Optimizing the Machinery System Selection in a Ship Design Process, Taking Environmental Regulations and Uncertainty into Consideration

Selection in a Ship Design Process, Taking Environmental Regulations and Uncertainty into Consideration

Karoline Jespersen

Marine Technology

Supervisor: Bjørn Egil Asbjørnslett, IMT

Department of Marine Technology Submission date: June 2015

Norwegian University of Science and Technology

P REFACE

This is a master’s thesis written as the final part of the Master of Science degree within Marine Technology at the Norwegian University of Science and Technology. The candidate has a specialization in Marine Systems Design with an extra focus on Operations Research.

This master’s thesis is conducted as a concluding piece of the academic work, written by the candidate alone with a 30 credits work load.

The thesis present the development and evaluation of a stochastic optimization model that aims to optimize selections regarding machinery solutions for a ship in the design phase.

Special considerations are made for air emission regulations and uncertain future elements.

The work with the master’s thesis has provided me with a lot of knowledge within the maritime industry, especially regarding environmental footprints by the industry, and how these can be reduced.

I would like to thank my supervisor, Professor Bjørn Egil Asbjørnslett, for valuable advices and support during the work of this thesis. Thanks also to Professor Kjetil Fagerholt for helpful modelling input. Finally, I want to thank my Jørgen, for patience and helpful feedback during my months of writing.

Trondheim, June 10^th, 2015

Karoline Jespersen

E XECUTIVE S UMMARY

Shipping is known for being the most efficient mode of transportation. The world's seaborne trade totalled to almost 9.6 billion tons in 2013, and the maritime shipping industry carries about 90% of globally transported goods (UNCTAD, 2014, IMO, 2014a). This implies significant environmental footprints, and a big issue is how to reduce the harmful emissions from ships.

The objective in this thesis is developing an optimization model that optimizes the concurrent selection of machinery system, fuel type used and air emission controls installed on a ship. An emission control is any measure that can be installed on the ship with the intention of

reducing emissions. The model is applied for a ship in the design phase, and an evaluation of uncertain elements to consider for the operation of the ship in the future, is performed.

In this thesis, the regulations of CO2, SOx and NOx are considered and challenges related to the stricter regulations applying in special emission control areas (ECAs) is accounted for. The revised Annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL) is reference for the emission goals for the work presented.

The optimization model developed is a stochastic two-stage recourse model. The stochastic aspect account for uncertain fuel prices in the future. The model’s objective function is cost driven and summarizes the total installation and operational cost for the machinery system and emission controls selected. Interaction effects are taken into consideration. One of the main constraints are ensuring compliance with the regulations given by considering the reduction in emissions from the selected main fuel type and emission controls.

The first-stage of the stochastic model assumes known values for all elements considered.

The second-stage, evaluates a set of scenarios where possible fuel projections are

represented. Each scenario is represented with a given probability. The goal of the model is to provide a balanced view of the future in order to make better decisions today.

Scenarios are generated with different projections of the fuel types HFO, MGO and LNG from 2020 to 2030. Emission controls are ensuring compliance with the regulations together with the selection of fuel, and the number of emission controls included in the case study for the model totals to 42. In the scenario generation a dependence between the development of HFO and MGO price is taken into account while the price development of LNG is considered independent. This results in 18 scenarios to evaluate, where eight of these have been taken into further consideration.

A splitting of the execution of the model is found necessary for obtaining a reasonable

running time. The running of the model on 13 SOx and NOx controls only, reduces the running

time to 7 seconds with 18 scenarios and 15 time periods. Similarly, when the model is running for the 29 additional CO2 controls, optimal solution is found in 930 seconds. The splitting of the execution of the model can be justified from the fact that the CO2 measures are

independent of the scenarios, and there are not significant interaction effects present between the two parts.

The stochastic model is executed with input data evaluated in a case study. The results from the model, concludes with running on HFO and installing an exhaust gas recirculation system and a seawater scrubber as emission controls, in order to comply with given regulations. The equivalent deterministic model applied for comparison, presents the same conclusion.

The presented results include special attention to certain changes in the input data, and how these changes affect the optimal solution presented by the model. The stochastic model did not come to a different conclusion than the corresponding deterministic model when original input data was applied. The stochastic model is still considered a favourable approach, as it represents possible outcomes of the future in a better way. The results presented show that when several outcomes of the future are assessed, a more diverse evaluation can be

performed. Especially positive, is the short running time of the presented stochastic model when executing the model with NOx and SOx reducing measures only. This model is promising for producing decision support that is valuable in a real-life setting.

S AMMENDRAG

Skipsfart er kjent for å være det mest energieffektive transportmiddelet. Nesten 9,6 milliarder tonn ble transportert via sjøveien i 2013, og den maritime skipsfarten står for omtrent 90 % av globalt transporterte handelsvarer (UNCTAD, 2014, IMO, 2014a). Dette innebærer betydelige miljøavtrykk, og et problem er reduksjonen av skadelige utslipp fra skip.

Målet i denne masteroppgaven, er å utvikle en optimeringsmodell hvor det samtidige valget av maskinerisystem, drivstoff og utslippsreduserende tiltak er optimert. Modellen anvendes for et skip i designfasen, og en vurdering av usikre elementer for evaluering av skipet i fremtiden er utført.

I denne oppgaven betraktes grenser for utslipp av CO2, SOx og NOx, og utfordringer knyttet til strengere reglement, spesielt i særlig utslippskontrollerte områder, er gjort rede for. Det reviderte Annex VI i den internasjonale konvensjonen for forebyggelse av forurensning fra skip (MARPOL) er referanse for de utslippsmålene som er presentert i oppgaven.

Optimeringsmodellen utviklet, er en stokastisk to-stegsmodell. Det stokastiske aspektet tar høyde for usikre drivstoffpriser i fremtiden. Modellens målfunksjon er kostnadsdrevet og summerer de totale installasjons- og driftskostnadene for et maskinerisystem og

utslippsreduserende tiltak. En av de viktigste restriksjonene sikrer at utslippsreduksjonsmålet blir oppfylt ved å legge sammen reduksjonen i utslippene ved valg av drivstofftype og

utslippsreduserende tiltak.

Det første steget i den stokastiske modellen antar kjente verdier for alle betraktede

elementer. Andre steg, evaluerer et sett med scenarier hvor forskjellige mulige drivstoffpriser er representert. Hvert scenario er tilknyttet en gitt sannsynlighet. Målet med en stokastisk modell er å gjøre en balansert vurdering av fremtiden for å ta bedre beslutninger i dag.

Scenarier er generert med forskjellige projeksjoner av drivstofftypene HFO, MGO og LNG fra 2020 til 2030. Antall utslippsreduserende teknologier inkludert i casestudie for modellen er til sammen 42. I genereringen av scenariene er en avhengighet mellom utviklingen av HFO- og MGO-prisen er tatt hensyn til, mens prisutviklingen på LNG anses som uavhengig. Dette resulterer i totalt 18 scenarier til evaluering, hvor åtte av disse har blitt tatt i videre betraktning.

Det er funnet nødvendig å splitte kjøringen av modellen for å oppnå en fornuftig kjøretid. Ved å kjøre modellen for 13 SOx og NOx reduserende tiltak blir driftstiden redusert til 7 sekunder med 18 scenarier og 15 tidsperioder. Tilsvarende når modellen kjøres for de 29 CO2

reduserende teknologiene, er optimal løsning funnet på 930 sekunder. Dette kan med fordel

utføres ettersom de CO2 reduserende tiltakene ikke blir påvirket av scenariene som betraktes, og det heller ikke er noen særlige påvirkninger mellom teknologiene, som forsvinner.

Den stokastiske modellen er kjørt med data evaluert i et casestudie. Resultatene fra

modellen, konkluderer med å bruke HFO som drivstoff og installere et resirkuleringssystem for eksos (EGR) og en sjøvannsscrubber, som sørger for at forskriftene for utslipp er

overholdt. En tilsvarende deterministisk modell blir brukt til sammenligning, og denne presenterer den samme konklusjonen.

Resultatene presentert i oppgaven inkluderer visse endinger av inputdata som er foretatt for å evaluere modellen. Hvordan disse endringene påvirker den optimale løsningen er forklart.

Den stokastiske modellen kom ikke til en annen konklusjon enn den tilsvarende

deterministiske modellen med de opprinnelige inputverdiene. Den stokastiske modellen er uansett betraktet som en gunstig tilnærming til problemet, ettersom den representerer mulige fremtidige aspekter på en bedre måte. Resultatene som er presentert viser at det å inkludere usikre scenarier for fremtiden, som gjøres i en stokastisk modell, kan gi et mer nyansert og mangfoldig resultat. Spesielt positivt er det for modellen, at kort kjøretid ble vist når modellen kjøres med kun NOx og SOx reduserende tiltak. Dette viser at modellen har et potensiale som gjør at en videre utvikling av modellen kan gjøre den til verdifull

beslutningsstøtte i et virkelig tilfelle.

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T ABLE OF C ONTENT

1 Introduction ... 1

2 Problem Description ... 3

2.1 Machinery Systems ... 3

2.2 Air Emission Regulations ... 5

2.3 Fuel ... 9

2.4 Air Emission Controls ... 11

2.5 Addressing Uncertainty ... 12

3 State of The Art ... 13

4 Model Description ... 17

4.1 Stochastic Programming ... 20

4.2 Special Considerations for the Adopted Deterministic Model ... 21

4.3 Uncertain Elements ... 22

4.3.1 Alteration of Future Regulations and Emission Control Areas ... 22

4.3.2 Change in Fuel Prices ... 23

4.3.3 Change in Availability of Fuel ... 23

4.3.4 Reliability of Air Emission Reduction Technologies ... 24

4.3.5 Change in Power Demand ... 25

4.3.6 Concluding Remarks Regarding Uncertain Elements for the Model ... 25

4.4 Timing of Decisions ... 26

4.5 Scenarios ... 26

5 Mathematical Model ... 29

5.1.1 Modelling Assumptions and Formulation ... 29

6 Case Study ... 37

6.1 The Machinery System ... 38

6.2 Fuel ... 40

6.3 Emission Goals ... 44

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6.4 Emission Controls ... 44

6.5 Evaluation of The Selection of Scenarios ... 48

6.6 Scenarios ... 51

7 Results ... 57

7.1 The Effect on The Optimal Solution By Changing The Probabilities Applied to Each Scenario ... 60

7.2 The Effect on The Optimal Solution By Applying a Reduced Installation Cost of The Dual Fuel Alternatives... 61

7.3 The Effect on The Optimal Solution By Applying Altered Initial Fuel Prices ... 62

7.4 The Effect on The Optimal Solution By Considering a Different Time Spent in ECAs . 63 8 Discussion ... 65

8.1 Discussion of Modelling Assumptions ... 65

8.2 Discussion of The Input Data Assumptions ... 67

9 Conclusion and Further Work ... 73

9.1 Further Work ... 74

References ... 77

Appendix A – Deterministic Model Used as Basis ... 79

Appendix B – Stochastic Model ... 83

Appendix C – Xpress Model ... 87

Appendix D – Results from Evaluation of Scenarios ... 102

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T ABLES

Table 1: Outline of IMO Regulations 13 and 14 on NOx and SOx respectively. (IMO) ... 6 Table 2: Potential alternative fuels or energy carriers for the future as presented by DNV GL (2014a) ... 9 Table 3: A list of assumptions made in the optimization model by Balland et al. (2014) ... 21 Table 4: Uncertain elements related to the reliability of air emission reduction technologies in the deterministic model by Balland et al. (2014) ... 25 Table 5: Presentation of the eighteen fuel price scenarios to include in the initial version of the model ... 42 Table 6: Presentation of emission controls considered in the case, including reduction effect, cost and lifetime. Alphebetically sorted ... 47 Table 7: Presentation of CO2 emission controls considered in the case, including reduction effect, cost and lifetime. Alphabetically sorted. ... 48 Table 8: Strategies for selecting different composition of scenarios to evaluate in the model 49 Table 9: Presentation of eight fuel price scenarios to include in the final version of the model ... 51 Table 10: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 1... 52 Table 11: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 2... 52 Table 12: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 7... 53 Table 13: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 8... 53 Table 14: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 10 ... 54 Table 15: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 11 ... 54 Table 16: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 14 ... 55 Table 17: Graphical representation of the fuel price projections of HFO, MGO and LNG in Scenario 16 ... 55 Table 18: Results from the running of the stochastic optimization model for NOx and SOx

measures ... 57 Table 19: Results from the running of the deterministic optimization model for NOx and SOx

measures ... 57

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Table 20: Results from the running of the stochastic optimization model for CO2 measures .. 58 Table 21: Presentation of an alternative probability distribution for the scenarios in the case study ... 60 Table 22: Alternative initial prices applied to the input values for the model ... 62 Table 23: Sets, parameters and variables in the optimization model given by Balland et al.

(2014) ... 79 Table 24: Sets, parameters and variables in the optimization model ... 83 Table 25: Strategies for selecting different composition of scenarios to evaluate in the model ... 102 Table 26: Second-stage decisions in the evaluation of included scenarios with Strategy 1 ... 102 Table 27: Second-stage decisions in the evaluation of included scenarios with Strategy 3 ... 103 Table 28: Second-stage decisions in the evaluation of included scenarios with Strategy 4 ... 103 Table 29: Second-stage decisions in the evaluation of included scenarios with Strategy 6 ... 104 Table 30: Second-stage decisions in the evaluation of included scenarios with Strategy 7 ... 104 Table 31: Second-stage decisions in the evaluation of included scenarios with Strategy 8 ... 104 Table 32: Second-stage decisions in the evaluation of included scenarios with Strategy 9 ... 105

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F IGURES

Figure 1: Flow process and typical exhaust gas composition in a diesel engine (MAN B&W

Diesel A/S) ... 4

Figure 2: Map over present and possible future emission control areas as defined by IMO (DNV GL, 2011) ... 6

Figure 3: Diagram illustrating the sulphur limits’ development over time based on IMO regulations ... 7

Figure 4: Diagram illustrating the NOx Tiers' development over time based on IMO regulations for ships with rpm < 130 ... 8

Figure 5: Diagram illustrating the NOx Tiers and the correlation with the engine's rated speed based on IMO regulations ... 8

Figure 6: WTP GHG emissions results for marine alternative fuels (DNV GL, 2014a) ... 10

Figure 7: Flow chart showing the overall dynamics of the two-stage stochastic model ... 19

Figure 8: Scenario tree showing the structure of the model ... 27

Figure 9: Timeline illustrating interaction with regulations and steps in the stochastic model 28 Figure 10: Fuel projections for the HFO price at Low, Reference and High level ... 40

Figure 11: Fuel projections for the MGO price at Low, Reference and High level ... 41

Figure 12: Fuel projections for the LNG price at Low, Reference and High level ... 41

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xiv

A BBREVIATIONS

CDD Conventional Direct Drive

CMDE Combined Mechanical Drive and Diesel Electric

CO2 Carbon Dioxide

DE Diesel Electric DME Di-Methyl Ether DWI Direct Water Injection ECA Emission Control Areas EEDI Energy Efficiency Design Index

EIAPP Engine International Air Pollution Prevention gCO2eq Grams of CO2 Equivalent

GHG Greenhouse Gas

HAM Humid Air Motor HFO Heavy Fuel Oil

IMO International Maritime Organization LNG Liquefied Natural Gas

LPG Liquefied Petroleum Gas

MARPOL International Convention for the Prevention of Pollution from Ships

MD Mechanical Drive

MDO Marine Diesel Oil MGO Marine Gas Oil MJF Mega Joule of Fuel

MPEC Marine Environment Protection Committee NECA Nitrogen Regulated Emission Control Area NOx Nitric Oxides

NTNU Norwegian University of Science and Technology

OR Operations Research

PM Particulate Matter RPM Revolutions per Minute

SECA Sulphur Regulated Emission Control Area SEEMP Ship Energy Efficiency Management Plan

SOx Sulphur Oxides

TTP Tank-To-Propeller

VOC Volatile Organic Compounds VRP Vehicle Routing Problem WHR Waste Heat Recovery WTP Well-To-Propeller

WTT Well-To-Tank

1

1 I NTRODUCTION

In 2013, the world’s seaborne trade totalled to a volume of almost 9.6 billion tons (UNCTAD, 2014). Shipping has been generally accepted as the most cost-efficient mode of

transportation, especially when you consider bulk transportation over long distances. This is reflected in the fact that the international maritime shipping industry carries about 90% of the world trade (IMO, 2014a). To keep up with international economic growth, it is expected that the shipping industry will follow, and the world fleet will expand with the economic development.

The world’s ships play a vital role in the global economy, and although shipping is considered an effective mode of transportation, such a role comes with a significant responsibility for ensuring a sustainable future.

Big actors in the maritime business have recognised, and are promoting some of the

challenges that shipping is facing. DNV GL published in 2014 a report on their outlook for the future of shipping (Longva et al., 2014) where environmental sustainability received great focus. The International Maritime Organization (IMO) has acknowledged this focus a long time. In 1973, the International Convention for the Prevention of Pollution from Ships (MARPOL) was adopted at IMO, and emphasis was made on air pollution when Annex VI was added in 1997, and entered into force for the first time in 2005.

Hence it is a widely known issue that shipping leave significant environmental footprints and reducing these will be of great importance for the global climate. Several pollutants receive attention in the climate discussions, both pollutants to air and sea. The MARPOL Convention contains six annexes addressing pollution from ships by oil; by noxious liquid substances; by harmful substances in packaged form; by sewage; by garbage; and the prevention of air pollution from ships. In addition, there exists a Ballast Water Management Convention and a Ship Recycling Convention (IMO).

The third IMO GHG Study 2014 states that shipping was accountable for approximately 3.1%

of CO2 emissions on average in the period 2007 – 2012 (IMO, 2014c). This corresponds to an average of 1 016 million tonnes carbon dioxide (CO2) each year (IMO, 2014c). Shipping also contributed to 15% of nitric oxide (NOx) and 13% of sulphur oxide (SOx) emissions, or 22 and 12 million tonnes respectively, as a yearly average between 2003 and 2012 (Longva et al., 2014). By 2050, it is likely that CO2 emissions from shipping will increase with 50 - 250%

depending on the future scenarios (IMO, 2014c). This will have serious consequences for the

2

world’s environment and much stronger regulations on air emissions than what exist today can be expected.

With these regulations, ship owners and operators face large challenges in a competitive industry. There exists means of reducing air emissions, these can however be costly to install and some of the existing emission reducing technologies is not sufficiently proven. Hence, because some of the emission controls are new in use, it could be uncertain exactly how efficient they are and how they interact with the rest of the ship.

Nevertheless, there is a trend of presenting improved and more energy efficient engine technology, several alternatives to traditional fuel already exist and new alternatives are discussed for the future. Possible combination of technologies and fuel in order to comply with new regulations are many, and when taking into consideration the compatibility of the different measures, the problem gets complicated. Most shipowners focus on costs, and that is probably the most important parameter to consider when constructing a ship for the future. It is important to take into consideration possible future regulations, as this might imply costly but necessary alterations or retrofitting on the ship at a later point in time.

So, if you are to build a ship today, a ship that is flexible regarding future regulations and markets, what is the best solution? The objective of this thesis is to optimize the selection of machinery components with fuel type and emission reducing technologies when considering given emission limits and uncertain elements for the future. Will the selection result

differently if the uncertain elements are included? Which uncertain elements are important to consider? This thesis will present a stochastic optimization model that will try to answer those questions.

The issue of optimizing the selection of machinery system including main fuel type and air emission reducing technologies on a ship is assessed. A description of the problem and the included components in the problem is given in Chapter 2. Relevant literature has been researched and presented in Chapter 3. The theories and methods used in the development of the optimization model are presented in Chapter 4. In that chapter the model is presented as a whole, and assumptions and modelling choices are presented. Chapter 5 presents the case and input values used to produce results and evaluate the selection of included scenarios that reflects the uncertainty in future fuel prices. The results from running the model with the case and input values presented, is given in Chapter 6. Here possible changes in the result are evaluated by applying certain changes to the original input values. The results and modelling choices and assumptions are discussed in Chapter 7. Finally, Chapter 8,

presents a conclusion of the work and suggests possible expansions and changes that can be applied in a further work.

3

2 P ROBLEM D ESCRIPTION

The optimization model developed in this thesis, considers the selection of a machinery system for a ship, fuel type and the installation of emission reducing technologies. The machinery system is an important component to consider in the design and construction phase of a ship, especially because it is essential for lifecycle costs. It is also of great

importance to make the right decisions regarding the machinery system in the design phase, as any alterations to the machinery will be significantly more costly when the ship is already build.

An optimization model with the intension of providing decision support regarding machinery selection in the design phase of a ship, must take into consideration the essential parameters for the whole lifetime. To build a proper model, the right decisions must be made. The right decisions regarding simplification and level of detail included, in order to get an efficient model representing the problem in the best way according to the circumstances.

The following part of this chapter will be used to outline the components included in the optimization model to be developed. It is necessary to obtain the full picture of the practical problem behind the model and the complex system that the model is based on must be mapped in order to be able to simplify the model in a proper manner.

2.1 M

S

One concern in this thesis, is the selection of machinery system. The machinery system selection is of great importance, and the reason is that with it comes the parameter for fuel consumption, air emissions and a maintenance plan. The machinery system on a ship is defined by the physical components included in the complete system that runs the electrical systems on-board and the mechanical or electrical propulsion system. A machinery system is here described by the structure and interconnections in the machinery. Different machinery systems will have different specifications, and a set of compliant fuel types. For a specific machinery system, it is important to note the component for air emissions, and how and why this differs between the machinery systems. The NOx content of the exhaust gas is dependent on the ignition and combustion type of the engine. A typical gas engine for example, is a conventional spark ignition engine with a premixed, or a homogenous combustion. For this kind of engines the NOx emissions are influenced by dilution and ignition timing, and the concentration of NOx is the highest when the fuel/air equivalence ratio is close to 1, hence the combustion is stoichiometric (Heywood, 1988). A diesel engine can be characterized with a compression ignition and heterogeneous fuel spray combustion. The amount of NOx formed

4

in the combustion process is then dependent on the temperature and residence time of the burned gas. Figure 1 shows a sketch of the overall flow process in a diesel engine.

Figure 1: Flow process and typical exhaust gas composition in a diesel engine (MAN B&W Diesel A/S) It is not necessary to consider the specific propulsion directly in the model, but this is

incorporated as the required power in the different operational modes is used to calculate the fuel consumption. The number of propellers is however relevant for the machinery configuration of a conventional machinery system. The power transferred to the propulsion system, the braking effect, is dependent on the power delivered from the engines and the loss in the machinery components, represented by the mechanical efficiency. The actual effect from the propulsion system is then again dependent on the braking effect and the propulsion efficiency. Hence, the required power will be dependent on both the propulsion and mechanical efficiency for the specific machinery system. The required power must be outlined in the operational profile for the ship. For simplicity, it is considered that all of the different machinery systems will be operated at the same engine load in the specific operational mode and the auxiliary engines will not be included in the model.

A specific machinery system will have specific operational and investment costs. The

operational costs are dependent on maintenance and general running costs in addition to the fuel consumption. The operational and investment costs for the machinery will be the main drivers in the objective function in the optimization model. For the consideration of several

5

machinery systems, it the difference in investment costs and operational costs defined by fuel consumption, that will be important.

2.2 A

E

R

The emission types to consider in this problem are defined by given air emission regulations from maritime authorities. If a regulation is enforced in the operational area of a ship, the ship must comply with these in order to be allowed to sail. Some shipowners might have the motivation to define emission goals that are stricter than the given regulations. This could for example be due to a certain environmentally friendly focus area defined by the shipowner, or because it is considered positive to be highly flexible towards possible stricter regulations in the future. Another example is governmental rewards for improved environmental qualities on a ship. This is a fact in Norway, where it is possible to apply for a grant that is given to actors that can verify a certain emission reduction of NOx (NHO).

The latest revisions on MARPOL Annex VI include the introduction of emission control areas (ECAs) and a new Chapter 4, “Regulations on energy efficiency for ships” that launches the Energy Efficiency Design Index (EEDI) and the Ship Energy Efficiency Management Plan (SEEMP). The regulations on energy efficiency entered into force on January 1^st 2013, where the EEDI applies for new ships over 400 gross tonnage and the SEEMP applies for all ships over 400 gross tonnage (IMO).

MARPOL Annex VI also regulates the content of SOx and NOx, in addition to prohibit and regulate the emissions of ozone depleting substances and volatile organic compounds (VOC) (IMO).

Certain sea areas are defined by the MARPOL Convention as special areas, where technical reasons ensure that special rules apply. For Annex VI these areas are called ECAs and a large portion of the ship traffic is in these areas today. In the Baltic Sea and the North Sea, ECAs are defined and special SOx requirements apply here, making these areas so called SECAs. There is also a North American ECA and a United Stated Caribbean Sea ECA where special

requirements apply for SOx, NOx and particulate matter (PM) (IMO). In this thesis, these areas are referred to as NECAs. The ECAs are illustrated on a map in Figure 2. In addition, several new ECAs in are discussed for the future (DNV GL, 2011).

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Figure 2: Map over present and possible future emission control areas as defined by IMO (DNV GL, 2011)

The regulations on SOx and NOx have a big impact on the decisions shipowners and operators make regarding machinery and fuel. In the revised MARPOL Annex VI, the regulations on SOx

and NOx are as shown in Table 1.

SOx NOx

Outside an ECA established to limit SOx and particulate

matter emissions

Inside an ECA established to limit SOx and particulate matter

emissions

Tier Ship construction date on or after

Total weighted cycle emission limit (g/kWh) n = engine’s rated speed

(rpm) n <

130

n = 130 - 1999

n ≥ 2000

4.50% m/m prior to January 1^st 2012

1.50% m/m prior to July 1^st

2010 I January 1^st 2000 17.0 45·n^(-0.2) 9.8

3.50% m/m on and after January 1^st 2012

1.00% m/m on and after July

1^st 2010 II January 1^st 2011 14.4 44·n^(-0.23) 7.7

0.50% m/m on and after January 1^st 2020

0.10% m/m on and after

January 1^st 2015 III January 1^st 2016 3.4 9·n^(-0.2) 2.0

Table 1: Outline of IMO Regulations 13 and 14 on NOx and SOx respectively. (IMO) What is known today is that regulation 14 of IMO’s MARPOL Annex VI requires a maximum limit of 0.1% sulphur content in fuels used inside ECAs. Outside ECAs, the same regulation allows a maximum sulphur content of 3.5% as of today, falling to 0.5% on and after January 1^st 2020 if the Marine Environment Protection Committee (MPEC) supports this in a review to be

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completed by 2018 (IMO, 2014b). Depending on the outcome of the review by MPEC, the 0.5% sulphur content requirement will enter into force from January 1^st in either 2020 or 2025. This is illustrated by Figure 3.

Figure 4 and Figure 5, illustrates the development of NOx regulations from 2010 to 2035. The NOx limits are dependent on theengine’s rated speed in revolutions per minute (rpm), and therefore the NOx regulations are illustrated by two diagrams. Figure 4 shows the

development of the regulation over time for low-speed engines with rpm lower than 130, while Figure 5 illustrates the dependency of rpm.

Hence, the relative change in regulations is quite significant. In order for shipowners and operators to comply with regulations, they have to consider the fuel type together with the possible installation of air emission reducing technology.

Figure 3: Diagram illustrating the sulphur limits’ development over time based on IMO regulations

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Figure 4: Diagram illustrating the NOx Tiers' development over time based on IMO regulations for ships with rpm < 130

Figure 5: Diagram illustrating the NOx Tiers and the correlation with the engine's rated speed based on IMO regulations

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2.3 F

The global merchant fleet currently consumes approximately 330 million tonnes of fuel annually, where 80-85% is high sulphur residual fuel (DNV GL, 2014a). Because of new and stricter regulations, the demand for low-sulphur fuel is likely to increase and significantly increased fuel prices for distillate fuels can be a result. Climate change concerns will put more pressure on the reduction of greenhouse gas (GHG) emissions in shipping (DNV GL, 2014a).

Additionally, the content of sulphur in fuels is creating a harmful environment in areas with a lot of ship traffic. The introduction of alternative, low carbon fuels can address the

requirement of reduced GHG emissions. In addition, several low-sulphur fuels exists that will provide compliance with sulphur regulations. Hence, in a problem where you are to select the machinery system, fuel type and emission controls, it is relevant to consider those fuel types that will eliminate the need of extra emission controls. Different emission regulations is enforced in different areas, and a fuel switch to a low-sulphur fuel can be considered an emission control that can be applied the moment the ship enters an ECA. The application of a fuel switch require minimum extra installations dependent on the type of ECA entered as extra consideration is required for NECAs.

The most commonly used fuels today are heavy fuel oil (HFO), marine diesel oil (MDO) and marine gas oil (MGO). Liquefied natural gas (LNG) is being used more frequently, but it is still only about 100 vessels that are confirmed LNG fuelled ships, either in operation today or in a confirmed order book for ships that will be in operation by 2018 (DNV GL, 2014a).

When trying to predict fuel types that will become more common, DNV GL (2014a) has presented a position paper that reviews the alternative fuels for the future. In this paper, what they consider as alternative fuels and energy carriers for shipping today and potentially for the future are given in Table 2.

Liquefied Natural Gas (LNG) Biodiesel

Liquefied Petroleum Gas (LPG) Biogas

Methanol and Ethanol Use of electricity for charging batteries and cold ironing

Di-Methyl Ether (DME) Hydrogen

Synthetic Fuels (Fischer-Tropsh) Nuclear Fuel

Table 2: Potential alternative fuels or energy carriers for the future as presented by DNV GL (2014a) In the paper by DNV GL (2014a), the given fuels are evaluated and explained. In addition, there has been given a life cycle assessment of the different fuels. This life cycle assessment is a Well-To-Propeller (WTP) study that assesses the potential climate impacts of alternative fuel systems at all stages in their life cycle; from oil & gas wells (or from farming) to the propeller (Chryssakis and Stahl, 2013).

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WTP can be separated into Well-To-Tank (WTT) and Tank-To-Propeller (TTP) in order to see where the footprint is created. The WTT and TTP results given by DNV GL (2014a) for the GHG emissions in grams of CO2 equivalent (gCO2eq) to Mega Joule of fuel (MJf) gCO2eq/MJf related to the different pathways for the maritime transport alternative fuels considered are

displayed in Figure 6. What is interesting when evaluating the entire history of footprints created for each fuel type, is that a fuel that has been pictured as an environmentally friendly fuel, due to low emissions in the combustion process, is proven less environmentally friendly when the WTT phase is evaluated.

Figure 6: WTP GHG emissions results for marine alternative fuels (DNV GL, 2014a)

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2.4 A

E

C

An air emission control is here defined as any measure that can be installed on the ship in order to reduce the air emissions. Emission controls can be divided in sets according to the type of emission they are reducing. What is crucial for an emission control is the efficiency and reliability regarding reduction. When you decide to install and use an emission control on your ship, you want to know that it actually works the way it is supposed to. Most shipowners and operators chose to install emission controls on their ships because they have to according to regulations, and when this is verified by authorities, it is the actual measured reduction effect and not the theoretical effect that decides if they comply with the regulation or not.

Therefore, if the emission control is not working as planned, a fine might be given as an extra expense, and in the worst case the ship will not be allowed to operate in that area.

There is a difference in how emission controls actually reduce emissions. Some controls reduce resistance; hence reduce fuel consumption and the general emissions. These are considered CO2 reducing measures as it is mostly reducing the amount of fuel burned and hence the amount of hydrocarbons released. These measures are improving energy

efficiency. Other emission controls work more directly on the specific emission, and goes into the exhaust gas to remove the given emission type. There are several ways of cleaning the exhaust gas, some more efficient than others, and there are differences in space

requirements and price. As an example the different scrubber types can be considered. You can choose between using a dry or a wet scrubber, and for the wet scrubber you can again choose between open-loop, closed-loop and a hybrid solution. A dry scrubber can use limestone as sulphur absorbent and all residues will be collected locally on the ship. An open- loop scrubber exploits the natural bicarbonate content of seawater to absorb the sulphur from the exhaust gas, and flushes the treated washwater back into the sea. A closed-loop scrubber however, use freshwater combined with a suitable alkaline chemical and collects the washwater in a tank (Kjølholt et al., 2012). An hybrid solution is a combination of an open- loop and a closed-loop scrubber that can switch between modes. The dry scrubber will be the most space and weight consuming alternative, while the open-loop seawater scrubber (SWS) is the simplest scrubber system.

Additionally, there could be issues related to having several emission controls installed and used at the same time, in that case interaction effects are present. An example of this is the use of both selective catalytic reduction (SCR) and a SWS that both cleanses the exhaust gas and reduces the emissions of NOx and SOx respectively. If they are installed and used on the same ship, they would both lower the temperature of the exhaust gas making it necessary to heat up the exhaust gas again for the control that comes second in line.

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Interactions in emission reduction or incompatibility between controls occur when the controls are affecting the same sub-system of the vessel. An example is direct water injection (DWI) installed at the same time as humid air motor (HAM). Both controls are NOx reducing measures that lower the temperature in the combustion chamber by adding water into the chamber. Hence, the emission reduction of adding one of these controls when the other is already installed will be significantly smaller.

Some of the technologies create a by-product, or residues from the exhaust. An example is a SWS that cleanses the exhaust gas with seawater, and flushes the residues into the sea again.

Hence, the emission created, in this case emissions of SOx, is removed from the exhaust gas and partly flushed into the sea. This technology is therefore debated, and in some waters, they are not allowing this open-loop version of a scrubber. In these areas other scrubber types can be applied.

2.5 A

U

One goal in this thesis is to develop a model that is addressing the issue of uncertain future events related to the design of a machinery system that complies with future emission regulations and emission reduction goals.

In the development of an optimization model that aims to function as decision support in the design process for a ship, the operation of the ship in its entire lifecycle must be considered.

The operation of a ship can never be predicted exactly as markets to operate in, and the circumstances of operating in those areas can be altered. To a certain extent, this uncertainty must be evaluated. Assumptions and simplifications of future events must be made in order to create a model that is less complex than the reality, the quality of the model will however be improved if possible scenarios for the future is included.

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3 S TATE OF T HE A RT

A lot has been done on the subject of optimization models and operations research (OR). For maritime applications of OR, the amount of previous work is growing and especially within the field of maritime transportation, quite a lot has been produced lately. One paper within this field is “Uncertainty in fleet renewal: a case from maritime transportation” by Pantuso et al.

(2014). In this paper, an optimization model is used to address the fleet renewal problem and particularly the issue of uncertainty is being evaluated. Here the use of stochastic

programming is analysed and the quality of decision support is compared against the

averaging of uncertain data in a deterministic model. In the paper, they emphasize on the role of uncertainty, which is directly applicable to this thesis.

A similar application of OR is found in the article “The Fleet Renewal Problem with Regional Emission Limitations: Case study from Roll-on/Roll-off Shipping” by Patricksson et al. (2015).

This paper extends the maritime fleet renewal problem to include the special regulations applied in ECAs. A recognisable motivation is present for including the aspect of stricter emission regulations being applied for deep sea shipping. The fleet renewal problem in this paper is also modelled by a stochastic model, and the uncertainty is applied to the future fuel prices. The application of uncertain fuel prices is especially relevant, and both the motivation for considering special ECAs and the procedure when applying the case is applicable when developing the optimization model in this thesis.

However, not so many OR models is presented that aims to optimize ship design. As a part of a doctoral thesis (2013), Oceané Balland presents a paper on the “Concurrent design of vessel machinery system and air emission controls to meet future air emission regulations” (Balland et al., 2014). This paper presents a cost driven optimization model that is handling the

selection of main machinery system on a ship, together with the selection of air emission controls. Combined, these choices must comply with a given air emission goal defined by governmental regulations. The optimization model is a deterministic integer linear programming model. The model aims to solve the problem of designing the machinery system and developing a strategic emission reduction plan in a cost-efficient way for a new build vessel so it will meet future operational requirements and current emission regulations (Balland et al., 2014). The model’s objective function minimizes costs, which include the costs of installing and running the machinery and installing, running and removing emission

controls. This model is directly applicable to this thesis and it is therefore used as a basis for further development. The model is given as a whole in the paper by Balland et al. (2014) and is copied into this thesis in Appendix .

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A similar approach to the selection of a machinery system is presented in the paper

“Optimization of diesel electric machinery system configuration in conceptual ship design”

(Solem et al., 2015), where an optimization model for the design configuration of complex diesel electric (DE) machinery systems is given. The purpose of this paper is to provide decision support on selecting configuration of diesel engines in a DE machinery system in the conceptual phase of ship design. Investment and operational costs are to be minimized over the ship’s lifetime with the operational profile taken into account. The objective function in the optimization model is driven by costs, and represented by the net present value of all costs in the ship’s lifetime. Investment costs are dependent on the engine model. Operational costs are represented by fuel costs and NOx taxes, with fuel consumption and costs being calculated based on the different operational modes and power demand. The model has been verified with a case study and the model was proven efficient for the evaluation of

operational costs and the selection of optimal machinery system. In the presented paper, a following key finding regarding machinery systems can be considered directly relevant and as a motivation factor for the development in the model in this thesis. “The presented case study indicates that increased investment costs, if resulting in reduced fuel consumption, can give lifetime savings, and therefore it can be economically beneficial to consider investment and operational costs simultaneously.”(Solem et al., 2015).

A study assessing the challenges with balancing costs and performance capabilities in the conceptual design phase of the ship is presented in the article “Assessing air emissions for uncertain life-cycle scenarios via responsive systems comparison method” by Gaspar et al.

(2014). In the article the responsive systems comparison method has been applied to the problem and the total air emissions from the ship is considered for a life cycle where the vessel’s mission might change. Possible mission changes includes future market changes, changes in regulations and changes in contract opportunities. The design of a vessel is

evaluated by considering an appropriate propulsion system and air emission controls, and the uncertainty in the vessel’s future mission is applied by considering possible realizations of uncertain operational life-cycle scenarios. This article provides valuable insight to the problem in this thesis, and it demonstrates the application of a different method to the similar

problem.

Stochastic programming is essential to consider when applying uncertain elements in an optimization model. An introduction to stochastic programming models and methodology is given by Higle (2005) in the paper “Stochastic Programming: Optimization When Uncertainty Matters”. In this paper, a simple deterministic linear programming problem is used to illustrate how a stochastic programming extension will work as a good solution. Issues illustrating the pros for stochastic programming are sensitivity analysis and data accuracy,

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which shows how uncertainty can be introduced in the linear programming model. In the paper it is discussed how some variables are uncertain while others can be considered known.

This reflects how decisions can be delayed the concept of recourse introduced, as some decisions are fixed before information about uncertain data is obtainable and other decisions can be delayed until afterward in order to exploit the new information. “Decisions that can be delayed until after information about the uncertain data is available offer an opportunity to adjust or adapt to the information that is received.“ (Higle, 2005) There is a variety of

modelling choices when dealing with uncertainty in a linear programming problem. This paper discusses and evaluates how the possible outcomes of different modelling strategies can be interpreted. The expected value, the calculation of the individual scenarios and solving the problem as a recourse problem are the three methods that are compared.

In the discussion of using a stochastic model, it is relevant to consider and compare the quality of applying a sensitivity analysis to a traditional deterministic linear programming model. In the paper “Sensitivity Analysis and Uncertainty in Linear Programming” by Higle and Wallace (2003) discuss shortcomings with applying sensitivity analysis to a postoptimalty investigation and compare the conclusions from sensitivity analysis for different models.

The issue of uncertainty connected to the reduction effect of an emission control is not applied in this thesis. This was however discussed and evaluated in a paper by Balland et al.

(2013), where a stochastic optimization model was used to evaluate possible scenarios for the actual emission reduction by the controls.

Many reports and studies on the future of shipping, previous developments and trends in the maritime transportation industry and how to adapt to future environmental regulations have been published (Wärtsilä, 2010, IMO, 2014c, Buhaug et al., 2009, DNV GL, 2014b, DNV GL, 2014a, Brynolf et al., 2014, McGill et al., 2013, Ritchie et al., 2005, Longva et al., 2014, UNCTAD, 2014, GL and MAN, 2010). These publications give an insight to possible challenges and solutions to these challenges in the future shipping industry.

In a project report by Det Norske Veritas (2012), they have used a scenario approach to discuss technology uptake in the maritime industry and create a picture of how the shipping fleet could appear in 2020. The project is basing the scenarios on trends in the world

economy and estimated transport demands for 2020. They present their suggestion to possible scenarios for shipping in 2020, and a key element in this project is the fuel trends.

They discuss the use of maritime fuels from fossil resources, and connect the fuel trends to emission levels in the maritime industry. In the report, they present fuel projections for HFO, MGO and LNG.

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4 M ODEL D ESCRIPTION

According to modelling theory, a model is a representation of a system and the features included should be only those that are relevant for achieving a specific goal. Models can predict nothing with certainty. Their proper function is to develop judgement, not a substitute for it. Models never represent the entire system, assumptions have been made and points have been omitted, hence it must be clear that the model is partial (Pedersen and Engja, 2008). Modelling however, is the process of applying physical laws and principles to establish a set of relations that describe a given system.

This chapter describes the process of developing a model that answer questions regarding the selection of a machinery system, fuel type and emission controls for a ship during its lifetime.

Flexibility in future operational area and uncertainties linked to the operation of the ship are issues that the model addresses. The model includes the key factors in the problem to solve, in order for the result to provide comprehensive decision support. However, when developing a model, it is essential to make simplifications as this distinct a model from the reality. The modelling procedure reduces the complexity of the problem, by excluding certain aspects, in order to obtain a solvable model that covers the essence of the problem. In this chapter an evaluation of which aspects that must be included in the model is given.

A model was presented by Balland et al. (2014) that in a similar manner presents a model with a concurrent selection of main machinery system and air emission controls on-board a ship. This model is a cost-driven deterministic optimization model that will be adapted in this thesis and used as basis for further development.

One of the modelling simplifications made in the deterministic model by Balland et al. (2014), is the assumption that all future values are known. A natural expansion in this thesis is

therefore to evaluate whether different future values would generate a different optimal solution. This is achieved by presenting a stochastic optimization model with the same goal as the deterministic model by Balland et al. (2014), hence optimizing the selection of machinery system with a costs-minimizing focus and necessary compliance with defined air emission regulations.

In the stochastic model, several future outcomes is included in order to get a more diverse representation of the future. For the possible future outcomes, key issues to consider are, which elements will change, how will they change, and when will they change.

The model presented in this thesis is a stochastic two-stage recourse model where the term

“recourse” indicate that there is an opportunity to adapt the solution to a specific outcome

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observed (Higle, 2005). The term “two-stage” refers to the dual nature of the model. The first-stage is a deterministic stage where all values are considered known, while the second- stage applies the stochastic programming theory and evaluates given future outcomes. The outcome to observe in this problem is experiences and expectations to the future market of operation when you are to design and build a ship. In order to evaluate possible outcomes of the model and to include a more diverse future than what you would expect in a

deterministic model, the model need to address relevant uncertain elements. There is a probability linked to each uncertain element and it will take different values according to an expectation of future values. The collection of all uncertain elements makes up the possible scenarios.

The mechanisms in the model developed here are illustrated by the flow chart in Figure 7. A general model with given equations to represent the addressed problem, receives input data that gives information on the specific problem to solve. This data is given in an appropriate format that allows the model to sort the information properly. The model presents a solution for the first-stage that must be feasible, hence lie within a feasible region limited by given model constraints. Simultaneously, the model also presents feasible solutions for all the scenarios evaluated in the second-stage. The result is an optimal solution for the first-stage decision when possible future outcomes is included.

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Figure 7: Flow chart showing the overall dynamics of the two-stage stochastic model

Start

Initialize data from input file

Sort data into given subsets

Generate a valid solution for the firs-stage

Is the solution feasible according to given constraints?

Yes No

Generate a valid solution for each of the scenarios

No

Yes

Minimize costs and fnd optmal solution

End

Is the solution feasible according to given constraints?

Ensure a connection between the first and second-stage

Apply information to uncertain elements

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4.1 S

P

Stochastic programming can be thought of as one of the ultimate operations research models as it is a mix between several traditional mathematical programs and stochastic models (Higle, 2005). Stochastic programming is one part of operations research (OR) and mathematical programming that incorporates uncertainty and random variables into the problems.

A deterministic model assumes to know all data for the entire scope of the model. In many cases, this will not be an accurate modelling assumption as essential data could change along the process. As an example, if you are to order a ship at a shipyard you can assume that installation and building costs will be the same as promised when you decide to build the ship.

Although this might not be the case, it is a reasonable assumption and you can establish safety margins on the price to be completely certain. However, when it comes to the running and operation of the ship, it is difficult to estimate the exact costs and income for a big part of the ship’s lifetime. Future regulations and fuel prices could change drastically during the ship’s lifecycle, and freight rates are dependent on the future markets. These issues are important for shipowners and you must consider several scenarios when providing good decision support to a design phase. Stochastic programming provides the modelling

techniques for taking these different scenarios and uncertain parameters into consideration.

In a mathematical programming problem, it is necessary to figure out how essential the uncertainty in each unknown parameters is for the optimal solution. This is necessary in order to evaluate the quality of the solution to your problem. There are numerous methods to obtain information on this, and if you have experience and expertise on the subject, you can provide some of the necessary insight yourself. In addition, you can use expected values for the unknown parameters or apply a sensitivity analysis to the problem.

A sensitivity analysis helps you see how and when the solution will change when moving away from the initial parameters. The extent to which sensitivity analysis is a trustworthy tool can however be discussed (King and Wallace, 2012, Higle, 2005). Sensitivity analysis is not really considered a suitable approach for uncertainty as it evaluates uncertain parameters in a fixed setting, and not the setting itself (King and Wallace, 2012).

Hence, when approaching uncertainty, exploration of possible scenarios is essential and you should re-evaluate the setting for each scenario. When using a stochastic multistep

procedure, you can delay the decision of some parameters to a later point where more information is available. As King and Wallace (2012) puts it in the book Modelling with

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Stochastic Programming; “Some decisions must be made today, but important information will not be available until after the decision is made.”

4.2 S

C

A

D

M

The problem in this thesis is to decide on a machinery system for a new vessel when focusing on energy efficiency, future emission regulations and lifecycle costs for the ship. This problem has already been introduced by Balland et al. (2014) and that model is therefore used as a basis when developing the stochastic model, and also as a foundation when evaluating the model in this thesis. I simplify the deterministic model in order to reduce the level of

complexity a bit before making the model stochastic. In the deterministic model, the number of constraints is numerous and the present running time is too long for a convenient

development and evaluation of the program.

To simplify the problem, I remove the set of machinery arrangement that was essential in most parameters and variables. The result of this is a reduction of a dimension in several parameters and the elimination of some constraints. From this, I observe a specific result of the simplification when running the program in FICO Xpress 7.7. The observations was a drop in number of constraints from 474 818 to 26 447, and a reduction in solving time from 7993 seconds to 217 seconds. A presentation of the full model is given in Appendix A.

This simplified version of the model will make it easier to adjust and see results with changes in details in the model. This will also make the implementation of scenarios and stochastic parameters more convenient.

In the model by Balland et al. (2014) , the authors make frequent assumptions regarding the future that is necessary for executing the model. Table 3 presents a list of some of these assumptions.

Elements considered known for all time periods in the model by Balland et al. (2014) The operational states including required power demand

The sailing time spent in SECAs The vessel’s trade route

The maintenance, insurance and running cost for the machinery The emission goals

The daily fuel consumption The fuel prices

Table 3: A list of assumptions made in the optimization model by Balland et al. (2014) The nature of some of the assumptions shows that there are several parameters to consider when making this model stochastic. The challenges that meet shipowners when deciding on the future ships are complex as the time horizon for a ship is quite long. If you are to start the

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building of your ship today, it could be finished within a year, brought into operation and ready for scrapping again in about 30 years. What will happen in those years? How could it affect the design of the ship you are building? How would the selection of machinery system and air emission controls differ for each of the future scenarios?

The results from the model should contribute to a decision of the design of a ship. As a difference from the deterministic model, it is advantageous here with a greater consideration of uncertain elements. This is important because it could be more profitable to make a certain design decisions if a specific outcome of the future is realized. The problem is that you never know the outcome before you make that design decision. A discussion of the uncertain elements to consider for the development of the model is therefore important when considering the different modelling options.

In a two-stage recourse model, the model make an initial decision based on a deterministic expected value, and at a certain point in time assume to know more about the problem and can make recourse decisions to make the best out of the situation. Hence, in a modelling perspective I need to consider the uncertain elements that I know could change. At a certain point in time, new information on how this element would change is available.

4.3 U

E

The elements discussed in the following are included with the assumptions made in the deterministic model by Balland et al. (2014) in mind, but most importantly they are thought of because they are essential when considering the machinery system and compliance with air emission regulations. A discussion of the representation of these elements in the

deterministic model by Balland et al. (2014) will follow for each element, then by referring to it as the deterministic model.

4.3.1 Alteration of Future Regulations and Emission Control Areas

If the emission reduction goal for a given time period is changed, this would imply that either there has been a change in government regulations, the area of operation has been changed to an area with different regulations than expected, or the ship operators wants a different emission profile. The emission reduction goal is the required reduction of an emission for each of the emission types considered, and can vary for with time periods. The selection of machinery system and emission reduction technologies depends largely on the given emission reduction goal.

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If the future sailing route for the ship is uncertain, the future emission goal for the ship is uncertain. This is because a new operational area might be subjected to different emission regulations.

The emission reduction goal parameter is in the deterministic model represented by 𝐺_𝑡𝑎𝑘 [%], where t is the index for the time period in question, a is the specific machinery arrangement and k is the index for emission types.

4.3.2 Change in Fuel Prices

The actual future fuel prices will be a crucial uncertain element. Fuel costs are a big part of the shipowner’s or ship operator’s expenses and it is always an objective to reduce fuel consumption or select the cheapest fuel available. With new regulations, it will not be possible to run on traditional HFO in ECAs without installing an emission cleaning technology or by switching to low sulphur HFO that is more expensive due to a more extensive

production process. This makes it necessary for the operators to evaluate the consequence of selecting a fuel type to run on.

In the deterministic model, fuel costs are represented by the product of the fuel cost

parameter, 𝐶_𝑡𝑓^𝐹 [€/ton], the fuel consumption parameter, 𝐷_𝑡𝑎𝑓 [ton/day], and the time spent in SECAs and deep sea as this is relevant for the fuel type used. Here, t is the index for the given time period, a is the specific machinery arrangement and f is the index for fuel types.

Engine power, operational profile and engine fuel consumption is the basis for the fuel consumption parameter.

4.3.3 Change in Availability of Fuel

Some fuel types have limited availability, like for example LNG which is per today only possible to bunker to ships in various places in Norway, Stockholm in Sweden, Buenos Aires in

Argentina, Incheon in South-Korea and Gaolan in China. There is a number of bunkering facilities planned and proposed, but there is an uncertainty related to the actual development of these facilities (DNV GL, 2014c). This information is relevant for shipowners and operators when deciding on the machinery system, as this is crucial for the set of compatible fuel types.

If LNG is available on a certain route of operation, this might be the best option if the price is right, as using LNG as fuel usually ensures compliance with the emission regulations that is known today. This is the case for LNG, and it might be similar for other fuel types being introduced within the lifecycle of a ship.

The possible consequences of increased availability of LNG are several. The immediate aspect to consider is the increased flexibility for the ship in operation. If new LNG bunkering facilities opens, it introduces new possible markets for a LNG-fuelled ship as several ports can be visited. Another aspect to consider is that if the availability of LNG increases, you can expect