Improving ship energy efficiency through a systems

THESIS FOR THE DEGREE OF LICENTIATE OF ENGINEERING

Improving ship energy efficiency through a systems perspective

FRANCESCO BALDI

Department of Shipping and Marine Technology CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2013

Improving ship energy efficiency through a systems perspective FRANCESCO BALDI [email protected] +46 (0)31 77 22 615 © FRANCESCO BALDI, 2013 ISSN: 1652-9189 Technical report no 13:147 Department of Shipping and Marine Technology Chalmers University of Technology SE-412 96 Gothenburg Sweden Telephone + 46 (0)31-772 1000

Cover: Artist impression of ship energy systems Printed by Chalmers Reproservice Gothenburg, Sweden 2013

Abstract

The last years have been particularly challenging for the shipping industry. Fuel prices have increased to levels only seen during the oil crisis in the 70’s, and environmental regulations have grown much stricter than in the past. Climate change, at a global level, is going to become a major threat to society. Increasing energy efficiency is one of the only possibilities of reducing fuel costs and environmental impact of the shipping sector without influencing the output. However, despite the recent developments in several aspects of ship technology, little effort has been made in looking at the whole ship as an energy system. This licentiate thesis aims at filling a gap in the existing scientific knowledge on the way energy in its different forms is generated, converted, and used on board of a vessel. This is done by applying energy and exergy analysis to ship energy system analysis. The results of this analysis allow improving the understanding of energy flows on board and identifying the main inefficiencies and waste flows. As a further development of this work, these results are used as a basis for generation and evaluation of alternatives for improving ship energy efficiency. This is applied to the three main categories of: ship operations, retrofitting, and design. Engine-propeller interaction, waste heat recovery systems and the early stages of ship design are identified as relevant aspects and their evaluation indicates that there is a relevant potential of improvement.

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Acknowledgements The first version of my licentiate thesis was more than 100 pages long. After discussion with my supervisors, it went down to roughly 60 pages. I will therefore take what is my only chance to be lengthy for thanking those who have had, in different ways, a role in the achievement of this key stone of my education, and of my life. First of all, thanks to my supervisors, Karin and Cecilia. It must not have been easy, I acknowledge that, as my way of moving forward is not always structured and methodical. This is why I need to thank you twice: once for helping me, and once for standing me. Big thanks also go to Bengt-Olof, Pär Brandholm, Mikael Karlsson, and in general all the people at Laurin Maritime who contributed to the development of my work providing me with data and support. I enjoyed very much your expertise, your professionalism, and your cordiality in my regards. Thanks also to Jon Agust, Kristinn and Stefn at Marorka. Working with you has been motivating and I learnt a lot during my visits to Iceland. All people I collaborated to, both in Laurin and Marorka, have been examples of what a company should have for being successful in its business. Thank you Gesa, thank you very much. You deserve your own little paragraph, for all your support, ranging from work to personal life. If there is one person I can say ”I would not be here without you”, that is definitely you. A lot of thanks to all my colleagues in the environmental group. Thanks to Selma, for making a patient smile every time I came to your door just for having a short chat. Thanks to Mathias, for your lessons on the Swedes, and for coming to the cinema with me for watching trash movies. Thanks to, Hannes for introducing me to Laurin and for the numerous confrontations on energy efficiency, and research in general. Thanks to Philip for making me feel you counted on me also outside of the working hours. Thanks to Erik, for the all the afterworks we had together that reminded me there is life out there. Thanks to Steven, Luis, Florian, Hedy, Nicole, Henrik, and all those who make me feel, every time I wake up in the morning, that I am happy to go to work because I am surrounded by wonderful people. Thanks to my friends, for their support in the happy and the sad moments. Thanks to (in random order, I hate alphabetic rankings!) Erry, Marco, Stefano, Silvia, Ann, Alberto, Oana, Raquel, Jack, Pablo, Gabo, Bernadette,

Roberta, Ignacio, Saimir, Stella, Chiara, Dado, Karl, Josefin, Angela, Luana, Suny, Romolo, Bartolo, Loris, Marzi, Ale, Giaele, Filo, Fierro, ... I am not thanking you individually for the only reason that would require a whole thesis alone. But I cannot help giving a special thank to Maurizio, who introduced me to sailing, the new little world where I just can feel good when I need to escape from everything else. Last, but definitely not least, thanks to Antonella and Sandro, papá e mamma. I thanked all the people who had a role in making me the researcher I am, but if I have to thank somebody for being the person I am, that is you.

Appended papers This thesis represents the combination of the research presented in the three following appended papers:

Paper I : Baldi, F. , Gabrielii, C. & Anderssson, K. (2013) Energy and exergy analysis of a ship: the case study of a chemical tanker, ready for submission to Energy. The study focuses on the analysis of ship energy system using energy and exergy analysis. These methods are applied to a specific case-study. The results identify the main energy flows and the most important inefficiencies on board of the selected vessel. Paper II : Baldi, F. , Larsen, U. , Gabrielii, C. & Andersson, K. (2013) A validated zero-dimensional four-stroke medium speed Diesel engine model for waste heat recovery marine applications, submitted to Applied Energy on the 2013-06-10. The study focuses on the description of a model for predicting mediumspeed marine Diesel engine performance and on its application to the case of engine-propeller interaction modelling for a case-study ship. Results underscore how the use of presented models can help identifying more efficient operations. Paper III : Baldi, F. , Bengtsson, S. & Andersson, K. (2013) The influence of propulsion system design on the carbon footprint of different marine fuels, Low Carbon Shipping Conference, London, 9-10 September 2013. The study focuses on proposing a method for comparing the carbon footprint of different fuels and propulsion systems. A number of possible arrangements for the case-study ship were analysed and compared in their energy performance and carbon footprint. The results show how fuel and propulsion system choice in the design phase must be tackled contemporarily and how a carbon footprint analysis can give a much more detailed understanding of the environmental performance of different propulsion systems and fuels. For all the appended papers, the author of this thesis contributed to the ideas presented and had a major role in planning the paper, data collection, performing the analysis, and writing the manuscript.

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Contents List of Illustrations vii Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Symbols and abbreviations 1 Introduction 1.1 Background . . . 1.2 Aim and research 1.3 Delimitations . . 1.4 Thesis outline . .

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1 1 5 6 7

2 The ship as complex energy system 2.1 An introduction to complexity . . . . . . . . . . . . . . . . . . . . . . . 2.2 The ship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 10

3 The 3.1 3.2 3.3

17 18 22 27

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study of ship energy systems with a systems The case study of a chemical tanker . . . . . . . . The analysis of ship energy systems . . . . . . . . The improvement of ship energy performance . . .

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approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Ship energy system analysis 37 4.1 Energy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 Exergy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5 Ship energy performance improvement 5.1 Ship operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Retrofitting and waste heat recovery . . . . . . . . . . . . . . . . . . . . 5.3 Ship design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43 43 44 46

6 Discussion 6.1 Energy and exergy analysis . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Energy performance improvement . . . . . . . . . . . . . . . . . . . . . . 6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

49 49 50 52

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CONTENTS

7 Future work and recommendations 57 7.1 Proposals to the scientific community . . . . . . . . . . . . . . . . . . . 57 7.2 Recommendations to the industry . . . . . . . . . . . . . . . . . . . . . 58 8 Conclusions

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References

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vi

List of Illustrations Figures 1.1 1.2 1.3

World trade evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . Residual fuel price evolution and forecast . . . . . . . . . . . . . . . . . Forecasted shipping contribution to global CO2 emissions . . . . . . . .

2 3 4

2.1 2.2

Schematic representation of ship energy system . . . . . . . . . . . . . . Schmeatic representation of ship energy flows . . . . . . . . . . . . . . .

15 16

3.1 3.2 3.3

Graphical representation of thesis methodology . . . . . . . . . . . . . . Conceptual representation of ship energy systems and flows . . . . . . . Conceptual representation of the coherency of mechanical and thermal energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual representation of the property of exergy . . . . . . . . . . . Conceptual representation of white and black box modeling . . . . . . . Ship speed distribution over one year of operations for the case study ship Propulsion system and individual engines load distribution over one year of operations for the case study ship . . . . . . . . . . . . . . . . . . . . Schematic representation of the two operational modes compared in the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic representation of the propulsion arrangement with WHR system installed: Case 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18 20

3.4 3.5 3.6 3.7 3.8 3.9

4.1 4.2

Sankey diagram for the case study ship . . . . . . . . . . . . . . . . . . Grassmann diagram for the case study ship . . . . . . . . . . . . . . . .

5.1

Comparison of case study ship specific fuel consumption, fixed-speed versus variable-speed setup . . . . . . . . . . . . . . . . . . . . . . . . . Propeller power versus propeller speed, for different values of ship speed and propeller pitch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fractional coverage of the auxiliary need for different recovery arrangements versus cycle exergy efficiency . . . . . . . . . . . . . . . . . . . . . Comparison of case study ship specific consumption, standard setup versus WHR retrofitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.2 5.3 5.4

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24 25 29 32 32 33 35 39 40

44 45 45 46

LIST OF ILLUSTRATIONS

5.5

Cumulative fossil energy consumption over one year of operation for the alternative design cases. . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Tables 2.1

Performance parameters of Diesel engines . . . . . . . . . . . . . . . . .

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3.1 3.2 3.3

Available measurements from on board energy management system . . . Comparison of mechanistic and empirical models . . . . . . . . . . . . . Alternative arrangements evaluated in ship design . . . . . . . . . . . .

21 29 36

4.1 4.2 4.3

Energy flows and efficiencies for system components . . . . . . . . . . . Exergy flows and efficiencies for system components . . . . . . . . . . . Energy and exergy analysis of waste heat flows . . . . . . . . . . . . . .

41 42 42

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ex

Exergy

Abbreviations AE

Auxiliary engine

CAC

Charge air cooler

CO2

Carbon dioxide

CPP

Controllable pitch propeller

ECA

Emission controlled area

EEDI

Energy Efficiency Design Index

EGE

Exhaust gas economiser

Roman Symbols

FPP

Fixed pitch propeller

EX

Exergy [kW ]

GHG

Greenhouse gas

H

Enthalpy [kJ]

HFO

Heavy fuel oil

I

Irreversibility [kJ]

HHV

Higher heating value

m

Mass [kg]

HT

High Temperature

M CR

Maximum continuous rate [kW ]

HVAC

P

Power [kW ]

Heat, ventilation, and air conditioning

Q

Heat [kJ]

IMO

International Maritime Organisation

S

Entropy [ kJ ] K

JW

Jacket wataer

SF OC

g ] Specific fuel oil consumption [ kW h

LHV

Lower heating value

SSF C

kg Specific ship fuel consumption [ nm ]

LNG

Liquified Natural Gas

T

Temperature [K]

LO

Lubricating oil

W

Work [kJ]

LT

Low Temperature

MDO

Marine diesel oil

Greek Symbols

ME

Main engine

δ

Contribution to total exergy destruction

NOX

Nitrogen oxides

PM

Particulate matter

η

Efficiency

S/G

Shaft generator

λ

Irreversibility ratio SEEMP

Ship Energy Efficiency Management Plan

SOX

Sulphur oxides

WHR

Waste heat recovery

Symbols and abbreviations

Subscripts 0

Reference ambient conditions

en

Energy

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SYMBOLS AND ABBREVIATIONS

x

1

Introduction The shipping industry is today facing very strong challenges. In a period of low freight rates, fuel prices have increased to levels only seen during the oil crisis in the 70’s. Stricter environmental regulations are putting additional stress on the sector. Meanwhile, the latest IPCC report highlighted the increased confidence in the existence of an anthropic contribution to global warming. Shipping, though only contributing by an estimated 3% to global CO2 emissions, is expected to increase its share in the future. In such a context, it is not surprising that the interest in energy efficiency has exponentially grown during last years. The critical role of shipping in global economy implies that increasing the efficiency of the sector is one of the only ways to reduce its consumption without decreasing its output. There is the need of addressing energy efficiency in shipping from a number of different angles. This thesis approaches this challenge from a technical perspective1 . This chapter provides an introduction to the subject of the thesis. In Section 1.1 a short description of the shipping sector and its current challenges is presented, leading to the identification of the problem. The aim of the thesis and the research questions are then explicitly defined in Section 1.2, while Section 1.3 draws the delimitations. Finally, the thesis outline presented in Section 1.4 helps the reader in the orientation through the different chapters and sections of this work.

1.1

Background

Shipping has always been intrinsically related to the history of mankind, from the Pheonicians to the Romans, the Venetians, the Hanseatic League and the journeys of Colombo, Diaz, Caboto, Zheng He. Today, shipping is one of the largest drives of world’s globalised economy, as it contributes to more than 80% of global world trade by volume, and 70% by value (UNCTAD, 2012). Figure 1.1 shows the evolution of global trade in the last decades; even after the step back caused by the economic crisis in 2008, global trade has already taken back on its previous pace, and most analysts 1

For a more interdisciplinary perspective, Towards understanding energy efficiency in shipping, by Johnson et al. (2013), is a very informative reading.

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1. INTRODUCTION

Figure 1.1: World trade evolution, 1950-2012; detail of the 2007-2012 period1

agree that this growing trend is very likely to continue in the future, fostered by the growth in non-OECD countries (UN, 2013). As a result of this trend, merchant shipping has been growing steadily over the past years, hand in hand with world trade. In the period between 1999 and 2004 merchant shipping increased its economic turnover by a striking average of 22% per year2 . This astonishing growth, together with the rising global economy, is explained by phenomena like containerisation, increased economy of scale, and advances in marine engineering. Under these conditions, the cost of freight is not a major concern anymore when deciding where to purchase goods and materials (Stopford, 2009).

1.1.1

The evolution of bunker prices

The low cost of transport by sea has also been historically connected to very low prices for marine fuels (normally referred as ”bunkers”). During latest years, however, the increase in bunker prices has made fuel cost the largest element for virtually every shipping company (DNV, 2012c). If as late as in the early 70s the fuel bill accounted for around 13% of total ship costs, for the period between 2006 and 2008, fuel costs were estimated to account for between 43% and 67% of total operating costs depending on vessel type (Kalli et al., 2009). Figure 1.2 presents bunker prices evolution from 1984 until 2012, showing how they have seen a sharp increase since the 80’s and, 1 2

Author’s elaboration, from WTO (2012). Source: Douglas-Westwood Ltd, available on (Stopford, 2009)

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1.1 Background

'History' 'Forecast'

Bunker price (HFO) [USD/ton]

800

600

400

200

1984

1994

2004

2014 Time

2024

2034

Figure 1.2: HFO price historical evolution (1984-2012) and forecast (2012-2035)1

especially, in the last ten years. As it can be observed, even if prices fell around 2009 in coincidence with the economic crisis, they have already promptly recovered. In the moment this thesis is going to print, the price for residual fuels ranged from 586 USD/ton in Rotterdam to 718 USD/ton in Sydney3 . This is not the first period in history when oil prices (and, consequently, bunker prices) have experienced this kind of increase. During the oil crisis of the 70s fuel costs had risen to over 50% of ship operating costs (Buxton, 1985), creating the deepest recession for the maritime sector since the Great Depression (Böme, 1983). Nevertheless, in spite of the large number of studies connected to the reduction of fuel consumption that was produced (Gauthey & DeTolla, 1974; DeTolla & Fleming, 1981; Brady, 1981; Sack, 1981), most of the technical and managerial improvements discussed in those years just faded as bunker prices dropped (Johnson et al., 2013). However, even if there is disagreement among experts on the forecasts, reference scenarios hypothesised by the major international agencies assume increasing prices in medium to far time horizons (EIA, 2013; DNV, 2012b). This is a crucial matter for the subject of this thesis, since fuel prices have a direct, strong impact on the uptake of new technologies for increasing energy efficiency, as well as on the implementation of existing ones (DNV, 2012c).

3

Source:bunkerworld.com. Prices refer to IFO380, a type of residual fuel. Last updated 2013-10-20 Author’s elaboration from Brett (2008), Mazraati (2011), EPA (2008), Vivid & Economics (2010), Sabinsky, SCC (2005), SSPA (2011) 1

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1. INTRODUCTION

1.1.2

The influence of environmental concerns on fuel costs

Yet, this is only the economic part of the picture. In 2013 there could be no discussion connected to the transport sector without a mention to greenhouse gases (GHG) and, in general, emissions to air. Transportation by sea requires energy for propulsion, which with today’s technological standard is provided by the combustion of fossil fuels. The oxidation of carbon content in the fuel, in turn, releases carbon dioxide (CO2 ), which stays in the atmosphere for centuries and contributes to global warming. Shipping contribution to global CO2 emissions is relatively low and hard to evaluate. Estimates relative to 2007 give an upper and lower boundary of respectively around 600 M T CO2 T CO2 (IEA, 2012) and around 1250 Myear (Buhaug et al., 2009), which correspond year to a share of global CO2 emissions of between 2.7% and 3.6%. Taking into account the contribution to the overall emissions of GHG, shipping is estimated to account for 1.2% to 2.5% of the total. According to Rogelj et al. (2011), global GHG emissions need to be reduced to GT CO2,eq GT CO2,eq by 2020 and 17 by 2050 in order to have a 90% approximately 43 year year likelihood of keeping the temperature from increasing more than 2◦ C compared to preindustrial levels. On the other hand, shipping emissions are not expected to decrease at all. Even when the implementation of all cost efficient measures in a high carbon-tax scenario is accounted for, projections do not forecast any reduction in total emissions (Buhaug et al., 2009; Eide et al., 2011; Faber et al., 2009). As shown in Figure 1.3, shipping might become the major contributor to global GHG emissions if present trends are not diverted. Two main policy instruments have been issued by the International Maritime Or-

Figure 1.3: Forecasted shipping contribution to global CO2 emissions. Lines represent different scenarios for global CO2 emissions, while the gray area represents the possibility space for shipping-related CO2 emissions as forecasted by Buhaug et al. (2009). From Gilbert & Bows (2012)

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1.2 Aim and research questions

ganisation (IMO)1 in the effort of reducing shipping impact on global warming: the Energy Efficiency Design Index (EEDI), which sets minimum limits on the emissions of CO2 per unit of transport work from newly built vessels, and the Ship Energy Efficiency Management Plan (SEEMP), which aims at improving awareness for energy efficiency on existing vessels2 . Their effectiveness, however, has been put under question (Johnson et al., 2013; Bazari & Longva, 2011; Devanney, 2011). A part from GHG3 , sulphur oxides (SOX ) and nitrogen oxides (SOX ) are often discussed as they have an influence on fuel costs. New, stringent limits on the emissions of these two pollutants are expected to be enforced in the coming years. As reported by the European Environmental Agency (EEA), shipping contribution to the national SOX and N OX deposition is estimated to be between 10% and 30% of the total for most of the European countries having a significant portion of their borders facing the sea (EEA, 2013). Meeting the requirements imposed by new regulations on the matter (especially in Emission Control Areas (ECAs), where limits are even more stringent) will require either the installation of costly equipment on board, or the switch to cleaner and more expensive fuels4 . In both cases, fuel-related costs are expected to increase in the near future because of the more stringent requirements on emissions to air (DNV, 2012c).

1.1.3

Energy efficiency and the need for a systems approach

This thesis deals with the challenge of increasing ships energy efficiency, i.e. reducing fuel consumption without decreasing the output. But if research and development have reached very high standards in the technology of engines and propellers, the same cannot be said of the design of ship energy systems. Until the recent past, low fuel prices have generated very little demand for more energy efficient ships from the industry; as a consequence, technical knowledge in this field has stagnated. There is the need of approaching the subject with systems perspective, which regards the ship as a complex system rather than focus on individual components (Lassesson & Andersson, 2009; DNV, 2012a). This thesis aims at addressing this gap in the scientific knowledge in the field.

1.2

Aim and research questions

The aim of this licentiate thesis is to contribute to a better understanding of the ship as an energy system and to use this acquired knowledge in order to analyse possible 1

The processes inside the International Maritime Organisation (IMO), the body of the UN responsible for international shipping, can be very complex. An analysis of one such example, related to sulphur dioxides emissions from shipping, can be found in Svensson (2011). 2 EEDI and SEEMP have been adopted at MEPC 62 in July 2012 as an amendment to the Maritime Pollution (MARPOL) convention. 3 There is still an open discussion, especially from a juridical point of view, on whether CO2 should be considered a pollutant or not. For further details on the subject please refer to (Linné, 2012) 4 There is a large debate on what will be the marine fuels in the future. A thorough environmental comparison of different marine fuels is presented in Bengtsson (2011).

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1. INTRODUCTION

improvements to its energy efficiency. To improve the understanding of ship energy systems translates in the objective of evaluating components performances and energy flows sizes. The importance and efficiency of producers, converters and consumers from one side and the size of inputs, outputs and internal flows from the other should be evaluated. This can be translated in the following research questions: How can energy and exergy analysis be used to identify: • identify the main energy producers and consumers on board? (Paper I) • the main waste heat flows? (Paper I) Once the main possibilities for improvement are identified, alternatives to the current practice should be generated and evaluated. In general, three kinds of intervention are considered: ship operations, retrofitting and design. In this thesis, this challenge is addressed using a systems approach, focusing on the system as a whole rather than on its individual components. This objective can be translated in the following research questions: How can the use of a systems perspective and of mathematical modelling assist in: • the improvement of the energy efficiency of the propulsion system through a better engine-propeller interaction? (Paper II) • the evaluation of the feasibility of the installation of waste heat recovery systems on ships? (Paper I, Paper II) • the estimation and comparison of the energy consumption and the carbon footprint of alternative arrangements in the initial stage of ship design? (Paper III) Results from the application of energy and exergy analysis are further developed in Paper I in order to evaluate WHR feasibility. In Paper II, models for the main part of ship energy systems are built in order to evaluate alternative operational modes for propulsion, also including a WHR system. Finally, in Paper III alternative fuels and propulsion system designs are evaluated in their annual energy consumption and carbon footprint.

1.3

Delimitations

International shipping is the largest contributor both to the benefits (trade volume) and to some of the drawbacks (GHG emissions) of shipping in general. This thesis will hence focus on the types of vessels mostly used in international shipping. This kind of ships are generally operated in stable conditions over long distances, which makes a steady-state approach the most appropriate for tackling this subject. Dynamic effects are, in most cases, of little interest and they are therefore not taken into account. Furthermore, this thesis aims at providing tools and information on how to improve the energy efficiency of ship energy systems, by means of generating and evaluating

6

1.4 Thesis outline

alternatives to current setups. The results of the thesis should, as a consequence, be seen as a part of a larger picture, also including economic, environmental, and human aspects. From an energy perspective, the analysis sets its boundaries at the ship as a system, and again the results should be seen as part of a larger figure, where energy needs for fuel production and transportation, and for ship production are also taken into account.

1.4

Thesis outline

This thesis presents a synthesis of the research conducted over two and a half years on the subject of improving ship energy efficiency through a systems perspective. Although additional details can be found in the three appended papers, the thesis is supposed to be understandable as a stand-alone work. This thesis is subdivided in eight chapters; after the introduction to the background, subject and scope of the thesis, Chapter 2 describes the ship as an engineering and energy system and motivates the use of a systems approach by introducing to its complexity. Chapter 3 presents the main methodological framework of the work, first describing the case study that have been used in the work, and then introducing the reader to the concepts of systems analysis, energy analysis and system modeling. Chapters 4 and 5 represent the core of the thesis, presenting the results of energy and exergy analysis of ship systems and providing relevant examples of how these can be used in order to generate and evaluate alternatives for the improvement of ship energy efficiency. Results and methodology are then discussed in Chapter 6, while Chapters 7 and 8 respectively present suggestions for further research and recommendations to the industry, and draws conclusions from the work and.

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1. INTRODUCTION

8

2

The ship as complex energy system Concepts such as ”energy system” or ”systems approach” have already been mentioned many times in this work. In particular, the aim of the thesis lies in the utilisation of a systems approach in order to identify possible improvements of ship energy efficiency. This is justified by the fact that in complex systems a major contributory factor [to erroneous predictions of systems behavior] has been the unwitting adoption of piecemeal thinking, which sees only parts and neglects to deal with the whole ˝Flood & Carson (1993, p. 14). Inefficient design is often connected to erroneous predictions of system behavior, which are normally originated by counter-intuitive behavior. However, referring again to Flood & Carson (1993, p. 14), this [counter-intuitive behavior] is not an intrinsic property of phenomena; rather, it is largely caused by our neglect of, or lack or respect being paid to, the nature and complexity that we are trying to represent. That is one reason why we need systems thinking, methodologies, and models. We argue that without this formal thinking we see only parts, the extremes, the simple explanations or solutions. This thesis addresses the subject of ship energy efficiency using a systems perspective. This kind of approach is most suitable, if not required, when a complex system is to be understood and improved without risking sub-optimisation. Section 2.1 introduces the subject of complexity and identifies what features of the ship make it identifiable as a complex system. This leads to a description of the ship as an engineering system, and in particular of the features related to its energy efficiency, presented in Section 2.2.

2.1

An introduction to complexity

Scientific literature can propose several examples which refer to the ship as a complex system from the point of view of control theory, social sciences, environmental

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2. THE SHIP AS COMPLEX ENERGY SYSTEM

sciences, and other more (Dupuis & Neilson, 1997). The interest of this work, however, lies specifically in looking at the ship as an energy system, and components and sub-systems are considered under the light of their influence on the energy balance of the ship. Components which are extremely relevant from other perspectives, as for example navigation equipment, can be considered as a mere fixed, small contribution to the overall energy balance of the ship. The complexity of a system can be broken down to a number of attributes (Yates, 1978; Flood & Carson, 1993; Checkland, 1999): • Large number of parts and significant interactions • Non-linearity • Emergence Ship energy systems have a number of significantly interacting parts that is large enough not allow intuitive prediction of all input-output relations, but small enough to enable a clear identification of all components and of their interactions. A system is non linear when at least one element in the system relates to and varies in a non-linear way with another ˝ (Flood & Carson, 1993, p. 28). Linearity allows very simple intuitive estimations. A ship is made of several complex components, few of which can be described with a linear behavior. These components also give rise to asymmetrical relationships. Emergence refers to the property of the system to be more than the sum of its parts. The ability of a ship to fulfil its mission depends on the coexistence of a number of interacting components. A ship is more than the sum of an engine, and propeller, a hull, and many other subsystems; it is the unique combination of all these components that makes the system to be able to deliver its final function. The ship, and in particular its energy systems, can therefore be said to be a complex system which can take a benefit from the application of a systems approach. Before proceeding further with the description of the work the led to the results of this thesis, the most relevant features of a ship are introduced, with a special focus on those that are most closely connected to the thematic of energy efficiency.

2.2

The ship

A ship is a floating, autonomously propelled platform which is designed for performing a specific mission. As this generally implies moving the ship through water, propulsion is often one of the major sources of energy consumption. The propulsion system fulfils this function on board of the ship. On the other hand, a ship has additional functional requirements, such as providing accommodation to the crew, supplying cooling and lubrication, etc. These functions require additional power, in the form of electric, mechanical, and thermal energy. A short overview of the most relevant auxiliary consumers and of the most common technologies for the generation of auxiliary power and

10

2.2 The ship

heat is then presented. A visual representation of ship energy systems is provided in Figures 2.1 and 2.21 .

2.2.1

The propulsion system

A detailed description of ship resistance would be out of the scope of this work; however, for giving an estimation of how the system is influenced by external parameters, it is often assumed that the power required for ship propulsion can be approximated as shown in equation 2.1 (Woud & Stapersma, 2008, p. 52). P = y · c0 (v)v 3

(2.1)

The factor y accounts for the influence of non-design conditions, such as hull fouling, displacement, sea state, and water depth. The coefficient c0 , representing the characteristic behavior of a specific ship, is normally increasing with speed, meaning that the final dependance of ship propulsion power requirement with speed is not exactly a third power curve (Woud & Stapersma, 2008, p. 52). The function of the propulsion system is to provide the ship with the ability to move. Even if the propulsion arrangement can vary substantially from vessel to vessel, the most common configuration can be described by one or more prime movers, coupled to one or more propellers. This is the typical arrangement for most of today’s commercial vessels. Propellers The propeller is the most widespread solution among for converting of the rotating mechanical power from the engine shaft into a thrust force. Thrust bearings connect the shaft to the ship, thus allowing to convert thrust force into ship motion. Fixed pitch propellers (FPP) are characterized by having blades whose angle relative to the axis of the shaft (pitch) is fixed. FPPs are generally directly connected to low-speed two-stroke engines, therefore building a very solid, reliable, and efficient propulsion train. However, this system suffers low flexibility and scarce manoeuvrability (Molland, 2008). FPPs are the most widespread solution for ship propulsion, and are particularly common among container ships, tankers, and bulk carriers (Carlton, 2007). Controllable pitch propellers (CPP) allow the variation of the pitch. This ability provides the CPP with an extra degree of freedom in addition to its rotational speed. As a consequence, CPPs are installed for increasing ship manoeuvrability, for improving the ability of adapting load to drive characteristic, and for giving the possibility to generate constant-frequency electric power with a generator coupled to the main engines (Woud & Stapersma, 2008). As a consequence of the system being more complex, CPPs are more expensive and delicate than FPPs. CPPs are most favoured in passenger ships, ferries, general cargo ships, tugs, and fishing vessels (Carlton, 2007). 1

Please note that the arrangement represented in these figure is only intended to give a feeling of what are the main components positioned on board of the case study vessel. Their location in the actual arrangement might differ substantially.

11


Prime movers Diesel1 engines are the most widespread solution for the generation of mechanical power from chemical energy. Firs installed on a ship in 1903, Diesel engine finally substituted steam turbines in the 60s, and today constitute 96% of installed power on board of merchant vessels larger than 100 gross tons (Eyring et al., 2010). Marine Diesel engines, in fact, can achieve efficiencies up to 50%, allow operations to very low load (down to 10% of maximum continuous rating, MCR (Laerke, 2012)), and are designed to burn both residual fuels (heavy fuel oil, HFO, and intermediate fuel oil, IFO), and distillates (marine diesel oil, MDO, and marine gas oil, MGO) (Woud & Stapersma, 2008, p. 132). Recent developments also made dual-fuel engines available on the market, which can run both on liquid fuels and on natural gas (Aesoy et al., 2011). The most relevant features of Diesel engines are summarised in Table 2.12 . Diesel Engines

Process Construction Output power range [kW] Output speed range [rpm] Fuel type SFOC [g/kWh] Specific mass [kg/kW]

Low-speed

Medium-speed

High-speed

2-stroke Crosshead 8000 - 80000 80 - 300 HFO/MDO 160 - 180 60 - 17

4-stroke Trunk piston 500 - 35000 300 - 1000 HFO/MDO 170 - 210 20 - 5

4-stroke Trunk piston 500 - 9000 1000 - 3500 MDO 200 - 220 6 - 2.3

Table 2.1: Performance parameters of Diesel engines, state of art 2001 (Woud & Stapersma, 2008, p. 136)

Gas turbines are today the only alternative to Diesel engines for ship propulsion. Despite being less efficient (efficiency for gas turbines ranges between 30% and 40%), and less flexible with load and fuel quality (Woud & Stapersma, 2008, p. 138) than Diesel engines, their main advantage lies in their higher power density. This makes them suitable for applications where high power and low weight are required, as in the case of fast ferries or naval vessels.

2.2.2

Auxiliaries

Ship auxiliary systems are a vital part of the ship. They are generally connected with energy demands that can, depending on ship type, represent a significant portion of 1 As Diesel is the surname of the inventor of this type of engine, Rudolf Diesel, I will refer to Diesel engines with capital letter. 2 For a detailed description of the different principles the reader is invited to refer to the extensive literature on the subject, such as (Stone, 1999; Heywood, 1988; Kuiken, 2008).

12

2.2 The ship

overall ship energy use. As a ship at sea cannot use an external source of energy for its auxiliary needs, these must be provided by on board machinery. This can be divided as a function of the purpose (consumers, producers) or of the type of energy processed (thermal, electrical). Auxiliary consumers A number of components on board require electric or mechanical power. Pumps are often a major consumer in this category, and they can be found in fuel and lubrication systems. Compressors are installed on board for air conditioning (HVAC, especially when additional accommodation is required for passengers), refrigeration and compressed air systems. Fans, both in the engine room and in cargo spaces, cargo handling, in the form of pumps or cranes, lighting, especially for passenger vessels, and all navigation equipment can also be a relevant source of electric power consumption. Ballast water pumps also constitute large auxiliary consumers, as most ships need to load water into specifically allocated tanks in order to maintain stability, especially when sailing with empty holds1 . Most ships are also equipped with bow thrusters, propulsors located in the fore of the ship, that provide additional manoeuvrability in port. Cooling demand is met by using sea water as a cooling flow and is also associated to relevant power demand, especially for pumps. The main cooling demand is represented by the main engines, which is subdivided into cooling of the cylinder walls (normally referred as jacket cooling, JW), of the charge air flow (charge air cooler, CAC), and of the lubricating oil (lubricating oil cooler, LO). In general, a high temperature (HT) and a low temperature (LT) circuits are used not to provoke too high thermal stress in the components. Additional systems are installed when refrigeration or air conditioning are required. The first is often needed by fishing vessels and reefers, while the second is connected to accommodation facilities and is a major consumer in cruise ships and ferries. Cooling demand generally translates in additional auxiliary power requirement for the operation of cooling pumps and refrigeration systems. Heat consumers can also be relevant to the overall energy balance of the ship, especially for some specific vessel types, such as tankers and cruise ships. A large heat demand is often connected to accommodation, both for heating and fresh water generation. As HFO has very high viscosity at ambient temperature, fuel heating is also often a major figure in this category. Finally, some vessel types (e.g. tankers) can have specific mission-related heat demands, such as cargo heating. Auxiliary producers Electric power is often generated using auxiliary Diesel engines (AEs, also referred to as auxiliary generators), coupled to electric generators and, in turn, to a main switchboard. This solution is the most common when a 2-stroke engine is used for propulsion. When a larger amount of auxiliary power is needed, a generator (in this case normally referred 1

A ship is said to be ”sailing ballast” when it is navigating with empty cargo holds for picking a new cargo.

13


to as ”shaft generator”, S/G) can be coupled to the main engine shaft. This increases the efficiency of auxiliary power generation since the main engines are generally more efficient than the auxiliary engines. This solution requires however either a constant engine speed (and, thus, a CPP), or the installation of power electronics for frequency conversion, since the frequency of the current generated by the S/G directly depends on the speed of the main engine (Woud & Stapersma, 2008). Auxiliary heat is generated in different ways depending on the required quality (temperature) and quantity. Heat exchangers recovering energy from the exhaust gas (also referred to as exhaust gas economisers, EGE) are often employed when a relatively small amount of process heat is required (i.e. only for fuel heating and accommodation, which is the most usual case on merchant ships). A separate boiler is necessary for higher heat demand, such as in the case of tankers and cruise ships. Heat is normally distributed to different consumers either using steam or thermal oil. The freshwater generator is a special case, as it can use low-grade heat and is therefore often located on the high temperature cooling water systems. Waste heat recovery systems WHR systems refer to technical devices designed to make use of thermal energy that would otherwise be wasted to the environment, a solution which is widely used in various industrial sectors. The possibility of recovering waste heat from the main engines exhaust gas to meet auxiliary heat demand has already been mentioned in the previous section, and conceptually falls in this category. However the acronym WHR will be used, in the continuation of this thesis, to identify systems whose main purpose is to generate mechanical and/or electric power from a flow of waste heat. This distinction is used since the technology required for the conversion of waste heat into mechanical/electric power are conceptually different from what needed for the heat-toheat conversion. Some different technologies exist for the conversion from thermal to mechanical energy (Shu et al., 2013). However, this work focuses on the utilisation of systems based on Rankine cycles. This technology has been particularly successful because of its simplicity, safety, and relatively high efficiency (Tchanche et al., 2011). A Rankine cycle is based on the generation of high-pressure steam and its subsequent expansion in a turbine, which generates mechanical power. Organic Rankine cycles (ORC) are often used when only low-temperature waste heat is available; their working process is analogous to that of a standard Rankine cycle, but they make use of different working fluids which allow additional freedom in the choice of the evaporating temperature. On a ship, the main engines are the principal source of waste heat on board of most of vessels, in particular through the exhaust gas and the cooling water flows. Despite the application of WHR systems is still quite rare in shipping, they are considered to have a high likelihood to be retrofitted on existent ships in the future (DNV, 2012c).

14

15

Propeller

Engine room auxiliaries

Auxiliary engines

Auxilary boilers

Gearbox

Main engines

Exhaust gas economisers

Accomodation

N2

Fuel heating

Tank cleaning

Bow thrusters

2.2 The ship

Figure 2.1: Schematic representation of ship energy system

16

Propeller

Gearbox Shaft generator

Engine room auxiliaries

Auxiliary engines

Auxilary boilers

Exhaust gas economisers

Main engines

HVAC

Accomodation

Cargo pumps

Nitrogen compressors Tank cleaning

Fuel heating

Bow thrusters

Electric energy consumer / producer

Heat consumer / producer

Mechanical energy consumer / producer


Figure 2.2: Schmeatic representation of ship energy flows

3

The study of ship energy systems with a systems approach In Chapter 2 ship energy systems have been described, and they have been shortly analysed from a systems science perspective. Having assessed the complexity of ship energy systems, it is necessary to employ a systems perspective rather than a component-wise optimisation. Systems analysis is a methodology suggested when dealing with problemsolving in complex systems (Flood & Carson, 1993, p. 100). The community of systems scientists only partially agrees on the specific procedures to be applied in systems analysis (Checkland, 1999, p. 139), and it is common to adapt the methodology to the specific need of the situation. In the case of this thesis, the task of problem solving has to coexist with the more scientific need of improving the understanding of a system. The analysis of the problem constitutes the first part of a systems analysis. The focus can be summarised in the question What are the limitations of the present system? (Flood & Carson, 1993). Valuable information for answering to this question can be provided by the results of energy and exergy analysis. A description of these methods, together with some required thermodynamic background to the concept of exergy and with an investigation of the existing literature, are provided in section 3.2. The results from the energy and exergy analysis provide the basis for the following steps of systems analysis: the generation and evaluation of alternative solutions. This process requires an additional premise; in fact, during specific and in-depth studies, conceptual or mental models are often not sufficient to cope with the type of complexity involved. [...] It is, therefore, necessary to seek more formal structured approaches to modeling (Flood & Carson, 1993). The second part of this thesis, hence, focuses on the application of models to the evaluation of alternative solutions for improving the system. This process is subdivided in its application to ship operations, retrofitting, and design. More details on systems modeling and its application to ships in existing literature are provided in Section 3.3. The main interest of this study is to argue for the application of a systems perspective to ship energy systems. However, the presentation of such a method without any

17

3. THE STUDY OF SHIP ENERGY SYSTEMS WITH A SYSTEMS APPROACH

Figure 3.1: Graphical representation of thesis methodology

reference to real cases would fall short both in explanatory power and persuasion. As observed in most literature in the field, in the present work the proposed methods are applied to a case study. The technical details of the selected case study ship and more information about the data sources used in this study are here presented.

3.1

The case study of a chemical tanker

The case study represents an existing vessel, for which the company owning the vessel (named the ”partner company”) provided extensive operational measurements and technical information.

3.1.1

Description of the ship

The case study ship is a chemical/product tanker of 45 000 tons of deadweight which largely operates in international waters and, often, in ECA areas. The propulsion system is composed of two equally sized medium speed 4-stroke engines for a total installed power of 7 680 kW. Both engines are connected to a common gearbox, which in turn is connected to the propeller shaft, which provides the required thrust for propulsion. The ship is equipped with a S/G (rated 3200 kW), connected to the main gearbox, which can provide auxiliary power. When operating in this configuration (”generator mode”) both the main engines and the propeller need to be run at constant speed, while acting on the pitch of the CPP enables setting the speed. Alternatively, the ship can run in ”combinator mode”; this operational mode allows for variable propeller speed, and consequently requires the use of at least on the two auxiliary engines (rated

18

3.1 The case study of a chemical tanker

682 kW each) for power generation. The ship is, however, operated in generator mode during most of its operations. When the main engines are running, the auxiliary heat is supplied by two EGEs, capable of generating 700 kg/h of steam at 10 bar each, while two large auxiliary boilers, rated 14 000 kg/h of steam at 14 bar, are used for peak demands and when the main engines are not in operation. For both electric power and heat, most auxiliary consumers are the same that can typically be found on most merchant ships. Special functions connected to the ship mission are the following: Inert gas production and compression: Nitrogen needs to be produced on board and pumped into cargo tanks when inflammable liquids are transported. Nitrogen compressors have a high power demand, but are only operated intermittently. Cargo pumping: When unloading the vessel, cargo pumps are required (high pressure in the shore-based tanks is normally sufficient for cargo loading). They can require a large amount of power when operated simultaneously. Tank cleaning: After one cargo has been unloaded, tank cleaning is generally necessary in order to prepare the cargo tanks for the following shipment. This operation is performed either directly in port or during ballast trips, and requires the use of the auxiliary boilers. Cargo heating: Some specific liquids are characterized by very high viscosity at ambient temperature, which makes them unsuitable for handling. For this reason, cargo heating can be ensured by means of process steam. This operation is, however, very seldom required. The energy system of the case study ship is schematically represented in Figure 3.2.

3.1.2

Input data

Measured data and technical information for the case study ship were provided by the partner company. The different sources of quantitative and qualitative information are hereafter described. Continuous monitoring The case study ship is equipped with an energy monitoring system which logs on board measurements on a dedicated server with a frequency of acquisition ranging between 1 and 15 seconds. Data are automatically processed by the systems in order to produce 15 minutes averages, to check data reliability, and to filter output values. The list of the measurements available on the energy monitoring system is presented in Table 3.1.

19


Mechanical energy Electric energy Thermal energy

Producers

Thermal auxiliary consumers

Converters

Engine room Auxiliary boilers

Nitrogen Compressor Boiler auxiliaries Engine room

Fuel input

Cargo pumps Auxiliary engines

Nitrogen Compressor

Switchboard HVAC

Boiler auxiliaries Other consumers

S/G

Electric auxilairy consumers Main engines

Gearbox

Propeller

Propulsion EGE

Lub oil cooler

LT cooling

Jacket cooler HT cooling CAC

Cooling systems

Figure 3.2: Conceptual representation of ship energy systems and flows

20

3.1 The case study of a chemical tanker

Measured variable Ambient air Dew point temperature Relative humidity Auxiliary engines Fuel consumption Power output Shaft generator power output Propeller Power Speed Torque Main engines fuel consumption Fuel temperature Seawater temperature

Unit ◦C

% ton 15mins

kW kW kW rpm kNm ton 15mins ◦C ◦C

Table 3.1: Available measurements from on board energy management system

Technical documentation When direct measurements of ship and components performance are not available, they can be calculated starting from available knowledge of the system. In this sense quite extensive technical documentation was made available by the partner company for the different components installed on board. Main engines project guide contains information directly provided by the engine manufacturer and publicly available online (MaK). Data here provided comply with ISO 3046/1 and 15550 standards. Information connected to engine performance, inlet and outlet flows, and thermal losses to the environment are used in the study. Main engine shop test contains experimental data provided by the manufacturer and measured under well-defined conditions. Information on engine performance for different load, including efficiency and exhaust temperature, is available from this type of technical document. Ship sea trials are performed when the construction of the ship is completed in order to verify that the actual vessel performance conforms to the initial requirements set by the customer. Ship sea trials were available for the case study ship and for all its sister ships1 . On board sensors are generally used in this phase, but a 1

In shipping jargon, ”sister ships” are vessels built according to the same design

21


reasonable level of accuracy is guaranteed. Propeller curves are represented as a diagram provided by the propeller manufacturer and generated through numerical codes. They provide information on propeller performance for different values of propeller pitch, speed and power and for different ship speeds. Combinator diagram maps the characteristics of the control system installed on board for engine-propeller interaction. The combinator diagram is used when the ship is run in combinator mode, and is needed for engine protection versus too high torque at low speed, which would result in excessive thermal loading for the engine. Ship electric balance is provided by the shipyard and summarises the expected power consumption of different auxiliary components depending on ship operational mode. Ship heat balance is supplied by the shipyard and provides details on the different parameters used in the calculations such as heat exchange areas and heat transfer coefficients. Other technical documents provide design information to be used for the estimation of the efficiency of the auxiliary engines, the shaft generator, and of other ship components. Noon reports and other data sources On the case study ship, as on most vessels, information and measurements related to on board fuel consumption and machinery relevant parameters is manually collected daily by the crew and logged in paper and electronic format. Although the accuracy and reliability of these data is often questioned (Aldous et al., 2013), they constitute a broad source of knowledge and are used in this thesis when none of the previously mentioned sources could provide the required information.

3.2

The analysis of ship energy systems

In this thesis, as energy efficiency is the main focus, the analysis of ship energy systems is performed as first step of the systems analysis methodology. The analysis of ship energy systems is subdivided in two main parts, energy and exergy analysis. While the former is quite known in various fields of science and in the industry, exergy is only seldom used, and will hence require some additional background. The analysis of energy systems is often also divided between two main approaches A top-down approach mostly relies on the analysis of extensive measurements carried out in existing facilities (see, for example, Basurko et al. (2013)). On the other hand, a bottom-up approach uses mechanistic knowledge of the system in order to simulate its

22

3.2 The analysis of ship energy systems

behavior and draw conclusions based on simulation results (see, for example, Nguyen et al. (2013)). The choice of the type of analysis strongly depends on the availability of input data and other information about the system.

3.2.1

Energy analysis

Energy analysis is defined as the process of determining the energy required directly and indirectly to allow a system to produce a specified good or service(IFIAS, 1974), and refers to the application of the first law of thermodynamics, which states the principle of energy conservation (Clausius, 1850). Energy analysis provides a quantitative insight of the relevance of different energy flows. It can help to identify, for example, the largest consumers in a system, on which efforts should be focused in order to improve the overall efficiency1 . Studies reporting the analysis of the whole ship energy system are not common in literature. The work of Thomas et al. (2010) and Basurko et al. (2013), related to fishing vessels, propose an estimation of ship energy consumption and its repartition among different consumers, followed by the generation of a number of different alternative solutions for decreasing fuel consumption and their evaluation.

3.2.2

Exergy analysis

Energy analysis becomes incomplete and can be misleading when thermal energy flows are compared to electric and mechanical flows; the first law of thermodynamics, in fact, does not include any consideration about energy quality (Dincer & Rosen, 2013). The concept of exergy can be of particular use in this case, as the exergy content of a flow depends both on the quantity and on the quality of its energy content. Heat and disorder The need of introducing a new concept lies in the discrepancies between different energy types and, especially, in the conversion from one to another. Electric energy can be easily transformed into mechanical power and vice verse (electric motors have efficiencies of more than 90%, and the same holds for electric generators). Both these forms of energy can also be easily converted into thermal energy (respectively, for instance, through a fan and a resistance), with efficiencies close to 100%. The same cannot be said of the opposite: in practical applications the conversion from thermal to mechanical energy reaches maximum values of roughly 60% in advanced combined cycles. There is always a part of the thermal energy input which cannot be converted into work. This asymmetry is expressed by the second law of thermodynamics, which states that it is not possible to have a cycle whose only result is to convert a given amount of heat into work. 1 Using high efficiency led lamps can be a very good idea for an office building, where large amounts of energy is required for lighting. On a container-ship, on the other hand, the same measure would lead to much less rewarding improvements as overall consumption for lighting is almost negligible in comparison to other needs.

23


No coherent movement

Coherent movement

(a) Mechanical energy

(b) Thermal energy

Figure 3.3: Conceptual representation of the coherency of mechanical and thermal energy

Explaining the reasons of this asymmetry requires to look into matter at microscopic level. With reference to Figure 3.3, it is easy to observe that mechanical energy is characterised by a coherent motion of particles, for which it is possible to identify a principal direction. On the other hand, thermal energy is characterised by a completely random motion, for which it is not possible to identify any main pattern. A cost is associated to the passage from chaos to order, and the second law of thermodynamics qualitatively describes this cost (Atkins, 1984). A quantification of this cost would allow to compare thermal flows among each other and versus more coherent forms of energy. Sadi Carnot provided the tools for this quantification: the Carnot efficiency is defined as the maximum efficiency that could be achieved by an ideal engine in generating mechanical power when receiving heat from a thermal source at a temperature Th and rejecting the waste heat to a thermal sink at a temperature Tc , lower than Th : ηex = 1 −

Tc Th

(3.1)

Exergy The concept of exergy derives from a generalisation of the Carnot efficiency. As conceptually represented in Figure 3.4, exergy represents the fraction of a given energy flow that could be converted into work using an ideal, Carnot engine. In fact, for a given amount of matter, its thermal exergy content is defined as showed in Equation 3.2. The remaining fraction of the initial energy flow represents the part that cannot

24

3.2 The analysis of ship energy systems

Energy

Exergy

Anergy

Figure 3.4: Conceptual representation of the property of exergy

be converted into work, even in ideal conditions, and is called anergy. EX = m[(h − h0 ) + T0 (s − s0 )]

(3.2)

where EX, h, T and s respectively represent exergy, specific enthalpy, temperature and specific entropy, while the subscript 0 represents reference conditions. It is possible to demonstrate that, under certain assumptions, Equation 3.2 can be derived from Equation 3.1. For energy in a coherent form, such as in the case of mechanical, chemical, and electric, energy and exergy flows coincide1 . Exergy and energy flows are instead remarkably different in the case of thermal energy. As it appears from Equation 3.1, the higher the temperature of a flow, the higher the fraction of that flow that can be converted to work. Equation 3.2 shows how the exergy content of a flow of thermal energy also depends on the ambient temperature, which in general represents the cold sink of the hypothetical Carnot cycle. A flow of steam at 500◦ C could not be converted into work if its environment was at the same temperature2 . 1

In this thesis, differently from common practice in exergy analysis, the lower heating value (LHV) is used instead of the higher heating value (HHV) for the quantification of chemical exergy. The reason for this choice lies in the fact that as conditions of exhaust gas condensation are never reached in ship energy systems, the use of HHV does not add any additional accuracy to the analysis. 2 Despite this argument, there is still debate on whether a fixed reference temperature should be

25


Exergy flows and efficiencies According to Dincer & Rosen (2013) exergy flows calculated according to equation 3.2 can be divided in three main categories: ˙ in ) : the flow of exergy entering the component. Input (EX ˙ out ) : the flow of exergy leaving the component. Output (EX ˙ : the amount of exergy lost in the component operation (also Irreversibility (I) known as exergy distruction). This part represents energy quality deterioration and is defined as I˙ = T0 S˙ gen , where S˙ gen represents the rate of entropy generation in the component. In practical terms, however, exergy destruction is normally calculated as the difference between input and output exergy flows. There are several different figures of merit that are commonly used in exergy analysis. In this thesis, four different quantities are used: ˙ EX

p Exergy efficiency ηex is defined for this study as ηex = EX ˙ in , where the subscripts p and in respectively refer to products and inputs. This definition of exergy efficiency gives an estimation of how efficient the component is in the generation of useful products. In the case of heat exchangers, as there is not a clear distinction ˙ c ∆EX between inputs and products, the alternative definition of ηex = ∆ ˙ h is used, EX where subscripts c and h respectively refer to the cold and the hot fluid. The definition of the ∆EX are adapted depending whether the component is meant for cooling or heating.

Irreversibility ratio λ is used according to the definition proposed by Kotas (1980), I˙ i.e. λ = EX ˙ in . The irreversibility ratio gives an estimation of how much energy quality is lost in the component. Irreversibility share δ is defined as the ratio between the exergy destroyed in the component and the total rate of exergy destruction in the whole system, i.e. ˙ δ = II˙i . tot

Task efficiency ηtask is used in this thesis in a modified form from what proposed in Dincer & Rosen (2013), and is defined as the ratio between the irreversibility in an ideal exchange at constant temperature difference (here arbitrarily fixed to ˙ 10◦ C ) and the irreversibility in the actual process, i.e. ηtask = IIid˙ . The task efficiency gives an estimation of how close the component behavior is to an ideal process. Exergy analysis allows overcoming many of the shortcomings related to energy analysis. The most typical and clear example is that of a heat exchanger: from a firstlaw analysis, the component can be assumed to have a 100% efficiency, if the small used instead for the evaluation of exergy flows (Pons, 2009).

26

3.3 The improvement of ship energy performance

heat losses to the environment are neglected, regardless the temperatures of inlet and outlet flows. If a second-law analysis is performed instead, the exergy losses connected to inefficient thermal exchanges (those, for instance, in which a low-temperature fluid is heated using a very high-temperature fluid, as generally happens in households boilers) can be identified. Exergy analysis provides, in general, an insight when both thermal and mechanical/electrical energy flows are present in the systems (Dincer & Rosen, 2013). Exergy analysis, however, is not widely used in shipping. Only few examples of the application of this method to ships can be found in literature, and only focus on individual components (Matuszak, 2008; Leo et al., 2010) or subsystems (Lijun et al., 1996; Choi & Kim, 2012). In the only example of the application of exergy analysis to the whole ship, Dimopoulos et al. (2012) presented the utilisation of exergy analysis for the optimisation of a marine WHR system.

3.2.3

Procedure

In the application of the analysis of energy systems to the case study presented in this thesis, a top-down approach is used, as extensive measurements are available. Input data from the continuous monitoring system for propeller power demand, MEs and AEs fuel consumption, S/G and AEs power output are used. The whole analysis of energy and exergy flows represents an aggregation over one year of operation of the case study ship. Mechanistic knowledge of the system is used to break down the analysis of the energy flows among different components. Data from the different sources presented in Section 3.1.2 are used to identify thermal flows in the exhaust gas, cooling systems, and in the heat consumers. Data from the electric balance is elaborated to subdivide the overall auxiliary power consumption among different users. Records from noon reports are used to calculate the fuel input to auxiliary boilers. Heat exchange areas and coefficients from the heat balance are used to estimate auxiliary heat consumption. Available measurements of external seawater temperature are used as reference temperature for the calculation of exergy flows.

3.3

The improvement of ship energy performance

As introduced in the beginning of this chapter, the results provided by the application of energy and exergy analysis to ship energy systems should be used for the subsequent process of proposing and evaluating possible improvements to the system under study. This thesis focuses on the application of such process to the three main types of intervention in systems improvement: operational, retrofitting, and design. In Section 2.1 the ship has been identified as a complex energy system. As suggested by Flood & Carson (1993, p. 151) during specific and in-depth studies, conceptual or mental models are often not sufficient to cope with the type of complexity involved.[...] It is, therefore, necessary to seek more formal structured approaches to modeling. The

27


subject of system modeling is therefore hereafter introduced.

3.3.1

Systems modeling

Structured, mathematical modeling allows predicting the response of a system to different inputs without the need of experimentation or prototyping (Blanchard & Fabrycky, 2006, p. 164). Depending on the problem to solve, different modeling approaches can be used; a basic description of different criteria used when making decisions on approaches and assumptions to be used in models is here provided, adapted from Grimmelius et al. (2007) and Flood & Carson (1993, p.155): Physical requirements: The model must be able to produce the outputs required by its utilisation and must to reproduce the required physics. This for instance might include the decision of whether the model should be dynamic or stationary, or of which variables it is required to predict. Accuracy requirements: Higher accuracy means better results. However, as resources are limited, a target for model accuracy should be set in order to optimise computational effort and final result. Accuracy requirements for a specific model are normally related to the accuracy of other models in the same systems (in order to avoid the ”bottleneck effect”), and to the accuracy of model inputs. Data availability: A model should be constructed in accordance with the available information on the system, both in terms of system parameters and system inputs. Some parameters can be assumed based on previous work (physical constants, empirical correlations), while in presence of experimental data it is also possible to let some of the parameters vary in a calibration process, as long as enough experimental points are available for performing both the calibration and the validation of the model. The main choice connected to the modeling approach refers to the use of mechanistic (often referred to also as white-box, bottom-up, or deterministic) models, as opposed to empirical (also referred to as black-box or top-down) models. Mechanistic models attempt to describe the physical phenomena that characterise a system, thereby assuming a deterministic approach; they typically make use of physical laws or empirical correlations in order to model and predict the behavior of a system. In contrast, empirical models treat the system to model as a black-box, and have no interest in the description of the underlying physical phenomena; starting from input-output databases, the empirical modeller employes regression techniques in order to generate a model able to predict system’s output. The difference between the two approaches is conceptually visualised in Figure 3.5, while their characteristics are compared in Table 3.2.0 Finally, hybrid models (also known as gray-box or semi-empirical models) attempt to mix the positive properties of both white- and black-box models. 1

The information summarised in this table is taken from (Duarte et al., 2004; Bieler et al., 2003, 2004; Braake et al., 1998; Groscurth et al., 1995; Bontempi et al., 2004; Grimmelius et al., 2007; Oliveira, 2004), which the reader is also referred to for further reading on various types of system modeling

28


(a) White box

(b) Black box

Figure 3.5: Conceptual representation of white and black box modeling

Mechanistic models

Empirical models

High High High Low High Low

Low Low Low High Low High

System knowledge required Validity of extrapolation Applicability in design phase Accuracy Improvement of system knowledge Amount of input data required

Table 3.2: Comparison of mechanistic and empirical models1

29


3.3.2

Ship systems modeling

Application of energy system analysis in shipping dates as back as 1979 (Fake & Pundyk, 1979). More recently, Shi et al. proposed models for predicting ship fuel consumption in design and off-design conditions, both aiming at ships in general (Shi et al., 2009, 2010) and in the specific case of dredgers1 (Shi & Grimmelius, 2010; Shi, 2013). The work of Shi (2013) focuses on the dynamic modeling of dredger energy systems with the aim predicting energy consumption starting from the knowledge of a limited number of external variables. More focus on energy and fuel consumption while considering the ship as a system was introduced by Dimopoulos et al., whose work first focused on LNG carriers (Dimopoulos & Frangopoulos, 2008a,b) and then more generally on marine energy systems (Dimopoulos & Kakalis, 2010). In Dimopoulos et al. (2011) the use of optimisation algorithms in order to optimise the design of marine energy systems, with a particular focus on the implementation of WHR solutions and to the optimisation of related design parameters is proposed. The same authors also introduced exergy analysis applied to ship energy systems (Dimopoulos et al., 2012). Propulsion system modeling The largest quantity of work in the field of ship systems modeling with an energy perspective has been devoted to the propulsion system, as it constitutes the largest share of ship energy consumption. Attempts in this sense started from the works of DeTolla & Fleming (1981) in the US Navy during the oil crisis, and went to the tool presented by Dupuis & Neilson (1997) and the modeling analysis described by Neilson & Tarbet (1997). Work by Benvenuto et al. (2005); Benvenuto & Figari (2011); Campora & Figari (2003); Figari & Altosole (2007) focused on the propulsion system, proposing different alternatives for the modeling of Diesel engines and gas turbines, ship dynamics, and different control systems. Similar work was presented by Theotokatos (2007, 2008), while Schulten (2005) focused on the interaction between the engines, the propeller and the hull, with a particular focus of the dynamic events occurring during manoeuvring. Grimmelius (2003); Grimmelius et al. (2007) also relates to modeling of ship propulsion system, with a specific focus on the main engines. Tian et al. (2012) proposed and validated a model for the prediction of the behavior of the propulsion system of a RoRo vessel. Some examples exist of the application of pure black-box models to ship propulsion systems modeling. Among them, work from Leifsson et al. (2008) and Shi & Grimmelius (2010) showed the comparison of the application of white-, grey-,and black-box models to the prediction of ship performance. The results, in accordance to the theory, show that black-box models provide the most effective prediction, but only when applied inside the initial data range and when a large amount of input data is available. Other 1

A dredger is a vessel whose purpose is to excavate and remove material from the bottom of a body of water.

30


attempts of predicting the influence of different control variables (speed, trim, etc.) are mostly related to navigation, as proposed for instance by (Petersen et al., 2012). In relation to the objectives of this thesis, however, black-box models fail to provide additional insight on flows and phenomena in the energy system; furthermore, blackbox models are not suitable for the evaluation of alternative solutions, as they provide only little possibility for extrapolation. Auxiliary modeling Studies related to ship auxiliaries are not common, as auxiliary power and heat demand often constitute a negligible part of ship energy consumption. Some of the studies mentioned in previous sections also include some part of the auxiliaries, such as in Shi (2013) for the case of dredger pumps. Among the few examples of auxiliary system modeling in literature, Balaji & Yaakob (2012) analysed ship heat availability for use in ballast water thermal treatment technologies; others studied very specific types of auxiliary power consumption, such as Fitzgerald et al. (2011) who focused on the consumption of refrigerated containers, where Tilke et al. (2010) directed their interest on ship unloaders from bulk carriers. Hulskotte & Denier van der Gon (2010) specifically studied ship consumption when at berth, which is of particular relevance for the high impact of ship emissions when released in densely populated areas (Winnes, 2010). Bidini et al. (2005) proposed instead an analysis of the combined heat and power energy consumption of a small ferry operating in lakes. Work has been published on the design and optimisation of WHR applications to ships already starting from the 70’s (Tarkir, 1979). Some studies, such as Tien et al. (2007); Larsen et al. (2013), focused on a theoretical investigation of the WHR cycle, while Ma et al. (2012); Grimmelius et al. (2010); Theotokatos & Livanos (2013); Dimopoulos et al. (2011) proposed and evaluated different designs for the installation of WHR systems on ships.

3.3.3

Ship operations

The first possibility for reducing ship energy consumption relates to the improvement of ship operations. This alternative is of particular interest as it does not require any installation of new equipment and, therefore, only limited investment. Ships are normally optimised for one specific design point, while energy efficiency deteriorates in off-design conditions. The design power for the case study ship refers to the condition of sailing at a speed of approximately 15 kn in calm sea. Figures 3.6 and 3.7 show the frequency distribution for ship speed, propulsion system load, and individual engines load over one year of operations. The figures show that the condition at design load (85% MCR) is far from being the most frequent condition for ship operations, a consequence of the fact that the ship is very seldom sailing at its design speed. In fact, it appears that the engines are mostly operating between 40% and 70% load and that the ship mostly sails in the of range 8-12

31


Frequency of occurrence

0.20

0.15

0.10

0.05

0.00 0

5

10

15

20

Ship speed [kn] Figure 3.6: Ship speed distribution over one year of operations for the case study ship

Individual engines Propulsion system

Frequency of occurrence

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 0

20

40

60

80

100

Load [%]

Figure 3.7: Propulsion system and individual engines load distribution over one year of operations for the case study ship

32


knots. Ship energy performance at low loads becomes therefore of primary importance. This consideration, here deducted from the analysis of the case study ship, has also been proved to apply to many other vessels operated today (Banks et al., 2013). Propulsion is the main source of energy consumption on board of the ship and should be addressed first. The systems approach suggests to look at interactions between components rather than at components themselves, which in the specific case is corroborated by discussions with the partner company. The issue of engine-propeller coupling, especially when the presence of a shaft generator requires to operate at constant propeller speed, has also already been treated in literature (Woud & Stapersma, 2008; Van Beek & Van Der Steenhoven, 2005). In this case, two alternative arrangements for ship propulsion are evaluated and compared (see Figure 3.8): Case 1 Fixed engine speed, auxiliary power provided by the shaft generator. This arrangement corresponds to the standard operations in today’s settings. Case 2 Variable engine speed, auxiliary power provided by the auxiliary engines. The advantage in this case is the possibility of modifying propeller speed in order to adapt to the conditions of best efficiency for different ship speeds. This case represents the proposed alternative to be evaluated.

ME 1

ME 1

ME 2

ME 2

AE1

AE1

AE2

AE2

(a) Case 1

(b) Case 2

Figure 3.8: Schematic representation of the two operational modes compared in the study

As mentioned in the introduction to this section, the use of models rather than prototypes or intuitive estimation is used to evaluate the different proposed alternatives. In the case of the propeller, curves produced by the manufacturer displaying propeller power as a function of ship speed, propeller pitch, and engine speed are available and used in this study. In the case of the engine, however, more modeling effort is required. In fact, all available information is related to engine operation at constant speed, which does not reflect the operating conditions for Case 2. For this reason, an in-house engine model is built in the Matlab© environment1 . The modeling of auxiliary engines and 1

A detailed description of the model is provided in Paper II.

33


of the shaft generator is based on simpler numerical regressions, widely used in this field of engineering. All the different models are finally coupled and the behavior of the system for the two different alternatives at different ship speed is evaluated.

3.3.4

Retrofitting

Retrofitting, i.e. the modification of an already existing vessel, is also considered as a possible alternative for the improvement of ship energy performance. Despite their high efficiency, Diesel engines reject to the environment a considerable amount of energy, especially in the form of exhaust gas and cooling water. The implementation of WHR technologies is not new in shipping, even if still not common. The utilisation of a systems perspective in the analysis of the feasibility of such systems is hence employed. In particular, this thesis focuses on evaluating the recovery potential compared to the actual need of auxiliary power, based on measurements relative to one year of operations of the case study ship1 . A criteria was set for the evaluation of the possible installation of such systems, as compared to standard operations: if installed, it should provide the totality of ship auxiliary power need for a minimum of 80% of the time spent sailing. The exergy efficiency of the WHR system required to achieve this goal is then calculated for a number of different possible sources of waste heat. The availability of waste heat is also influenced by engine load, and therefore by ship operations. For this reason, in the second part of the study the focus is shifted to the evaluation of the WHR potential as a function of ship speed. The same modeling approach as proposed for ship operations is used, where the potential for WHR is evaluated using model output on exhaust gas temperature and mass flow. The arrangement here proposed consists of positioning a WHR system on the exhaust gas flows of the main engines (Case 3, see Figure 3.9). Case 3 is compared to both the previously proposed Case 1 and Case 2. In both parts of this study, the concept of exergy efficiency was used in order to give an estimation of the technical complexity of the recovery system. This approach, though only partially corresponding to real conditions, represents an improvement from the use of energy efficiency, which does not allow to account for differences in energy quality.

3.3.5

Design

As possible improvements both in operations and retrofitting are identified, the question can be further moved to investigating how it is possible to improve the ship directly from the design stage. Compared to common practice, the work presented in this thesis aims at proposing a more holistic perspective, thus taking into account aspects such as fuel selection, engine selection, WHR installation, energy performance and environmental 1 It should be noted that, as reviewed by Shu et al. (2013), several alternatives to power generation exist for the exploitation of ship waste heat. However, given the ship needs of auxiliary heat are already fulfilled by heat recovery and no additional requirement of, for instance, refrigeration was measured, auxiliary power generation was the only possible utilisation of waste heat taken into account.

34


ME 1 ME 2

AE1 AE2

Figure 3.9: Schematic representation of the propulsion arrangement with WHR system installed: Case 3

performance in the same study. In addition, the influence of the utilisation of a real operational profile instead of a single design point, as suggested by Motley et al. (2012), is evaluated. The study is carried out by comparing a total of 11 different arrangements, as showed in Table 3.3, which are generated assuming that the system should comply with future emission limits in ECAs for SOX and NOX . The arrangements are generated combining different alternatives of fuel type (HFO, MGO, or LNG), type of engine (2-stroke and 4-stroke) and the utilisation of a WHR system1 . The performance of a database of engines collected from technical documentation is evaluated using numerical regressions. For each of the 11 proposed arrangements, the best performing engine was selected. This allowed to compare ”best practice to best practice”. The analysis is then completed by looking at how the picture changed when including a carbon footprint analysis, thus including focusing on GHG emissions and employing an LCA approach.

1

The case of two-stroke engine with WHR using HFO as a fuel was not taken into account. This case would require too complex arrangements in order to fulfil future ECA emission limits and therefore was excluded from the study.

35


Case

Fuel

Engine

WHR

1 2 3 4 5 6 7 8 9 10 11

HFO HFO HFO MDO MDO MDO MDO LNG LNG LNG LNG

2-st 4-st 4-st 2-st 2-st 4-st 4-st 2-st 2-st 4-st 4-st

N N Y N Y N Y N Y N Y

Table 3.3: Alternative arrangements evaluated in ship design. 2-st and 4-st respectively stand for two-stroke and four-stroke engines

36

4

Ship energy system analysis The analysis of ship energy systems is applied to the case study ship. The objective is to show the typical results of this application and the kind of information that it is possible to extract. Energy and exergy analysis are applied to the case study ship.

4.1

Energy analysis

The results of the application of energy analysis to the case study ship are shown in Figure 4.1 and in Table 4.1. The first observable result is that consumption related to propulsion is the largest figure, as expected, accounting for 70% of the overall energy consumption. This also translates in the main engines consuming the largest share of the overall energy input of the system, representing around 89% of the total. However, the contribution from the auxiliaries cannot be neglected, particularly because the ship spends a significant part of time waiting in port, when the only energy demand comes from ship auxiliaries. Both auxiliary engines and boilers (respectively representing 8.0% and 2.6% of ship energy input) on one side, and auxiliary power and heat consumers (16% and 14% of ship energy output) on the other, should be given significant attention. Boiler auxiliaries should be added to these considerations, as they also represent a significant share of the total output (2.7%). Auxiliary boilers are also run at low load most of the time, leading to low efficiency. Fuel heating also represents a surprisingly high share of the overall ship energy consumption (7.8%). Finally, a large amount of energy is wasted to the environment through the exhaust gas (41% of main engines power output), the CAC (20%), JW cooler (22%) and the LO cooler (24%). This suggests that there is a potential for the utilisation of these waste flows for the generation of additional useful power.

4.2

Exergy analysis

Results of the application of exergy analysis to the case study ship are presented in Figure 4.2 and in Table 4.2.

37

4. SHIP ENERGY SYSTEM ANALYSIS

The exergy analysis provides a different type of information on the system under study. The irreversibility ratio (λ) quantifies the tendency of a component to deteriorate energy quality in its internal processes. High values of λ correspond to high losses in energy quality. It can be seen, for example, that according to this definition, boilers (λ = 64%) are much less efficient than both main (37%) and auxiliary engines (38%). In the case of heat exchangers, the utilisation of the irreversibility ratio as a figure of merit can be misleading: a heat exchanger located on a large exergy flow (e.g. on the exhaust gas) could have very high λ only because of its low heat exchange area. This is the case, for example, of the EGE, which has a very low rate of exergy destruction compared to the total exergy input (λ = 6.5%) and therefore appears to be a very efficient component. If task efficiency (ηtask ) is used instead, it appears that the EGE and all heat consumers are very badly designed exchangers, from an exergy perspective, as they could achieve the same task with a much lower exergy destruction. On the other hand the LT/SW exchanger, which is responsible of the highest rate of exergy destruction among heat exchangers (δ = 5.3%), shows that this is connected to its particular function; its task efficiency is not, in fact, particularly low (ηtask = 32%) Figure 4.2 can be helpful in the evaluation of different waste flows with respect to both energy quantity and quality. It is here shown that the exhaust gas is a much larger source of potentially recoverable heat than the cooling systems, contrarily to what could be deducted from Figure 4.1. When looking at the results of the exergy analysis, exergy flow in the exhaust gas represent 54% of the total recoverable energy, compared to 38% in energy analysis. The absolute estimation of the recoverable energy is also redimensioned: the energy flow in the exhaust is estimated to be 41% of the main engine output, while this value is decreased to 18% from an exergy perspective. This reduction is even more pronounced when looking at the cooling systems. In this case, it should also be noted that every step of heat exchange brings a decrease in the recovery potential; as an example, the exergy flow entering the jacket cooling alone is almost the same size as that flowing into the LT/SW heat exchanger. These results show how exergy gives a much more realistic estimation of the actual power that could be generated through a WHR system. Even if smaller in size than that of the exhaust gas, the energy flow in the cooling systems should not be discarded as it still constitute a relevant source of potentially recoverable energy. Looking at task efficiencies leads to the identification of which components could be improved in order to perform the same task while reducing exergy destruction. This is particularly true for the charge air cooler (ηtask = 27.5%). From the point of view of energy use, the very low efficiency of all auxiliary heat consumers (tank cleaning, hotelling, and fuel heating respectively have task efficiencies of 4.7%, 7.2% and 9.1%) indicates that it would be possible, by using a different heat transfer fluid (or, in alternative, steam at a lower pressure), to generate the same heat requirements while using much lower heat-grade sources. It is noted, for instance, that fuel handling and hotelling only require temperatures as low as 70-80 ◦ C (a part from fuel heaters before the engine, which warm HFO up to around 100◦ C ), which could be provided at much lower temperature than by 9 bar steam.

38

4.2 Exergy analysis

Figure 4.1: Sankey diagram for the case study ship

39


Figure 4.2: Grassmann diagram for the case study ship

40

4.2 Exergy analysis

Energy analysis %inp Producers Main engines Auxiliary engines Boilers

%out

88.4 8.0 2.6

Consumers Propeller Nitrogen compressors Cargo pumps Boiler auxiliaries Engine room HVAC Miscellaneous Tank cleaning Hotelling Fuel heating Internal flows Gearbox Shaft generator Turbocharger EGE Exhaust (after EGE) Charge air cooler Lub oil cooler Jacket water cooler HT/LT heat exchanger LT/SW heat exchanger

ηen 42 36 81

70.0 2.1 0.8 2.7 3.5 1.8 2.6 3.1 5.5 7.8

37.0 4.0 34.1 3.2 22.0 7.6 9.0 8.4 14.0 28.0

99 91 26

Table 4.1: Energy flows and efficiencies for system components

41


Exergy analysis ηex

λ

δ

ηtask

Main engines Engine Turbocharger Exhaust gas economiser Charge air cooler Lubricating oil cooler Jacket water cooler HT/LT heat exchanger LT/SW heat exchanger

42.9% 35.9% 67.0% 66.4% 59.3% 50.1% 59.5% 2.1%

37.2 % 24.4% 6.5% 6.8% 20.1% 15.9% 11.6% 85.3%

64.2 % 6.4% 1.1% 1.1% 1.1% 2.5% 1.8% 5.3%

4.8% 27.5% 38.8% 49.5% 38.5% 31.5%

Auxiliary power Auxiliary engines

61.7%

38.3%

5.3%

-

Auxiliary heat Boilers Tank cleaning Hotelling Fuel heating

29.7% 25.3% 37.8% 26.2%

63.7% 61.1% 46.7% 62.1%

5.4% 0.8% 0.6% 1.8%

4.7% 7.2% 9.1%

Table 4.2: Exergy flows and efficiencies for system components

Energy

Exhaust gas Charge air cooler Jacket water cooler Lubricating oil cooler

Exergy

%rec

%M E,out

%rec

%M E,out

38 19 21 22

41 20 22 24

54 14 20 12

17.7 4.6 6.7 3.8

Table 4.3: Energy and exergy analysis of waste heat flows

42

5

Ship energy performance improvement Departing from the results of the first part of the thesis, the newly acquired knowledge of the system can be used in order to improve its performance from an energy perspective. Measures of intervention are generally subdivided in the categories of operational, retrofitting, and design, which are presented in Sections 5.1 to 5.3.

5.1

Ship operations

Figure 5.1 presents the result of the comparison of Case 1 and Case 2 operational modes. The specific ship fuel consumption (SSFC), defined as the amount of fuel consumed by the both the main and the auxiliary engines over one nautical mile distance (see Paper II, equations 15 and 16), is plotted versus ship speed. The dashed and the solid lines respectively represent the SSFC of the Case 1 and Case 2 arrangements. The results indicate that a large reduction in SSFC can be obtained by modifying the operating mode from fixed to variable propeller speed. The reason lies in the very different efficiency of the propeller, at given ship speed, depending on propeller speed, as shown in Figure 5.2. The propeller is designed for a ship speed of 15 knots, where its best efficiency point corresponds to a rotational speed of 105 rpm. At lower ship speeds, efficient propeller operations would require a slower rotation. Operations in combinator mode do not allow reducing engine speed when only running on one, high-loaded engine. As soon as operating on two engines is permitted, the reduction of propeller speed brings an improvement of ship energy efficiency, as can be seen in Figure 5.1 in the range between 11 and 13 kn. This advantage peaks at around 12 kn and then diminishes when increasing ship speed. At around 14 knots, close to design conditions, the performance of Case 1 becomes again more efficient than that of Case 2, as expected. With reference to Figure 5.1, it is possible to observe the moment when the second main engine is clutched in, which in both cases corresponds to a sharp increase in SSFC.

43

5. SHIP ENERGY PERFORMANCE IMPROVEMENT

Specific ship consumption [kg/nm]


80

80

60

40

20

Case 1: Fixed rpm Case 2: Variable rpm 0

78

10

11

12

13

14

Ship speed [kn]

76 74 72 70 68

Case 1: Fixed rpm Case 2: Variable rpm

66 10

11

12

13

14

Ship speed [kn] Figure 5.1: Comparison of case study ship specific fuel consumption, fixed-speed versus variable-speed setup

5.2

Retrofitting and waste heat recovery

The results of the first part of the analysis of WHR potential for the case study ship are presented in Figure 5.3; for each value of WHR cycle exergy efficiency (X-axis), the correspondent generated power is compared to the actual ship demand in auxiliary power. The percentage of time during which the WHR system is able to generate the required amount of auxiliary power is calculated (Y-axis). This process is repeated for different choices on which waste heat flow is used as an energy source for the WHR system. For each of these possibilities, the exergy efficiency of the WHR system required for meeting auxiliary power demand for at least 80% of the time is shown. When recovering on the exhaust gas alone the required efficiency amounts to approximately 58% if the WHR system is installed before the EGE (continuous line, circular marks), while this value increases up to around 62% if the easier retrofitting arrangement of installing the WHR after the EGE was employed (dashed line, triangular marks). The required efficiency of the recovery cycle can be drastically reduced by taking the cooling systems into account: either for the generation of auxiliary heat (chained line, square marks) where 50% efficiency is required, and where all the energy in the exhaust gas is available for WHR purposes; or for the WHR system itself (dashed line, plus marks, and dotted line, rhombus marks) where 48% efficiency is required. It should be noted however that these solutions would imply a shift in system complexity,

44

5.2 Retrofitting and waste heat recovery

Figure 5.2: Propeller power versus propeller speed, for different values of ship speed and propeller pitch; kindly provided by the partner company

Fulfillment of auxiliary power need [%]

100

Only Exhaust, EGE before WHR boiler Only exhaust Only exhaust, no aux heat generation Exhaust + LT cooling Exhaust + CAC

80

60

40

20

0 30

40

50

60

70

WHR system exergy efficiency [%]

Figure 5.3: Fractional coverage of the auxiliary need for different recovery arrangements versus cycle exergy efficiency

45



Case 1: Fixed rpm Case 2: Variable rpm Case 3: WHR

80

75

70

65

60

10

11

12

13

14

Ship speed [kn] Figure 5.4: Comparison of case study ship specific consumption, standard setup versus WHR retrofitting

from the efficiency of the WHR cycle to the equipment required for recovering waste heat from the cooling systems. The possibility of adapting ship operations towards the optimisation of the efficiency of the whole system (including WHR) was also explored. The results indicate that the installation of a WHR system on the case study ship becomes easier if both engines are operated simultaneously. In this case, in fact, the advantages of permitting low-propeller speed operations and having additional exhaust flow neutralise the disadvantage of operating at low load. This is graphically shown in Figure 5.4, where the SSFC of the propulsion system with WHR (Case 3) is compared to operations in the current arrangement of the case study ship (Case 1 and Case 2, see Section 5.1). For operating speeds high enough to allow the utilisation of two engines (around 11 kn), the calculated minimum exergy efficiency required in order to provide auxiliary power (364 kW) on board was estimated at 39% (auxiliary needs are lower or equal to 364 kW for 80% of the sailing time). The utilisation of a WHR system for auxiliary power production also allows operating the engine in combinator mode, as presented in the previous section, hence contributing to the large improvement displayed in figure 5.4.

5.3

Ship design

The results related to the application of a method for the comparison of different propulsion arrangements, presented in Section 3.3.5, are shown in Figure 5.5. Different

46

5.3 Ship design

fuels perform in dissimilar manners depending on the selected propulsion system arrangement, and vice verse. This distinction is particularly strong when WHR is taken into account. It is therefore considered important to include the choice of both the propulsion system and the fuel in the early phases of ship design. Additionally, the study aims at evaluating the possible advantages of selecting the propulsion system based on the whole operational cycle rather than on the design point. The results show that there is no difference in the two approaches for 2-stroke engines, which means that employing an engine selected based on its high efficiency at design point also leads to the most efficient solution when the whole operational cycle is taken into account. For 4-stroke engines instead a small improvement is computed when using the ”operational-cycle approach”, evaluated in a decrease in fuel consumption of 0.56% for the cases of HFO and MGO and 1.7% in the case of LNG. This latter result is of particular interest and is connected to the fact that 4-stroke LNG-powered engines operate according to a Otto cycle, which is known to have worse performance at low-load than a comparable Diesel cycle-based engine. The use of the ”operationalcycle approach” is therefore particularly advised when the choice of LNG as a fuel is associated to a propulsion arrangement where 4-stroke engines are the prime mover, which is becoming quite a common choice for ferries operated in ECAs (Aesoy et al., 2011).

2

Note that in the LNG case pilot fuel injection is also taken into account

47


Yearly energy consumption [TJ]

0

2 HFO

4 MGO

LNG 6

8Reference case10

12

150

100

50

0 2-stroke

4-stroke

2-stroke

4-stroke

2-stroke

4-stroke

Propulsion arrangement

Figure 5.5: Cumulative fossil energy consumption over one year of operation for the alternative design cases. Each group of bars represents the main fuel (HFO, MGO, LNG2 ). In each group of bars, the first two represent the performance of a two-stroke engine arrangement, while the last two that of a four-stroke engine arrangement. Finally, for each pair, the first bar represent the yearly consumption of the vessel without WHR, while the second also accounts for such system to be installed. The horizontal line gives the calculated consumption of the existing ship arrangement. Finally, the error bars represent the extent of the range of results for all the evaluated arrangements for each specific case. As the main bars represent the most efficient engine choice for each arrangement, error bars only stretch above the main level

48

6

Discussion In Chapter 6 the implications connected to the results of this thesis are discussed, together with the validity and generality of the conclusions that can be deducted (Sections6.1 and 6.2). A discussion related to the methodology employed in the study is proposed in Section 6.3, referring to the validity of the thesis and to the quality of the data.

6.1

Energy and exergy analysis

The application of energy and exergy analysis to the case study ship shows how these methods can be used in order to improve the understanding of ship energy flows. These methods can guide in the identification of main energy flows, therefore getting an understanding of where intervention should be prioritised, and main inefficiencies, which in turn can foster the ability of understanding where the potential for improvement is located. In this thesis, energy analysis led to results conceptually similar to what presented in energy audits available in literature, such as Thomas et al. (2010) and Basurko et al. (2013), which also include the identification of energy flows. The energy analysis proposed by the author, however, brings further detail in the analysis of internal energy flows in systems such as the turbocharger, the cooling systems, the heat distribution systems and auxiliary power generators. This level of detail, to the best of my knowledge, has never been presented in literature before and provides an improved understanding of the relative and absolute sizes of different flows of different energy types on board the ship. The analysis of energy consumption led to the unexpected result of showing that thermal (14% of total energy use) and electric (16%) users make up a large part of the total ship consumption. Even if this result underscores the fact that the propulsion line should be the first priority when approaching the issue of reducing ship fuel consumption, it also shows that optimising ship energy systems performance based on the propulsion power demand alone is not justified. One additional contribution from this work is give by the introduction of exergy

49

6. DISCUSSION

analysis to the whole ship, which provided the basis for approaching the possibility of implementing WHR systems on the ship as a way of reducing fuel consumption need for auxiliary power generation. The estimation of the different energy flows including considerations on energy quality allowed to select which flows could be of major interest for energy recovery way that would not be allowed by energy analysis. Additionally, exergy analysis allowed the identification of those components which could be targeted in order to improve the overall potential for WHR. However, from a technical point of view, improving the task efficiency of a heat exchanger is achieved by reducing the temperature difference across the component. This reflects into an increase in the exchange area required for maintaining the same amount of heat exchange, which de facto translates into additional space and weight requirements. Furthermore, improvements in the design of the network of heat exchangers could only be beneficial if additional use of waste heat is planned. In the current arrangement all heat needs, when the main engines are running, are met by the EGE and there is no need for an optimisation.

6.2

Energy performance improvement

The results from the application of energy and exergy analysis to ship systems can be used as the basis to ship energy performance improvement. The attention focused on engine-propeller interaction, WHR systems, and whole propulsion system design. It should be noted that the results presented in Chapter 5 only attempt to see the matter from an energy perspective. Details regarding other aspects, such as the economic (balance between savings and additional costs for installation, training, contingent increased maintenance), human (increased workload in the engine room, lack of expertise, safety), structural (additional space/weight requirements), and environmental (use of dangerous chemicals) were either not included, or considered qualitatively. The results presented in this thesis should be seen as information to be put in a larger context by the interested stakeholders when a decision is to be taken on how to operate, retrofit, or build a ship. The investigation of engine-propeller interaction led to the identification of possibilities for improvement in this area for the case study ship. The results obtained through the application of mathematical models to the interaction between engine and propeller in the case study ship suggested that a reduction in ship fuel consumption of 5.8% can be achieved while increasing ship speed from 10 to 12 kn if the ship is run in variable propeller speed. Even though the outcome could vary for different ship types, the results suggest that this analysis can lead to the identification of possible improvements of ship energy efficiency. Furthermore, although in this thesis the interaction between engine and propeller was studied from an operational perspective, efforts can also be directed to the retrofitting of existing ships and to the design of new vessels. WHR systems are often suggested as one possible solution for decreasing ship energy consumption, but the actual feasibility is seldom analysed in all its complexity. In this sense, the main limitation in the approach proposed by the cited authors lies in the

50

6.2 Energy performance improvement

accounting of the operational mode of the selected ship. Ma et al. (2012) assume that the system is operated at design conditions for 280 days a year, an assumption that rarely reflects real operational conditions (Banks et al., 2013). Grimmelius et al. (2010) and Theotokatos & Livanos (2013) both take into account a typical voyage and quantitatively use this assumption for the calculation of expected system performance. The ”typical voyage” approach represents a widely accepted approximation, but is only partially able to account for the impact of real operations on fluctuations in ship operational pattern (typical of ships operating on the spot market) and in boundary conditions (increased resistance due to weather and waves). In the work presented by Dimopoulos et al. (2011) ship operations are divided in 4 well-defined categories with an assumption for the time spent during each operation, which additionally increases the detail, but is more suitable for the operational pattern typical of container ships. The main contribution of this thesis to the subject consists in the evaluation of the WHR potential over one year of real ship operations. The results indicate a profitable application of WHR to the case study ship would require a rather complex recovery system, either from the point of view of the recovery cycle (efficiencies of around 60% are required, which could only be met by advanced Organic Rankine Cycles (ORC) with high-performance fluids (Larsen et al., 2013)), or from that of the type and amount of waste heat to be recovered (by making use of the waste heat in the cooling system the required efficiency can be reduced to 48%1 ). The possibility of adapting ship operations in order to maximise the energy efficiency of the propulsion system including WHR was also explored. The results suggest that by adapting operations for running the ship on two engines, even at low load, the advantages from improved propeller and WHR performance would outweigh the loss in engine efficiency, while requiring a relatively simple recovery system (16% reduction of fuel consumption with a required ηex of 39% recovering waste heat on the exhaust gas alone). This result indicates that the installation of WHR systems should be evaluated taking also operational aspects into account. This results is considered to be of particular relevance as it underscores the importance of addressing ship energy systems with a systems approach. For the case study ship, both the improved enginepropeller interaction and the installation of WHR systems, if taken alone, would provide much smaller benefits than the two applied together. Looking at ship energy systems as a whole allowed the identification of this synergy, something that would not have been possible otherwise. A similar consideration can be done on possible energy savings on auxiliary power consumers. Small reductions in auxiliary electric demand can make the difference on the feasibility of a WHR system. The availability of measured data on auxiliary consumption would allow further research in this direction. Finally, the possibility of influencing the system from the design stage was explored. Even if results in the initial phases of the design are only partly representative of 1

It should be noted that the use of cooling systems as a source of waste heat is not technically easy. Cooling is vital to engine operations, and introducing an additional system could generate issues in safety and control.

51

6. DISCUSSION

what the real systems will look like, the methods proposed in this thesis show very promising results. Different engines, arrangements, and fuels can be compared and the resulting estimated yearly consumption can be used as one of the basis for the decision of which propulsive arrangement should be installed. The importance of including several different parameters in the early design phase is demonstrated by the large variance in the results depending on the chosen arrangement. Finally, the influence of including the operational cycle in the evaluation proved rather limited, and only relevant in the case of four-stroke dual fuel engines based on an Otto-cycle.

6.3

Methodology

As part of the analysis of the scientific value of this thesis, the quality of the work should be critically discussed. Validity is often referred to as a measure of the quality of a scientific work. It is often subdivided in internal validity, which refers to the extent to which the results represent the real problem to be studied, and external validity, which concerns instead the generalisability of the results to a larger sample than what specifically addressed in the work. Finally, the quality of the data also has a very important influence on the value of the results.

6.3.1

External validity and case studies

External validity deals with the extent to which the results and the methods presented in a scientific study can be extended outside of the specific application presented in the work (Mitchell & Jolley, 2001). In the case of this thesis, external validity is a subject of major importance, as the use of case studies is often connected to a loss generalisability. The methods proposed in this thesis are potentially applicable to all ship types. The main restriction to the external validity of the methods here presented is the assumption of steady-state behavior, as all models and analysis are conceived for steady state operation. This requirement implies that vessel types characterised by a very dynamic behavior, such as tugs and inland ferries, would not be suitable for the application of the proposed methods. Other possible differences, such as a different engine type (two-stroke Diesel engine, gas turbine), different propeller type (FPP, water jet), and propulsion arrangement would just require an additional effort in the modeling of a different component. On the other hand, the external validity of the numerical results is more limited. For what concerns the analysis of ship energy systems, it has been observed that time spent in different operational modes tends to remain approximately constant over different years (Banks et al., 2013). The possibility of extending the results of this thesis to future operations of the case study, is correlated to vessel speed distribution, as it has a strong influence on fuel consumption for propulsion, and can be subject to large variations over the years (Banks et al., 2013). When it comes to the extension to other ship types, the possibility of extending the results presented in this thesis is limited by:

52

6.3 Methodology

Engine type: Two-stroke engines have lower exhaust temperatures (Theotokatos & Livanos, 2013), therefore revolting all conclusion drawn in connection to the evaluation of WHR potential. Propeller type: FPPs behave substantially differently from CPPs, and therefore all results related to engine-propeller interaction would differ if a FPP is used instead. Auxiliary power generation arrangement: Results related to engine-propeller interaction are connected to the use of a shaft generator. Ship type: All results, especially those connected to the evaluation of WHR potential, are strictly related to the balance between ship requirement of propulsion power, auxiliary heat, and auxiliary power. Different ship types might differ sensibly in this matter. These restrictions, should not lead the reader to think that the arrangement featured by the case study ship is a special one. Four-stroke engines represent a significant portion of the market (27.7% in terms of number of installed engines and 10.5% in terms of installed power for engines with MCR > 2 MW (Haight, 2012)). Similarly, CPPs have reached a stable market share of around 35% of the total number of propellers installed; this is particularly true for some specific ship types, such as general cargo vessels (80%), ferries (63%), tugs and offshore vessels (78%), and fishing vessels (89%)1 . The utilisation of case studies is very widespread among applied researchers, especially when methods are proposed rather than specific designs. This is the case for almost all work mentioned or referred to in this thesis: (Dimopoulos et al., 2011, 2012) refer to a specific containership with well defined loads and operational pattens; Theotokatos & Livanos (2013) study one specific application to a bulk carrier, while Thomas et al. (2010) present their method for energy audit applied to a fishing vessel. Therefore, even if in applied science there is not such thing as a well defined scientific method, as is instead the case in basic science (Niiniluoto, 1993), the thesis reflects common practice in the field. As an additional motivation to the choice of working on a case study, it should be noticed that the close interaction with the company allowed to get a much better insight of how the academic work here presented could be used in the ”real world”. It is sometimes observed in academic researchers to become too much theoretical and forgetting the challenges that arise when theories and models need to be applied in practice. As highlighted by Cross et al. (1981), ”knowing how” is often as important as ”knowing that”, where the first refers to explicit, procedural knowledge and the second to the knowledge that, despite its evident existence, is not structured.

6.3.2

Internal validity

Internal validity refers to the extent to which it is possible to identify a causal connection between a study and its results (Brewer, 2000). Verification and validation are two of 1

Data referring to the 2000 to 2004 period, (Carlton, 2007, p. 21)

53

6. DISCUSSION

the main methods employed in the evaluation of internal validity (Oberkampf et al., 2002). Verification refers to the process of determining that a model implementation accurately represents the developer’s conceptual description of the model and the solution to the model ˝(DoD). Verification is very important in those cases, such as system design, when no data for validation is available. Validation refers to the process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended use of the model ˝(AIAA, 1998). The validation of a model is performed by comparing model outputs versus experimental measurements. Different levels of validation can be achieved depending on the thoroughness of this process (Oberkampf et al., 2002). As described in the dedicated methodology section, energy and exergy analysis involve both a top-down and a bottom-up approach. The two parts are connected to different issues when it comes to internal validity. From a top-down perspective, the analysis simply relates to the observation of existing measurements, which allows no validation. From a bottom-up perspective, instead, the individual models used to simulate different components could, and should, be validated. In particular, the absence of data for model validation when the propulsion system is operating at variable speed can cast some doubts on the applicability of the model to such cases. Furthermore, no information was available for the validation of individual components either. The main uncertainty is related to the main engine model; even though a validation of the model is proposed (see Paper II), it only refers to operations at fixed speed, as no information is available on variable speed operations. However, the absence of the related data was also a major justification for employing a larger mechanistic content in the modelling: in fact, white-box models provide much larger safety when extrapolating them out of the initial boundaries. This explains why not only pure black-box, but also hybrid models that employ a lower grade of mechanistic content (such as mean-value models) were excluded. If extensive data were available on off-load engine speed, the task of modeling would have been approached in a different way, giving larger room to the black-box part of the model. It should be noted, additionally, that despite the modifications proposed in Paper II, the modeling approach used in this thesis is not dissimilar to what can be found in literature (e.g. Scappin et al. (2012); Payri et al. (2011); Benvenuto et al. (1998)) and therefore represents an application of common practice. The validation of the overall results is discussed with the partner company, and tests are being carried out for the purpose. Unfortunately, to the moment the thesis is going to print, this kind of validation data has not been provided yet. A verification process, comparing findings versus existing literature, was performed instead. As studies available from scientific literature normally only include sailing operations, the results from the energy analysis used in this thesis were recalculated for only accounting for the time spent by the ship at sea. From the energy analysis perspective, results presented by Thomas et al. (2010) and Basurko et al. (2013) can be used as a confirmation of the orders of magnitude. Figures for total propulsion need, for example, are quite in accordance in identifying propulsion as the main consumer

54

6.3 Methodology

on board (75.7% in the author’s work, 76% in Thomas et al. (2010) and 84.3%, 87.3%, and 87.8% in Basurko et al. (2013)). The results of the exergy analysis here proposed could be partially compared to what was obtained by Dimopoulos et al. (2012). The paper confirms the fact that most of system irreversibilities are connected to main engine operations (79% in this thesis compared to 82% in literature1 ), while relevant losses are also connected to turbocharger operations (respectively 7.5% and 3.9%). As the exergy analysis proposed by Dimopoulos et al. (2012) refers to a system equipped with a WHR system it is not possible to compare the results connected to other components, as the resulting arrangement is much different for the two case-studies. Results related to the evaluation of WHR systems are rather consistent with what expressed in similar studies. The results presented by Dimopoulos et al. (2011) relate to a similar ratio of propulsive over auxiliary power and show how auxiliary power demand can be completely met by a recovery system only for high loads. In this case, full power production using WHR system is guaranteed for only 17% of the time, but this is mostly connected to the higher auxiliary power need compared to propulsion (for normal speed transit the auxiliary power need represents 11.4% of total demand, while on the case study ship this value is normally lower then 10%) and to the use of two-stroke engines, known for their lower availability of waste heat (Theotokatos & Livanos, 2013). Similar results are also obtained by Theotokatos & Livanos (2013), who present the analysis of a simpler recovery cycle (around 34% exergy efficiency2 ) which leads to a higher need for the operation of auxiliary engines in order to generate the required electric power. It should be noted that even in this case, in sea-going mode, the WHR system is able to provide around 72% of the power requirement.

6.3.3

Data quality

The availability and quality of the available information used in a scientific study are of vital importance for the quality of the work itself. A perfectly built model can still give misleading results if data are not handled correctly. A model can only be as accurate as the data it is used to process. Data quality is often defined as ”fitness for use”, i.e. can only be evaluated in light of the purpose data are used for (Haug et al., 2001). According to Wang (1996) the data qualities most found in reviewed scientific literature are: Intrinsic: The extent to which data values conform to the actual variable to be measured. Contextual: The extent to which data are useful to the purpose. 1

Note that the value referred to the result presented in this thesis refers to the aggregation of losses in the main engine and in the cooling systems. 2 It was not possible, unfortunately, to evaluate the exergy efficiency of the WHR system proposed by Dimopoulos et al. (2011) based on the information provided in the article. The estimation of complexity is based on a purely qualitative evaluation of system design, ad described by the authors of the two studies

55

6. DISCUSSION

Representational: The extent to which data are presented in a clear and unambiguous manner. Accessibility: The extent to which data are easy to obtain. As already mentioned in section 3.1.2, it has not been possible to perform extensive, own measurements. Data were made available from one shipping company, who collaborated with the author, for one of their ships. The strong collaborative framework with a ship operator in the related industrial sector (in this case a shipping company) have both positive and negative sides. On the one hand, in fact, it has not been possible to run own experiments, as the ship was sailing in very different locations under hardly predictable schedules; moreover, the company manifested the interest in sharing large amount of available information with the author, but did not back up any experimental campaign. This translates in inconsistencies in data quality: continuous monitoring, for instance, provides higher data quality than noon reports, which is reflected in the accuracy of the results (Aldous et al., 2013). Referring to the categories of data quality presented before, intrinsic quality was very variable depending on the specific measurement; contextual quality was limited, as extensive data were available on variables of little interest to the scope of the thesis and lacked for other, more important variables. Additionally, data consistency was rather poor, as very different sources had to be used to be able to perform the analysis. No issue can instead be reported on data representational and accessibility quality. On the other hand, the process showed that the proposed methodology is very resilient to different data quality (this is often referred to as ecological validity (Shadish et al., 2002)). The analyses was conducted using data from the real world, meaning that the presented methods do not belong to a restricted community of researchers but can be used in virtually any real life situation. The alternative of tailoring the method on measurements performed in a proper scientific way would have improved the accuracy of the results, but would have made the model of limited use outside the specific proposed application.

56

7

Future work and recommendations Suggestions on how to proceed from the results of this thesis are here presented. These are divided into proposals to the scientific community (Section 7.1) and recommendations to the industry (Section 7.2).

7.1

Proposals to the scientific community

• The application of energy and exergy analysis to ship energy systems should be expanded to include different ship types. This would bring a larger literature on which to base further studies like the one presented in this thesis. • This thesis focuses on large vessels, operating on mostly international routes, for which it is legitimate to assume a steady-state behavior. Some specific applications do not allow this kind of assumption, such as small inland ferries and fishing vessels. The influence of transient phenomena can become relevant in particular when phenomena characterised by different inertiae are coupled. This is for example the case of WHR systems, where fast mechanical transients are coupled to slower thermal ones. • Engine-propeller interaction showed large margins of improvements in arrangements were the propulsion system is operated at constant speed for using a shaft generator, especially at low propeller load. The findings presented in this thesis should be extended to other types of propulsion systems, including slow-speed engines and fixed pitch propellers. • Additional focus should be put on the environmental impact from the initial stage of ship design. Future work should be focused on including not only GHGs but also other relevant pollutant emissions, such as N OX , SOX , and PM to the analysis. Issues related to different pollutants are, in fact, strongly interconnected and should not be addressed separately.

57

7. FUTURE WORK AND RECOMMENDATIONS

• Even if extensive work on Diesel engine models has been performed to date, very little has been done on their modeling for implementation into energy systems modeling, especially when it comes to the cooling systems. As showed in paper I there are large amounts of energy available through different flows from engine cooling, and a more detailed modeling of thermal losses and cooling systems should be available for the optimisation of WHR systems for using the highest possible amount of available waste heat.

7.2

Recommendations to the industry

• ”Know what you do, know what to do”. The application of energy and exergy analysis to the case study ship has been facilitated by the existence of a large, reliable dataset based on a continuous monitoring system. The availability of such large and complete databases is still not common in shipping. Resources should be invested on reliable sensors and online data storage, since this would expand the information available for a correct planning actions to improve energy efficiency. • WHR systems are a promising solution for improving ship energy efficiency. It is recommended to consider this possibility for both retrofitting and new buildings. However, as showed in this thesis, the profitability of this technology strongly depends on ship operational mode, engine type and auxiliary demands. For this reason, it is recommended to increase the expertise in such systems and in the evaluation of waste heat availability and quality. • As suggested by the results of this thesis and by existing literature, enginepropeller interaction can have a strong influence on ship energy efficiency. This aspect should be kept under strong focus, both in the operation, retrofitting, and design of marine vessels. The impact of this interaction on propulsion systems based on FPPs and two-stroke engines, as well as the influence of sea state and hull fouling, should be investigated. • The initial design phase of a vessel should be emphasised more. New knowledge in the field of systems optimisation and environmental studies should not be relegated to the last phases of the design, where most of the choices are already taken, but rather be included in the initial phases of ship design. • Sampling data is not enough. The analysis of these data is crucial. In this sense, I would personally like to further emphasise the importance of academic collaboration. We, as researchers, have an interest in improving our understanding of systems and reality; you, running a business, have an interest in having access to knowledge and expertise that might be missing in your human resources. It is a win-win situation that should happen more often!

58

8

Conclusions The main findings of this work can here be summarised: • Energy and exergy analysis applied to ship energy systems provide a useful tool for the identification of main energy flows and of inefficiencies. For the case study ship, auxiliary heat and power consumption was found to account together for 30% of the total ship energy consumption. The evaluation of the waste flows allowed to estimate that a large potential for WHR exists on the case study ship, particularly in the exhaust gas. • Engine-propeller interaction can have a strong impact on ship energy efficiency. In the case study ship, the application of such models suggests that specific ship fuel consumption can be reduced by 5.8% while increasing the speed from 10 to 12 knots by operating engine and propeller at variable speed instead of at constant speed. The benefit of an increased energy efficiency of the propulsion train outweighs the disadvantage of using the auxiliary engines instead of the shaft generator for meeting on board auxiliary power demand. • The profitable application of WHR systems is subjected to an analysis of the availability of waste energy in comparison of auxiliary power demand. For the case study ship, results suggest that a rather effective system (ηex of 58%) is required if only heat in the exhaust gas is recovered. The situation can be improved either by increasing the number of sources of waste heat (the required ηex when including cooling water goes down to 48%) or by adapting ship operations in order to always run on two engines (ηex = 39%.) • Ship design can be improved, with both economic and environmental benefits, if parameters such as engine type and fuel choice are taken into account from the initial stages of the design. Results from the application of mathematical models to the evaluation of alternative arrangements suggested that both fuel consumption and carbon footprint are strongly influenced by these choices. All the results and findings presented in this thesis were obtained by addressing the challenge of improving ship energy efficiency with a systems perspective. This

59

8. CONCLUSIONS

translated in the application of energy and exergy analysis to ship energy systems, as well in the use of mathematical models for the simulation of these systems without the need to resort to time consuming sea trials. This approach proved to be particularly beneficial, and systems thinking is expected to become more and more important in the challenge of making shipping a cleaner and more energy efficient mean of transportation.

60

Bieler, P., Fischer, U. & Hungerb¨ uhler, K. (2004). Modeling the energy consumption of chemical batch plants: Bottomup approach. Industrial and Engineering Chemistry Research, 43, 7785–7795. 28 Blanchard, B. & Fabrycky, W. (2006). Systems engineering and analysis. Prentice-Hall international series in industrial and systems engineering, Pearson Prentice Hall, Upper Saddle River, N.J, 4th edn. 28

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64

Paper I

Energy and exergy analysis of a ship, the case study of a chemical tanker Francesco Baldi, Cecilia Gabrielii, Karin Andersson October 23, 2013 Manuscript ready for submission

Abstract Shipping is already a relevant contributor to global carbon dioxide emissions, and its share is expected to grow together with global trade in the coming years. At the same time, bunker prices are growing and companies start to feel the pressure of growing fuel bills in their balance sheet. In order to address both challenges, it is important to improve the understanding of how ship energy consumption is generated, through a detailed analysis of its energy systems. In this paper, data from one year of operations of a chemical tanker were used, together with some mechanistic knowledge of ship systems, in order to improve the knowledge of ship energy use. Energy analysis applied to the case-study vessel allowed comparing different energy flows and therefore identifying system components and interactions critical for ship energy consumption. Exergy analysis allowed instead identifying main inefficiencies and evaluate waste flows. This last information was then processed in order to estimate the potential for waste energy recovery under different conditions. Results showed that propulsion is the main contributor to ship energy consumption (70%), but that also auxiliary heat (16.5%) and power (13.5%) needs are relevant sources of energy consumption. The potential for waste heat recovery is relevant, especially in the exhaust gas, which contain an exergy flow sized 14.4% of engine power output. However, the analysis of feasibility for WHR systems showed how the exhaust gas alone would not suffice for providing at least the auxiliary power need for at least 80% of the sailing time, which would require also harvesting waste energy from the cooling systems.

1

1 1.1

Introduction Background

The shipping business is currently facing a number of challenges. The recent global economic crisis and the increase in oil price have influenced negatively most economic activities based on fossil fuels, a phenomenon that can be observed very strongly in shipping. Shipping activities are strongly related to trade, (more than 80% of global trade by mass is transported on sea [1]) and this sector is therefore directly connected to the global economy. Freight rates have already recovered in several sectors after the global economic crisis, and the same has happened to bunker fuel prices, which have increased by a factor of 3 since the 80’s [2] and have already returned to the pre-crisis levels. For the period between 2006 and 2008, fuel costs were estimated for between 43% and 67% of total operating costs depending on vessel type [3]. This situation is not expected to improve in the future: stricter environmental regulations on sulphur oxides, nitrogen oxides, and greenhouse gases will exert an additional leverage to fuel costs [4]. This phenomenon will be even more pronounced in emission controlled areas (ECA), i.e. USA coastal waters, the Baltic Sea, and the North Sea, where regulations will be even stricter [5], and new ECAs (Japan coastal waters, Caribbean Sea) are expected in the coming years. These conditions combined have motivated the shipping business to start focusing on fuel efficiency. However, unlike what happened during the oil crisis in the 80’s, efforts are expected to be more durable, as the increase in fuel prices is not generated by a crisis, but by market evolution. In order to save fuel, various measures are available and currently implemented. They are often divided into operational and design measures, where the first mostly relate to improving the performance of existing vessels, while the second refer to a more efficient ship design for new-buildings. Some operational measures are improvements in voyage execution, engine monitoring, reduction of auxiliary power consumption, trim/draft optimization, weather routing, hull/propeller polishing, slow-steaming. Examples of design related measures can be the use of more efficient engines and propellers, improved hull design, air cavity lubrication, wind propulsion, fuel cells for auxiliary power generation, waste heat recovery, LNG as fuel, pump frequency converters, cold ironing [6]. Several scientific studies have been conducted on these measures; however, a more detailed treatise of these technologies is out of the scope of this work. Even if efforts have been put in order to evaluate the benefits associated with the use of each of these solutions and of their combined effect [6], these advantages should be evaluated on a ship-to-ship basis. For this reason, a deeper understanding of energy use on board is considered to be vital.

1.2

Previous work

Studies can be found in scientific literature that report figures connected to ship propulsion or for selected components. Thomas et al.[7] presented the energy audit of a fishing vessel, also providing a certain detail about end energy use, but did not provide any thermodynamic analysis of the different processes and flows; Shi et al.[8, 9] proposed models for predicting ship fuel consumption in design and off-design conditions, without considering auxiliary machinery and

2

with no attempt to refer to real operative cycles; Balaji and Yaakob[10] analysed ship heat availability for use in ballast water treatment technologies, which is an interesting but very limited domain of application. A more thorough, holistic thermodynamic analysis of a ship, such as that proposed by Nguyen et al.[11] for oil platforms energy systems, is lacking in scientific literature. A study on the ship systems energy analysis, providing a detail modeling and understanding of the different energy flows encountered on a commercial vessel, is therefore needed. Analysis purely based on the first law of thermodynamics lack insight of the irreversibilities of the systems, as they do not account for the additional knowledge provided by the second law of thermodynamics. Exergy analysis, which is based on both the first and the second laws of thermodynamics, can help addressing this shortcoming. Unlike energy, exergy can be destroyed in real processes, and the rate of destruction is directly connected to the departure of the analysed process from a reversible one. Exergy analysis also takes into account energy quality, and not only quantity. Dincer et al. have thoroughly explained the use of exergy analysis in several industrial applications [12], while its utilisation in the shipping sector is still reduced to a limited amount of applications, mostly related to waste heat recovery systems [13, 14] and refrigeration plants [15, 16]. Literature on exergy analysis applied to the overall ship energy systems is however lacking.

1.3

Aim

The aim of this paper is to analyse the energy and exergy flows of an entire ship, using a mixed top-down and bottom-up approach. The scope of the analysis is to provide a better understanding of how energy is used on board and where the largest potential for improvement is located.

2

Methodology

In this section, the methods of energy and exergy analysis are described for the application to ship energy systems. Their application to a case-study vessel is proposed.

2.1

Combined top-down and bottom-up approach

The analysis of energy systems is often categorised either top-down or bottomup. When a top-down approach is used to model the system’s behavior, large amounts of experimental data points regarding system inputs and outputs are processed leading to the construction of an empirical model which, based on past experience, can predict the behavior of the system. These models tend to be rather accurate but need large initial data sets for calibration and are not suitable for extrapolation [17]. On the other hand, a white-box approach makes use of mechanistic knowledge of the system in order to model each component, link them together, and produce the output of the system based on the assumptions made on the single components. Bottom-up approaches are less accurate, but provide a larger insight of the system, as well as allow a safer extrapolation of the results [17]. The use of white- and black-box models is thoroughly discussed

3

in literature, and each of the two approaches might be more or less suitable depending on the application, i.e. on the desired output, and the availability of initial information. In the present case, a combined approach was used, as some features of both strategies were needed. Pure top-down analysis does not provide any insight of the system under study, as no detailed analysis of the connections and the flows is performed [17, 18]. On the other hand, bottom-up analysis does not allow handling the large amount of data on different system inputs and outputs that is available for the ship under study [17]. A combination of the two, however, may be the most appropriate way to make the most use of the available information.

2.2

Exergy analysis

Exergy is defind as the maximum shaft work that can be done by the composite of the system and a specified reference environment [12]. The exergy content of a flow depends on the quality of the energy content. Additionally, differently from energy, exergy is not conserved and can be destroyed, representing the deterioration of energy quality. For electrical, potential, kinetic, and mechanical energy exergy and energy flows coincide, while this is not true for chemical and thermal energies. For a given amount of matter, its thermal exergy content is defined as showed in equation 2.2: EX = m[(h − h0 ) + T0 (s − s0 )] (1) where EX, h, and s respectively stand for exergy, specific enthalpy, and specific entropy, while the subscript 0 represents reference conditions. According to Dincer and Rosen [12] exergy flows calculated according to equation 2.2 can be divided in three main categories: ˙ in ) : the flow of exergy entering the component. Input (EX ˙ out ) : the flow of exergy leaving the component. Output (EX ˙ : the amount of exergy lost in the component operation Irreversibility (I) (also known as exergy distruction). This part represents energy quality deterioration and is defined as I˙ = T0 S˙ gen , where S˙ gen represents the rate of entropy generation in the component. The definition of the exergy efficiency of a component is quite debated, and several authors propose different possible alternative definitions. In this study, three different quantities will be used: ˙ EX

p Exergy efficiency is defined for this study as ηex = EX ˙ in , where the subscripts p and in respectively refer to products and inputs. In the case of ˙ c ∆EX heat exchangers, the alternative definition of ηex = ∆ ˙ h is used, where EX subscripts c and h respectively refer to the cold and the hot fluid. The definition of the ∆EX are adapted depending whether the component is meant for cooling or heating. The exergy efficiency gives an estimation of how efficient the component is in the generation of useful products.

Irreversibility ratio is used according to the definition proposed by Kotas et I˙ al. [19], i.e. λ = EX ˙ in . The irreversibility ratio gives an estimation of how much energy quality is lost in the component. 4

Task efficiency is here slightly modified from what proposed by Dincer adn Rosent [12], and is defined as the ratio between the irreversibility in an ideal exchange at constant temperature difference (here arbitrarily fixed to 10°C) and the irreversibility in the actual process. The task efficiency gives an estimation of how close the component behavior is to an ideal process. Measurements of seawater temperature are used as reference point for exergy calculations throughout the entire study, as they represent ambient condition with which to compare conditions of thermal equilibrium.

2.3

Ship description

The ship here selected as a case study is a Panamax chemical / product tanker. Relevant ship features are provided in Table 1 Ship feature Deadweight Overall length Draft Installed power (main engines) Installed power (auxiliary engines) Shaft generator power Exhaust boilers Auxiliary boilers

Value

Unit

46760 183 12.2 7680 1364 3200 1400 28000

dwt m m kW kW kW kgsteam /h kgsteam /h

Table 1: Selected ship main features The ship is propelled by two 4-stroke Diesel engines rated 3840 kW each. The two engine shafts are connected to a common gearbox. One of the gears reduces the rotational speed from 600 rpm to 105.7 rpm, the design speed for the controllable pitch propeller (CPP). Another shaft from the gearbox connects it to the electric generator (S/G) which provides 60 Hz current to the ship. Additionally, two auxiliary engines rated 682 kW can provide electric power when the main engine is not in operation, or whenever there is a failure in the shaft generator system. Figure 1 graphically represents the propulsion arrangement.

2.4

Energy conversion

Energy needs on board of the case-study vessel are provided by: the main engines (MEs), the auxiliary engines (AEs), and the auxiliary boilers (ABs). In addition, a shaft generator (S/G) is used to convert mechanical energy generated by the main engines to electrical energy, while an exhaust gas boiler (EGB) is used to recovery heat from the main engine exhaust. Main engine fuel consumption is known from online measurements, while its power output can be derived starting from measurements of propulsion and auxiliary power using the following expression: ( PM E =

Pprop ηS

+

PS/G ηS/G )

ηGB 5

(2)

Figure 1: Ship propulsion system arrangement Where the variables P and η refer to power and efficiency and subscripts ME, prop, S/G, S, and GB respectively refer to main engines, propeller, shaft generator, shaft, and gearbox. PS/G and Pprop are available from online measurements; ηS is assumed equal to 0.99, as suggested by Shi et al. [9]; ηGB is assumed equal to 0.983, as reported by the shipyard. ηS/G,des is assumed equal to 0.95, as reported in dedicated technical papers. As the shaft generator is often operating at very low load (see Figure 2), its efficiency dependence on load needs is modelled using a polynomial regression calibrated on the experimental points reported by Hau [20]. Inlet air conditions are determined using a polynomial regression for compression ratio in the turbocharger based on engine technical data; air temperature in the engine room, before the turbocharger, is assumed equal to 35°C. Air is cooled after the turbocharger in order to increase its density through a charge air cooler (CAC); the air temperature before the charge air cooler is calculated assuming a polytropic compression, whose efficiency is based on a polynomial regression; the air temperature after the charge air cooler is assumed equal to 55°C according to common practice on the selected ship; the air mass flow is calculated assuming unitary volumetric efficiency. The heat exchanged to the cooling system in the charge air cooler can therefore be calculated as PCAC = m ˙ air cp,air ∆TCAC . The temperature of the exhaust gas is estimated through a polynomial regression, while the mass flow is obtained combining air and fuel flows. The energy content in the exhaust flow is therefore simply calculated from the temperkJ ature difference with reference conditions, assuming a constant cp,eg of 1.08 kgK . Heat flows to engine jacket water (JW) cooling, lubricating oil and radiation are estimated from design values available from technical documentation, under the assumption that their relative share of the residual heat available in the engine energy balance remains constant with load. The engine is connected to a double-level cooling system, composed of a lowtemperature (LT) and a high-temperature (HT) part. JW heat is transferred to

6

the HT circuit, while heat from the CAC is subdivided among the two circuits. Elaborating data from technical documentation, 22.4% of the charge air cooling heat is transferred to the low-temperature circuit, while the rest is transferred to the high-temperature circuit. Mass flows in the HT cooling, LT cooling and lubricating oil (LO) circuit are assumed to be constant with engine load as they are operated by engine-driven pumps. LT circuit inlet temperature, HT circuit outlet temperature, and LO circuit inlet temperature are respectively assumed equal to 34°C, 85°C, and 60°C according to operative experience coming from communication with on board technical personnel. Little technical documentation was available for auxiliary engines. The efficiency is calculated as a polynomial regression of available data points and corrected according to ISO standards, while the residual energy is subdivided among charge air cooling, jacket water, lubricating oil, and radiation according to values at design point.

2.5

Consumers

Propulsion constitutes the largest energy need on board of the selected vessel. As shown in figure 1 both main engines are coupled to one CP propeller through a gearbox. The CP propeller allows operating at fixed main engine speed, thus allowing the utilisation of a shaft generator. In this study, measured data for propeller shaft power will be used in order to estimate, using a top-down approach, the ship propulsion need. In a similar way, auxiliary power consumption is available as measured value from online measurements. The distribution of auxiliary loads varies between different operational modes; this is exemplified in figure 2, which shows the example of sailing loaded and ballast conditions.

Figure 2: Auxiliary power, frequency of occurrence for different operational modes In order to give an estimation of the power needed by different consumers, data from the electric balance was used. This operation, however, required some assumptions. • For seagoing mode (loaded), it is assumed that the power consumption is subdivided as suggested by the shipyard. Therefore, proportions between different consumers are maintained. For all points were auxiliary load is

7

larger than 500 kW nitrogen compressors are assumed to account for the difference between with actual consumption. • For seagoing mode (ballast) the same repartition is assumed as for seagoing mode (loaded) if auxiliary power is lower than 500 kW. If power consumption is higher the difference is assumed to be connected to the operations of nitrogen compressors and boilers auxiliaries (in connection to tank cleaning), which are subdivided according to design power. • For manoeuvring the same assumptions as for seagoing mode (loaded) are employed. • For cargo loading and unloading all consumption going over 500 kW is allocated to nitrogen compressors and cargo pumps, with repartition according to maximum installed power. It should be noted that cargo loading operations normally do not require the use of cargo pumps, as port storage facilities can provide the needed overpressure for loading the cargo, which is shown by the comparison of the respective distributions in figure 2. • For waiting time the same proportions as reported in the ship electric balance are used, with the exception of engine room consumption, which is halved, since when waiting in port only auxiliary engines are used. Fuel heating need is needed because of high fuel viscosity, and is computed starting from the design heat balance and using sea water temperature and outer air temperature measurements. Hotel facilities needs are calculated assuming a linear correlation between the value given in the heat balance, assumed at an outer temperature of 2°C, depending on outer air temperature. Fuel heating, hotel facilities, and other needs are fulfilled by recovering heat from the exhaust through an Exhaust Gas Economiser (EGE), when the main engine is active, or by boilers, when the main engine is shut off. Heat consumption for fresh water generation is assumed according to common practice equal to 7.5 kg h per crew member. Since the generation of fresh water is connected to the low temperature (LT) cooling systems, the value of heat of vaporisation for water was taken at 50°C and equal to 2382 kJ kg . During ballast legs, steam is needed for tank cleaning. This is a very energy intensive process, and requires the operation of the auxiliary boilers. Energy use for tank cleaning is derived from the aggregated boiler fuel consumption, under the assumption of 90% boiler efficiency accounting for combustion losses and heat flow in the exhaust gas, limited at 200°C. Auxiliary boilers are also used when the main engines are not in operation. In this condition, as boilers are operated at very low load, a reduced efficiency of 80% was assumed instead. A summary of the main auxiliary consumers is shown in Table 2:

2.6

Data sources and quality

As mentioned in section 2.1 a large amount of measured and technical data were available and were used in the modeling and analysis process. This section will shortly clarify the origin and the quality of the different data sources. The main source of data for the top-down part of the analysis is the continuous monitoring system (CMS) installed onboard. The accuracy of these 8

Electricity consumers

Heat consumers

Nitrogen compressors Cargo pumps Boiler auxiliaries Engine room HVAC Miscellaneous

Fuel handling Hotel facilities Tank cleaning

Table 2: Main auxiliary consumers on the case-study ship data, with a sampling time of 15 minutes, is only related to the accuracy of the sensors on board. Technical documentation (TD) was available for the main engines and the shaft generator and used as input for numerical regressions. Ship sea trials (ST) performed by the shipyard when the ship was first sailed were also available. Finally, some values that were not available by any of the previous sources were obtained by verbal input from the crew.

3

Results

In this section the results of the energy and exergy analysis of the case-study ship are presented and analysed.

3.1

Energy analysis

Figure 3 represents the Sankey diagram of ship energy systems, while Table 3 summarises the energy inputs and outputs to different ship components. Propulsion represents the main source of energy consumption on the overall yearly operations of the vessel, representing the 70% of the overall ship energy demand. Hence, efforts directed towards the reduction of propulsive power are therefore highly justified for the ship under study. Electric (13.5%) and heat (16.5%) needs are however not negligible and should be taken also into account, especially because of the large time spent in port. Figure 3 also shows the large amount of energy that is released to the environment through the funnel and through the sea water cooling systems. The recovery of this waste energy is limited to the auxiliary heat needs for accommodation and fuel handling, which however leave a large part of the energy unused.

3.2

Exergy analysis

However, energy analysis becomes partly inappropriate and can be misleading when thermal energy flows are compared to electric and mechanical flows; the first law of thermodynamics, in fact, does not include any consideration about energy quality [12]. For this reason, the concept of exergy is introduced and employed in this work, in order to get a better understanding of the amount of waste energy that can be recovered in order to produce additional electric or mechanical power. This kind of information can be deduced from the observation of the Grassmann diagram (Figure 4). Here exergy flows, instead of energy

9

Figure 3: Sankey diagram of ship systems

10

Energy analysis E˙ in Main engines Engine Turbocharger Exhaust gas economiser Charge air cooler Lub oil cooler Jacket water cooler HT/LT heat exchanger LT/SW heat exchanger

E˙ out

ηen

192.0 80.2 73.3 19.3 9.0 16.3 19.3 18.0 30.7 52.6

41.7% 26.4% -

80.2 79.2 70.5 8.68 7.87

98.7% 90.6%

Auxiliary power Auxiliary engines Nitrogen compressors Cargo pumps Boiler auxiliaries Engine room HVAC Miscellaneous

17.2 6.12 2.15 0.79 2.68 3.55 1.82 2.63

35.5% -

Auxiliary heat Boilers Tank cleaning Hotelling Fuel heating 7.84

5.56 4.46 3.16 5.56 -

80.2% -

Converters Gearbox Propeller Shaft generator

Table 3: Energy flows and efficiencies for system components. All flows are given in TJ/year

11

flows, are represented, and a new entry (symbolised as red arrows in the figure) representing energy destruction is included.

Figure 4: Grassmann diagram of ship systems

12

The results from the exergy analysis are presented in Figure 4.Table 4 shows values of exergy efficiency (ηex ),

Exergy analysis Component

ηex

λ

δ

ηtask

Main engines Engine Turbocharger Exhaust gas economiser Charge air cooler Lubricating oil cooler Jacket water cooler HT/LT heat exchanger LT/SW heat exchanger

42.9% 35.9% 67.0% 66.4% 59.3% 50.1% 59.5% 2.1%

37.2 % 24.4% 6.5% 6.8% 20.1% 15.9% 11.6% 85.3%

64.2 % 6.4% 1.1% 1.1% 1.1% 2.5% 1.8% 5.3%

4.8% 27.5% 38.8% 49.5% 38.5% 31.5%

Auxiliary power Auxiliary engines

61.7%

38.3%

5.3%

-

Auxiliary heat Boilers Tank cleaning Hotelling Fuel heating

29.7% 25.3% 37.8% 26.2%

63.7% 61.1% 46.7% 62.1%

5.4% 0.8% 0.6% 1.8%

4.7% 7.2% 9.1%

Table 4: Exergy flows and efficiencies for system components. All flows are given in TJ/year The largest share of this exergy destruction is originated in a small number of components: the main engines (64.2% of total exergy destruction) and their turbochargers (6.4%), the auxiliary engines (6.1%) and the auxiliary boilers (5.4%). Possible improvements in the performance of these components are a concern of dedicated research and will not be dealt further in this work. Figure 4 also helps in the evaluation of the different waste flows with respect to both energy quantity and quality. It is here shown that the exhaust gas is a much larger source of potentially recoverable heat then the cooling systems, contrarily to what could be deducted from Figure 3. In addition, looking a the task efficiency of the EGE (4.8%) allows understanding how its location and use could be improved in order to increase the available waste heat. The same amount of heat could be generated using much lower-grade energy. Even though their exergy flow is lower than the exhaust gas, the cooling systems should not be discarded as they still constitute a relevant source of potentially recoverable energy. Here again the observation of the task efficiency leads to the identification of which components could be improved in order to perform the same task while reducing exergy destruction. This is particularly true for the charge air cooler (ηtask = 27.5%), while improvements could also be programmed in the lubricating oil cooler (38.8%) and the HT/LT heat exchanger (38.5%). It should be noted, however, that improvements in the task efficiencies of heat exchangers would only be beneficial in case installation of a WHR system is expected, as heat is already supplied to auxiliary consumers in sufficient quantity otherwise. 13

From the point of view of energy use, the very low efficiency of all auxiliary heat consumers (tank cleaning, hotelling, and fuel heating respectively have task efficiencies of 4.7%, 7.2% and 9.1%) shows that it would be possible, by using a different heat transfer fluid (or, in alternative, steam at a lower pressure), to generate the same heat requirements while using much lower heat-grade sources. It is noted, for instance, that fuel handling and hotelling only require temperatures as low as 70-80 °C (a part from fuel heaters before the engine, which warm HFO up to around 100°C), which could be provided at much lower temperature than by 9 bar steam. Finally, figure 4 shows how the on board management of thermal energy can be improved. Firstly, the use of boilers for generation of auxiliary heat should be reduced to a minimum, as underlined by the very low exergy efficiency of both these components.

3.3

Potential for waste heat recovery

Heat recovery is already performed on board, and when the ship is sailing no additional fuel is required for providing energy for the auxiliary heat needs. This does not mean, however, that the ship systems are thermodynamically optimised. In particular, the possibility for installing a WHR system in order to utilise waste heat for providing auxiliary power can be an option to be explored in order to reduce the consumption from auxiliary engines. This study focused on the use of the results from the exergy analysis in order to estimate the feasibility of the installation of a WHR system on board of the selected case-study ship. The method here presented is expected to allow a much finer estimation of whether and how WHR systems should be retrofitted on existing vessels. Figure 5 shows the percentage of time during which the WHR system pro-

Fulfillment of auxiliary power need [%]

100

Only Exhaust, EGE before WHR boiler Only exhaust Only exhaust, no aux heat generation Exhaust + LT cooling Exhaust + CAC

80

60

40

20

0 30

40

50

60

70

WHR system exergy efficiency [%]

Figure 5: Fractional coverage of the auxiliary need for different recovery arrangement versus cycle exergetic efficiency

14

vides sufficient power for the auxiliaries, as a function of the recovery arrangement and of the exergy efficiency of the WHR system (the fraction only refers to the time sailing, both loaded and ballast). The range for WHR exergy efficiency was set between 0.4 (referring to the simple pressure cycle, as proposed by [21]) and 0.6 (referring to complex, optimized ORCs as proposed by [22]). The different lines represent the possibility of recovering energy from different sources: • Exhaust gas plus LT cooling (auxiliary heat harvested from the exhaust, after the WHR boiler) • Exhaust gas plus Charge air cooling (auxiliary heat harvested from the exhaust, after the WHR boiler) • Exhaust gas (auxiliary heat harvested from the exhaust, before the WHR boiler; this solution would mean constitute the easiest modification of the current arrangement) • Exhaust gas (auxiliary heat harvested from the exhaust, after the WHR boiler) • Exhaust gas (auxiliary heat harvested from the cooling flows) Vertical lines are drawn in order to show, for each arrangement, the efficiency that grants that auxiliary power demand is fulfilled for at least 80% of the time. Observing this figure it is possible to notice that fulfilling both heat and power needs based on the exhaust alone is not a viable solution, unless very complex recovery systems are employed (minimum efficiencies of 58% and 64%, the latter not showed in the figure). When low-grade waste heat is harvested as well more reasonable efficiencies become sufficient for justifying the installation and use of WHR systems on board. If the efficiency required in order to provide the totality of the auxiliary power demand for the 80% of the time spent sailing, fuel consumption can be reduced by 350 tons, representing a saving of 6.78% of the overall yearly bunker needs.

3.4

Discussion

The assumptions made in the analysis of the data collected were explained in detail in section 2. The implications of these hypothesis will be here further discussed, with a focus on their impact on the validity of the results as presented in this section. One strong limitation of the procedure employed lies in the inconsistency of the input data used in order to elaborate the structure of on board energy flows. Input data for calculations were obtained from online measurements, manufacturers technical documentation, shipyard technical documentation, and reported measurements from the crew. This mixture of different data sources made it possible to use all available information, with the drawback of giving up to data consistency. More accurate data concerning boiler fuel consumption, temperatures across the engines and the different cooling flows, as well as more detailed information about all different auxiliary needs, would make the analysis much more consistent and accurate. However, this information was not available, and

15

the authors resolved that employing all usable data, even if coming from different sources, was the best solution for getting the highest amount of insight on the system under study, without compromising the scientific quality of the work. On the other hand, the availability of such a relatively wide and detailed dataset of online measurements for ship propulsion and electric load, as well as for main engine fuel consumption, is quite uncommon in the shipping sector (only limited examples of similar datasets are reported in literature, such as what presented in [23]). Therefore, even if the methodology described is supposed to be applicable to any kind of ship, the specific choices and assumptions employed in the modeling are almost exclusive of the specific case. The composition of data would be hardly found in exactly the same form for other ships. However, the overall method of decomposition and analysis, both from the energy and exergy perspectives, does not depend strongly on the input data. In fact, the composition of different data sources presented in this study shows indeed how such analysis can be performed even if no complete database is available. The results presented in the energy and exergy analysis are expected to be valid for the selected vessel and its sister ships: as aggregated data over one year of operation were used any voyage-specific feature (weather influence on propulsive power, sea water temperature, etc.) is supposed to be cancelled out when accounting for longer periods of time. It should be noted, however, that some phenomena can be observable only under longer time perspectives. In particular, today’s low markets and high fuel prices have pushed down the operative speed of the vessel, and it is reasonable to expect that the share of propulsive power would be larger (together with recoverable energy) if the vessel were to operate at higher speed. The validity of the results can also be extended to vessels of similar scope, even if of different sizes. It is legitimate to expect that while quantities will vary sensibly from ship to ship, the conclusions and considerations derived from the analysis of this ship can still be accounted as valid. Much more care should be put when extending the validity of the analysis to other ship types. As can be easily observed, the largest share of energy demands are not type-specific: heat demand for fuel handling and accommodation, power demand for auxiliary, and propulsion power need are present on most of merchant vessels in operation. However, the applicability should, in this case, be analysed on a case-by-case principle.

4

Conclusion

The paper presented the energy and exergy analysis of a chemical / product tanker, based on a mixed top-down and bottom-up approach applied to one year of ship operation. The exergy analysis was used as a basis for evaluating the installation of waste heat recovery on the vessel. The analysis showed, as expected, that propulsion power is the major energy consumption, while also demonstrating that auxiliary demands of both electric power and heat are not negligible. A large amount of energy is wasted to the environment through engine cooling and exhaust gas. Through the exergy analysis it has been possible however to identify what the largest sources of exergy loss and destruction are. The largest exergy losses is connected to the exhaust, while losses connected to engine cooling are negligible. Large amounts

16

of exergy are instead destructed in the boiler and in the cooling systems, as exchanges are not optimised for conserving energy quality. The analysis of the possibilities for energy recovery showed that there is large potential for the application of such technology to the selected vessel. Assuming the operation of a WHR system in sailing conditions the exergetic efficiency required by the system in order to fulfil a minimum value of 80% of the auxiliary power demand would be between 48% and 64% depending on the exergy flows selected for energy recovery. Such a system would allow the ship saving an estimated 6.78% of the yearly fuel consumption.

References [1] UNCTAD, Review of maritime transport, Tech. rep. (2012). [2] IEA, Co2 emissions from fuel combustion - highlights, Tech. rep., IEA International Energy Agency OECD, Paris, France (2012). [3] J. Kalli, T. Karvonen, T. Makkonen, Sulphur content in ships bunker fule in 2015 - a study on the impacts of the new imo regulations and transportation costs, Tech. rep., Ministry of Transport and Communications, Finland (2009). [4] DNV, Shipping 2020, Tech. rep., Det Norske Veritas (2012). [5] Vivid, Economics, Assessment of the economic impact of market-based measures, Tech. rep., Prepared for the Expert Group on Market-based measures, International Maritime Organization (2010). [6] DNV, Pathways to low carbon shipping - abatement potential towards 2030, Tech. rep., Det Norske Veritas (2010). [7] G. Thomas, D. O’Doherty, D. Sterling, C. Chin, Energy audit of fishing vessels, Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment 224 (2) (2010) 87– 101. [8] W. Shi, D. Stapersma, H. T. Grimmelius, Analysis of energy conversion in ship propulsion system in off-design operation conditions, Energy and Sustainability 1 (2009) 461–472. [9] W. Shi, H. T. Grimmelius, D. Stapersma, Analysis of ship propulsion system behaviour and the impact on fuel consumption, International Shipbuilding Progress 57 (1-2) (2010) 35–64. [10] R. Balaji, O. Yaakob, An analysis of shipboard waste heat availability for ballast water treatment, Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology 11 (2) (2012) 15–29. [11] T. Nguyen, L. Pierobon, B. Elmegaard, F. Haglind, P. Breuhaus, M. Voldsund, Exergetic assessment of energy systems on north sea oil and gas platforms, Energy (0).

17

[12] I. Dincer, M. Rosen, Exergy: Energy, Environment and Sustainable Development, 2nd Edition, Elsevier, 2013. [13] B. C. Choi, Y. M. Kim, Exhaust-gas heat-recovery system of marine diesel engine (ii) - exergy analysis for working fluids of r245fa and water, Transactions of the Korean Society of Mechanical Engineers, B 36 (6) (2012) 593–600. [14] G. Dimopoulos, C. Georgopoulou, N. Kakalis, The introduction of exergy analysis to the thermo-economic modelling and optimisation of a marine combined cycle system, in: Proceedings of ECOS, Perugia, Italy, 2012. [15] R. Y. Lin, X. Yu, J. Li, Y. Li, W. Wang, Exergy analysis for lng refrigeration cycle, in: International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM), Changsha, Hunan, CH, 2011, pp. 211–214. [16] L. Mattarolo, Energy economy and heat recovery with particular reference to sea transport, International Journal of Refrigeration. [17] B. Duarte, P. Saraiva, C. Pantelides, Combined mechanistic and empirical modelling, International Journal of Chemical Reactor Engineering 2. [18] G. Bontempi, A. Vaccaro, D. Villacci, Semiphysical modelling architecture for dynamic assessment of power components loading capability, IEEE Proceedings: Generation, Transmission and Distribution 151 (2004) 533–542. [19] T. Kotas, Exergy criteria of performance for thermal plant: second of two papers on exergy techniques in thermal plant analysis, International Journal of Heat and Fluid Flow 2 (4) (1980) 147–163. [20] E. Hau, Wind Turbines, Springer Berlin Heidelberg, 2013. [21] G. Theotokatos, G. A. Livanos, Techno-economic analysis of single pressure exhaust gas waste heat recovery systems in marine propulsion plants, Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment 227 (2) (2013) 83–97. [22] U. Larsen, L. Pierobon, F. Haglind, C. Gabrielii, Design and optimisation of organic rankine cycles for waste heat recovery in marine applications using the principles of natural selection, Energy (0). [23] J. P. Petersen, D. J. Jacobsen, O. Winther, Statistical modelling for ship propulsion efficiency, Journal of Marine Science and Technology 17 (1) (2012) 30–39.

18

Paper II

A validated zero-dimensional four-stroke medium speed Diesel engine model for waste heat recovery marine applications Francesco Baldia,∗, Ulrik Larsenb , Cecilia Gabrieliia , Karin Anderssona a Chalmers

University of Technology; Department of Shipping and Marine Technology, SE-41296 Gothenburg, Sweden b Technical University of Denmark; Department of Mechanical Engineering, DK-2800 Kgs. Lyngby, Denmark

Abstract As fuel prices increase and global warming awareness raises, there is need for more advanced tools for improving design and operation of energy systems toward increasing energy efficiency. In this framework, the aim of this paper is to derive a zero-dimensional single zone mathematical model for predicting outputs suitable for a combined energy system analysis of four-stroke marine medium speed Diesel engines and waste heat recovery systems. The use of Redlich-Kwong real gas equation for high pressure phenomena is proposed; Chen and Winterbone correlations for friction losses are tested and compared. The model is calibrated and validated on acceptance test records for a Caterpillar MaK 8M32C and a MAN 9L27/38 marine engine, with very accurate predictive results. Tests on operational data show similarly high accuracy. The model is applied to the optimization of the propulsive efficiency of a merchant vessel. Analysis using the model shows that, at medium-low ship speeds, the higher efficiency in auxiliary power generation related to the use of a generator connected to the main engine instead of auxiliary engines is overcome by the decreased propeller efficiency when operating at fixed speed. Moreover, the evaluation of the waste heat recovery potential from the exhaust gas leads to the conclusion that the standard auxiliary power needed onboard the vessel can be provided by a rather simple recovery cycle (ηex = 0.32), thus generating large potential reduction in fuel consumption. Keywords: Diesel engine, Modeling, gray-box, zero-dimensional, waste heat recovery

∗ Corresponding

author Email addresses: [email protected] (Francesco Baldi), [email protected] (Ulrik Larsen), [email protected] (Cecilia Gabrielii), [email protected] (Karin Andersson)

Preprint submitted to Applied Energy

October 9, 2013

Nomenclature DAE

Differential Algebraic Equation

EVO

Exhaust Valve Opening

GA

Genetic Algorithm

LHV

Lower Heating Value

WHR Waste heat recovery Ea

Activation energy [kJ]

αf r

Friction weighting coefficient

s¯p

Mean piston speed [m/s]

s¯p

Mean piston speed

∆i

Combustion duration [CAD]

η

Efficiency

ω

Engine speed [rad/s]

Φ

kg Ship specific fuel consumption [ nm ]

τid

Ignition delay [ms]

θi

Crank angle [CAD]

bsf c

Brake specific fuel consumption

g kW h

f mep Friction mean effective pressure [bar] H, h

J ] Enthalpy [J, kg

m

Mass [kg]

n

Engine speed [rpm]

P

Power

p

pressure, [bar]

Q

Heat [J]

R

kJ ] Specific gas constant[ kgK

R0

kJ Universal gas constant [ kmolK ]

T

Temperature [K]

U, u

J Internal energy [J, kg ]

2

V

Cylinder volume [m3 ]

v

kg Specific volume [ m 3]

vship

Ship speed [kn]

Vsw

Swept volume, [m3 ]

W

Work [W]

y

Gas mass fraction

AE

Auxiliary engine

b

Brake

ex

Exergy

f

Fuel

f

Fuel

fr

Friction

fs

Fixed speed

GB

Gearbox

in

Inlet manifold

in

Inlet

max

Maximum

ME

Main engine

min

Minimum

out

Outlet manifold

out

Outlet

s

Propeller shaft

SG

Shaft generator

vs

Variable speed

w

Wall

3

1. Introduction 1.1. Background In recent times two main factors have exerted a strong influence on the development and the use of fossil-fueled energy systems and, in particular, of internal combustion engines. On one hand, the awareness is growing about human contribution to climate changes in terms of greenhouse gases emissions [1]. On the other hand, the fuel market is experiencing a new, large increase in demand, mainly triggered by developing countries growing economies, that is not matched by a proportionate increase in resources availability [2]. The joint influence of these two elements has brought a new, rising interest in technologies for reducing engine fuel consumption. One of the directions researchers have started to look at is a better understanding of the connections that can be found in complex energy systems. In this kind of structures a simple component-by-component optimization could be inefficient and even lead to the undesired phenomenon of sub-optimization. However, the many possible configurations that could be applied for each energy system do not allow a simple, straight-forward experimentation process, that would turn out to be too expensive and time consuming. For this reason, the analysis and optimization of energy systems is subject to the use of accurate predictive calculation models. If countless examples of the application of modeling and optimization techniques can be found for land-based systems, such as cogeneration or industrial plants, the activity in the sector of ship design and operation control has not been put under the same focus. Rather extensive research has been published focusing on the main propulsion systems: Theotokatos presents a simplified modeling approach for the overall ship propulsion system model, both in steady-state and in transient operations [3]; Campora and Figari propose a similar analysis making use of models with higher mechanistic content and providing validation of the system transient behavior [4]; Schulten and Stapersma present an analysis of the uncertainty in relation with the validity of a ship’s model as a complex system. [5]; Vrijdag et al. propose a modeling procedure, complete of verification, calibration and validation, of ship propulsion [6]. However, only little work has been done on the concept of energy system modeling in shipping, even though very interesting results have been showed by Dimopoulos [7, 8] and Grimmelius [9, 10]. 1.2. Diesel engine modeling Diesel engines are used as prime movers in most of the energy systems they are part of, meaning that every error in their modeling will propagate all through the system. Hence, a large effort is needed in the Diesel engine modeling. On the other hand, as the model is used for energy system optimization, computational time must be kept reasonably low. Two main alternatives have been presented and used for energy system modeling in shipping. Grimmelius et al. [9, 10] make use of mean value first principle models, which are an evolution of ideal models, where a higher level of detail 4

in the description of the cycle is employed. Dimopoulos et al. [7, 8] instead use a Diesel engine mean value model where the Diesel cycle is simulated as a flow restriction with heat addiction. In spite of their apparent simplicity, both modeling techniques show very impressive results in the prediction of engine power output and efficiency. Zero-dimensional thermodynamic models, also known as ”filling and emptying” or ”crank angle” models, can be a possible solution for increasing the simulation while keeping model complexity and calculation time at reasonable levels [11]. In these models, the mass and energy conservation equations, together with the gas state equation, are solved in their differential form all along the cycle, so that the properties of the gas within the combustion chamber, such as pressure and temperature, can be calculated. Zero-dimensional models are often further subdivided in single-zone and multi-zone models, the latter subdividing the combustion zone in two ore more sub-zones for a better prediction of local temperature peaks. As showed by both Pariotis et al.[12] and Kumar et al. [13], single-zone models provide a reasonable accuracy for in-cylinder pressure, while keeping computational time low. Several studies have been performed on single-zone models for compression ignition engines. However, medium-speed marine engines, even though still based on a Diesel process, are significantly different from automotive engines. Different limitations in terms of weight, space, cost, and maintenance allow marine engines to reach much higher pressures (up to 200 bar and more) and, therefore, much higher efficiencies than what is observed in most automotive application, with values for brake specific fuel consumption (bsf c) down to 160 g kW h . High pressures in particular, as will be explained in section 2, do not allow using the ideal gas equation for modeling gas behavior during compression and combustion. Therefore, previous work on zero-dimensional single-zone modeling of Diesel engines was deemed unfit for the application to marine energy systems. Papagiannakis et al. [14], Kannan et al. [15], Kökk¨ ul¨ unk et al. [16], and Awad et al. [17] validate their results on engines characterized by lower maximum pressures g (60 to 100 bar) and higher design bf sc (200 to 265 kW h ). Several additional studies are available, sharing the same limitation to marine applications. Applications of zero-dimensional models explicitly for marine engines are less numerous. Scappin et al. [18] apply a two-zone model to a marine engine in a similar pressure and efficiency range, but with a two-stroke cycle. Benvenuto et al. [19] focus on a 4-stroke cycle instead, but employ a more computationalintense two-zone model. Finally, friction is often overlooked in most modeling studies. Benvenuto et al. [19] refer to an ”ad-hoc” correlation provided by the manufacturer, while none of the other cited references mention correlations or methods for calculating friction losses. Livanos et al. [20] showed how, for a marine engine, friction losses account for 2.6% to 5% of fuel energy input. This contribution is far from being negligible.

5

1.3. Objective The objective of this study is to provide a numerical tool for the modeling of four-stroke marine Diesel engine performances to be used as part of a larger energy system model. The model should therefore be able to combine good accuracy and low computational time. Additionally, high cylinder pressure and need for a friction correlation are addressed in the modeling. The model should adapt to several different engines with relatively low effort, i.e. not requiring extensive experimental campaigns and easily available calibration data. The model focuses on the ability to predict engine main outputs for use in energy system models, i.e. shaft power and exhaust gas energy content and quality. The model is used in the operational optimization of the propulsion system of a merchant vessel. In section 2 model equations and assumptions are presented. In section 3 the calibration and validation of the model are described. In section 4 results from the application of the model are presented and discussed. Finally, in section 5, conclusions on the presented work are drawn. 2. Mathematical model Engine evolution was modeled throughout five main phases: compression, injection, combustion, expansion, and post-exhaust valve opening (EVO)blowdown. Each phase was modeled using a different set of differential equations, that will be described in the following sections. Modeling the thermodynamic properties evolution during the gas-exchange phase is omitted, as the gas-exchange phase involves complex thermo-fluid dynamics interactions, which are hardly predictable with a 0-dimensional model. An approximation, described later in this section, is therefore used for the exhaust and intake strokes. A set of case-dependent parameters employed by the model needs to be defined. This involves engine geometrical parameters (number of cylinders, cylinder bore and stroke) and calibration parameters, which will be described in detail in section 3. Five input variables are needed to the model: inlet air temperature and pressure from the turbocharger (after the cooling stage, if present), inlet fuel flow, engine speed, and fuel lower heating value (LHV) . Inlet air flow is calculated assuming unitary volumetric efficiency. 2.1. Model equations Energy conservation is implemented in the form of the following differential equation: ˙ − Q˙ w + m U˙ = Q˙ f − W ˙ in hin − m ˙ out hout (1) Where U represents the internal energy, Qf the heat released by the combustion, W the work, Qw the heat lost to the cylinder walls, and m ˙ in hin and m ˙ out hout the inlet and outlet enthalpy flows.

6

Equations for work, specific internal energy and specific enthalpy come from basic thermodynamic principles. Volume evolution is represented by equation: i p Vsw h V = Vmin + f + 1 − cosθ − (f 2 − sin2 θ) (2) 2 where f represents the ratio between connecting rod length and crank shaft diameter, Vmin the minimum cylinder volume, Vsw the swept volume, and θ the piston position, in crank angle degrees. All these values were made available by the manufacturer. The ignition delay (τid ) is calculated as proposed by Heywood [11]: ( " 0.63 #) 1 1 21.2 1 (0.36 + s¯p )exp Ea − (3) τid = 6n R0 T 17190 p − 12.4 where n represents the engine speed [rpm], s¯p the mean piston speed [m/s], Ea the activation energy [kJ], R0 the universal gas constant, T the gas temperature [K], and p the pressure inside the cylinder. Combustion is subdivided in a first, fast premixed combustion, resulting from the mixing of fuel and air charge that occurs during the ignition delay, and a second, slower mixing-controlled combustion [18, 21, 22, 23]. Both heat releases are modeled using a Wiebe function, which was showed to be the most reliable approximation, among others, by Ding et al. [23]: " m m+1 # Q θ − θ θ − θ 180ω c,i 0 0 a(m + 1) exp −a (4) Q˙ f = π ∆θi ∆θi ∆θi where ω represents the angular speed rad and ∆θi the duration of the coms bustion phase [CA], while a and m are parameters depending on the fuel. Since no experimental data were available for heat release, values of respectively 5 and 2 where used, as suggested by Heywood [11]. Premixed combustion duration was set to 7◦ according to Miyamoto et al.[24], while diffusive combustion duration was subjected to calibration. Gas composition is calculated under the hypothesis of complete combustion. Several empirical correlations are available for modeling heat losses to cylinder walls, as reviewed by [13]. Both Annand and Woschni correlation are used in other examples of marine engine modeling [18] [19] . However, as the single zone approximation averages temperatures over the whole combustion chamber, the use of Annand correlation, which involves non-linear dependence on gas temperature, was deemed unsuitable. Therefore the heat transfer coefficient is approximated using Woschini correlation: W 0.8 0.8 −0.2 −0.55 h = 129.9p u d T (5) m2 K where p represents the cylinder pressure [bar], d the cylinder diameter [m], T the cylinder gas temperature [K] and u is a coefficient depending on the mean piston 7

speed, cylinder swept volume and compression ratio. Cylinder wall temperature is assumed to be constant, as experimentally verified by Rakopoulos et al. [25]. The blow-down that follows the opening of the exhaust valves is responsible for a considerable loss of indicated work that should be accounted for. Equation 6 can be used in order to avoid discontinuities in the model and to predict engine performance with reasonable accuracy without requiring detailed knowledge of valve geometry [18]: p=

pEV O − pout eθEV O −θ−1 + pout e−1 −θEV O + θ + 1

(6)

where pEV O represents the cylinder pressure at the instant when the outlet valve opens, pout the pressure in the exhaust manifold, and θEV O the angle of exhaust gas opening. As the model refers to heavily turbocharged engines, additional work is produced during the intake/exhaust strokes. As these phases are not modeled in detail, equation 7 is here proposed in order to approximate the additional work produced in the pumping cycle: Wpc = Vsw (pin − pout )ηpump

(7)

where Wpc represents the additional work produced by the pumping cycle, pi n the inlet pressure, po ut the outlet pressure, and ηpump the loss of efficiency due to the non-ideal valve behavior and is subject to model calibration. Heat losses occurring in the gas-exchange phase are assumed to be negligible, the largest part of heat transfer taking place during the combustion phase [26, 22]. Exhaust gas temperature is calculated through an energy balance of the engine over the entire cycle: ∆heg =

˙b−W ˙ f r − Q˙ w m ˙ f LHV − W m ˙ air + m ˙f

(8)

exhaust gas temperature is calculated starting from its enthalpy value and composition. Ideal gas behavior is assumed in this case, hence no dependence on outlet pressure is considered. Enthalpy difference in both compressor and turbine is calculated using the concept of adiabatic (also referred as polytropic) efficiency, which was preferred to the isentropic efficiency since it allows to take into account the variation in the efficiency due to varying pressure drops through the turbomachine [27]. ˙C =W ˙ T is used for iteratively calculating the pressure in Finally, the balance W the exhaust manifold. Both the turbine and the compressor are assumed to be adiabatic. 2.2. Real gas versus ideal gas equation The choice of the state equation for gas evolution in the cylinder is a controvert subject. Most researches make use of the ideal gas law, which has been proved effective in a dedicated study by Lapuerta et all [28]. However it is well 8

known that for high pressures the ideal gas approximation becomes less justified. Danov et al. [29] showed how for pressure values higher than 80-90 bar the difference between ideal and real gas behavior stops being negligible. This model is built for heavy duty Diesel engines where pressure can reach values up to more than 200 bar. As suggested by Wark [30], for pressure values up to 300 bar, the Redlich-Kwong equation of state (equation 9) is recommended for describing the thermodynamic behavior of gases: a √ (v − b) = RT (9) p v(v + b) T where p, T and v respectively represent pressure [Pa], temperature [K] and specific volume of the gas inside the cylinder [m3 /kg], while a and b are two coefficients that can be analytically derived for each gas component. According to Danov et al. [29], the thermodynamic behavior of the mixture subordinates to the Amago law up to pressures of 300 bar and temperatures of 3300 K. Therefore, the following mixing rule applies for real gas mixtures: X w= yi wi (10) i

where w represents a generic thermodynamic property and y the mass fraction of the pure gas in the mixture. 2.3. Friction The application of a numerical correlation was preferred to a more detailed modeling of friction-related phenomena. Friction losses can increase up to 40 % because of high pressures [31], and marine Diesel engines generally feature very high compression pressures. Therefore, correlations 11 and 12 respectively proposed by Chen et al. [32] and Winterbone et al. [33] where considered for use: pmax f mepC = 0.137 + + 0.162¯ vsp (11) 200 pmax n f mepW = 0.061 + + 0.294 (12) 60 1000 where pmax represents the maximum pressure in the cylinder [bar], n the engine speed [rpm] and s¯p the mean piston speed [m/s]. The equations are conceptually identical and only differ by the coefficients, and different authors implement different correlations. For instance, Yun et al. [34] use the Winterbone correlation, while Schulten, after calibration, gets to the same coefficients as Chen et al. [35]. The two correlations were separately tested versus the third possibility of using a weighted average of equations 11 and 12: f mep = αf r f mepC + (1 − αf r )f mepW

(13)

where the friction weighting coefficient αf r was subject to model calibration. 9

3. Model calibration and validation 3.1. Parameters sensitivity analysis Since a few parameters necessary for engine simulation were not available from engine technical documentation, an estimation was needed. A serial graybox approach was used, in which experimental results were used to calibrate a set of parameters. Data from two engines were used: a MaK 8M32C (A engine) and a MAN 9L27/38 (B engine). Their main parameters are showed in Table 1. For a more detailed description of the engines please refer to the engine technical guides, available on MaK and MAN websites [36, 37].

Rated power [kW] Cylinders Bore [mm] Stroke [mm] Design speed [rpm]

A 3840 8 320 480 600

B 3060 9 270 380 800

Table 1: MaK 8M32C and MAN 9L27/38 main technical details Experimental measurements were available from acceptance test records from both engines. For comparison with model results, data regarding SFOC and exhaust gas temperature were used. Eight parameters needed to be estimated: diffusive combustion duration [CA], injection duration [CA], pumping cycle efficiency, turbine and compressor polytropic efficiency, engine wall temperature [K], injection timing [CA, after BDC], inlet valve timing [CA, after BDC], and compression ratio. Injection timing is often a function of engine load, which in the model is taken into account using a linear correlation, whose two parameters are evaluated through model calibration. The influence of different calibration parameters on model output was estimated. A default value and a range of variation were assigned to each parameter, according to the incertitude of the value. Injection timing was observed being the most relevant parameter, followed by combustion duration and wall temperature. These three parameters were therefore employed in the calibration process. The remaining parameters, when unknown, were arbitrarily assumed, under the assumption that their influence on model output is limited. Assumptions are 50 CA degrees for injection duration, 0.85 for turbocharger polytropic efficiency, and 0.9 for pumping cycle efficiency. 3.2. Calibration A Genetic Algorithm (GA) minimizing the total weighted residual error () for the three variables was used. Based on the principles of natural selection, first formulated by Charles Darwin, the GA can be applied to resolve a large variety of simulation issues, as for example an optimization or calibration [38].

10

In this work a GA coded as a Matlab tool box (GATbx) has been obtained from the Department of Automatic Control and System Engineering of The University of Sheffield, UK and it is freely available online [39]. The accordance between calibrated and experimental data is finally showed in Tables 2 and 3, where M and P respectively refer to measured and predicted ones: Test [% MCR] 110% 100% 85% 50%

bsfc[g/kWh] M P 192.9 192.5 -0.21% 190.9 190.8 -0.05% 189.0 190.1 0.58% 199.0 198.8 -0.10%

Exhaust Temp. [K] M P 799 802 0.38% 777 780 0.39% 761 761 0% 756 766 1.32%

Table 2: Calibration results for A engine

Test [% MCR] 110% 100% 85% 75% 50%

bsfc[g/kWh] M P 203.2 202.8 -0.19% 198.5 198.2 -0.16% 195.0 194.8 -0.13% 193.8 193.9 0.06% 197.5 198.3 0.41%

Exhaust Temp. [K] M P 826 853 3.21% 777 799 2.84% 737 758 2.85% 716 734 2.45% 678 693 2.32%

Table 3: Calibration results for B engine

3.3. Validation The proposed model was tested versus the calibration dataset of both mentioned engines, through the calculation of the ”prediction coefficient of determi2 nation” RP rediction statistics. When the lack of a large amount of experimental points does not allow the repartition of the original dataset into a calibration and a validation part, the predictive ability of the model can be estimated through 2 the RP rediction statistic [40]. The analysis shows that the model, regardless the engine used for testing, 2 features a good accuracy in the prediction of both bf sc (RP rediction for A and B engine respectively of 0.820 and 0.858) and exhaust temperature (0.913 and 0.852). Contribution to the cumulated error is generated evenly over all different points, thus showing that the model is able to predict the physical behavior of the engine even for extrapolation cases. In order to further verify model accuracy, measurements from six ships employing engine A were used. On the selected ship, the engine shaft is connected to a gearbox, which transfers mechanical power to the propeller shaft and to an electrical generator (hereafter called ”shaft generator”). The propeller shaft power (Ps ) and the shaft generator power (PSG ) are calculated after measuring 11

shaft speed and torque (for the propeller shaft) and output voltage and current (for the shaft generator). In order to calculate engines brake power (Pb ), equation 14 was employed: Ps PSG 1 Pb = + (14) ηs ηSG 2ηGB where ηs , ηSG , and ηGB represents the efficiencies of respectively propeller shaft, shaft generator, and gearbox. According to Shi et al.[41] shaft efficiency can range between 98% and 99%, therefore a mid-range value of 98.5% was used. ηSG was assumed equal to 90% referring to manufacturers technical papers. A value of 98.3% for ηGB was given by the shipyard. The total brake power is divided by 2, since two main engines of the same model are simultaneously used. Only fuel flow, engine speed and fuel LHV were available as inputs to the model. The inlet temperature was set to a constant value of 55 ◦ C, which is used as standard set point for ship operations. Other values were assumed as stated in section 2. Fig. 1 shows model results compared to measured values. Error bars for the measured SFOC are calculated starting from a respective 2% and 3% measurement accuracy for propeller power and fuel mass flow, as reported in technical documents. Fig. 1a shows how the model is able to predict the engine output largely within the accuracy of the measurements. The residuals are plotted versus load in Fig. 1b, showing no specific pattern. The final regression accuracy is R2 = 0.742

(a) SFOC, predicted versus measured

(b) SFOC, model residuals

Figure 1: Model validation

3.4. Application of a real gas equation in high-pressure Diesel engine models The high maximum pressures in marine Diesel engines, that can reach up to 200 bar, challenge the validity of the application of the ideal gas law. Fig. 2 illustrates the difference in pressure between the application of the RedlichKwong and the ideal gas equation for engine A, at 110%, 100%, 85%, and 50% engine load. The use of the ideal gas equation underestimates maximum 12

pressure by between 8.89% and 3,6% depending on the load. Hence, at these pressure levels the use of a the Redlich-Kwong equation instead of the ideal gas equation for determining gas state is necessary.

Figure 2: Cylinder pressure for different engine loads, ideal gas versus RedlichKwong state equation The use of the two different equations was studied in the calibration process. 2 Observing the evolution in the RP rediction statistics for engine efficiency for both engines (0.82 and 0.98 with real gas equation implemented, 0.71 and 0.97 with ideal gas equation implemented) leads to the conclusion that the use of the real gas equation in the model improves its predictive performance. 3.5. Comparison of friction correlations The evaluation of two existing correlations for the calculation of friction losses was proposed and compared with the use of a weighting coefficient as additional calibration parameter to the mode. Results show clearly that the correlation proposed by Chen et al. is leading to 2 the highest prediction ability for the model. RP rediction for power calculated in the three different cases (respectively using Chen et al. correlation, Winterbone et al. correlation, and the weighting approach) is equal to 0.820, -0.916, and 0.708 for the A engine and 0.966, 0.232, and 0.573 for the B engine. It is clear that not only is the Chen et al. correlation more appropriate than Winterbone et al.’s, but also that allowing an additional calibration parameter for friction leads to over-fitting the model and, therefore, to a reduced predictive capability.

13

(a) Case 1

(b) Case 2

(c) Case 3

Figure 3: Schematic representation of the different propulsion system arrangements 4. Simulation results The model was applied in the analysis the propulsion system as described in the validation chapter. The ship under analysis is operated at constant engine speed in order to use the shaft generator for auxiliary power generation. As the shaft generator is connected to he main engine, this arrangement allows generating auxiliary power more efficiently than by using auxiliary engines. However, propeller efficiency varies with its speed, and especially during slow operations the optimal working condition is not located at the design propeller speed. For this reason the possibility of generating electricity by using auxiliary engines while running the engine at variable speed is studied. The option of installing a WHR system on the exhaust gas, to be used for electric power generation, is also evaluated. The three different concepts are shown in Fig. 3. In this case the use of engine modeling is deemed necessary for the prediction of both engine efficiency and exhaust gas temperatures and flows, as a standard regression of engine shop test would not allow to make any prediction for performance at a different speed than the design. The potential for energy recovery was evaluated using the concept of exergy, often used for WHR applications [42]. Exergy efficiency is then defined as the ratio between the amount of exergy contained in the useful outputs of the system (i.e. mechanical power) and the exergy input to the system, and it represents how close the cycle gets to a Carnot engine. As exergy efficiency depends on the specific cycle employed, the choice was made to calculate, for each condition, the minimum exergy efficiency needed by the WHR system for producing the needed amount of auxiliary power onboard. The value issued from this calculation can give an estimation of the level of complexity needed by the recovery cycle in order to fulfill its purpose.

14

Some additional values are needed for the calculation. The power need (Pprop ) and the corresponding engine speed (n) are summarized in Table 4 at different ship speeds (vship ), both for constant engine speed and for the condition corresponding to the highest propeller efficiency permitted by engine safety limits. Subscripts f s and vs respectively refer to fixed and variable speed operations. Ship speed kn 11.0 11.5 12.0 12.5 13.0 13.5 14.0

Pprop,f s kW 2959 3286 3578 3949 4332 4797 5274

n rpm 600 600 600 600 600 600 600

Pprop,vs kW 2959 3072 3281 3734 4188 4725 5263

nM E,vs rpm 600 551 499 521 543 569 595

Table 4: Propulsion power need for different ship speed for fixed and variable engine speed Efficiency for auxiliary generators (ηAG ) is assumed at 92%. Auxiliary engines efficiency (bsf cAE ) at full load was available from technical documentation (195 g/kWh), while their off-load behavior was assumed by similitude with the main engine. The auxiliary power (Pel ) was assumed equal to 364 kW, which includes 80% of sailing time. The process heat needed onboard is assumed to account for 300 kW, which is generated using heat from the exhaust gas and that consequently cannot be used for auxiliary power generation. The three propulsion system arrangements are compared through their respective ship specific fuel consumption (Φ), calculated as fuel consumed per nautical mile traveled , a common evaluation figure of merit used for shipping applications: Pprop,f s Pel φ1 = vs−1 bsf cM E + (15) ηGB ηs ηGB ηSG Pprop,vs Pel φ2 = vs−1 bsf cM E + bsf cAE (16) ηGB ηs ηAG Pprop,vs φ3 = vs−1 bsf cM E (17) ηGB ηs Results for Φ are shown in Fig. 4a. Relative Φ compared to the reference case (Case 1) is shown in figure Fig. 4b. It is clear from the figures that the benefit deriving from running the propeller at its optimal point largely overcomes the loss due to the less efficient auxiliary power generation. The advantage deriving from the use of WHR is also clear, but is limited to ship operations on two engines. In this case, however, the minimum efficiency required for fulfilling the required auxiliary power need is of 39%; according 15

(a)

(b) Case 1

Figure 4: Comparison of ship specific fuel consumption to Larsen et al.[43], the maximum exergy efficiency for organic Rankine cycles (ORCs) can range between 53% and 66% depending on the level of hazard related to the working fluid, therefore demonstrating that the required power needs could easily be met even with very low complexity in the recovery cycle. 5. Conclusion A zero-dimensional gray-box model for four-stroke medium speed marine Diesel engines was derived and validated with acceptance tests records from a MaK 832C and a MAN 9L27/38 engines. The predictive ability of the model 2 was tested using the RP rediction statistic for both engines, while for the MaK engine an additional dataset was also available. Results from the validation procedure indicate a very good ability in predicting both engine power output and exhaust gas temperature. The application of the Redlich-Kwong real gas equation instead of the ideal gas equation was shown to be required due to the very high pressures in marine engines. The influence of such modeling choice was tested and shows significants improvements in power prediction. Similarly, two different friction correlations were tested; the comparison showed that the regression proposed by Chen et al. features better performance when applied to the modeling of large 4-stroke marine Diesel engines. The model was applied to the propulsion system of a merchant vessel with the aim of evaluating three different arrangements influencing both the propulsion and the auxiliary power generation onboard. The application of the model showed that relying on the shaft generator in order to generate auxiliary power does not bring any advantage to the overall fuel consumption as this choice does not allow operating the propeller at its maximum efficiency. Moreover, the evaluation of the WHR potential from the exhaust gas leads to the conclusion that the standard auxiliary power need can be met by using a rather simple recovery cycle (ηex = 0.32). 16

Acknowledgments This work is part of a larger project about ship energy systems modeling, performed at the Shipping and Marine Technology Department of Chalmers University of Technology and financed by the Swedish Energy Agency. The authors would also like to thank Bengt-Olof Petersen and Laurin Maritime for their support and discussions. A special acknowledgment is also given to professor Sven Andersson from the Department of Applied Mechanics in Chalmers University of Technology for the productive discussions that lasted all over the development of this work. References [1] J. T. Houghton, G. J. Jenkins, J. J. Ephraums, Climate change: the IPCC scientific assessment, Cambridge University Press, Cambridge, UK, 1990. [2] R. W. Bentley, Global oil and gas depletion: An overview, Energy Policy 30 (2002) 189–205. [3] G. P. Theotokatos, A modelling approach for the overall ship propulsion plant simulation, in: 6th WSEAS International Conference on System Science and Simulation in Engineering, Venice, Italy, 2007, pp. 80–87. [4] U. Campora, M. Figari, Numerical simulation of ship propulsion transients and full-scale validation, Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment 217 (2003) 40–52. [5] P. Schulten, D. Stapersma, A study of the validity of a complex simulation model, Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology (2007) 67–77. [6] A. Vrijdag, D. Stapersma, T. Van Terwisga, Systematic modelling, verification, calibration and validation of a ship propulsion simulation model, Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology (2009) 3–20. [7] G. G. Dimopoulos, N. M. P. Kakalis, An integrated modelling framework for the design, operation and control of marine energy systems, Paper 154, in: CIMAC Congress, Bergen, Norway, 2010. [8] G. G. Dimopoulos, C. A. Georgopoulou, N. M. P. Kakalis, Modelling and optimization of an integrated marine combined cycle system, in: Proceedings of ECOS, Novi Sad, Serbia, 2011, pp. 1283–1298. [9] H. T. Grimmelius, E. Mesbahi, P. Schulten, D. Stapersma, The use of Diesel engine simulation models in ship propulsion plant design and operation, Paper 227, in: CIMAC Congress, Wien, Austria, 2007. 17

[10] H. T. Grimmelius, E. J. Boonen, H. Nicolai, D. Stapersma, The integration of mean value first principle Diesel engine models in dynamic waste heat and cooling load analysis, Paper 280, in: CIMAC Congress, Bergen, Norway, 2010. [11] J. N. Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill, New Yori, USA, 1988. [12] E. G. Priotis, G. M. Kosmadakis, C. D. Rakopoulos, Comparative analysis of three simulation models applied on a motored internal combustion engine, Energy Conversion and Management 60 (2012) 45–55. [13] S. Kumar, M. Kumar Chauhan, Varun, Numerical modeling of compression ignition engine: A review, Renewable and Sustainable Energy Reviews 19 (2013) 517–530. [14] R. G. Papagiannakis, D. T. Hountalas, Combustion and exhaust emission characteristics of a dual fuel compression ignition engine operated with pilot diesel fuel and natural gas, Energy Conversion and Management 45 (18-19) (2004) 2971–2987. [15] K. Kannan, M. Udayakumar, Modeling of nitric oxide formation in single cylinder direct injection diesel engine using diesel-water emulsion, American Journal of Applied Sciences 6 (7) (2009) 1313–1320. [16] G. K¨ okk¨ ul¨ unk, G. Gonca, V. Ayhan, I. Cesur, A. Parlak, Theoretical and experimental investigation od diesel engine with steam injection system on performance and emission parameters, Applied Thermal Engineering 54 (2013) 161–170. [17] S. Awad, E. G. Varuvel, K. Loubar, M. Tazerout, Single zone combustion modeling of biodiesel from wastes in diesel engine, Fuel. [18] F. Scappin, S. H. Stefansson, F. Haglind, A. Andreasen, U. Larsen, Validation of a zero-dimensional model for prediction of NO x and engine performance for electronically controlled marine two-stroke diesel engines, Applied Thermal Engineering 37 (2012) 344–352. [19] G. Benvenuto, U. Campora, G. Carrera, P. Casoli, A two-zone diesel engine model for the simulation of marine propulsion plant transients, in: MARIND 98, Second International Conference on Marine Industry, Varna, Bulgaria, 1998. [20] G. A. Livanos, N. P. Kyrtatos, Friction model of a marine diesel engine piston assembly, Tribology International 40 (2007) 1441–1453. [21] S. N. Danov, A. K. Gupta, Modeling the performance characteristics of diesel engine based combined-cycle power plants - Part I: Mathematical model, Journal of Engineering for Gas Turbines and Power 126 (2004) 28– 34. 18

[22] R. Stone, Introduction to internal combustion engines, third edit Edition, Palgrave MacMillan, London, UK, 1999. [23] Y. Ding, D. Stapersma, H. Knoll, H. T. Grimmelius, Characterising heat release in a diesel engine: A comparison between seiliger process and vibe model, in: CIMAC Congress, Bergen, Norway, 2010. [24] N. Miyamoto, T. Chikahisa, T. Murayama, R. Sawyer, Description and analysis of a Diesel engine rate of combustion and performance using Wiebe’s functions, SAE Paper 850107 (1985). [25] C. D. Rakopoulos, E. G. Giakoumis, D. C. Rakopoulos, Study of the shortterm cylinder wall temperature oscillations during transient operation of a turbocharged diesel engine with various insulation schemes, International Journal of Engine Research 9 (2008) 177–193. [26] H. Hiroyasu, Diesel engine combustion and its modeling, in: Proceedings of 1st International Symposium on Diagnostics and Modeling of Combustion in internal Combustion Engines, Tokyo, 1985, pp. 53–75. [27] M. V. Casey, T. M. Fesich, The efficiency of turbocharger compressors with diabatic flows, Journal of Engineering for Gas Turbines and Power 132 (2010) 1–9. [28] M. Lapuerta, R. Ballesteros, J. R. Agudelo, Effects of the gas state equation on the thermodynamic diagnostic of diesel combustion, Applied Thermal Engineering 26 (2006) 1492–1499. [29] S. G. A. K. Danov, Influence of imperfections in working media on diesel engine indicator process, Journal of Engineering for Gas Turbines and Power 123 (2001) 231–239. [30] K. Wark, Advanced thermodynamics for engineers, McGraw Hill, New York, USA, 1995. [31] D. E. Richardson, Review of power cylinder friction for diesel engines, Journal of Engineering for Gas Turbines and Power 122 (2000) 506–519. [32] S. K. Chen, P. Flynn, Development of a compression ignition research engine, SAE Paper 650733 (1965). [33] D. E. Winterbone, Transient performance, in: The Thermodynamics and gas dynamics of internal combustion engines, Vol. II, Oxford University Press, Oxford, UK, 1986. [34] K. T. Yun, H. Cho, R. Luck, P. Mago, Modeling of reciprocating internal combustion engines for power generation and heat recovery, Applied Energy 102 (2013) 327–335. [35] P. Schulten, The interaction between diesel engines, ship and propellers during maneuvering, Tech. rep., TU Delft. 19

[36] MAN, Project guides, L27-2 Marine, http://www.mandieselturbo. com/0001425/Products/Marine-Engines-and-Systems/Medium-Speed/ GenSets/Project-Guides.html. [37] MaK, M32c project guide, propulsion, http://marine.cat.com/cda/ files/953438/7/. [38] J. H. Holland, Genetic algorithms, Scientific American 267 (1992) 66–72. [39] A. Chepperfield, P. Flemming, H. Pohlheim, C. Fonseca, Genetic algorithm toolbox: for use with matlab, University of Sheffield - Department of Automatic Control and Systems Engineering. [40] D. C. Montgomery, Design and Analysis of Experiments, 8th Edition, John Wiley & Sons, Singapore, 2009. [41] W. Shi, H. T. Grimmelius, D. Stapersma, Analysis of ship propulsion system behaviour and the impact on fuel consumption, International Shipbuilding Progress 57 (2010) 35–64. [42] I. Dincer, M. Rosen, Exergy, 2nd Edition, Elsevier Ltd, 2013. [43] U. Larsen, L. Pierobon, F. Haglind, C. Gabrielii, Design and optimisation of organic rankine cycles for waste heat recovery in marine applications using the principles of natural selection, Energydoi:http://dx.doi.org/ 10.1016/j.energy.2013.03.021.

20

Paper III

Low Carbon Shipping Conference, London 2013

The influence of propulsion system design on the carbon footprint of different marine fuels Francesco Baldi*a, Selma Bengtssona, Karin Anderssona ªShipping and Marine Technology, Chalmers University of Technology, 412 96 Gothenburg, Sweden

Abstract Rising environmental awareness and stricter environmental regulations have increased the interest in new fuels and energy efficiency measures in the shipping industry. Different fuels have different physical and chemical properties that affect the performance of internal combustion engines, e.g. the efficiency, the exhaust gas emissions, and the potential for energy recovery. This has an impact on the potential propulsion efficiency as well as on the life cycle environmental performance. The aim of this study is therefore twofold. First, to assess the potential for optimising the energy use of the propulsion system dependent on fuel choice and second, to assess the overall life cycle global warming potential of the optimised systems. Three fuels are compared, heavy fuel oil (HFO), marine gas oil (MGO), and liquefied natural gas (LNG), in combination with two exhaust gas cleaning technologies, scrubbers and selective catalytic reduction (SCR) units. Data from one year of actual operation with a product tanker are used as a base for the optimization. The results show that the solution with the lowest fuel consumption and carbon footprint is a two-stroke engine with waste heat recovery (WHR) powered by LNG. The synthesis of an optimization procedure for the propulsion system and an LCA approach leads to very interesting results. The different carbon content of different fuels, together with methane slip, leads to a better estimation to the carbon footprint of different propulsion systems. On the other hand, a better insight of the differences between different propulsion arrangements allows performing a more accurate comparison between different fuels. The potential for WHR has a particularly relevant influence on the final result. Keywords: Propulsion system optimization, shipping, carbon footprint, marine fuels

1. Introduction The shipping sector is going to face, in the coming years, a number of decisive challenges, which are expected to modify ship design as it has not happened in the last decades. Rising bunker prices and low freight rates demand for increasing fuel efficiency; rising global warming awareness is starting to also affect the shipping sector, which despite its high transportation efficiency has become a significant contributor of greenhouse gases (Buhaug et al., 2009); finally, new environmental regulations, mainly related to the emissions of sulphur and nitrogen oxides (IMO, 2013a), will add complexity to the design and operation of merchant vessels. 1.1 Background In the preliminary phase of ship design, several different choices have to be made. In particular, decisions connected to the propulsion system will strongly affect the carbon footprint of the ship, and it is therefore of primary importance not to underestimate this phase. The selection of the fuel, the type of engine, and the propulsion arrangement should be performed with maximum care. The use of computer simulations for the identification of optimum designs has a long history of application in several fields of knowledge, not last that of engineering. In naval architecture, applications of computer models for energy systems can be dated as back as 1984 (DeTolla and Fleming, 1984), while the work from Dimopoulos et al. (2011) is just a more recent example of optimization procedures applied to ship propulsion systems. However, two aspects are very seldom treated in such scientific publications: the accounting of the operational profile, and the selection of the prime mover.

*

Corresponding author. Tel: +46-31-772-2615 Email address: [email protected]

1


As stressed by Motley et al., traditionally propulsion systems are designed to optimize performance at a single or few design points (Motley et al., 2012), while the importance of taking the whole operational profile into account was already highlighted by Dupuis and Neilson (1997). Only few examples of different approach are available in scientific literature; Dimopoulos et al. propose the optimization of a combined cycle for marine applications, subdividing ship operations in four well-defined conditions (Dimopoulos et al., 2011); Doulgeris et al. propose the optimization of a propulsion system considering a reference journey, with varying environmental conditions (Doulgeris et al., 2012); Sciberras and Norman, in their work about multi-objective optimization of hybrid propulsion systems refer to the use of extensive data on the operational profile; Motley et al. (2012) also propose a tentative for operational lifetime optimization of a propulsion system, focusing on propeller design. In the same line, the choice of the prime mover is often left outside of the scope of the work (Dimopoulos et al., 2011, Motley et al., 2012); Doulgeris et al. propose the choice between two alternative gas turbines ((Doulgeris et al., 2012); Sciberras and Norman, instead, use an extensive component database (Sciberras and Norman, 2012); this latter method is applied in the present study. From the technical point of view, when compared to traditional design practices, two new subjects are today on the agenda for addressing fuel consumption and environmental concerns: fuel choice and waste heat recovery (WHR) systems installation. As a consequence of new environmental regulations and high fuel prices, the choice of heavy fuel oil (HFO) as ship fuel is not anymore to be given for granted. Emission Control Area (ECA) emission limits can be met either with additional machinery on board (scrubbers, catalysts), or by employing a different fuel, such as marine gas oil (MGO), for compliance with SOX emissions, or liquefied natural gas (LNG), for compliance with both SOX and NOX emissions. On the other hand, WHR systems are getting largely on the agenda of shipyards and naval architects for the design of new ships; several installations can already be observed on-board existing vessels, and there are expectations for the number to grow rapidly in the future. WHR is a well-known technology which is extensively employed in land-based applications and it allows significant improvements in the efficiency of Diesel engines with using a proven technology and having a rather short payback time(Dimopoulos et al., 2011). It should be noted, however, that WHR systems do not come without any drawback. Lowering exhaust gas outlet temperature can lead to condensation of particles and sulphuric acids on the heat exchangers, thus leading to corrosion. Additional space and weight for the exhaust boiler, turbine, and condenser need to be allocated. Furthermore, the interaction of WHR systems with abatement technologies can lead to complex configurations. The implications of these aspects in the modelling will be discussed in sections 2.1 and 2.2. The evaluation of different design alternatives is often performed on the basis of energetic or economic principles. However, it is in the beliefs of the authors that environmental impact should be a major parameter to take into account in order to compare design alternatives, especially when the use of different fuels involves diversity both in direct and indirect carbon emissions. In order to evaluate the environmental performance of possible marine fuels life cycle assessment (LCA) can be used. LCA is a method where the environmental performance of products or services throughout the life cycle of the product or service is assessed, ideally including all processes in the life cycle from raw material acquisition to end-of-life disposal. Several LCAs of marine fuels have been published previously (Corbett and Winebrake, 2008, Winebrake et al., 2007, Bengtsson et al., 2011, Bengtsson et al., 2012, Bengtsson et al., 2013). Corbett and Winebrake (2008) and Winebrake et al. (2007) considered a specific trip with different engine load for different parts of the trip, while Bengtsson et al. (2011, 2013) only considered operation at sea with 85% engine load. Furthermore, Bengtsson et al. (2012) considered the energy use during one year of operation. The engine efficiency was assumed to be the same independently on fuel used in all studies. Nor did any of these studies try to optimize the propulsion system with regard to the fuel. If one fuel has a larger potential for gaining higher propulsion system efficiency than the others, this could have an impact on its environmental performance and should ideally be considered in an LCA.

2


1.2 Aim The aim of this paper is twofold. From one perspective, a procedure is proposed for the optimization of the choice of the propulsion system with regard to energy efficiency, taking into account the environmental constraints, and with respect to the choice of fuel, engine type, and possible application of WHR systems. To add one more dimension to the work the overall life cycle global warming potential (GWP) of the optimised systems is compared using an LCA approach, i.e. the carbon footprint.

2. Method The method here proposed for propulsion system optimization and GWP assessment is supposed to be applicable to ships of any kind and size. However, it will be presented applied to a specific case-study ship, as to make it easier to show its principles and potential. The case study of a product tanker with a deadweight of 45 000 tons will be used. In order to apply the propulsion system design optimization based on real operational data instead of a fixed design point, measured data from one year of operations of a similar ship were used in order to represent the expected ship behaviour. Data on propulsion and auxiliary power needs in kW are available. The inclusion of propeller design in the optimization process would involve additional complexity, and is left for future studies. The implications of the use of different propeller types and diameters, as well as the complex issues of engine-propeller coupling will therefore not be considered here. The use of a controllable pitch propeller, associated with a gearbox and a shaft generator for auxiliary power generation is assumed in every case, even if this corresponds to an important limitation in the process. The main engine load used in the calculations is presented in Figure 1. Three main operational patterns can be identified: high-load operation (peak at 90% load), when sailing at maximum speed, low-load operation (peak at 55% load), when sailing at economical speed, and port operations (peak at 20% load), when unloading the cargo

Figure 1. Frequency of occurrence at different engine loads

2.1 Environmental implications The ship is supposed to to sail into ECA areas. Hence, the chosen design must be able to fulfil ECA areas requirements, i.e. maximum of 0.1% sulphur in the fuel starting from 1st of January 2015 and Tier III NOX requirements for new buildings starting from 1st of January 2016.† Sulphur emission limits require additional technical arrangements depending on the fuel choice. MGO and LNG have sulphur content below the level required in ECAs. HFO, however, has much higher sulphur content in the fuel (average 2.51 wt. % in 2012 (IMO, 2013b)) that has to be taken care of. The use of a scrubber is assumed in this case, which allows for even high sulphur heavy fuels to respect ECA limits. The electricity needed for pumps and the backpressure imposed on the engine are

†

This regulation is under review by the International Maritime Organization (IMO).

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estimated to generate a fuel penalty between 1-3% of the engine capacity (Kjølholt et al., 2012). A 2% fuel penalty is added for scrubbers in this study. Limitations on NOX emissions require the installation of a SCR unit, apart from the case of 4-stroke LNG powered engines. While little literature is published on the influence of SCRs on engine performance, manufacturers often claim the absence of any fuel penalty (Hagström, 2013); no increased fuel consumption connected to the use of SCRs for NOX emissions reduction is therefore considered in this study. Furthermore, the effect on exhaust gas temperature is considered to be negligible. The carbon footprint implications of SCRs (direct CO2 emissions and the GHG impact of urea production and transportation) will be further explored in section 2.4. 2.2 Propulsion system optimization This paper’s first aim is that of describing a methodology to optimize the design of a propulsion system in the early stages of the design. A number of different variable design choices are proposed and analysed separately. The design choices concern: 1. The number and type of engines: starting from the experience of today existing installation, two possible arrangements were selected:  One 2-stroke engine  One or two 4-stroke engines 2. The availability of a waste heat recovery system 3. The primary fuel used for propulsion:  Heavy fuel oil (HFO)  Marine gas oil (MGO)  Liquefied natural gas (LNG) The combination of all the three possible design variables generates 12 possible cases. However, the case of 2-stroke engine, HFO fuelled, equipped with WHR system is not considered because of the technical difficulties in providing the good combination of high temperatures and sulphur concentration in the exhaust required by SCR operations (Magnusson et al., 2012). This leaves a total of 11 different cases to be separately optimized and compared. The authors have gathered a large dataset from the documentation of the main manufacturers active on the market of Diesel engines for marine propulsion. Data from each engine referred to engine power, efficiency, and exhaust gas properties (temperature and mass flow) for a number of operational points. For 2-stroke engines, data was available for a large number of operative points; hence, spline curves were deemed the most suitable regression technique. For 4-stroke engines data were available only for 3-5 operative points, and second degree polynomial regressions were used instead. For all types of engines the extrapolation of engine performances outside of the original load range was performed using a linear approximation, in order to avoid numerical “explosion” of efficiencies, temperatures, and flows, particularly at very low loads. Efficiency for all engines was corrected in order to account for the typical 5% tolerance allowed under ISO conditions; furthermore, ISO corrections for the addition of engine-driven pumps and for more realistic ambient conditions were applied. The range of total installed power was set between 7500 and 10000 kW, where the original installed power was equal to 7700 kW. This limited the amount of possible propulsion trains to:  18 possibilities for 2-stroke engines  14 possibilities for 2-stroke dual fuel engines  143 possibilities for 4-stroke engines  8 possibilities for 4-stroke dual fuel engines The relatively low number of total runs (183 runs) allowed testing all possible configurations, therefore avoiding any numerical uncertainty in the results. The computational time for the overall evaluation process is 30 seconds on an Intel(R) Core(TM) i7-2620M CPU at 2.7 GHz processor. The WHR system is accounted for as a net, free contribution to on-board power production. Exhaust gas temperature and flow are interpolated as a function of load for each engine starting from available 4


data, thus allowing calculating the energy content in the exhaust, which is considered in this work to be the input to the WHR system. Despite the existence of several proposals for WHR systems also harvesting other sources of waste heat, such as charge air cooling (Teng et al., 2007), only the exhaust will be considered in this study. In order to account for variations in energy quality connected to exhaust temperature, the concepts of exergy is used. Exhaust exergy flows are evaluated, and the power available from WHR system is finally calculated assuming a fixed value for the recovery cycle’s exergy efficiency. This approach allows making an assumption of how much the system approaches the ideal, which can be considered a constant for all propulsion system scenarios (Dincer and Rosen, 2013). Values up to 60% can be reached by optimized ORCs (Larsen et al., 2013). However, for the present study, a more traditional single pressure steam based cycle is assumed, as it represents more closely the state-of-the-art in the shipping industry. A reference value of 40% exergy efficiency, consistent with the design proposed by Theotokatos and Livanos (2013), will be used. The implications of using a WHR system in the design are strongly connected to both the choice of the prime mover (2-stroke or 4-stroke engines) and of the fuel. The implications of fuel choice are mostly related to the environmental impact of the propulsion system, and in particular with its compliance to regulations on the emissions of NOX and SOX. Fuel choice also has a strong influence on the efficiency of the propulsion system when the installation of a WHR system is taken into account. A non-negligible amount of sulphur is contained in HFO and MGO. For this reason, sulphur oxides and, as a consequence, sulphuric acid will still be generated after the combustion process. In order to prevent sulphuric acid condensation on the heat exchangers and on the walls of the funnel exhaust gas temperature is not allowed to drop below 150 °C. This limit can be discarded when LNG is used as fuel, as it usually contains less than 10ppm sulphur by mass. The handling of high-viscosity HFO also adds to an additional heat requirement, which was assumed equal to 300 kW regardless the load of the engine, and needs to be produced using exhaust gas heat. 2.3 Analysis of the GHG potential of the optimized designs: an LCA approach It is important to consider the whole life cycle when comparing the carbon footprint of the optimised designs. Carbon footprint is a limited life cycle assessment considering only the emissions of greenhouse gases in the life cycle (Weidema et al., 2008). The carbon footprint of the optimised designs will be compared by calculating the global warming potential at a 100 year time perspective (GWP), considering emissions of CO2, CH4 (1 g CH4=25 g CO2-equvivalent) and N2O (1 g N2O=298 g CO2-equivalent) (IPCC, 2007). The whole fuel life cycle is considered starting from raw material acquisition, i.e. crude oil or natural gas, followed by fuel production, distribution and finally combustion in the marine engines. However, the manufacturing, maintenance and scrapping of the vessel is not included. Nor is the manufacturing of the exhaust abatement technologies. The greenhouse emissions and energy use during the manufacturing of the ship has been shown to be very low, less than 3%, when considering the life cycle of the vessel according to Johnsen and MagerholmFet (1998) and Walsh and Bows (2011). Data for the raw material acquisition, fuel production and fuel distribution for the fuels are from Bengtsson et al. (2011). These data are representative for shipping in the northern part of Europe. Two distribution alternatives for LNG are included in Bengtsson et al. (2011), i.e. from the North Sea or from Qatar. Data for natural gas transported from the North Sea is considered in this comparison. Ammonia is used as a reducing agent in the SCR, normally supplied by a water solution of urea. The data for urea production and distribution are taken from Andersson and Winnes (2011). The emission of CO2 from the use of urea is included in the data for production of urea (Davis and Haglund, 1999). The estimates for the needed amount of urea is calculated based on data for the NOX emissions from existing engines (Bäckström, 2010, NTM, 2008) and the amount of urea needed to reduce the NOX emissions to the Tier III level Is calculated based on the assumption that in order to reduce the NOX emissions with 2 moles, 1 mole of urea needs to be added to the SCR unit. The CO2 emissions from the fuel combustion are calculated from the carbon content in the fuel assuming that 99.5% of the fuel forms carbon dioxide. Fuel properties for MGO and HFO are average data reported in scientific journals (Fridell et al., 2008, Cooper, 2003, Cooper, 2005, Winnes and Fridell, 2009, Winnes and Fridell, 2010), while the fuel properties for Norwegian LNG are from

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Edwards et al. (2011). The emissions of methane are taken from NTM (2008) for MGO and HFO (both for 4-stroke and 2-stroke engines), from Bäckström (2010) for 2-stroke LNG engines, and from Wärtsilä published by Bengtsson et al. (2011) and from Nielsen and Stenersen (2010) for 4-stroke LNG engines . The methane emissions from the dual fuel engines are uncertain and very high emissions, i.e. averaging 8 wt. % of the fuel, is reported for the first generation of 4-stroke dual fuel engines (Nielsen and Stenersen, 2010). A 4 wt. % methane slip for the 4-stroke dual fuel engine is considered as a base in the calculations. The emissions of nitrous oxide for the existing engines are taken from Cooper and Gustafsson (2004). Information regarding emissions of nitrous oxide from the gas engines has not been found. The data used is presented in Table 1. Table 1. Data used for the carbon footprint calculations. HFO (1 MJ) MGO (1 MJ) LNG (1 MJ) Urea (1 g) Well-to-tank Primary energy use (MJ) Emissions of CO2 (g) Emissions of CH4 (g) Emissions of N2O (g) Tank-to-propeller Urea consumption (2-stroke) (g) Urea consumption (4-stroke) (g) Emissions of CO2 (g) Emissions of CH4 (2-stroke) (g) Emissions of CH4 (4-stroke) (g) Emissions of N2O (2-stroke) (g) Emissions of N2O (4-stroke) (g)

1.09 6.68a 0.073 0.0002

1.16 7.02b 0.078 0.0002

1.1 6.97 0.046 0.0036

0.03 1.99 0.0021 -

1.19 0.835 77.2 0.000756 0.000463 0.00388 0.00352

1.10 0.835 73.1 0.000744 0.000465 0.0039 0.00352

1.06 0 54.4 0.04609 0.816c -

-

a

The open-loop sea water scrubbers discharge the scrubber water in the open sea, thereby indirectly releasing CO2 to the atmosphere. Approximately 2 moles of CO 2 is formed for every mole of SO2 released. This would increase the CO2 emissions from use of scrubbers with approximately 1.5 g/MJ HFO combusted and is not included in the LCA. b Increased energy use in the refineries for producing MGO after 2015 is not included in Bengtsson et al. (2011). However, the CO 2 emissions are estimated to increase with approximately 7 g/MJ MGO based on Avis and Birch (2009), when allocating the total increase in CO2 to the production of MGO. b This parameter have been varied between 0.28 g/MJ and 1.63 g/MJ representing between 1.4 and 8 wt. % methane slip.

3. Results Here follow the results for the propulsion system optimization as well as the carbon footprint calculations. 3.1 Propulsion systems A total of 183 design alternatives were evaluated and compared. In order to validate the results, the modelled consumption was tested versus the measured one using the data corresponding to the arrangement of the original ship. This showed reasonable accuracy, as calculated consumption differs from measured consumption by 5%, which could be explained by engine wear and non-optimal operating conditions. Moreover, measurement accuracy derived from fuel meter and shaft power meter is equal to around 5%, meaning that the two results are not significantly different. Results for the total fuel consumption over one year of operation (valid only for the main engines) are shown in Figure 2, displayed as TJ/year. Values are given for each fuel choice, and for each type of engine selected. Two values are showed for each combination, referring to the case with and without the installation of WHR systems, apart from the case of 2-stroke propulsion arrangement with HFO as a fuel, where technical limitations do not allow the application of WHR. The horizontal red line refers to the original arrangement for the selected ship (hereafter referred to as “reference case”). For each scenario “range bars” are also displayed, representing the average, maximum and minimum results for each possible arrangement. It should be noted however that only the best point was used for the further analysis. The best selected arrangement for all 2-stroke cases coincided with the most efficient engine at design point, as is also showed in Table 2. In the case of 4-stroke arrangements, instead, a father-and-son arrangement proved to be the best efficient in all cases, with a large, efficient engine (rated power of 7200 kW in HFO and MGO cases, 5850 kW in the LNG case) supported by a smaller engine (1020 kW

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and 2700 kW) at low and peak loads. The benefit of operating in off-design conditions for shorter time in the case of 4-stroke, dual fuel engines is justified by the lower efficiency of these engines at lowload operations, as they operate according to a Otto-cycle (Aesoy et al., 2011).

Figure 2. Energy consumption from one year of operation for the presented cases.

One of the aims of this paper was that of estimating the relevance of different details related to the design of the propulsion system when evaluating the carbon footprint of the selected ship, both in absolute and relative terms for comparison between fuels and arrangements. In order to compare the influence of the different aspects, Table 2 shows, for each case, the modifications observable in the yearly energy consumption when adding more detail to the modelling, where:  Level 1: Constant engine load, constant engine efficiency assumed based on existing data (engine load fixed at 75% according to EEDI calculation; engine efficiency assumed from Buhaug et al. (2009) equal to 170 g/kWh for 2-stroke engines and 190 g/kWh for 4-stroke engines).  Level 2: Real operational load, constant engine efficiency based on the most efficient arrangement at design conditions.  Level 3: Real operational load, load-dependent engine efficiency based on the most efficient arrangement at design conditions.  Level 4: Real operational load, load-dependent engine efficiency based on the most efficient arrangement taking the operational cycle into account.  Level 5: Real operational load, load-dependent engine efficiency based on the most efficient arrangement taking the operational cycle into account, with WHR. Table 2. Energy consumption in TJ for one year of operation and for different levels of modeling detail.

Level 1 Level 2 Level 3 Level 4 Level 5

2-stroke engine HFO MGO 223 151(-33%) 148(-34%) 166(9.9%) 163(10.1%) 166(0.00%) 163(0.00%) 162(-2.4%) 157(-3.7%)

LNG 156(-30%) 164(5.1%) 164(0.00%) 150(-8.0%)

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4-stroke engine HFO MGO 249 168(-33%) 165(-34%) 178(6.0%) 175(6.1%) 177(-0.56%) 174(-0.57%) 164(-7.3%) 159(-8.6%)

LNG 164(-34%) 179(9.1%) 176(-1.7%) 156(-11%)


3.2 Carbon footprint The carbon footprints of the optimised systems are presented in Figure 2. The case with the overall lowest carbon footprint is LNG with 2-stroke engines combined with WHR. The 4-stroke dual fuel engines have a significant higher carbon footprint caused by a large methane slip from the engine during combustion. The 4-stroke dual fuel engine cases vary from having the third and fourth lowest carbon footprint to the highest dependent on the estimates of the methane slip. The largest contribution to the carbon footprint originates from the combustion of fuels in the marine engines (between 84-88%). Acquisition of raw material, fuel production and distribution stands for between 10-13% while urea production and transportation stands for between 0-3% of the carbon footprint.

Figure 3. The carbon footprint of the optimised systems divided on the contributing greenhouse gases and the different phases in the life cycle. The range for the 4-stroke dual fuel engines represents the difference between a methane slip of 1.4 and 8 wt. % during combustion of LNG. Results with and without WHR are presented for all alternative except for the alternative with HFO and 2-stroke engines. The second bar for each engine arrangement represents the system with WHR.

Table 1Table 3 gives an overview of the result comparing the propulsion system energy use and the carbon footprint for the selected cases compared to the reference case, representing the modelled energy use with today’s propulsion system on the product tanker. The propulsion system energy use is reduced by 28% while the carbon footprint is reduced by 36% for the case with a 2-stroke LNG engine with WHR. Table 3. Comparison of the propulsion energy use and the carbon footprint for the selected cases. The difference compared to the reference case (182 TJ) is presented in parenthesis.

HFO 2-stroke HFO 4-stroke HFO 4-stroke with WHR MGO 2-stroke MGO 2-stroke with WHR MGO 4-stroke MGO 4-stroke with WHR LNG 2-stroke LNG 2-stroke with WHR LNG 4-stroke LNG 4-stroke with WHR

Propulsion energy use (TJ/year) 166 (91%) 177 (97%) 164 (90%) 163 (90%) 157 (86%) 174 (96%) 159 (87%) 164 (90%) 150 (82%) 176 (97%) 156 (86%)

Carbon footprint (tonne CO2-eq./y) 14800 (91%) 15700 (97%) 14500 (89%) 13900 (86%) 13400 (82%) 14700 (90%) 13500 (83%) 11300 (70%) 10400 (64%) 14800 (91%) 13100 (81%)

4. Discussion The discussion is divided in three parts discussion on the results, on the method and on potential future work.

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4.1 Result discussion The analysis of Figure 1 shows that 2-stroke dual fuel engines are the most efficient solution from a purely energetic perspective. The 4-stroke engine solution (which is also the current arrangement for the existing ship) is a competitive alternative only when a WHR system is in place, as 4-stroke engines generally feature higher exhaust temperature, when only direct fuel consumption is accounted for. The observation of the results for the carbon footprint, however, has a strong influence on the evaluation. On the one hand, the lower carbon content in the fuel and the very low sulphur content makes 2-stroke LNG-powered solutions even more convenient (carbon footprint is reduced by 36% compared to the reference case when WHR is used). On the other hand, taking the methane slip into account makes the LNG 4-stroke solution much less viable, even if this result is strongly affected by the uncertainty in the data. The observation of the range bars shows the size of the variation in the results, thus confirming the additional value of implementing a propulsion system optimization algorithm in order to evaluate the overall yearly consumption. The variation between the two extremes of the range varies between 6% and 10% depending on the case, while the standard deviation lies between 1.5% and 3%. The utilization of a complete operational profile for the evaluation of the propulsion arrangement instead of a single operational point only has an influence when a multi-engine arrangement is employed. In fact, for all 4-stroke engine cases, multi-engine solutions prove being the most efficient, regardless the fuel. The advantage compared to the use of the most efficient engine alone is rather small in the HFO and MGO cases (0.7% improvement), but larger in the LNG case (1.7% improvement). The presence of WHR has no influence on the choice of the engine, regardless the case. The small amount of power produced by the WHR system does not justify the choice of an engine with lower efficiency but, for instance, higher exhaust temperature. However, as observable in Figure 1, taking WHR systems into account can significantly decrease the overall consumption (between 2.4% and 6.7%, with the assumed recovery efficiency). The propulsion system optimization has shown to have a significant impact on the life cycle GWP. The propulsion energy needed spans between 150-177 TJ per year depending on the selected designs. The increased potential for WHR for LNG compared to HFO and MGO is important and should be considered when evaluating the environmental performance of LNG for future marine propulsion. The carbon footprint when using 2-stroke or 4-stoke dual fuel engines was significantly different, showing that the propulsion system should not be neglected when calculating the carbon footprint. From an engine optimization perspective, this approach provides additional insight about the climate impact of the propulsion system. It is here shown that the energy use and the carbon footprint are not directly proportional, as the carbon content is different between the different fuels and the non-CO2 emissions contributing to global warming are different dependent on the different propulsion systems, especially in the case of dual fuel engines. From an LCA perspective it is possible to gain insight of the potential for energy efficiency improvement for the different fuels, e.g. the LNG propulsion systems can extract more energy from a WHR system due to the very low sulphur content. We suggest that this approach could contribute to a more complete understanding of the propulsion systems available and their pros and cons. This approach could therefore be used to provide decision support when selecting propulsion systems for new vessels.

4.2 Method discussion The implications of the results presented in this paper would not be complete without a discussion of the methodology employed in this paper, and specifically of the different assumptions that had to be made. Data for different engines were obtained from publicly available technical documents from different engine manufacturers. Data for 2-stroke engines from MAN were very extensive, with detailed fuel consumption, exhaust mass flow and temperature for 14 different load points. The same cannot be said, unfortunately, for data concerning 4-stroke engines, obtained from Wärtsilä and MaK: in these cases, in fact, data points were available for only 3 to 5 different loads, which involves less accurate approximations in the mathematical regression. For all engines, in addition, the variations generated by the possibility of influencing cylinder loading, valve timing, and turbocharger matching were

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neglected. The availability of better and more extensive data would definitely improve the quality of the approximation. The installation of the SCR was also taken into account, with no fuel penalty. There is some controversy on this point, as manufacturers claim no fuel penalty (Hagström, 2013) while some tests have shown the opposite (Subramaniam et al., 2011). Moreover, the presence of an SCR would allow a much more efficiency-driven engine tuning, as Tier II engines normally comply with NO X emissions regulations by reducing combustion pressures and temperatures and, thus, engine thermodynamic efficiency. No study is known to the authors that could show the effect of the combination of the two different phenomena. Similarly, the approximation employed in this study in order to account for the scrubber power needs is quite strong, and, in presence of more extensive data, could be further improved. The result of an LCA study depends on the goal and scope of the study and the data used in this paper are specific for operation in the northern part of Europe considering marine transportation in 20152020. Ideally, the data used in the LCA should be specific for the region and the time in which the new ship will operate. In this study the geographical operation and time scale are not defined. However, the part of the life cycle that contributes the most to the carbon footprint is the combustion of fuel in the tank-to-propeller phase and this data is specific for the different types of engines used. The second biggest contributor to the carbon footprint is the fuel production phase. This data may vary between different production facilities, but still when considering future operation it can be expected that stateof-the-art production today independent on geographical scope can be representative. It is highlighted that the methane slip from the dual fuel engines has a significant impact on the carbon footprint. The uncertainty on this value is very high and needs to be verified with actual on-board measurements during operation. The only data available today are from engine manufacturers and a few measurements on vessels operating in Norway (Nielsen and Stenersen, 2010). 2-stoke dual fuel engines are operating according to the diesel process and have a much lower methane slip than the 4stroke lean burn dual fuel engines included in this analysis. It is also possible to use 4-stroke dual fuel engines operating according to diesel process; this would result in lower methane slip and higher NOX emissions, which is the case for the 2-stroke LNG engines. Another possibility may also be to use oxidation catalysts in order to reduce the CH4 emissions. Furthermore, there are no emission factors for nitrous oxides for the dual fuel engines.

4.3 Future work This study opens for a number of alternatives for future work. As mentioned in the description of the method employed in this study, leaving the propeller out of the optimization procedure constitutes a very strong approximation, which is hardly justifiable in practical terms. In particular, 2-stroke engines performance would then be even higher, as no gearbox losses are involved in a directly coupled arrangement between engine and propeller. Moreover, engine speed should then be taken into account, as it strongly influences propeller efficiency, and vice versa. As an additional consequence of this simplification, auxiliary needs (though rather limited in the case study taken into account), are assumed to be a part of the main engine load, as the current arrangement is provided with a shaft generator. This is definitely a viable alternative, but accounting of different possible designs for auxiliary power generation would be an improvement to the quality of the results. The methane emission from the combustion of natural gas in marine engines is shown to have a great impact on the overall result. The methane slip is markedly load-dependent and could potentially be as high as 15 wt. % at low engine loads (Nielsen and Stenersen, 2010). Hence, the availability of loaddependent methane slip emissions would be an additional improvement to the calculation of the greenhouse gas emissions from combustion. The approach of combining a propulsion system optimization with carbon footprint calculations gives information on two important aspects when selecting the propulsion system in the design of a new ship. It would, however, be possible to combine the engine optimization with a full LCA and not only the carbon footprint and thereby providing information about a number of different environmental impacts.

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As have been mentioned before a number of LCA studies on marine fuels have been published and data from these could rather easily be combined with data from engine optimizations. When performing a complete LCA there are more emissions from combustion that could be calculated based one the operational profile of the ship, e.g. nitrogen oxides, carbon monoxide and particles; thus, making the result even more accurate. When making a decision of which propulsion system to choose there are more parameters that are important to consider then only the energy use and the environmental impacts, e.g. the cost of the system, the reliability and ease to operate, the knowledge about the system and so on. This could, for example, be considered in a multi criteria decision analysis.

5. Conclusions In this paper we have presented a combination of a design optimization methodology with a carbon footprint analysis which could be used as decision support in the design process for new ships. The most efficient propulsion system was selected for a number of different engine types and fuels, taking into account a real operational pattern, referred to one year of operation of a similar existing ship. This procedure showed to improve the efficiency of the design when 4-stroke engines are employed, especially with LNG as fuel. The 2-stroke dual fuel engine, equipped with a WHR system, proved to be the arrangement with the lowest carbon footprint. The WHR system proved to significantly improve the overall performance. The 4-stroke dual fuel engine solution, even though showing promising performance from an energy perspective, loses appeal when methane slip is also taken into account. When compared to traditional LCA studies, this work showed the importance of the selection of the propulsion system, as compared to the assumption of a common efficiency for all engines, in particular when off-design performance is taken into account.

Acknowledgments This work is the synthesis between two distinct projects, “life cycle assessment of marine fuels” and “energy systems modelling in shipping”. The authors want therefore to acknowledge the contribution of the financers of the studies, respectively Vinnova and the Swedish Energy Agency.

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