engines) with and four
stroke diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0591 kWh per tonne
transported cargo and km see Appendix I Specifications of the modelled vessels for more
details.
The efficiency of the engine is 41%
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
“THC” in the data from Hattar (2010) is assumed to be CH4.
The data from Hattar is from an engine at 100 % load and nominal speed this is not
representative for real operating conditions, but it is assumed that it is approximately the
same.
Emissions from the gas engine are approximately proportional to the engine efficiency in a
small efficiency range
Data used:
From Hattar (2010)
•
•
•
Emissions of CO2:
Emissions of NOX:
Emissions of CH4:
From Stenhede (2009)
•
•
•
Efficiency:
Emissions of CO:
Emissions of particles:
425 g/kWh
1.3 g/kWh
2.1 g/kWh
Calculations of primary energy use:
•
•
48%
2.1 g/kWh
0.07 g/kWh
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ)/ 0.41 = 0.1829268... kg LNG/kWh
≈ 0.183 kg LNG/kWh
f5a = f5b = 0.1829268... kg LNG/kWh × 0.0591 kWh /tonne km = 10.8110 g LNG / tonne km
xlvii
Calculations of CO2 emissions:
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
U5a, U5b: 2.73625 g CO2 /g LNG × f5a = 30 g CO2 /tonne km
Calculations of emissions from Hattar (2010):
•
•
•
•
•
•
•
0.995×0.75×44/12 = 2.73625 g CO2 /g LNG
2.73625 g CO2 /g LNG / 0.048 MJ /g LNG = 57.0052 g CO2 / MJ fuel
425 g CO2/ kWh / 3.6 MJ /kWh = 118.056 g CO2 / MJ energy from engine
(57.0052 g CO2 / MJ fuel) / (118.056 g CO2 / MJ energy from engine) = 0.4828676471 MJ
energy from engine / MJ fuel ≈ 48 % efficiency.
Emissions factor at 41% efficiency: 2.1 g CH4 /kWh × 0.4828676471 / 0.41 = 2.47322 g CH4
/kWh
Emissions factor per fuel input: 2.1 g CH4 /kWh × 0.4828676471 / 3.6 MJ/kWh × 48 MJ LNG
/kg = 0.013520 g CH4 /g LNG
U5a, U5b: 0.013520 g CH4 /g LNG × f5a = 0.15 g CH4 /tonne transported LNG
Calculations of emissions from Stenhede (2009):
•
•
•
•
•
•
2.1 g CO/kWh from engine × 0.48 = 1.008 g CO/kWh fuel input
1.008 g CO/kWh fuel input / 3.6 MJ/kWh = 0.28 g CO /MJ fuel
0.28 g CO /MJ fuel × 48 MJ /kg fuel = 13.44 g CO /kg fuel
13.44 g CO / kg fuel × 10.770952 kg LNG / tonne transported LNG = 144.762 g CO / tonne
transported LNG
144.762 g CO / tonne transported LNG × c5a = 0.016 g CO / tonne cargo km
U5a, U5b: 13.44 g CO / kg fuel × f5a = 0. 15 g CO/ tonne km
LCI data set:
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
Inputs:
LNG
Cargo
Outputs:
Cargo
Propulsion energy
CO2
Type of flow
Fuel
Functional unit
10,8110
1b
Functional unit
1b
0,0591
2,958E+01
1,453E-01
1,462E-01
9,048E-02
4,843E-03
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
CO
CH4
NOX
particles (PM10)
a Propulsion energy is in kJ per tonne km
b In tonne per tonne km
xlviii
U5a, U5b
[g (kJa)//tonne km]
Appendix D – LCI results for marine transportation with GTL
The two alternatives with GTL are described in this appendix. The system is illustrated in Figure C-1.
The data used for each process in the flowchart are described below.
9.973 kJ
primary energy / tonne km
9.973 kJ
primary energy / tonne km
22.23 kJ primary energy
/tonne km
22.23 kJ primary energy
/tonne km
Extraction and
transport of
natural gas (A6)
Extraction and
transport of
crude oil,
production of
MGO (G6)
Extraction and
transport of
crude oil,
production of
MGO (G7)
a6 =15.9099 g natural gas / tonne km
Fischer-Tropsch
diesel production
(B6)
g6 = 0.4440 g MGO
/tonne km
Unloading,
storage and
loading (H6)
h16 =0.3821 g MGO
/tonne km
a7 = 15.9099 g natural gas / tonne km
Fischer-Tropsch
diesel production
(B7)
g7 =0.4440 g MGO
/tonne km
b6 = 11.5324 g GTL / tonne km
Transportation
6000 NM with
product tanker
(C6)
Extraction and
transport of
natural gas (A7)
Unloading,
storage and
loading (H7)
b7 =11.5324 g GTL / tonne km
h17 = 0.3821 g MGO
/tonne km
c6 = 11.5324 g GTL / tonne km
h26 =0.0607 g MGO
/tonne km
c7 =11.5324 g GTL / tonne km
Transportation
1000 NM with
product tanker
(D6)
h27 =0607 g MGO
/tonne km
d6 = 11.5324 g GTL / tonne km
h36 = 0.0011 g MGO
/tonne km
e6 = 11.5304 g GTL / tonne km
Bunkering (F6)
Transportation
1000 NM with
product tanker
(D7)
d7 =11.5324 g GTL / tonne km
h37 = 0.0011 g MGO
/tonne km
Unloading, storage
and loading (E6)
Transportation
6000 NM with
product tanker
(C7)
Unloading,
storage and
loading (E7)
e7 = 11.5304 g LNG / tonne km
20.45 kJ
primary energy
/tonne km
Bunkering (F7)
Production and
transportation of
urea (P7)
f6 = 11.5304 g GTL / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U6)
0.852
g urea / tonne km
f7 = 11.5304 g LNG / tonne km
Transportation of 1
tonne cargo 1 km
in a RO-Ro vessel
with SCR (U7)
7
6
Figure D-1 Overview of the energy consumption for the two alternatives with GTL as fuel.
The total primary energy consumption is:
•
•
Fuel alternative 6: 22.23 kJ + 15.9099 g × 48 kJ/g = 785.9052 ≈ 790 kJ
Fuel alternative 7: 22.23 kJ + 15.9099 g × 48 kJ/g + 20.45 kJ = 806.3552 ≈ 810 kJ
xlix
Extraction and transportation of natural gas (A6, A7)
Process type: Cradle-to-gate
Reference flow: 1 tonne natural gas to the Fischer-Tropsch plant
Data source: SPINE LCI dataset (2008)
Representativeness:
•
•
•
Geographical: Norway
Technical: Technology for oil and gas extraction used in Norway. Comment from the data
provider: “Extraction of oil in Norway is internationally considered to be one of the most
environmental friendly, because the oil companies have to follow strict environmental laws and
therefore use the most advanced technology and equipment available on the market” (SPINELCI-dataset, 2008)
Time: Average data from 1991
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
The data is representative even if it is from 1991
Allocation between crude oil and natural gas is based on energy content (lower heating
valued and mass)
The lower heating value of crude oil is 42.3 MJ/kg and the lower heating value of natural gas
is 48 MJ/kg
Lower heating value of diesel is 43 MJ/kg
Lower heating value of fuel gas is 36.8 MJ/m3
Lower heating value of jet fuel is 44.1 MJ/kg
VOC is assumed to be NMVOC
Data used:
From SPINE LCI dataset (2008)
Inflows
Diesel
Fuel gas
Jet fuel
Outflows
CO2
CH4
nitrous oxide
CO
NOX
sulphur dioxide
NMVOC
Crude oil
Gas
Calculation of allocation factor:
•
3663
11000008
123
tonnes
m3
tonnes
61248
86
136
83
388
896
817
786000
213000
tonnes
tonnes
tonnes
tonnes
tonnes
tonnes
tonnes
tonnes
tonnes
213 000 tonnes × 48 000 MJ/tonne/(213 000 tonnes × 48 000 MJ/tonne + 786000 × 42 300
MJ/tonne) = 0.23518695
l
Calculations of primary energy use:
•
•
(3663 tonne × 43 000 MJ/tonne +11000008 m3 × 36.8 MJ/m3 +123 tonne × 44 100 MJ/tonne)
/ 213 000 tonnes natural gas × 0.23518695 = 626.871 MJ / tonnes natural gas
626.871 MJ / tonnes natural gas × a6 = 10 kJ /tonne km
Calculations of emissions:
•
•
61248 tonnes CO2 / 213000 tonnes natural gas × 0.23518695 = 67627.8 g CO2 / tonnes
natural gas
A6, A7: 67627.8 g CO2 / tonnes natural gas × a6 g natural gas /tonne km = 1.1 g CO2 /tonne
cargo km
LCI data set:
Extraction and transportation of natural gas (A6, A7)
Type of flow
Inputs:
Diesel
Fuel gas
Jet fuel
Outputs:
natural gas
CO2
CH4
nitrous oxide
CO
NOX
sulphur dioxide
NMVOC
a The fuel gas is expressed in m3 instead of g per kg fuel and tonne km.
li
Unit process
[g (m3a)/kg
natural gas]
A6, A7
[g (m3a)/tonne
km]
Refined
Refined
Refined
4,045E+03
1,215E+01
1,358E+02
6,435E-02
1,932E-04
2,161E-03
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00
6,763E+04
9,496E+01
1,502E+02
9,165E+01
4,284E+02
9,893E+02
9,021E+02
15,9099
1,076E+00
1,511E-03
2,389E-03
1,458E-03
6,816E-03
1,574E-02
1,435E-02
GTL production (B6, B7)
Process type: Unit process
Reference flow: 1 tonne GTL
Data sources:
•
•
JEC (2008b)
Marano and Ciferno (2001)
Representativeness:
•
•
•
Geographical: Europe and USA
Technical: From study by Marano and Ciferno (2001) - FT diesel produced from associated
gas with conventional product upgrading with 2000 years state of the art FT production and
cogeneration of electricity.
Time: 2010+ for the JEC Well-to-Wheels study
Other important information:
•
•
The JEC Well-to-Wheels study uses the following allocation method: “All energy and emissions
generated by the process are allocated to the main or desired product of that process. The byproduct generates an energy and emission credit equal to the energy and emissions saved by
not producing the material that the co-product is most likely to displace” (Edwards et al.,
2007)
The study by Marano and Ciferno (2001) has used allocation based on LHV and mass (due to
co-production of electricity).
Assumptions for modelling:
•
•
•
•
•
Two different studies have been used for different flows from the Fischer-Tropsch process
and it have been assumed that the result will still be representative even if the studies have
used different allocation methods and different system boundaries.
The lower heating value of GTL is 43 MJ/kg
The lower heating value of LNG is 48 MJ/kg
The density of GTL is 738.443683 kg /m3, calculated from Marano and Ciferno (2001)
All energy used in the process is supplied from natural gas
Data used:
From JEC (JEC)
•
•
Energy expended 0.54 MJx/MJf
Emissions of CO2 13.8 g/MJ
Marano and Ciferno (2001)
CH4
nitrous oxide
NOX
CO
particles (unspecified)
NMVOC
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
lii
30
20
632
154
14
27
mg/dm3
mg/dm3
mg/dm3
mg/dm3
mg/dm3
mg/dm3
Calculations of energy use:
•
•
•
•
0.54 MJ energy is expended when 1 MJ GTL is produced => 1.54 MJ natural gas is needed to
produce 1 MJ GTL if all energy is supplied from natural gas.
For 1 tonne GTL: 1.54 MJ × 43 000 MJ/tonne /48 000 MJ/tonne = 1.379583 tonne natural gas
a6 = b6 × 1.379583 = 15.9099 g/tonne km
a7 = b7 × 1.379583 = 15.9099 g/tonne km
Calculations of emissions:
•
•
•
•
13.8 g CO2 /MJ GTL × 43000 MJ /tonne GTL = 593400 g CO2 /tonne GTL
A6, A7: 593400 g CO2 /tonne GTL × b6 = 6.8 g /tonne km
30 mg CH4/dm3 GTL / 738.443683 kg /m3 GTL = 40.6 g CH4 /tonne GTL
A6, A7: 40.6 g CH4/tonne GTL × b6 = 0.00047 CH4 g/tonne km
LCI data set:
GTL production (B6, B7)
Type of flow
Inputs:
Natural gas
Outputs:
GTL
CO2
CH4
nitrous oxide
NOX
CO
particles (unspecified)
NMVOC
liii
Unit process
[g/tonne GTL]
A6, A7
[g /tonne km]
product
1,380E+00
15,9099
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00
5,934E+05
4,063E+01
2,708E+01
8,559E+02
2,085E+02
1,896E+01
3,656E+01
11,5324
6,843E+00
4,685E-04
3,123E-04
9,870E-03
2,405E-03
2,186E-04
4,217E-04
Transportation of GTL 6000 NM with product tanker (C6, C7)
Process type: Unit process
Reference flow: 1 kg of GTL
Data sources:
•
•
Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and fuel
consumption (g/kWh) for the for the bunker ship
World Port Distances Calculator (Distances.com, 2010) for transport distances from Qatar to
Gothenburg.
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the product tanker is estimated 0.0140 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The product tanker is assumed to have medium speed engines
The product tanker is assumed to be fuelled by HFO with a sulphur content of 2%.
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
213 g/kWh
677g/kWh
14 g/kWh
0.496 g/kWh
0.004 g/kWh
0.8 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
From World Port Distances Calculator (Distances.com, 2010)
•
Qatar – SECA border: 6000 NM
Calculations of primary energy consumption:
liv
•
•
•
213 g HFO/kWh × 0.0140 kWh/tonne GTL/ km × 6000 NM × 1.852 km/ NM = 33135.984 g
HFO/tonne GTL
h16= 33135.984 g HFO/tonne GTL × c6 = 0.3821 g HFO/tonne cargo km
h17= 33135.984 g HFO/tonne GTL × c7 = 0.3821 g HFO/tonne cargo km
Calculations of emissions of sulphur dioxide:
•
•
•
•
2% sulphur × 1 tonne HFO = 20 000 g sulphur /tonne HFO
20 000 g sulphur /tonne HFO × 2 g sulphur dioxide / g sulphur = 40 000 g sulphur
dioxide/tonne HFO
40 000 g sulphur dioxide/tonne HFO × 33135.984 g HFO/tonne GTL = 1325.44 g sulphur
dioxide /tonne GTL
C6, C7: 40 000 g sulphur dioxide/tonne HFO × h16 = 0.015 g sulphur dioxide /tonne cargo km
Calculations of the other emissions:
•
•
677 g CO2 /kWh × 0.0140 kWh/tonne GTL/ km × 6000 NM × 1.852 km/ NM = 105320 g CO2
/tonne GTL
C6, C7: 677 g CO2 /kWh × 0.0140 kWh/tonne GTL/ km × 6000 NM × 1.852 km/ NM × c6 = 1.2
g CO2 /tonne HFO
LCI data set:
Transportation of GTL 6000 NM with product tanker
(C6, C7)
Inputs:
HFO
GTL
Outputs:
GTL
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a In tonne per tonne GTL
Type of flow
Unit process
[g/tonne GTL]
C6, C7
[g/tonne km]
fuel
product
3,314E+04
1,000E+00a
0,3821
11,5324
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00a
4,667E-01
1,053E+05
1,711E+02
6,223E-01
2,178E+03
4,823E+00
9,287E+01
1,245E+02
1,325E+03
11,5324
5,382E-06
1,215E+00
1,973E-03
7,176E-06
2,512E-02
5,562E-05
1,071E-03
1,435E-03
1,529E-02
lv
Transportation of GTL 1000 NM with product tanker (D6, D7)
Process type: Unit process
Reference flow: 1 kg of GTL
Data sources:
•
•
Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and fuel
consumption (g/kWh) for the for the bunker ship
World Port Distances Calculator (Distances.com, 2010) for transport distances from Qatar to
Gothenburg.
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the product tanker is estimated 0.0140 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The product tanker is assumed to have medium speed engines
The product tanker is assumed to be fuelled by MGO with a sulphur content of 0.1 %.
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
From World Port Distances Calculator (Distances.com, 2010)
•
Qatar – Gothenburg: 7000 NM
Calculations of primary energy consumption:
lvi
•
•
203 g HFO/kWh × 0.0140 kWh/tonne GTL/ km × 1000 NM × 1.852 km/ NM = 5263.384 g
HFO/tonne GTL
h26 = 5263.384 g HFO/tonne GTL × d6 = 0.0607 g HFO/tonne cargo km
Calculations of emissions of sulphur dioxide:
•
•
•
0.1 % sulphur × 1 tonne MGO = 1 000 g sulphur /tonne MGO
1 000 g sulphur /tonne MGO × 2 g sulphur dioxide / g sulphur = 2 000 g sulphur
dioxide/tonne MGO
D6, D7: 2 000 g sulphur dioxide/tonne MGO × h26 = 1.2 E-04 g sulphur /tonne cargo km
dioxide
Calculations of the other emissions:
•
•
645 g CO2 /kWh × 0.0140 kWh/tonne GTL/ km × 1000 NM × 1.852 km/ NM = 16723.56 g
CO2 /tonne HFO
D6, D7: 645g CO2 /kWh × 0.0140 kWh/tonne GTL/ km × 1000 NM × 1.852 km/ NM × d6 =
0.19 g CO2 /tonne cargo km
LCI data set:
Transportation of GTL 1000 NM with product tanker
(D6, D7)
Inputs:
MGO
GTL
Outputs:
GTL
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a In tonne per tonne GTL
Type of flow
Unit process
[g/tonne GTL]
D6, D7
[g/tonne km]
fuel
product
5,263E+03
1,000E+00a
0,0607
11,5324
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00a
7,778E-02
1,672E+04
2,852E+01
1,037E-01
3,422E+02
8,038E-01
1,289E+01
7,778E+00
1,053E+01
11,5324
8,970E-07
1,929E-01
3,289E-04
1,196E-06
3,947E-03
9,269E-06
1,486E-04
8,970E-05
1,214E-04
lvii
Extraction, transportation and refining of crude oil to MGO (G6, G7)
Process type: Cradle-to-gate
Reference flow: 1 kg of MGO
Data source: ELCD core database version II (2009b)
Representativeness:
•
•
•
Geographical: Average data for EU-15 countries
Technical: “The data set describes a mass-weighted average refinery for the respective country
/ region.” (ELCD-core-database-version-II, 2009a)
Time: Average data for the year 2003
Other important information:
•
Allocation made by the data provider: “For the combined crude oil, natural gas and natural
gas liquids production allocation by net calorific value is applied. For all products of the
refinery, allocation by mass and net calorific value is applied. The manufacturing route of every
refinery product is modelled and so the effort of the production of these products is calculated
specifically. Two allocation rules are applied: The raw-material (crude oil) consumption of the
respective stages, which is necessary for the production of a product or an intermediate
product, is allocated by energy (mass of the product * calorific value of the product). In these
way products with high caloric values, e.g. gasoline or gases are assigned to higher raw
material consumption and so higher environmental impacts compared with low caloric value
products (e.g. asphalt, residual oil). The energy consumption (thermal energy, steam,
electricity) of a process, e.g. atmospheric distillation, being required by a product or a
intermediate product, are charged on the product according to the share of the throughput of
the stage (mass allocation). The products, which are more complex to produce and therefore
pass a lot of refinery facilities e.g. gasoline, are assigned with a higher energy consumption
(and so higher emissions) compared with e.g. straight run products” (ELCD-core-databaseversion-II, 2009b).
Assumptions for modelling:
•
•
•
The data set are representative for MGO produced in Europe with a sulphur content of 0.1%
The lower heating value of MGO is 43 MJ/kg
Only selected flows are modelled. Inputs considered is energy carriers and outputs
considered are emissions to air from the substances selected in the goal and scope definition
Data used: see LCI data set, column three, below
Calculations for primary energy consumption:
•
•
0.04746 kJ/g MGO + 47.13 KJ/g MGO + 0.1285 kJ/g MGO +2.695 kJ/kg MGO + 0.05568 kJ/g
MGO + 0.004795 kJ/g MGO + 0.005421 kJ/g MGO = 50.06 kJ/g MGO
G6, G7: 50.06 kJ/g MGO × g6 = 22 kJ/tonne km
Calculations for emissions:
•
G6, G7: 0.3013 kg CO2 /kg MGO × g6 = 0.13 g CO2 / tonne cargo km
lviii
LCI data set:
Extraction, transportation and refining of crude oil to
Type of flow
MGO (G6, G7)
Inputs:
brown coal; 11 MJ/kg
resource
crude oil; 42.3 MJ/kg
resource
hard coal; 26.3 MJ/kg
resource
natural gas; 44.1 MJ/kg
resource
primary energy from hydro power
resource
primary energy from solar energy
resource
primary energy from wind power
resource
Outputs:
MGO
reference flow
ammonia
emissions to air
CO2
emissions to air
CO
emissions to air
CH4
emissions to air
nitrogen dioxide
emissions to air
nitrogen monoxide
emissions to air
nitrous oxide
emissions to air
NMVOC
emissions to air
emissions to air
•
acetone
emissions to air
•
benzene
emissions to air
•
ethane
emissions to air
•
ethanol
emissions to air
•
ethylene
emissions to air
•
formaldehyde
emissions to air
•
methanol
emissions to air
•
n-butane
emissions to air
•
propane
emissions to air
•
toluene
particles (PM10)
emissions to air
sulphur dioxide
emissions to air
a The inputs are expressed in kJ instead of kg per kg fuel and tonne km.
lix
Unit process
[kg (kJa)/kg MGO]
G6, G7
[g (kJa)/tonne km]
4,746E-02
4,713E+01
1,285E-01
2,695E+00
5,568E-02
4,795E-03
5,421E-03
2,107E-02
2,092E+01
5,707E-02
1,196E+00
2,472E-02
2,129E-03
2,407E-03
1,000E+00
3,265E-06
3,013E-01
4,146E-04
3,348E-03
8,756E-04
7,529E-12
6,958E-06
2,601E-04
7,541E-08
1,319E-06
1,818E-04
2,647E-08
2,005E-08
2,662E-07
2,434E-08
6,821E-05
3,289E-04
6,685E-08
1,965E-05
1,752E-03
0,4440
1,450E-06
1,338E-01
1,841E-04
1,486E-03
3,887E-04
3,343E-12
3,089E-06
1,155E-04
3,348E-08
5,857E-07
8,073E-05
1,175E-08
8,901E-09
1,182E-07
1,081E-08
3,028E-05
1,460E-04
2,968E-08
8,725E-06
7,778E-04
Storage and loading of oil products (E6, E7, H6, H7)
Process type: Unit process
Reference flow: 1 kg of oil product
Data source: Port of Gothenburg (2009)
Representativeness:
•
•
•
Geographical: Oil harbour, Gothenburg
Technical: The oil harbour in Gothenburg has a vapour recovery unit used during loading of
oil products.
Time: Average data for the year 2008
Assumptions for modelling:
•
•
•
all fuels handled at the port emit the same proportions of volatile organic compounds
emission of volatile organic compound are assumed to be NMVOC
the energy consumption at the oil harbour and the emissions to water is not included
Data used:
•
•
products loaded to vessels
emissions of NMVOC
Calculations of energy consumption:
•
•
•
•
•
12996468 tonne
2220.620 tonne
2220.620 tonne evaporated MGO/ 12996468 tonne loaded HFO = 0.00017 g evaporated
fuel/ g loaded fuel
0.00017 g transported MGO/ g loaded MGO × b3 = 0.001960 g HFO/ tonne cargo km
d6 = e6 + e6 × 0.00017 g evaporated fuel/ g loaded fuel =11.5324 g GTL /tonne km
h16 + h26 + h36 + e6 = 11. 9743 g fuel /tonne km
g6 = h16 + h26 + h36 + (h16 + h26 + h36) × 0.00017 g evaporated fuel/ g loaded fuel = 0.4440 g
fuel /tonne km
Calculations for emissions:
•
•
•
2220.620 tonne NMVOC/ 12996468 tonne loaded products = 0.170836 kg NMVOC / tonne
loaded product
E6, E7: 0.170836 g NMVOC / kg loaded product × d6 = 0.0020 g NMVOC /tonne cargo km
H6, H7: 0.170836 g NMVOC / kg loaded product × g6 = 7.6 E-05 g NMVOC /tonne cargo km
LCI data set:
Storage and loading of oil products (E6, E7)
Inputs:
fuel
Outputs:
fuel
NMVOC
Type of flow
product
reference flow
emissions to air
lx
Unit process
[kg /kg fuel]
E6, E7
[g /tonne km]
1,0002E+00
11,5324
1,000E+03
1,709E-01
11,5304
1,970E-03
Storage and loading of oil products (H6, H7)
Inputs:
fuel
Outputs:
fuel
NMVOC
Type of flow
product
reference flow
emissions to air
lxi
Unit process
[kg /kg fuel]
H6, H7
[g /tonne km]
1,0002E+00
0,4440
1,000E+03
1,709E-01
0,4439
7,585E-05
Transportation and bunkering (F6, F7)
Process type: Unit process
Reference flow: 1 kg of fuel delivered to the Ro-Ro vessel
Data sources: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the bunker ship
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines
Time: The data used were published 2004 and 2008
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the bunker ship is estimated 0.0452 kWh per transported tonne
and km see Appendix I Specifications of the modelled vessels for more details.
The bunker ship is assumed to have medium speed engines
The bunker ship is assumed to be fuelled by MGO with a sulphur content of 1.5%.
The distribution of MGO from the port to the Ro-Ro vessel with the bunker vessel is assumed
to be 10 km.
The emissions during the actual bunkering are not included
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of primary energy consumption:
•
•
203 g fuel/kWh ×0.0452 kWh/tonne GTL/ km × 10 km =91.756 g fuel/tonne GTL
h36 = 91.756 g fuel/tonne GTL × f6 =0.0011 g fuel /tonne cargo km
lxii
Calculations of emissions of sulphur dioxide:
•
•
•
•
•
0.1% sulphur × 1 tonne MGO = 1 000 g sulphur /tonne MGO
1000 g sulphur /tonne MGO × 2 g sulphur dioxide / g sulphur = 2 000 g sulphur
dioxide/tonne MGO
203 g MGO/kWh × 0.0452 kWh/tonne transported MGO/km × 10 km = 91.756 g MGO/tonne
transported GTL
2 000 g sulphur dioxide/tonne MGO × 91.756 g MGO/tonne transported GTL = 0.183512 g
sulphur dioxide /tonne GTL
F6, F7: 0.183512 g sulphur dioxide /tonne GTL× f6 = 2.1 E-06 g sulphur dioxide /tonne cargo
km
Calculations of the other emissions:
•
•
645g CO2 /kWh × 0.0452 kWh /tonne GTL/ km × 10 km = 291.54 g CO2 /tonne GTL
F6, F7: 291.54 g CO2 /tonne GTL × f2 = 0.0034 g CO2 /tonne cargo km
LCI data set:
Transportation and bunkering (F6, F7)
Inputs:
MGO
GTL
Outputs:
GTL
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
a In tonne per tonne GTL
Type of flow
Unit process
[g/tonne GTL]
F6, F7
[g/tonne km]
fuel
product
9,176E+01
1,000E+00a
0,0011
11,5304
reference flow
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
emissions to air
1,000E+00a
1,356E-03
2,915E+02
4,972E-01
1,808E-03
5,966E+00
1,401E-02
2,246E-01
1,356E-01
1,835E-01
11,5304
1,564E-08
3,362E-03
5,733E-06
2,085E-08
6,879E-05
1,616E-07
2,590E-06
1,564E-06
2,116E-06
lxiii
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U6)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data source: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines fuelled with MGO
Time: The data used were published 2004 and 2001
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0568 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The Ro-Ro vessel is assumed to have medium speed engines
GTL has zero sulphur content
Emissions from medium speed diesel engines fuelled with MGO are roughly representative
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of energy use:
•
f6 = 203 g fuel /kWh × 0.0568 kWh /tonne cargo km =11.5304 g fuel/tonne cargo km
Calculations of emissions:
•
645 g CO2 /kWh × 0.0568 kWh /tonne cargo km = 36.636 g CO2 /tonne cargo km
lxiv
LCI data set:
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U6)
Type of flow
Inputs:
GTL
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a Propulsion energy is expressed in kWh instead of g.
fuel
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
lxv
U6
[g (kWha)/tonne km]
11,5304
5,680E-02
1,704E-04
3,664E+01
6,248E-02
2,272E-04
7,498E-01
1,761E-03
2,823E-02
1,704E-02
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U7)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data sources:
•
•
Cooper (2001) for emissions of ammonia from a medium speed diesel engine with SCR
Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission except of
ammonia and fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines fuelled with MGO, SCR
Time: The data used were published 2008, 2004 and 2001
Other important information:
Assumptions for modelling:
•
•
•
As process above
Only the emissions of ammonia and NOX is affected by the SCR unit
The SCR unit is assumed to reduce the emissions of NOX with 85 %. It is possible to reach
95% reduction according to Lövblad and Fridell (2006). An 85% has been used here as this
will be enough to fulfil the Tier III.
Data used:
From Cooper (2001)
•
Emissions of ammonia:
0.025 g /kWh
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of nitrous oxide:
0.031 g/kWh
Data from NTM (2008)
•
•
•
•
•
•
•
Data from Cooper and Gustafson (2004)
•
Calculations of energy use:
•
f7 = 203 g fuel /kWh × 0.0568 kWh /tonne cargo km =11.5304 g fuel/tonne cargo km
Calculations of emissions:
•
13.2 g NOX/ kWh × 0.15 × 0.0568 kWh /tonne cargo km = 0.112464 g NOX /tonne cargo km
lxvi
•
0.025 g ammonia /kWh × 0.0568 kWh/tonne cargo km = 0.00142 g ammonia /tonne cargo
km
LCI data set:
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U7)
Type of flow
Inputs:
GTL
urea
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a Propulsion energy is expressed in kWh instead of g.
fuel
product
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
lxvii
U7
[g (kWha)/tonne km]
11,5304
0,8520
5,680E-02
1,420E-03
3,664E+01
6,248E-02
2,272E-04
1,125E-01
1,761E-03
2,698E-02
1,704E-02
Production and transportation of urea (P7)
See process production and transportation of urea in the section, Appendix B – LCI results for marine
transportation with MGO.
lxviii
Appendix E – Uncertainty in emission from gas engines
The emissions to air from a gas fuelled engine at real operating conditions are uncertain. US EPA
(2000) has published data for emission test from stationary lean burn gas engine, which is used in
this scenario as a complement to the data from Wärtsilä. These emission factors do not fulfil the
coming Tier III requirements for emissions of NOX and the CH4 slip from the engine is much higher,
approximately 0.6 g/MJ fuel as compared to 0.3 g/MJ. The data for the processes that are yellow in
Figure E-1 have been changed from the base scenario.
lxix
7.422 kJ
primary energy / tonne km
7.903 kJ
primary energy / tonne km
Extraction and
transport of
natural gas (A5a)
Extraction and
transport of
natural gas (A5b)
a5a = 11.8567 g
natural gas / tonne km
a5b = 12.6068 g
natural gas / tonne km
Liquefaction (B5a)
Liquefaction (B5b)
b5a = 10.9291 g LNG / tonne km
b5b = 11.6206 g LNG / tonne km
Transportation
350 NM with
product tanker
(C5a)
Transportation
6500 NM with
LNG Carrier (C5b)
c5b = 10.9792 g LNG / tonne km
Unloading,
storage and
loading
c5b = 10.9792 g LNG / tonne km
c5a = 10.8127 g LNG / tonne km
Transportation
500 NM with Coral
Methane (D5b)
d5b = 10.8127 g LNG / tonne km
Unloading,
storage and
loading
Unloading,
storage and
loading
c5a = 10.8127 g LNG / tonne km
d5b = 10.8127 g LNG / tonne km
Bunkering (F5a)
Bunkering (F5b)
f5a = 10.8110 g LNG / tonne km
f5b = 10.8110 g LNG / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5a)
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5b)
5a
5b
Figure E-1 Overview of the energy consumption for the two alternatives with LNG as fuel. The uncoloured
processes these have not been included. The data for the yellow boxes are exchanged from the base
scenario.
lxx
Transportation of LNG 350 NM with product tanker (C5a)
Process type: Unit process
Reference flow: 1 tonne LNG
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from North
Sea to Gothenburg
Data from US EPA (2000) has been used other emissions from natural gas combustion in a
lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Product tanker with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the product tanker is estimated to 156.38 kWh per km and the
cargo capacity is 7500 m3, see Appendix I Specifications of the modelled vessels for more
details. The fillrate is set to 0.55, i.e. the vessels travels without cargo almost all the way back.
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
If more BOG is generated than needed for propulsion this is also assumed to be combusted in
the engine. If less BOG then needed for propulsion is generated this is disregarded in the
calculations as long as the fuel needed is less than the BOG and the heel together.
The lower heating value for natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
The engine efficiency is 41%.
1 Ib = 453.5924 g
1 MMBtu =1055.06 MJ
Data used:
From World Port Distances Calculator (Distances.com, 2010)
•
Bergen – Gothenburg: 350 NM, 1 day at 15 knots
From Hasan et al. (2009)
•
•
Heel 5% of total cargo capacity
Boil-off rate 0. 15% per day
lxxi
From U.S. EPA (2000)
Emissions to air from a lean burn four-stroke engine
CO
CH4
NOX
NMVOC
particles (PM10)
Ib/MMBtu
5.57E-01
1.31E+00
8.47E-01
0.223
7.71E-05
Calculation of transported cargo:
•
•
•
•
•
•
•
Cargo capacity: 7500 m3 × 440 kg/m3 × 0.55= 1815 tonne
Boil-off-gas: 1815 tonne × 0.0015 /day × 1 day = 2.7225 tonne
Heel: 1850 tonne × 0.05 = 90.75tonne
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ)/ 0.41 = 0.1829268... kg LNG/kWh
≈ 0.183 kg LNG/kWh
0.183 kg LNG/kWh × 156.38 kWh/km × 350 NM ×1.852 km/NM= 18542.47244 kg LNG ≈
19 tonne LNG > 2.7225 tonne boil-off-gas, 19 tonne LNG < 90.75 tonne
Transported cargo = 1815 tonne – 90.75 tonne – 2.7225 tonne = 1721.5275 tonne
156.38 kWh/km / 1721.5275 tonne LNG × 350 NM ×1.852 km/NM = 58.881148 kWh/
tonne transported LNG
Calculations of primary energy use:
•
•
•
58.881148 kWh/ tonne transported LNG × 0.182927 kg LNG/kWh = 10.770952 kg LNG /
tonne transported LNG
10.770952 kg LNG / tonne transported LNG × c5a = 0.116463 g LNG/ tonne km
b5a = c5a + 0.116463 g LNG/ tonne km = 10.9291 g LNG/ tonne km
Calculation of emissions:
•
•
•
•
0.557 Ib CO /MMBtu /( 1055.06 MJ /MMBtu) × 453,5924 g /Ib = 0.239466 g CO /MJ
0.239466 g CO /MJ × 48 MJ/kg LNG = 0.0115 g CO /g LNG
0.0115 g CO /g LNG ×10.770952 kg LNG / tonne transported LNG = 123.866 g CO / tonne
transported LNG
= 123.866 g CO / tonne transported LNG × c5a = 0.0013 g CO /tonne km
LCI data set:
Transportation of LNG 350 NM with product tanker (C5a)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
NMVOC
particles (PM10)
a In tonne per tonne LNG
lxxii
Unit process
[g/tonne LNG]
D5a
[g /tonne km]
fuel
product
1,077E+04
1,000E+00a
0,1165
10,8127
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
2,947E+04
1,238E+02
2,912E+02
1,883E+02
4,957E+01
1,714E-02
10,8127
3,187E-01
1,339E-03
3,148E-03
2,036E-03
5,359E-04
1,853E-07
Transportation of LNG 500 NM with product tanker (D5b)
Process type: Unit process
Reference flow: 1 tonne LNG
Data sources:
•
•
•
Hasan et al. (2009) for modelling the boil-off gas from LNG
World Port Distances Calculator (Distances.com, 2010) for transport distances from
Rotterdam to Gothenburg
Data from US EPA (2000) has been used other emissions from natural gas combustion in a
lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Product tanker with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the product tanker is estimated to 156.38 kWh per km and the
cargo capacity is 7500 m3, see Appendix I Specifications of the modelled vessels for more
details. The fillrate is set to 0.55, i.e. the vessels travels without cargo almost all the way back.
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
If more BOG is generated than needed for propulsion this is also assumed to be combusted in
the engine. If less BOG then needed for propulsion is generated this is disregarded in the
calculations as long as the fuel needed is less than the BOG and the heel together.
The lower heating value for natural gas is 48 MJ/kg.
The density of LNG is 440 kg/m3.
The engine efficiency is 41%.
1 Ib = 453.5924 g
1 MMBtu =1055.06 MJ
Data used:
From World Port Distances Calculator (Distances.com, 2010)
•
Rotterdam –Gothenburg: 500 NM, 1.5 days at 14 knots
From Hasan et al. (2009)
•
•
Boil-off rate 0. 15% per day
Heel 5% of cargo
lxxiii
From U.S. EPA (2000)
Emissions to air from a lean burn four-stroke engine
CO
CH4
NOX
NMVOC
particles (PM10)
Ib/MMBtu
5.57E-01
1.31E+00
8.47E-01
0.223
7.71E-05
Calculation of transported cargo:
•
•
•
•
•
•
•
Cargo capacity: 7500 m3 × 440 kg/m3 × 0.55= 1815 tonne
Boil-off-gas: 1815 tonne × 0.0015 /day × 1.5 day = 4.08375 tonne
Heel: 1850 tonne × 0.05 = 90.75 tonne
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ )/ 0.41 = 0.18292683 kg
LNG/kWh ≈ 0.183 kg LNG/kWh
0.18292683 kg LNG/kWh × 156.38 kWh/km × 500 NM ×1.852 km/NM= 26489.214634 kg
LNG ≈ 26 tonne LNG > 4.08375 tonne boil-off-gas, 26 tonne LNG < 90.75 tonne
Transported cargo = 1815 tonne – 90.75 tonne – 4.08375 tonne = 1720.16625 tonne
156.38 kWh/km / 1720.16625 tonne LNG × 500 NM ×1.852 km/NM = 84.18249108 kWh/
tonne transported LNG
Calculations of primary energy use:
•
•
•
84.18249108 kWh/ tonne transported LNG × 0.18292683 kg LNG/kWh = 15.399250 kg LNG
/ tonne transported LNG
15.399250 kg LNG / tonne transported LNG × d5a = 0.166507 g LNG/ tonne km
c5b = d5b + 0.166507 g LNG/ tonne km = 10.9792
Calculation of emissions:
•
•
•
•
0.557 Ib CO /MMBtu /( 1055.06 MJ /MMBtu) × 453,5924 g /Ib = 0.239466 g CO /MJ
0.239466 g CO /MJ × 48 MJ/kg LNG = 0.0115 g CO /g LNG
0.0115 g CO /g LNG × 15.399250 kg LNG / tonne transported LNG = 177.091 g CO/ tonne
transported LNG
0 177.091 g CO/ tonne transported LNG × d5b = 0.0019 g CO /tonne km
LCI data set:
Transportation of LNG 500 NM with product tanker (D5b)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
NMVOC
particles (PM10)
a In tonne per tonne LNG
lxxiv
Unit process
[g/tonne LNG]
D5b
[g /tonne km]
fuel
product
1,540E+04
1,000E+00a
0,1665
10,8127
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
4,213E+04
1,770E+02
4,163E+02
2,692E+02
7,086E+01
2,450E-02
10,8127
4,556E-01
1,914E-03
4,501E-03
2,910E-03
7,662E-04
2,649E-07
Transportation and bunkering (F5a, F5b)
Process type: Unit process
Reference flow: 1 tonne LNG delivered to the Ro-Ro vessel
Data sources: Data from US EPA (2000) has been used other emissions from natural gas combustion
in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Bunker ship with lean burn four stroke engines (main engines) and four stroke
diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0591 kWh per tonne
transported cargo and km see Appendix I Specifications of the modelled vessels for more
details.
The efficiency of the engine is 41%
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
1 MMBtu = 1055060000 J
1 Ib = 453,5924 g
The lower heating value of natural gas is 48 MJ/kg.
Data used:
From U.S. EPA (2000)
Emissions to air from a lean burn four-stroke engine
CO
Ib/MMBtu
5.57E-01
CH4
1.31E+00
NOX
8.47E-01
NMVOC
particles (PM10)
0.223
7.71E-05
Calculations of primary energy use:
•
•
•
•
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ)/ 0.41 = 0.1829268... kg LNG/kWh
≈ 0.183 kg LNG/kWh
0.1829268... kg LNG/kWh × 0.0858 kWh /tonne LNG km × 10 km= 156.951 g LNG
combusted / tonne LNG transported
156.951 g LNG / tonne × f5a = 0.00169679... g LNG combusted/tonne km
c5a = c5b = f5a + 0.0017 g LNG /tonne transported LNG = 10.8127 g / tonne km
Calculation of emissions:
•
•
0.557 Ib CO /MMBtu /( 1055.06 MJ /MMBtu) × 453,5924 g /Ib = 0.239466 g CO /MJ
0.239466 g CO /MJ × 48 MJ/kg LNG = 0.0115 g CO /g LNG
lxxv
•
•
0.0115 g CO /g LNG × 156.951 g LNG combusted g CO = 1.80494 g CO g CO
1.80494 g CO g CO × f5a= 1.6 E-05g CO /tonne km
LCI data set:
Transportation and bunkering (F5a, F5b)
Type of flow
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
NMVOC
particles (PM10)
a In tonne/tonne LNG
lxxvi
Unit process
[g/tonne LNG]
F5a, F5b
[g /tonne km]
fuel
product
1,569E+02
1,000E+00a
0,0017
10,8110
Reference flow
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
1,000E+00a
4,293E+02
1,803E+00
4,241E+00
2,742E+00
7,220E-01
2,496E-04
10,8110
4,641E-03
1,950E-05
4,585E-05
2,965E-05
7,805E-06
2,699E-09
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
Process type: Unit process
Reference flow: 1 tonne cargo transported 1 km
Data sources: Data from US EPA (2000) has been used other emissions from natural gas combustion
in a lean burn four-stroke engine
Representativeness:
•
•
•
Geographical: Technical: Ro-Ro vessel with lean burn four stroke engines (main engines) with and four
stroke diesel engines (auxiliary engines)
Time: -
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0591 kWh per tonne
transported cargo and km see Appendix I Specifications of the modelled vessels for more
details.
The efficiency of the engine is 41%
Natural gas has a carbon content of 75% and 99.5% of the carbon in natural gas is converted
to CO2 during combustion
1 MMBtu = 1055060000 J
1 Ib = 453,5924 g
The lower heating value of natural gas is 48 MJ/kg.
Data used:
From U.S. EPA (2000)
Emissions to air from a lean burn four-stroke engine
CO
Ib/MMBtu
5.57E-01
CH4
1.31E+00
NOX
8.47E-01
NMVOC
particles (PM10)
0.223
7.71E-05
Calculations of primary energy use:
•
•
Specific fuel consumption: 1 / (48 MJ/kg × 1/3.6 kWh /MJ)/ 0.41 = 0.1829268... kg LNG/kWh
≈ 0.183 kg LNG/kWh
f5a = f5b = 0.1829268... kg LNG/kWh × 0.0591 kWh /tonne km = 10.8110 g LNG / tonne km
Calculation of emissions:
•
•
•
0.557 Ib CO /MMBtu /( 1055.06 MJ /MMBtu) × 453,5924 g /Ib = 0.239466 g CO /MJ
0.239466 g CO /MJ × 48 MJ/kg LNG = 0.0115 g CO /g LNG
0.0115 g CO /g LNG × f5a = 0.1243 g CO /tonne km
lxxvii
LCI data set:
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
Inputs:
LNG
Cargo
Outputs:
Cargo
Propulsion energy
CO2
CO
CH4
NOX
NMVOC
particles (PM10)
a Propulsion energy is in kJ per tonne km
b In tonne per tonne km
Type of flow
Fuel
Functional unit
10,8110
1b
Functional unit
1b
0,0591
2,958E+01
1,243E-01
2,923E-01
1,890E-01
4,975E-02
1,720E-05
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
lxxviii
U5a, U5b
[g (kJa)//tonne km]
Appendix F – Uncertainty in the emissions from diesel engines fuelled
with GTL
Emissions of particles, NOX and CO were reduced by 33.5%, 5.2% and 19.5% with GTL compared to
conventional diesel in a test with an intercooled and turbocharged Euro III diesel engine by Wang et
al. (2009). The effect of similar reductions in marine engines is tested in this alternative scenario. The
processes that are changed from the base scenario are yellow in Figure F-1.
9.973 kJ
primary energy / tonne km
9.973 kJ
primary energy / tonne km
22.23 kJ primary energy
/tonne km
22.23 kJ primary energy
/tonne km
Extraction and
transport of
natural gas (A6)
Extraction and
transport of
crude oil,
production of
MGO (G6)
a6 =15.9099 g natural gas / tonne km
Fischer-Tropsch
diesel production
(B6)
g6 = 0.4440 g MGO
/tonne km
b6 = 11.5324 g GTL / tonne km
Unloading,
storage and
loading (H6)
h16 =0.3821 g MGO
/tonne km
Transportation
6000 NM with
product tanker
(C6)
Extraction and
transport of
crude oil,
production of
MGO (G7)
Extraction and
transport of
natural gas (A7)
a7 = 15.9099 g natural gas / tonne km
Fischer-Tropsch
diesel production
(B7)
g7 =0.4440 g MGO
/tonne km
Unloading,
storage and
loading (H7)
b7 =11.5324 g GTL / tonne km
h17 = 0.3821 g MGO
/tonne km
c6 = 11.5324 g GTL / tonne km
h26 =0.0607 g MGO
/tonne km
c7 =11.5324 g GTL / tonne km
Transportation
1000 NM with
product tanker
(D6)
h27 =0607 g MGO
/tonne km
d6 = 11.5324 g GTL / tonne km
h36 = 0.0011 g MGO
/tonne km
e6 = 11.5304 g GTL / tonne km
Bunkering (F6)
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U6)
Unloading,
storage and
loading (E7)
e7 = 11.5304 g LNG / tonne km
20.45 kJ
primary energy
/tonne km
f6 = 11.5304 g GTL / tonne km
Transportation
1000 NM with
product tanker
(D7)
d7 =11.5324 g GTL / tonne km
h37 = 0.0011 g MGO
/tonne km
Unloading, storage
and loading (E6)
Transportation
6000 NM with
product tanker
(C7)
Bunkering (F7)
Production and
transportation of
urea (P7)
0.852
g urea / tonne km
f7 = 11.5304 g LNG / tonne km
Transportation of 1
tonne cargo 1 km
in a RO-Ro vessel
with SCR (U7)
7
6
Figure F-1 Overview of the energy consumption for the two alternatives with GTL as fuel. The data for the
yellow boxes have been changed from the base scenario.
lxxix
Transportation of cargo (U6)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data source: Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission and
fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines fuelled with MGO
Time: The data used were published 2004 and 2001
Other important information:
Assumptions for modelling:
•
•
•
•
•
•
•
•
The energy consumption for the Ro-Ro vessel is estimated to 0.0568 kWh per transported
tonne and km see Appendix I Specifications of the modelled vessels for more details.
The Ro-Ro vessel is assumed to have medium speed engines
GTL has zero sulphur content
Emissions from medium speed diesel engines fuelled with MGO are roughly representative
“HC” minus CH4 in the data from NTM is assumed to be representative for NMVOC
“PM” in the data from NTM is assumed to be PM 10
Emission of sulphur dioxide is calculated from the chemical reaction,
under
the assumption the all sulphur in the fuel reacts with oxygen and forms sulphur dioxide.
Particles, NOX and CO (CO) are reduced by 33.5%, 5.2% and 19.5% compared to if MGO
would have been used
Data used:
Data from NTM (2008)
•
•
•
•
•
•
•
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of ammonia:
Emissions of nitrous oxide:
0.003 g/kWh
0.031 g/kWh
Data from Cooper and Gustafson (2004)
•
•
Calculations of energy use:
•
f6 = 203 g fuel /kWh × 0.0568 kWh /tonne cargo km =11.5304 g fuel/tonne cargo km
Calculations of emissions:
•
1.1 g CO/kWh × 0.0568 kWh /tonne cargo km ×(1- 0.052) = 36.636 g CO2 /tonne cargo km
lxxx
•
•
•
1.1 g CO/kWh × 0.0568 kWh /tonne cargo km ×(1- 0.195) = 0.050 g CO /tonne cargo km
0.3 g PM10 /kWh × 0.0568 kWh /tonne cargo km ×(1- 0.335) = 0.011 g PM10/tonne cargo
km
13.2 g NOX/kWh × 0.0568 kWh /tonne cargo km ×(1- 0.052) = 0.71 g NOX /tonne cargo km
LCI data set:
Transportation of cargo (U6)
Type of flow
Inputs:
GTL
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a Propulsion energy is expressed in kWh instead of g.
fuel
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
lxxxi
U6
[g (kWha)/tonne km]
11,5304
5,680E-02
1,704E-04
3,664E+01
5,030E-02
2,272E-04
7,108E-01
1,761E-03
2,823E-02
1,133E-02
Transportation of cargo (U7)
Process type: Unit process
Reference flow: 1 tonne cargo transported one km
Data sources:
•
•
Cooper (2001) for emissions of ammonia from a medium speed diesel engine with SCR
Data from NTM (2008) and Cooper and Gustafson (2004) were used for emission except of
ammonia and fuel consumption (g/kWh) for the for the Ro-Ro vessel
Representativeness:
•
•
•
Geographical: Emission inventory for marine transportation mainly in Sweden and Europe
Technical: Medium speed diesel engines fuelled with MGO, SCR
Time: The data used were published 2008, 2004 and 2001
Other important information:
Assumptions for modelling:
•
•
•
•
As process above
Particles, NOX and CO (CO) are reduced by 33.5%, 5.2% and 19.5% compared to if MGO
would have been used
Only the emissions of ammonia and NOX is affected by the SCR unit
The SCR unit is assumed to reduce the emissions of NOX with 85 %. It is possible to reach
95% reduction according to Lövblad and Fridell (2006). An 85% has been used here as this
will be enough to fulfil the Tier III.
Data used:
From Cooper (2001)
•
Emissions of ammonia:
0.025 g /kWh
Specific fuel consumption:
Emissions of CO2:
Emissions of NOX:
Emissions of NMVOC:
Emissions of CH4:
Emissions of particles (PM10):
Emissions of CO:
203 g/kWh
645g/kWh
13.2 g/kWh
0. 496 g/kWh
0.004 g/kWh
0.3 g/kWh
1.1 g/kWh
Emissions of nitrous oxide:
0.031 g/kWh
Data from NTM (2008)
•
•
•
•
•
•
•
Data from Cooper and Gustafson (2004)
•
Calculations of energy use:
•
f7 = 203 g fuel /kWh × 0.0568 kWh /tonne cargo km =11.5304 g fuel/tonne cargo km
Calculations of emissions:
lxxxii
•
13.2 g NOX/ kWh × 0.15 × 0.0568 kWh /tonne cargo km ×(1- 0.052) = 0.11 g NOX /tonne
cargo km
LCI data set:
Transportation of cargo (U7)
Type of flow
Inputs:
GTL
urea
Outputs:
propulsion energy
ammonia
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a Propulsion energy is expressed in kWh instead of g.
fuel
product
reference flow
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
emission to air
lxxxiii
U7
[g (kWha)/tonne km]
11,5304
0,8520
5,680E-02
1,420E-03
3,664E+01
5,030E-02
2,272E-04
1,066E-01
1,761E-03
2,698E-02
1,133E-02
Appendix G – Changes in engine efficiency
The same efficiency of about 41 % of the lean burn gas engine as for the four-stroke diesel engines
has been assumed in the modelling. In order to test how this assumption will affect the result an
increase and decrease of engine efficiency with 5 % has been tested. All processes are changed by this
and therefore yellow in Figure G-1 and Figure G-2. Figure G-1 represents the processes and their
energy use if the efficiency of the lean burn four stroke engine is 46% and Figure G-2 represents the
processes and their energy use if the efficiency of the lean burn four stroke engine is 36%.
lxxxiv
6.617 kJ
primary energy / tonne km
7.032 kJ
primary energy / tonne km
Extraction and
transport of
natural gas (A5a)
Extraction and
transport of
natural gas (A5b)
a5a = 10.5555 g
natural gas / tonne km
a5b = 11.2177 g
natural gas / tonne km
Liquefaction (B5a)
Liquefaction (B5b)
b5a = 9.7297 g LNG / tonne km
b5b = 10.3402 g LNG / tonne km
Transportation
350 NM with
product tanker
(C5a)
Transportation
6500 NM with
LNG Carrier (C5b)
c5b = 9.7695 g LNG / tonne km
Unloading,
storage and
loading
c5b = 9.7695 g LNG / tonne km
c5a = 9.6372 g LNG / tonne km
Transportation
500 NM with Coral
Methane (D5b)
d5b = 9.6372 g LNG / tonne km
Unloading,
storage and
loading
Unloading,
storage and
loading
c5a = 9.6372 g LNG / tonne km
d5b = 9.6372 g LNG / tonne km
Bunkering (F5a)
Bunkering (F5b)
f5a = 9.6359 g LNG / tonne km
f5b = 9.6359 g LNG / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5a)
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5b)
5a
5b
Figure G-1 Overview of the energy consumption for the two alternatives with LNG as fuel. The
uncoloured processes these have not been included. The data for the yellow boxes are exchanged from
the base scenario.
lxxxv
8.478 kJ
primary energy / tonne km
9.020 kJ
primary energy / tonne km
Extraction and
transport of
natural gas (A5a)
Extraction and
transport of
natural gas (A5b)
a5a = 13.5237 g
natural gas / tonne km
a5b = 14.3883 g
natural gas / tonne km
Liquefaction (B5a)
Liquefaction (B5b)
b5a = 12.4658 g LNG / tonne km
b5b = 13.2627 g LNG / tonne km
Transportation
350 NM with
product tanker
(C5a)
Transportation
6500 NM with
LNG Carrier (C5b)
c5b = 12.5307 g LNG / tonne km
Unloading,
storage and
loading
c5b = 12.5307 g LNG / tonne km
c5a = 12.3147 g LNG / tonne km
Transportation
500 NM with Coral
Methane (D5b)
d5b = 12.3147 g LNG / tonne km
Unloading,
storage and
loading
Unloading,
storage and
loading
c5a = 12.3147 g LNG / tonne km
d5b = 12.3147 g LNG / tonne km
Bunkering (F5a)
Bunkering (F5b)
f5a = 12.3125 g LNG / tonne km
f5b = 12.3125 g LNG / tonne km
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5a)
Transportation of
1 tonne cargo 1
km in a RO-Ro
vessel (U5b)
5a
5b
Figure G-2 Overview of the energy consumption for the two alternatives with LNG as fuel. The
uncoloured processes these have not been included. The data for the yellow boxes are exchanged from
the base scenario.
lxxxvi
Extraction and transportation of natural gas (A5a, A5b)
See Appendix C.
Calculations of primary energy use:
•
•
•
•
•
(3663 tonne × 43 000 MJ/tonne +11000008 m3 × 36.8 MJ/m3 +123 tonne × 44 100 MJ/tonne)
/ 213 000 tonnes natural gas × 0.23518695 = 626.871 MJ / tonnes natural gas
5a, 46%: 626.871 MJ / tonnes natural gas × a5a = 6.6 kJ /tonne km
5a, 36%: 626.871 MJ / tonnes natural gas × a5a = 8.5 kJ /tonne km
5b, 46%: 626.871 MJ / tonnes natural gas × a5b = 7.0 kJ /tonne km
5b, 36%: 626.871 MJ / tonnes natural gas × a5b = 9.0 kJ /tonne km
LCI data set:
Extraction and transportation of natural gas
Type of flow
Unit process
(A5a)
[ga/tonne natural gas]
Inputs:
Diesel
resource
4,045E+03
Fuel gas
resource
1,215E+01
Jet fuel
resource
1,358E+02
Outputs:
natural gas
Reference flow
1,000E+00b
Emissions to air
6,763E+04
CO2
Emissions to air
9,496E+01
CH4
Emissions to air
1,502E+02
nitrous oxide
Emissions to air
9,165E+01
CO
Emissions to air
4,284E+02
NOX
Emissions to air
9,893E+02
sulphur dioxide
Emissions to air
9,021E+02
NMVOC
a Fuel gas is expressed in Nm3 / tonne natural gas instead of g / tonne natural gas
b In tonne/tonne natural gas
A5a (46%)
[g /tonne km]
Extraction and transportation of natural gas
Type of flow
Unit process
(A5b)
[ga/tonne natural gas]
Inputs:
Diesel
resource
4,045E+03
Fuel gas
resource
1,215E+01
Jet fuel
resource
1,358E+02
Outputs:
natural gas
Reference flow
1,000E+00b
Emissions to air
6,763E+04
CO2
Emissions to air
9,496E+01
CH4
Emissions to air
1,502E+02
nitrous oxide
Emissions to air
9,165E+01
CO
Emissions to air
4,284E+02
NOX
Emissions to air
9,893E+02
sulphur dioxide
Emissions to air
9,021E+02
NMVOC
a Fuel gas is expressed in Nm3 / tonne natural gas instead of g / tonne natural gas
b In tonne/tonne natural gas
A5b (46%)
[g /tonne km]
lxxxvii
A5a (36%)
[g /tonne km]
4,269E-02
1,282E-04
1,434E-03
5,470E-02
1,643E-04
1,837E-03
10,5555
7,138E-01
1,002E-03
1,585E-03
9,674E-04
4,522E-03
1,044E-02
9,522E-03
13,5237
9,146E-01
1,284E-03
2,031E-03
1,239E-03
5,794E-03
1,338E-02
1,220E-02
A5b (36%)
[g /tonne km]
4,537E-02
1,362E-04
1,524E-03
5,819E-02
1,748E-04
1,954E-03
11,2177
7,586E-01
1,065E-03
1,685E-03
1,028E-03
4,806E-03
1,110E-02
1,012E-02
14,3883
9,730E-01
1,366E-03
2,161E-03
1,319E-03
6,164E-03
1,423E-02
1,298E-02
Liquefaction (B5a, B5b)
See Appendix C.
LCI data set:
Liquefaction (B5a)
Type of flow
Inflows:
Natural gas
resource
1,085E+00
10,5555
13,5237
Outflows:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
reference flow
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
1,000E+00
2,279E-01
1,244E-04
1,939E-03
1,866E-04
7,465E-06
1,244E-05
1,244E-06
1,268E-06
9,7297
2,217E+00
1,211E-03
1,887E-02
1,816E-03
7,263E-05
1,211E-04
1,211E-05
1,234E-05
12,4658
2,840E+00
1,551E-03
2,417E-02
2,326E-03
9,306E-05
1,551E-04
1,551E-05
1,580E-05
Liquefaction (B5b)
Type of flow
Inflows:
Natural gas
resource
1,085E+00
11,2177
14,3883
Outflows:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
reference flow
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
1,000E+00
2,279E-01
1,244E-04
1,939E-03
1,866E-04
7,465E-06
1,244E-05
1,244E-06
1,268E-06
10,3402
2,356E+00
1,286E-03
2,005E-02
1,930E-03
7,719E-05
1,286E-04
1,286E-05
1,311E-05
13,2627
3,022E+00
1,650E-03
2,572E-02
2,475E-03
9,901E-05
1,650E-04
1,650E-05
1,681E-05
lxxxviii
Unit process
[tonne/tonne LNG]
Unit process
[tonne/tonne LNG]
B5a (46%)
[g/tonne km]
B5b (46%)
[g/tonne km]
B5a (36%)
[g/tonne km]
B5b (36%)
[g/tonne km]
Transportation of LNG 350 NM with product tanker (C5a)
See Appendix C.
LCI data set:
Transportation of LNG 350 NM with product tanker (C5a)
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne per tonne LNG
Type of flow
Unit process
[g/tonne
LNG]
D5a (46%)
[g /tonne km]
D5a (36%)
[g /tonne km]
fuel
product
1,077E+04
1,000E+00a
0,0925
9,6372
0,1511
12,3147
Reference
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
1,000E+00a
2,947E+04
1,448E+02
1,456E+02
9,015E+01
4,825E+00
9,6372
2,531E-01
1,243E-03
1,251E-03
7,743E-04
4,145E-05
12,3147
4,133E-01
2,030E-03
2,042E-03
1,264E-03
6,767E-05
Transportation of LNG 6500 NM with LNG carrier (C5b)
See Appendix C.
LCI data set:
Transportation of LNG 6500 NM with LNG carrier (C5b)
Type of flow
Inputs:
LNG
LNG
Fuel
Product
5,842E-02a
1,000E+00a
0,5707
9,7695
0,7320
12,5307
Reference
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
1,000E+00a
1,598E+05
1,092E+02
2,924E+00
1,039E+02
2,924E+00
7,176E+00
9,834E+00
9,7695
1,562E+00
1,067E-03
2,856E-05
1,015E-03
2,856E-05
7,011E-05
9,607E-05
12,5307
2,003E+00
1,369E-03
3,663E-05
1,302E-03
3,663E-05
8,992E-05
1,232E-04
Outputs:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
a In tonne per tonne LNG
lxxxix
Unit process
[g /tonne
LNG]
C5b (46%)
[g /tonne km]
C5b (36%)
[g /tonne km]
Transportation of LNG 500 NM with product tanker (D5b)
See Appendix C.
LCI data set:
Transportation of LNG 500 NM with product tanker (D5b)
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne per tonne LNG
Type of flow
Unit process
[g/tonne
LNG]
D5b (46%)
[g /tonne km]
D5b (36%)
[g /tonne km]
fuel
product
1,540E+04
1,000E+00a
0,1323
9,6372
0,2160
12,3147
Reference
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
1,000E+00a
4,213E+04
2,070E+02
2,082E+02
1,289E+02
6,899E+00
9,6372
3,619E-01
1,778E-03
1,788E-03
1,107E-03
3,619E-01
12,3147
5,909E-01
2,903E-03
2,920E-03
1,808E-03
9,675E-05
Transportation and bunkering (F5a, F5b)
See Appendix C.
LCI data set:
Transportation and bunkering (F5a, F5b)
Inputs:
LNG
LNG
Outputs:
LNG
CO2
CO
CH4
NOX
particles (PM10)
a In tonne/tonne LNG
Type of flow
Unit process
[g/tonne
LNG]
F5a, F5b (46%)
[g /tonne km]
F5a, F5b (36%)
[g /tonne km]
fuel
product
1,569E+02
1,000E+00a
0,0013
9,6359
0,0022
12,3125
Reference
Emissions to
Emissions to
Emissions to
Emissions to
Emissions to
1,000E+00a
4,293E+02
2,109E+00
2,121E+00
1,313E+00
7,028E-02
9,6359
3,687E-03
1,811E-05
1,822E-05
1,128E-05
6,036E-07
12,3125
6,019E-03
2,957E-05
2,974E-05
1,841E-05
9,856E-07
xc
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
See Appendix C.
An engine efficiency of 36% and 46 % is modelled.
Calculations of primary energy use:
•
•
•
•
Specific fuel consumption (46%): 1 / (48 MJ/kg × 1/3.6 kWh /MJ )/ 0.46 = 0.163043 kg
LNG/kWh ≈ 0.163 kg LNG/kWh
f5a = f5b = 0.163043 kg LNG/kWh × 0.0591 kWh /tonne km = 9.6359 g LNG / tonne km
Specific fuel consumption (36%): 1 / (48 MJ/kg × 1/3.6 kWh /MJ )/ 0.36 = 0.208333 kg
LNG/kWh ≈ 0.208 kg LNG/kWh
f5a = f5b = 0.208333 kg LNG/kWh × 0.0591 kWh /tonne km = 12.3125 g LNG / tonne km
LCI data set:
Transportation of 1 tonne cargo 1 km with a Ro-Ro vessel (U5a, U5b)
Inputs:
LNG
Cargo
Outputs:
Cargo
Propulsion energy
CO2
Type of flow
a Propulsion energy is in kJ per tonne km
b In tonne per tonne km
xci
U5a, U5b (36%)
[g (kJa)//tonne
km]
Fuel
Functional unit
9,6359
1b
12,3125
1b
Functional unit
1b
0,0591
2,637E+01
1,295E-01
1,303E-01
8,065E-02
4,317E-03
1b
0,0591
3,369E+01
1,655E-01
1,665E-01
1,031E-01
5,516E-03
Emissions to air
Emissions to air
Emissions to air
Emissions to air
Emissions to air
CO
CH4
NOX
particles (PM10)
U5a, U5b (46 %)
[g (kJa)//tonne
km]
Appendix H – Changes in liquefaction efficiency
An alternative scenario there only 5% of the gas is consumed during liquefaction and no other
leakages of CH4 than from the combustion process is studied as an example on the effects of improved
liquefaction efficiency. An alternative there 12% of the gas is used in the liquefaction process is also
investigated. The processes marked with yellow in Figure H-1 are changed from the base scenario. It
is only the alternative with LNG produced in Norway that is analysed. LNG from Qatar will be affected
in the same way by increased and decreased liquefaction efficiency.
Figure H-1 Overview of the energy consumption for the alternative with LNG transported from North Sea
as fuel. The uncoloured processes these have not been included. The data for the yellow boxes are
changed from the base scenario.
xciii
Extraction and transportation of natural gas (A5a)
See Appendix C.
Calculations of primary energy use:
•
•
•
(3663 tonne × 43 000 MJ/tonne +11000008 m3 × 36.8 MJ/m3 +123 tonne × 44 100 MJ/tonne)
/ 213 000 tonnes natural gas × 0.23518695 = 626.871 MJ / tonnes natural gas
High efficiency, 5a: 626.871 MJ / tonnes natural gas × a5a = 7.2 kJ /tonne km
Low efficiency, 5a: 626.871 MJ / tonnes natural gas × a5a = 7.7 kJ /tonne km
LCI data set:
Extraction and transportation of natural gas
Type of flow
Unit process
(A5a)
[ga/tonne natural gas]
Inputs:
Diesel
resource
4,045E+03
Fuel gas
resource
1,215E+01
Jet fuel
resource
1,358E+02
Outputs:
natural gas
Reference flow
1,000E+00b
Emissions to air
6,763E+04
CO2
Emissions to air
9,496E+01
CH4
Emissions to air
1,502E+02
nitrous oxide
Emissions to air
9,165E+01
CO
Emissions to air
4,284E+02
NOX
Emissions to air
9,893E+02
sulphur dioxide
Emissions to air
9,021E+02
NMVOC
a Fuel gas is expressed in Nm3 / tonne natural gas instead of g / tonne natural gas
b In tonne/tonne natural gas
xciv
A5a (high)
[g /tonne km]
A5a (low)
[g /tonne km]
4,641E-02
1,394E-04
1,559E-03
4,951E-02
1,487E-04
1,662E-03
11,4756
7,761E-01
1,090E-03
1,723E-03
1,052E-03
4,916E-03
1,135E-02
1,035E-02
12,2406
8,278E-01
1,162E-03
1,838E-03
1,122E-03
5,244E-03
1,211E-02
1,104E-02
Liquefaction (B5a)
Process type: Unit process
Reference flow: 1 MJ LNG
Data source:
•
•
Data for energy use and greenhouse gas emissions from JEC (2008b, 2008a)
Data for other emissions from natural gas combustion in combined cycle gas turbine from
GEMIS version 4.5 (2010).
Representativeness:
•
•
•
Geographical: Europe
Technical: Consider technologies that are expected to be commercial available in 2010-2020
Time: 2010-2020
Other important information:
Assumptions for modelling:
•
•
•
•
•
The lower heating value of natural gas is 48 MJ/kg.
The combined cycle gas turbine have an efficiency of 57 % in the case with high efficiency
and an efficiency of 52.5% in the case with low liquefaction efficiency
5% of the gas is consumed in the case with high efficiency
12 % of the gas is consumed in the case with low efficiency
100% of the flared gas is converted to CO2 according to the following formula: CH4 + 2O2
CO2 + 2H2O. 1 g flared CH4 thus results in approximately 0.363636 g CO2 emissions.
(
•
.)
The liquefaction process is modelled according to Figure H-2.
o High efficiency: X + 1.02313 MJ natural gas = 1.05 MJ natural gas => X = 0.02687. Y =
X × 0.57 – 0.00085 = 0.0144659 MJ electricity
o Low efficiency: X + 1.02313 MJ natural gas = 1.12 MJ natural gas => X = 0.09687. Y =
X × 0.525 – 0.00085 = 0.05000675 MJ electricity
xcv
X MJ natural gas
1.02313 MJ natural gas
CCGT, 52.5% alt.
57% efficiency
Y MJ electricity
Liquefaction
1.01 MJ LNG
0.00085 MJ electricty
Storage at
terminal
1 MJ LNG
Figure H-2 Modelling of the liquefaction process.
Data used:
From JEC (2008b)
•
•
0.17% leakage of CH4 from the liquefaction process
0.25% of the gas is flared
From GEMIS version 4.5
Type of flow
Inflows:
Natural gas
fuel
1,754E+00
1,905E+00
TJ
Outflows:
Electricity
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
Energy carrier
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
1,000E+00
9,676E+04
7,366E+01
5,524E+00
1,105E+02
4,419E+00
7,366E+00
7,366E-01
7,506E-01
1,000E+00
1,051E+05
7,997E+01
5,998E+00
1,200E+02
4,798E+00
7,997E+00
7,997E-01
8,149E-01
TJ
kg
kg
kg
kg
kg
kg
kg
kg
Data as collected
(57 % efficiency)
Data as collected
(52.5 % efficiency)
Unit
Comment: The efficiency is 52.5%.
Calculations of primary energy consumption:
•
•
High efficiency:
o (0.02687 MJ natural gas + 1.02313 MJ natural gas) / MJ LNG = 1.05 MJ natural gas
/MJ LNG = 1.05 tonne natural gas / tonne LNG
o B5a: = 1.05 tonne natural gas / tonne LNG × b5a = 11.4756 g natural gas /tonne km
Low efficiency:
o (0.09687 MJ natural gas + 1.02313 MJ natural gas) / MJ LNG = 1.12 MJ natural gas
/MJ LNG = 1.12 tonne natural gas / tonne LNG
xcvi
o
B5a: 1.12 tonne natural gas / tonne LNG × b5a = 12.2406 g natural gas /tonne km
Calculations of CH4 emissions:
•
High efficiency:
o 0.00085 MJ electricity + 0.0144659 MJ electricity = 0.0153159 MJ electricity / MJ
LNG
o 5.524 kg CH4 / 1 TJ electricity × 0.0153159 MJ electricity / MJ LNG = 8.46050315 E05 g CH4 / MJ LNG
o 8.46050315 E-05 g CH4 / MJ LNG × 48 000 MJ /tonne LNG = 4.0610415 g CH4
/tonne LNG
o 4.0610415 g CH4 /tonne LNG + 1700 g CH4 / tonne LNG = 1704.0610415 g CH4 /
tonne LNG = 1.704 E-03 tonne CH4/ tonne LNG
o B5a: 1704.0610415 g CH4 / tonne LNG × b5a = 0.019 g CH4 / tonne cargo km
Calculations of other emissions:
•
High efficiency:
o 0.00085 MJ electricity + 0.0144659 MJ electricity = 0.0153159 MJ electricity / MJ
LNG
o 73.66 kg CO / 1 TJ electricity × 0.0153159 MJ electricity / MJ LNG = 0.001065558 g
CO / MJ LNG
o 0.001065558 g CO / MJ LNG × 48 000 MJ /tonne LNG = 54 g CO /tonne LNG
o B5a: 54 g CO /tonne LNG × b5a = 0.00059 g CO / tonne cargo km
LCI data set:
Liquefaction (B5a, high efficiency)
Type of flow
Inflows:
Natural gas
resource
Outflows:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
reference flow
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
xcvii
Unit process
[tonne /tonne LNG]
B5a (high)
[g/tonne km]
1,0500
11,4756
1,0000
7,204E-02
5,415E-05
1,704E-03
8,123E-05
3,249E-06
5,415E-06
5,415E-07
5,518E-07
10,9291
7,873E-01
5,918E-04
1,862E-02
8,877E-04
3,551E-05
5,918E-05
5,918E-06
6,031E-06
Liquefaction (B5a, low efficiency)
Type of flow
Inflows:
Natural gas
resource
Outflows:
LNG
CO2
CO
CH4
NOX
nitrous oxide
NMVOC
particles (PM10)
sulphur dioxide
reference flow
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
Emission to air
xcviii
Unit process
[tonne/tonne LNG]
B5a (low)
[g/tonne km]
1,1200
12,2406
1,0000
2,573E-01
1,952E-04
1,715E-03
2,928E-04
1,171E-05
1,952E-05
1,952E-06
1,989E-06
10,9291
2,813E+00
2,134E-03
1,874E-02
3,200E-03
1,280E-04
2,134E-04
2,134E-05
2,174E-05
Appendix I – Specifications of the modelled vessels
Eight different ship types has been modelled in this study, two bunker ships, three product tankers and two Ro-Ro vessels. The model parameters are
presented in Table I-1.
Table I-1: Specifications of the modelled ship types.
Ship
type
Engine
load
Engine
(kW)
Bunker
ship.
HFO
0.85
Bunker
ship,
LNG
Product
tanker/
Crude
carrier,
HFO
LNG
carrier,
LNG
Product
tanker,
LNG
Design speed
Cargo capacity
Load
factor
Pay
load
Fuel
Engine
0.55
0.95
HFO
MSD
2460
(knot)
8
(km/h)
14.816
DWT
6370
(m3)
6800
0.85
2460
8
14.816
6370
6800
0.55
0.95
SG
LNG
0.85
12330
15.2
28.15
50301
57741
0.55
0.95
HFO
0.85
27300
19.5
36.114
83050
147237
0.55
0.95
0.85
4770
14
25.928
4700
7500
0.55
Ro-Ro
vessel
0.85
14680
17.98
33.3
15000
-
Ro-Ro
vessel,
LNG
0.85
14680
17.98
33.3
15000
-
Comments
Reference
Whitona: double hull
bunkering tanker.
Bunkering capacity
3
1000 m /h
Bunkering capacity
3
1000 m /h
Significant ships of 2006
(Knaggs, 2007)
MSD
EMIRATRS STAR.
Handymax
Significant ships of 2008
(Knaggs, 2009)
LNG
Steam
turbine
Significant ships of 2008
(Knaggs, 2009)
0.95
LNG
0.88
0.5
0.88
0.48
HFO.
MGO
. GTL
LNG
Diesel
engine
and gas
engine
MSD
Dapeng sun: first
Chinese-built LNG
carrier
Coral CH4
xcix
4-stroke.
lean
burn
Significant ships of 2007
(Knaggs, 2008)
(Gullaksen, 2009)
Assumptions
Assumed bunker
3
capacity 1000 m
Assumptions based on
information from Gisle Rong
(2009) at Seatrans AS
The pay load for the Ro-Ro vessel fuelled with LNG is 0.48 instead of 0.50 as for the other Ro-Ro vessels. This depends on the extra space needed for the LNG
tank. Gisle Rong (2009) at Seatrans AS estimates that a volume of about 19.4 x 10.6 x 2.8 m would be available for cargo if their ordered LNG vessels were to be
fuelled with HFO instead. This space could be used for 9 containers, which corresponds to about 180 tonne (if 1 TEU in average represents 20 tonne as
suggested in IMO’s document Interim Guidelines for Voluntary Ship CO2 Emission Indexing for the Use in Trials (IMO, 2005)). Seatrans AS’s Ro-Ro vessel that
Gisle refers to have a tank capacity of about 460 m3 LNG which represents approximately 200 tonne LNG. The Ro-Ro vessel would need to bunker
approximately each forth day if about 50 tonnes LNG are used each day.
The energy consumption per tonne and kilometre for the modelled ship types are calculated either based on the dead weight tonne (dwt) or based on their
tank capacity. The following formulas are used:
(1.1)
(1.2)
(1.3)
(1.4)
(1.5)
c
The result of the calculations from equations (1.1) – (1.5) is presented in Table I-2. Calculations based on tank capacity are used for all tanker vessels (bunkers
ships. product tankers and the LNG carrier). For the Ro-Ro vessels calculations based on dwt is used instead.
Ship type
Bunker
ship. HFO
Bunker
ship. LNG
Product
tanker,
HFO
LNG
carrier.
10
LNG
Product
tanker.
10
LNG
Ro-Ro
vessel
Ro-Ro
vessel.
LNG
Table I-2 Energy consumption per tonne and kilometre for the modelled ships. The calculations are based on the information in Table I-1.
Energy consumption (kWh/km) Calculations based on dwt
Calculations based on tank capacity
Used value
Average cargo
loaded (tonne)
Energy consumption
(kWh/tonne km)
Average cargo mass
(tonne)
Energy consumption
(kWh/tonne km)
Energy consumption
(kWh/tonne km)
141.13
3328
0.0424
3123
0.0452
0.0452
141.13
3328
0.0424
1646
0.0858
0.0858
327.30
26282
0.0142
26518
0.0140
0.0140
642.55
-
-
-
-
-
156.38
-
-
-
-
-
374.71
6600
0.0568
-
-
0.0568
374.71
6336
0.0591
-
-
0.0591
10 Energy consumption (kWh/tonne km) depends on the duration of the journey since the boil-off gas differs and therefore the amount of transported cargo. The energy
consumption is thus modelled separately for each journey.
ci