Dry bulk terminal efficiency under changing input factors

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2628 CD D the Netherl Phone Fax www.mtt.tu Delft lands + + udelft.nl +31 (0)15-278 +31 (0)15-27882889 81397

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Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of

the EMO terminal

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Presented here is the result of my graduation research for the study Transportation, Infrastructure and Logistics at Delft University of Technology. It was realized in cooperation with the operational department of the EMO dry bulk terminal in Rotterdam.

I would like to thank Teus van Vianen, Daniël Mooijman, Hans van Ham and prof. Lodewijks for their support and valuable insights during my research. Also, the planning department of EMO has supported me greatly. They are a resource I haven’t consulted enough, both as professional and as colleagues. Finally, I thank my family, friends and girlfriend. They are always supportive in demanding times and stressful periods. Delft, June 2013 Thijs Vlaar

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REFACE

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Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal

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the EMO terminal

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Around the world, dry bulk import terminals facilitate the transhipment of large intercontinental flows to smaller inland flows of coal and iron ore. The Europees Massagoed Overslagbedrijf (EMO) is the largest terminal of this kind in Europe, with a supply of around 30 MT of coal and iron ore per year. EMO handles vessels from Panamax up to the largest dry bulk carriers presently trading and is a vital part of the supply chain of iron ore and coal for the European steel and electricity industries.

In the future, changes are expected in the supply volumes of coal and iron ore. Also, a new range of Very Large Ore Carriers (VLOC’s) was recently introduced. The question arises if such changes, over which EMO has little or no control, affect the efficiency of the terminal in any way. It is currently unknown which of these external or input factors affect the terminal efficiency, and to what extent.

To provide this knowledge, the following research question was formulated:

What input factors affect terminal efficiency and what are the quantitative effects on terminal efficiency when these factors change

The research was scoped to include the efficiency of the quayside, as well as the storage yard. The quayside can be seen as a queuing system, where the quay cranes are the servers. The service rate, or crane unloading rate, is an important factor in the efficiency of the quayside. Higher unloading rates mean more throughput of material with the same resources.

An initial selection of input factors was made based on the analysis of the EMO dry bulk terminals processes and equipment. This list was then used as a basis for the analysis of a historic dataset of ship and load properties and the realized unloading rate. Using a linear regression model, the factors that significantly affect the

S

UMMARY

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unloading rate were determined. With these factors a non‐linear model was made that is more anchored in theory and literature.

Only two significant input factors were found to affect the unloading rate, ships deadweight and the load being coking coal. Bigger ships are unloaded at a faster unloading rate [thr‐1], with an elasticity of 0.18, in line with literature values found. Ships carrying coking coal were found to have about 15% lower unloading rates due to the stickiness of the material and associated longer trimming stage. Different fleets are used to carry steam coal and iron ore. Iron ore is therefore on average unloaded at a faster rate than steam coal, but this is basically a ship size effect.

The unloading rate formula was used in a discrete event simulation model of the terminal to determine the effects on the entire queuing system. Scenarios based on an extensive throughput study for the Hamburg‐Le‐Havre range (Western‐Europe) were evaluated using this model. For the range of commodity mixes and ship size differences tested, only marginal changes in quayside system efficiency were found.

For the storage yard, the surface densities for coal and iron ore were determined using a new view on material reclaiming. Piles always occupy a certain area, regardless of the amount of material. Basically only the height of the pile is thought to change. Using this new view, historic storage yard data of EMO was used in a Monte Carlo simulation setting to come to a surface density estimate for coal and iron ore. The values found were found to be valid.

Using the same commodity mix scenarios used for the quayside, the surface density [tm‐2y‐2] for the storage yard was found to only marginally change with the commodity mix. The storage time is a much bigger determinant. With storage times increasing, the efficiency of the storage yard in terms of surface density can rapidly decrease. This means lower throughputs can be realized using the same amount of storage area. Investments in more storage area are then needed if higher throughputs are aspired in the future.

Overall, the efficiency of the quayside system and storage yard was found to only be marginally affected by changes in relevant input factors within the bounds of future predictions. Only the storage time developments call for closer attention by terminal management as this factor can have large effects on the storage yard efficiency.

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the EMO terminal

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Over de hele wereld faciliteren drogebulkterminals de overslag van grote intercontinentale stromen kolen en ijzererts naar kleinere binnenlandse stromen. Het Europees Massagoed Overslagbedrijf (EMO) is de grootste terminal van zijn soort in Europa, met een totale aanvoer van 30 MT kolen en ijzererts per jaar. EMO ontvangt schepen van panamax tot de grootste drogebulkschepen en is een vitaal onderdeel van de supply chain van kolen en ijzererts voor de Europese staal‐ en elektriciteitsinductie.

Voor de toekomst wordt verwacht dat de aanvoervolumes van kolen en ijzererts gaan veranderen. Verder is recentelijk een nieuwe lijn Very Large Ore Carriers (VLOC’s) in de vaart genomen. De vraag rijst of zulke veranderingen, waarover EMO weinig tot geen controle heeft, op enige manier invloed hebben op de efficiëntie van de terminal. Op dit moment is het onbekend welke van deze externe factoren of inputfactoren effect heeft op de terminalefficiëntie, en in welke mate.

Om in deze kennis te voorzien, is de volgende onderzoeksvraag opgesteld:

Welke inputfactoren hebben effect op de terminalefficiëntie, en wat zijn de kwantitatieve effecten op de terminalefficiëntie als deze factoren veranderen Het onderzoek is afgebakend op de efficiëntie van de kadezijde en het opslagveld. De kadezijde kan worden gezien als een wachtrijsysteem, waar de kadekranen de servers zijn. De service rate, of lossnelheid van de kranen, is een belangrijke factor voor de efficiëntie van het kadesysteem. Hogere lossnelheden betekent meer doorvoer van materiaal met dezelfde resources.

Een eerste selectie van inputfactoren is gemaakt, gebaseerd een analyse van de processen en machines op de EMO drogebulkterminal. Deze lijst is gebruikt als basis voor de analyse van historische data van lossnelheden met scheeps‐ en ladingeigenschappen. Een selectie van significante factoren is gemaakt door middel

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AMENVATTING

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Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal van een lineair regressiemodel. Met deze gegevens is een niet‐lineair model gemaakt dat meer is verankerd in theorie en literatuur.

Maar twee factoren bleken significante invloed te hebben op de lossnelheid, de deadweight van schepen en schepen geladen met cokes‐kolen. Grotere schepen kunnen sneller worden gelost [thr‐1] met een elasticiteit van 0.18. Dit is in lijn met waarden gevonden in literatuur. Voor schepen welke cokes‐kolen vervoeren worden 15% lagere lossnelheden gevonden. Oorzaak hiervan is de plakkerigheid van het materiaal en de daaraan gekoppelde langere trimming‐stage. Door het verschil in de gebruikte vloten, wordt ijzererts wel sneller gelost dan kolen. Dit is echter puur een scheepsgrootte‐effect.

De gevonden formule voor lossnelheid is gebruikt in een discreet‐event simulatiemodel van de haven om de effecten op het gehele kadesysteem te onderzoeken. Scenario’s gebaseerd op een uitgebreide doorvoerstudie voor de Hamburg‐Le Havre‐regio (West Europa) zijn met dit model geëvalueerd. Voor de verschillende aanvoermixscenario’s en scheepsgrootte‐scenario’s werden slechts marginale effecten op de efficiëntie van het kadesysteem gevonden. Voor het opslagveld zijn de oppervlaktedichtheden voor kolen en ijzererts bepaald middels een nieuwe kijk op afgraven van hopen. Hopen worden verondersteld altijd een bepaalde oppervlakte te bezetten, ongeacht hoeveel materiaal er daadwerkelijk ligt. Alleen de hoogte van een hoop wordt geacht te veranderen. Met deze nieuwe methode zijn de oppervlaktedichtheden van kolen en ijzererts geschat op basis van historische data door middel van Monte Carlo simulatie. Deze waarden zijn getest op validiteit.

Met dezelfde aanvoermixscenario’s die gebruikt zijn voor de kadezijde, blijkt de opslagfactor [tm‐2y‐1] nauwelijks te veranderen met een veranderende aanvoermix. De opslagtijd is een veel belangrijker factor. Met toenemende opslagtijden, kan de efficiëntie van het opslagveld in termen van opslagfactor snel afnemen. Dit betekend dat er minder hoge doorvoeren kunnen worden gerealiseerd met dezelfde hoeveelheid opslagruimte. Investeringen in meer opslagruimte zijn dan nodig als hogere doorvoeren in de toekomst gewenst zijn.

In het algemeen blijkt de efficiëntie van het terminalsysteem slechts marginaal afhankelijk van veranderingen in relevante inputfactoren binnen de grenzen van de toekomstige voorspellingen. Alleen de ontwikkelingen met betrekking tot de opslagtijd vragen nadere aandacht van het terminalmanagement, want deze factor kan grote effecten hebben op de efficiëntie van het opslagveld

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ry bulk termina e EMO termina Preface ... Summary .. Samenvatt Table of co Introdu 1 1.1 Re 1.2 O Dry bu 2 2.1 Th 2.1.1 2.2 Fu Equipm 3 3.1 D 3.2 D 3.2.1 3.2.2 3.2.3 3.2.4 3.3 Te

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OF CONT

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TENTS

put factors; an ... ... ... ... ine ... d research q ... minal function the global co ron ore supp oal and iron o he EMO term ry bulk term al system ... ss ... ... ... umptions ... fficiency ... historical analy ... ... ... ... ... uestion ... ... n ... oal and iron ply chain ... ore supply ch minal ... inal ... ... ... ... ... ... ... ysis and modelli ... ... ... ... ... ... ... ... ore supply c ... hain ... ... ... ... ... ... ... ... ... ling study of ... i ... iii ... v ... vii ... 1 ... 3 ... 5 ... 7 chain ... 7 ... 8 ... 11 ... 13 ... 14 ... 16 ... 17 ... 17 ... 18 ... 19 ... 19

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Dry bulk term the EMO term 3.4 3.4 3.4 3.5 An 4 4.1 4.1 4.1 4.2 4.2 4.2 4.2 4.2 4.3 4.3 4.3 4.4 4.4 4.4 4.4 4.5 4.5 4.5 4.6 Inp 5 5.1 5.1 5.1 5.1 5.1 5.2 5.2 5.3 minal efficiency minal input facto 4.1 Unloa 4.2 Stocky Summary . unloading ra Methodolo 1.1 Param 1.2 Impro Predictor e 2.1 Unloa 2.2 Predic 2.3 Regre 2.4 Predic Improved r 3.1 Relati 3.2 Impro Regression 4.1 Analy 4.2 Cross‐ 4.3 EMO Discussion 5.1 Datas 5.2 Rema Conclusion ut paramete Characteri 1.1 Classi 1.2 Globa 1.3 Devel 1.4 Gener Commodit 2.1 Globa Developme under changing ors; input par ading rate; in yard; input p ... ate estimatio ogy ... meter exclusi oved model .. elimination .. ading rate da ctor selectio ession model ctor selectio regression m ons between oved model o n model valid sis of the res ‐validation o unloading ra ... set ... ining varianc ns ... ers, historic t stics of ships fication of d al developme opments ob ral remarks o y mix develo al developme ents in stora g input factors; rameter sele nput parame parameters a ... on model ... ... ion using line ... ... ataset ... n ... l overview ... n results ... model ... n remaining overview ... dation ... siduals (rand on 2009 data ate estimates ... ... ce and expla ... trends and sc s ... ry bulk ships ents in ship s bserved in fle on fleet deve opments ... ents in the d ge time ... ; an historical a ection ... ters affectin affecting effi ... ... ... ear regressio ... ... ... ... ... ... ... parameters ... ... dom errors ε) a ... s ... ... ... anatory powe ... cenario deve ... s ... sizes ... eet calling at elopments ... ... ry bulk comm ... nalysis and mo ... g efficiency . ciency ... ... ... ... on ... ... ... ... ... ... ... ... ... ... ... ) ... ... ... ... ... er ... ... elopment ... ... ... ... EMO ... ... ... modity mix .. ... odelling study of ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... f 20 20 22 23 25 26 26 28 28 28 30 32 32 34 34 36 37 37 38 38 39 39 39 40 43 43 43 44 45 46 47 47 49

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Dr the ry bulk termina e EMO termina 5.4.1 5.4.2 5.5 Co Quaysi 6 6.1 in 6.1.1 6.1.2 6.2 Si 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5 6.2.6 6.3 Ve 6.3.1 6.3.2 6.3.3 6.4 Ex 6.5 Re 6.5.1 6.5.2 6.5.3 6.6 D 6.7 Co Storag 7 7.1 Ch 7.1.1 7.2 St 7.2.1 7.2.2 l efficiency und al Total ter Ship size onclusion .... ide modellin ntroduction . Method . Performa mulation mo Simulatio Function Conceptu Planner; stochasti Determin erification an Model ve Model va Conclusio xperimental esults ... Commod Ship size Evaluatio iscussion ... onclusions .. e modelling haracterizing The stora torage yard e A model Conceptu der changing inp minal throug developmen ... g ... ... ... ance indicato odel ... on method: T al demands . ual model .... control elem ic relations .. ning the theo nd validation erification .... alidation ... ons ... plan ... ... dity mix ... ... on of the De ... ... ... g the storage age system in efficiency; a for storage s ual efficiency put factors; an ghput and co nts ... ... ... ... ... or ... ... TOMAS Delp ... ... ments and as ... oretical max n ... ... ... ... ... ... ... ... Langen scen ... ... ... e system ... n broader pe storage fact space efficie y model base historical analy ommodity m ... ... ... ... ... ... ... phi ... ... ... ssumptions .. ... imum throug ... ... ... ... ... ... ... ... narios ... ... ... ... ... erspective .... or model ... ncy ... ed on single ysis and modelli ix ... ... ... ... ... ... ... ... ... ... ... ... ... ghput ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... pile inventor ling study of ... 50 ... 52 ... 53 ... 55 ... 55 ... 56 ... 57 ... 57 ... 57 ... 58 ... 58 ... 60 ... 61 ... 63 ... 63 ... 64 ... 67 ... 70 ... 70 ... 71 ... 71 ... 73 ... 74 ... 76 ... 76 ... 78 ... 78 ... 79 ... 81 ... 81 ry level .. 81

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Dry bulk term the EMO term 7.3 7.3 7.3 7.3 7.4 7.4 7.4 7.5 7.6 7.6 7.6 7.6 7.7 Con 8 Rec 9 Referen Append Append Append Append Append minal efficiency minal Surface de 3.1 Deter 3.2 Deter 3.3 Mont Input scen 4.1 Comm 4.2 Storag Verificatio Results ... 6.1 Comm 6.2 Storag 6.3 Storag Conclusion nclusions ... commendatio nces ... dix A Param dix B Resid dix C Ship s dix D Trace dix E Statis under changing nsity simulat rmining maxi rmining avera e Carlo simu arios ... modity mix a ge Time ... n ... ... modity mix sc ge time scen ge area for D ns ... ... ons ... ... meter elimina uals plots fo size developm outputs for tical validati g input factors; tion ... imum pile siz age inventor ulation for EM ... nd throughp ... ... ... cenarios ... narios ... De Langen sc ... ... ... ... ation Model r URE‐Mode ment tables simulation v on of the sim ; an historical a ... zes ... ry level ... MO surface d ... put ... ... ... ... ... ... cenarios ... ... ... ... ... results ... el ... ... verification .. mulation mo nalysis and mo ... ... ... density ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... del ... odelling study of ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 1 ... 1 ... 1 ... 1 ... 1 ... 1 f 85 85 87 88 89 89 90 90 91 91 92 93 94 95 99 101 105 109 111 113 115

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Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal Figure 1: Basic import dry bulk terminal Because import fluctuations may be out of phase with consumption fluctuations, a storage yard is needed (Kraaijeveld van Hemert, 1984). This effectively decouples the unloading side (sea‐side) form the loading side (land side) of the terminal. This view leads to a very basic depiction off a dry bulk import terminal consisting of unloading, storage and loading, where unloading takes place at the seaside and loading on the landside (Figure 1).

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Figure 2: Developments in the dry bulk trade and fleet (adapted from UNCTAD, 2011)

1.1 R

ESEARCH INTRODUCTION AND RESEARCH QUESTION

The global dry bulk trade has increased strongly in the last decades (Figure 2), and about one quarter of total seaborne trade volume currently consists of dry bulk trade (UNCTAD, 2011). Due to the high demand for energy and mineral resources many dry bulk terminals around the world are expanding and seriously increasing their capacity (Lodewijks, et al., 2009). It is clear to understand the amount of throughput a certain terminal can handle depends on the combination of installed handling capacities and buffer capacities in the system. However, these capacities are not fixed. For example, specified unloading rates for grab cranes depend on the material to be unloaded (Nautic Expo, sd), and the storage yard capacity depends on the number of different products stored (Lodewijks, et al., 2010) and the storage time of these products.

In other words, certain factors affect the efficiency of the unloading process or stockyard. For some changes such as a possible growing share of VLOC’s (Very Large Ore Carriers) of over 400.000 dwt (deadweight‐tonnes) (Figure 3), it is unknown whether or not they affect efficiency. 0 500 1000 1500 2000 2500

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spects of it e this closer rganised and in this chain nalysis and mo n Rotterdam n numerous ors. The term employees a t al., 2011). can be seen es and ther will be referr and to bette nderstandin how big th vidual extern nal performa estion was fo ncy and w these factor will have to examination d what is th odelling study of factors, wh minal operat and the rest Because the n as externa refore termin red to as inp er assess futu ng is needed he influence nal factors a ance is not y ormulated:

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the EMO terminal

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2. Which processes are executed at the terminal to perform this function and what are important input factors influencing for these processes

3. What are the historic developments of important input factors and how are they expected to develop in the future

4. What are the effects of input factor variations on terminal efficiency Knowing the effect of changing input factors on a large existing terminal can lead to more robust design of new terminals and large terminal expansions. Also research on more effective operations can benefit from a more quantitative description of these relations. Finally, development of strategic future plans benefits from knowing which factors are important for terminal efficiency.

The EMO‐terminal in the port of Rotterdam is the subject terminal for this research. The added insights can help EMO provide more efficient operations to its customers, and develop robust plans for the future.

1.2 O

UTLINE

This report consists of 9 chapters. After the problem definition and outline, chapter 2 focusses on the dry bulk terminal and supply chain, to provide a frame of reference for the function of dry bulk import terminals in general, and particularly the EMO‐terminal. In chapter 3, a description of the EMO‐terminal itself is provided. The terminal processes and possibly relevant input factors are discussed, as is the concept of process efficiency.

Chapter four deals with determining which factors affect the quayside efficiency through a regression analysis on unloading rate data. For these factors historic trends, both global and EMO‐specific, are investigated. Scenarios for future development of these factors are constructed here and presented.

Finally, chapters 6 and 7 deal with modelling the quayside and storage yard respectively. The constructed scenarios are used to come to an understanding of the effects of changing input factors on the quayside and storage yard.

Finally, overall conclusions and recommendations can be found in chapters 8 and 9.

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8 2.1.1 T

HE GLOBAL COAL AND IRON ORE SUPPLY CHAIN

To better understand the function of the EMO‐terminal, it is placed in the perspective of the global coal and iron ore supply chain. After a summary of global and specific European trade figures for commodities, the geographic dispersion and main players in the supply chain is discussed. Lastly, the position of EMO within the supply chain is elaborated on.

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NTERNATIONAL COAL TRADE

Worldwide, coal primarily serves two purposes: power generation and production of steel. For both purposes, different grades of coal are used. Coal for power generation is generally known as steam coal, where coal for steel production is known as metallurgic coal or coking coal.

Around 80‐85% of the 7.0 Gt of coal produced worldwide is used in the country where it is produced. The remaining 1.0 Gt is traded internationally and usually transported by large seagoing vessels [world coal factsheet]. The global seaborne coal trade accounts for about 12% of the total seaborne trade in terms of weight [derived from UNCTAD, 2011].

The European Union (EU‐27) is one of the biggest coal importing regions, with almost 200 Mt of coal imports 2009. Primary sources of coal for the European market are Australia, South‐Africa, Colombia and Russia (Figure 4). About 25% of the imports in the European Union enter the market through Dutch ports. The majority, 20 Mt of coal, is handled by the EMO‐terminal (Wilde‐Ramsing, et al., 2012)

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NTERNATIONAL IRON ORE TRADE

Iron ore is a collective name for rock holding iron‐oxides from which metallic iron can be extracted and is almost exclusively used as raw material in the production of steel. Global iron ore production amounts close to 1.9 Gt per year, of which about 55% or 1.1 Gt per year is traded globally (OECD, 2012). Like coal, global iron ore trade depends on large seagoing vessel for transportation. Import of iron ore into Europe amounts to some 130 Mt per year, with Germany and France as largest net importers (OECD, 2012). Main producing countries of iron ore for the European market are Brazil, Canada and Australia (Figure 4). EMO handles some 12 Mt of iron ore or 8‐9% of European iron ore imports.

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Australia an

by three m o major play iron ore pro ipping agen sportation. A historical analy red) trade to

have a large enerally, the

oal is effectiv the so‐called . Alone, or operations, ortant suppl nd Colombia major compa yers in coal oduction (UN

nts, steved As a stevedo

ysis and modelli Europe

geographic e supply cha

vely control d “big four”

r consortia mine‐to‐por iers of coal (Wilde‐Ram nies, Vale, B mining. Toge NCTAD, 2012) oring comp oring compa ling study of dispersion, ain of both

led by four are Anglo‐ in varying rt transport to Europe. msing, et al., BHP‐Billiton ether these ). panies and any, EMO is

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10

Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal Traders

Traders are companies involved in trading physical coal as to profit from price fluctuations. Biggest commodity trader is Glencore. Besides conventional traders, other companies are getting more involved in the physical coal trade. Utility companies nowadays have trading departments, trading either physical coal or financial derivatives. Traditional financial market players like Deutsche Bank also become involved in physical coal trade (Wlecke, 2006). Like coal, regular traders are active in the iron ore market. However, prospecting in the iron ore market is less developed because the industry used benchmark pricing and bilateral contracting for much longer. Utilities companies and steel mills Utilities companies and steel mills are on the receiving end of the supply chain of coal and iron ore. Large European utilities companies are E.ON, Vattenfall/Nuon, GDF Suez/Electrabel and RWE/Essent. Thermal coal imports are used by these companies to feed their coal‐fired power plants.

Major steel mills include ThyssenKrupp, Arcelor Mittal and Voest Alpine. They require iron ore and coking coal for production of steel.

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the EMO terminal

11 2.2 F

UNCTION OF

EMO

IN THE COAL AND IRON ORE SUPPLY

CHAIN

EMO is the biggest dry bulk terminal in Europe and is an important part of the coal and iron ore supply chain in Western Europe. Many power plants and steel mills depend partly of solely on the services provided by EMO.

Much coal and iron ore transhipped by EMO is bound for the German Ruhr district. Due to natural coal and iron ore deposits in the region and proximity to important waterways like the Rhine and Ruhr, power plants and steel production is heavily clustered in this region. In the end of last century, German coal and ore production declined and global sourcing of both commodities prevailed. The Ruhr district switched to importing and good rail and waterway connections made Dutch ports, especially Rotterdam, indispensable for coal and iron ore supplies

Besides the German market, EMO also has an important function in the Dutch coal market. Thermal coal for the nearby E.ON Maasvlakte power plant is exclusively handled by EMO. In the near future, GDF Suez’ Rotterdam power plant will become operational. This plant will also solely depend on EMO’s handling capacity for its coal supplies. As a terminal, main function of the EMO terminal is the transhipment of coal and iron ore. Main direction of transhipment is from ships to inland modalities. This type of terminal is called import terminal. In many ways processes at import terminals differ from export terminals.

Besides the transhipment, there are several secondary functions. Storing materials for clients is one of the most important of these functions. Other functions include washing, sieving and blending.

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12

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Dr the Ro of an an ex

Fig

3

ry bulk termina e EMO termina This chapte otterdam (Fi f the termina nd loading a nd finally an xecuting proc gure 5: Aerial

E

QUIP

3

TERM

l efficiency und al er describes gure 5). A g al is provide re described n overview cesses and in view of the E

PMENT

MINAL

der changing inp the subject eneral descr d. After that d. Efficiency of possible nput flows. EMO‐termina

AND

put factors; an terminal of ription of th t, three prim

of the diffe input facto al in 2009

PROCES

historical analy this researc e equipmen mary subsyst erent subsyst

ors is given

SSES AT

ysis and modelli ch, the EMO t, capacities ems unloadi tems is disc

. The focus

T THE

ling study of terminal in s and layout ing, storage ussed next, s is on the

EMO

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14 3.1 D

ESCRIPTION OF THE

EMO

DRY BULK TERMINAL

The characteristics of the quay side components at the terminal largely determine the service capacity of the terminal. Therefore, these characteristics are described here. First the nautical characteristics of basin are discussed, followed by the quay and finally the unloading equipment.

P

ORT ACCESS

,

BASIN

,

QUAY AND BERTHS

The deep‐sea port basin for the EMO‐terminal is the Mississippihaven, located south of the terminal. The basin is connected with the main access channel Euro‐ Maasgeul, by means of the Beerkanaal fairway. The port basin is approximately 2.5 km long and 380 m wide. Guaranteed draught in the Beerkanaal fairway and Mississippihaven basin is about 22.6 m at mean lower low water springtide (MLLWS). The basin width of 380 m is in accordance with design rules of basins for big dry bulk carriers (4 to 6B + 100 m) for even the largest bulk‐carriers (65m beam) currently in the fleet.

At the Mississippihaven, a 1275 m long quay provides 4 berths for bulk carriers. The first three provide berth for ships with a draught up to 21.65 m. For Large Capesize ships with draught exceeding 21.65 m, the fourth berth provides 23.00 m draught. This berth is located closest to the connecting Beerkanaal fairway. Ships are usually berth starboard side on, which requires no turning of the ship. Some situations call for berthing a ship port side on to save quay space (ship superstructure can extend beyond quay) or provide better access to the hatches. This is most common for berth 1.

U

NLOADING EQUIPMENT

For the unloading of ships, 4 travelling grab cranes are present at the quayside. Travelling grab cranes move parallel to the quay on rails, and provide a boom with outreach perpendicular to the quay. By means of a travelling trolley, a grab bucket can be lowered into the cargo holds, and hoist the material. It is then discharged into the hopper. A hopper discharge conveyor then supplies the system downstream with material at a constant rate. Four conveyor belts parallel to the quay are available to transport the material anywhere on the terminal. It is also possible to discharge the material directly on the quay under the crane.

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Dr the Fig w Th cr w di re un

S

T co st st ca

L

O pr co av lo co pu ry bulk termina e EMO termina gure 6: Unloa The grab c ith a length he lifting cap ranes have a

ith the sam scharge capa A floating equired and ndesirable. TORAGE YARD Through th onnected to orage area acker reclaim apacity is app OADING EQUIP The transp rimarily usin onveyor. For loadin vailable (Fig cated above oal and 30.0 ulling the tra l efficiency und al ading grab cra ranes are ap of 120 m of pacity of the an 85 tonne me specifica acity is listed crane is av the use of

D AND STORAG he system o the storage of over 100 mers for long proximately PMENT

port of coal ng one of f

g of trains, ure 7 right) e a rail track 000 tonnes o ains, and the der changing inp nes (left) and pproximately f which 45 m e first two c

lifting capac tions as th d at 42 Mt an vailable whe the conveyo GE YARD EQUIP

of belt conve yard. A tota 0ha. Extra st g‐term stora

7 Mt.

and iron ore four modalit

three train ). These tra k. Maximum of iron ore. train loading put factors; an d stacker‐recla y 90 m high, m is outreach cranes is 50 city. A fifth c he third and

nnually (4 cra en direct tra or network a PMENT

eyors, all lo l of 7 stacke torage space age of mostly

e from the ties; trains, n loaders (tw ain loaders daily outpu Semi‐autom g is also auto historical analy aimer (right) have a boom h over the q tonnes, whi crane recent d fourth cr anes). anshipment and barge lo ading and u er‐reclaimers e is availabl y compacted

terminal to barges, sho

wo for coal are effectiv t capacity is matic locomo omated. ysis and modelli m 45 m abov uay wall (Fig le the third tly became o rane. Maxim from ship t oaders is im unloading eq (Figure 6 rig e outside th d coal. The to the hinterla ort‐sea ship

one for iro vely discharg s 5,000‐6,000 otives are av ling study of ve the quay gure 6 left). and fourth operational, mum yearly

to barge is mpossible or

quipment is ght) serve a he reach of otal storage

and is done ps and belt

on ore) are ge hoppers 0 tonnes of vailable for

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16

Dry bulk term the EMO term Figure 7: Ba Barge lo quay or by discharge t by the ship of material For pow supply the

3.2 D

E

The ter terms of in Systems Ap important Empty vehicles Loaded ships Resources Figure 8: Te minal efficiency minal arge loader (le oading can b y the ship loa the material p loader, loc l from ship to wer plants lo plants direct

ESCRIPTI

rminal system nputs, outpu pproach (Fig parameters rminal system under changing eft) and train be done by o ader (Figure into the ba cated at the o barge or so cated on the tly from the

ON OF TH

m, with bul uts, buffers a gure 8). This that affect th Unloading process Seaside m model with g input factors; loader (right) one of three 7 left). Conv arges. Loadin deep sea qu ort sea ship c e EMO‐penin storage yard

HE TERMI

k transhipm and processe model will he terminal s Use Stock yard h input flows ; an historical a ) loaders loca veyor belts e ng of short‐s uay. Alternat can be done nsula, conve ds or the ble

INAL SYST

ment as main es based on serve as a f systems perf

Load

Loadin Lan nalysis and mo ated at the b extend beyon ea ships can tively, direct using the flo yor routes a nding silos.

TEM

n function, i the principl ramework fo formance an

ding

ng process ndside odelling study of barge handli nd the quay n be done o t transhipme oating crane are available is described es of the De or determini nd efficiency Loaded vehicle Resources Unloaded ship f ing to nly ent . to in elft ing . es ps

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the EMO terminal

17 3.2.1 T

HE UNLOADING PROCESS

The unloading process of the bulk material starts with the arrival of ships. During the voyage, the Estimated Time of Arrival (ETA) is regularly updated. Upon arrival, ships wait until a berth or when necessary a tidal window is available. When a berth and tidal window are available, the ships call in with Dirkzwager (port approach monitoring) and are piloted to the quay. From calling in until mooring is planned at 2.25 hours. Berthing and de‐berthing is planned to take approximately 1 hour each. When ready, the captain issues a Notice of Readiness (NOR) to notify the terminal the ship is ready for unloading.

When a ship berths, it is not necessarily unloaded immediately. A quay crane and a total downstream production group have to be available before unloading can begin. The busier the operations at the terminal, the less chance there is that a total production group is available. Also, when the storage yard occupancy is higher, the flexibility of choosing a storage location decreases. This reduces the chance that exactly the production group needed is actually available. A number of cranes is assigned to unload the ship, which may change during unloading. When a ship is unloaded, it is be de‐berthed and departs.

The function of the deep‐sea quayside is to unload ships (transform loaded ships into unloaded ships and bulk material). This transformation is executed by the unloading process. Loaded ships and terminal resources to be used (e.g. cranes and personnel) can be seen as process input. For loaded ships, two important buffers are available; an anchorage at the fairway entrance and the deep‐sea quay itself. The process outputs are then bulk material, unloaded ships and re‐available (or used) resources. It is clear that the unloading process partly determines the maximum amount of material that can be put through the entire terminal system (in steady state).

3.2.2 T

HE STOCKYARD

From a systems point of view, the main function of the stockyard is the decoupling of the unloading process and the loading process. It is therefore depicted as a buffer. No additional buffers are available between the unloading process and the stockyard, so storage space, stacking equipment and connecting conveyor capacity must always be available before unloading can commence. Using the stockyard as buffer is optional. Feeding the bulk material directly from unloading process to the loading process is known as direct throughput.

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18

Dry bulk term the EMO term Figure 9: A s The fun and iron or require inp performan are blende of material    A batch multiple b shipload (F Betwee buffer cap have to be high peaks process.

3.2.3 T

H Bulk ma sea ships. nearby pow can wait b and bulk m process (di It is clear material th minal efficiency minal ship containin nction of the re. Main EM put of mate

ce. To ensu ed together. l stored at th

originates fr has differen belongs to a h is not nec

atches, and Figure 9). n the stock pacity. There e available a s in the un HE LOADIN aterial is pri Also, outgoi wer stations efore being material eit irect through that the lo hat can be pu under changing ng multiple ba stockyard is O customers erial with p re this with Because of he terminal. A rom a differe nt material p a different cu essarily the an existing kyard and th efore, a stac at the time o loading pro G PROCESS marily loade ng conveyor . For trains, loaded. Loa her from th hput). Loade oading syste ut through th g input factors; atches, both f s not purely s are coal‐fir properties b a natural ra this, a single A batch usua ent source a roperties an ustomer

same as a g batch may he loading p cker‐reclaim of loading. B cess cannot S

ed onto one r belts are a barges and ding process he stockyard ed vehicles a em partly d he entire ter ; an historical a for a new pile buffering th red power p between nar aw material, e customer m ally: nd/or d/or shipload. In be supplem process ther er and conn Because EM t coincide w of 3 modal available for ships buffer s inputs are d or fed dir and resource determines t minal system nalysis and mo e and for supp e input and lants and ste rrow margin , several diff may own mu

n fact, a ship mented with

re is, again, necting conv

O uses stack with peaks i ities; trains, the supply s are availab empty vehic rectly from es are the pr the maximu m (in steady s odelling study of plementing output of co eelworks. Bo ns for optim ferent batch ultiple batch p may conta h a new (pa

no significa veyor capac ker‐reclaime in the loadi

barges, sho of coal to tw ble where th cles, resourc the unloadi rocess outpu um amount state). f oal oth mal hes hes ain art) ant city ers, ing ort‐ wo hey ces ing uts. of

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the EMO terminal

19 3.2.4 T

ERMINAL SYSTEM ASSUMPTIONS

Although the described terminal system captures a large part of the actual processes that take place at the EMO‐terminal, some assumptions are made in the system description. The use of shovels for reclaiming material, dumping it on the conveyor system and loading trains is not taken into account. So is stacking material outside the reach of stacker reclaimers and in mixing silo’s.

3.3 T

ERMINAL PERFORMANCE

;

EFFICIENCY

The performance of the terminal system and the processes in particular, depends on both efficiency and effectiveness (Veeke, et al., 2008). In this thesis, it is assumed that the effectiveness of the processes is fixed or that the process always achieves the desired goal (e.g. unload the ship). The amount of effort(s) required to do so determine the efficiency (Figure 10). Although no standard effort is set, the required effort for different input parameters can be compared and thus lead to a relative measure of efficiency. Figure 10: Process performance related to efficiency based on (Veeke, et al., 2008)) Ac tual Ac tual Stan dard Stan dard

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20

Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal For the stockyard, this analogy requires redefinition of the stockyard as a buffer. Although in the total terminal system is just a buffer between the unloading and loading process, storing bulk material can be seen as a processes. The amount of storage space needed to store a given amount of bulk material can be seen as the effort. Then the amount of storage material that can be stored on the storage yard over the course of a given period of time is the storage yard efficiency.

3.4 INPUT FACTORS

;

INPUT PARAMETER SELECTION

In the previous section, three main input flows for the terminal system were determined. It is clear, however, that these flows are not fixed. The exact composition and characteristics of these flows is different at any given time. More precisely, on the operational level the unloading of a ship and the loading of an outgoing modality is standardized and repetitive, but never exactly the same.

Large part of the efficiency depends on factors associated with the system (or process) inputs as presented in Figure 8. At least for large multi‐client input terminals, operators have little or no control on these factors. The terminal operator has to provide a quay wall, equipment, storage area and employees and the rest is determined by the clients of the terminals (Van Vianen, et al., 2011).

A list of input parameters that possibly affect the unloading rate is inexhaustible and never complete. First and foremost, parameter selection is limited by historic data availability at the subject terminal. Parameters that are not recorded are therefore not part of this list. Furthermore, the effect of a certain parameter must at least have some theoretically basis. The parameters chosen in this section are therefore a product of the limitations described above. The provided parameter list can therefore not be seen as conclusive or comprehensive in its own.

3.4.1 U

NLOADING RATE

;

INPUT PARAMETERS AFFECTING EFFICIENCY

The unloading process as a part of the terminal system model has 3 important input flows:

 Resources

 Load

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Dr the Fig un EM th cr ra ex im co di co is an al se un pr un ha tim ry bulk termina e EMO termina gure 11: Stage Input para nloading rate MO has 2 dif he unloading rane is logica ate. Also, for xperience etc Second so mportant fac ommodities, fferent com oking coal an present in t nd therefore When mult low identity‐ een as a pos nloaded is al Finally, the rovide longe nloading is a atches, more me and slow l efficiency und al es in unloadin ameters that e logically de fferent type g rate. Anoth ally presume other resou c. no data is urce of inp ctor is the c

different gra mmodities (N nd steam coa the original future throu tiple batches ‐preserved s ssible determ so considere e characterist er uninterrup ssumed to fa e trimming a wer unloading der changing inp ng rate efficie t affect the epends on th s of cranes a her resource ed to be the rces like am available. ut paramete commodity abs are used Nautic Expo, al. Reason fo data, and t ughput are e s are present storage of ba minant for u ed. tics of the sh pted unload all in the trim and more ha g. put factors; an ency (Ligtering system are he amount of at their disp e‐related fact single most ount of shov

ers are the type. Due t d and differe sd). A disti or this distinc hat both co expected to d t in one ship atches. There unloading ra hip might aff ing of mate mming stage atch‐changes historical analy gen, 2009) e associated f cranes assi posal, the typ tor is the do important f vels used for characteris to differenc ent unloading nction is m ction is the f mmodities s develop diffe , unloading h efore the nu ate. The am fect the unlo erial. Also a e (Figure 11). s are needed ysis and modelli with these gned to a sh pe of crane owntime of c factor for the r trimming, c

tics of the es in densit g rates are s ade betwee fact that this serve differe erently. has to be int mber of bat ount of mat

oading rate. L smaller frac . When a shi d, resulting in ling study of flows. The hip. Because also affects cranes. The e unloading crane driver load. Most ty between specified for en iron ore, s distinction ent markets

terrupted to tches is also terial to be

Larger ships ction of the ip has more n more idle

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22

Dry bulk terminal efficiency under changing input factors; an historical analysis and modelling study of the EMO terminal Figure 12: Input parameters for the unloading process A highlight of the unloading process, together with the input flows and associated parameters is presented in Figure 12. As a final note, there is a clear difference between the ship size and the amount of material to be unloaded. An 80.000 tonnes load of coal can be unloaded from an 85,000 dwt Panamax ship, or from an 185,000 dwt Capesize carrier that also calls at another port.

3.4.2 S

TOCKYARD

;

INPUT PARAMETERS AFFECTING EFFICIENCY

For the stockyard, different types of efficiency can be distinguished. First, the efficiency of the stacking operation mainly depends on the amount of stacker‐ reclaimers available for stacking, the conveyor belt routes available etc.

Second is the efficiency of the storage yard itself. How much material can be stored on a given square meter of yard in a given time‐period is determined by the geometry of the piles and how long the material remains at the yard.

Figure 13: Stockyard efficiency input parameters

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the EMO terminal

23

It is this last measure of storage yard efficiency that is the prevailing subject of this part of the research. To provide a measure of this storage yard efficiency, the storage factor is used. The storage factor breaks down into two different measures:  Surface density [tm‐2]: amount of bulk material per square meter. Depends on pile geometry and volumetric density of the bulk material  Inventory turnover [y‐1], number of times a unit area is cleared, consequently the number of times a pile can be formed at the same location. Calculated as the inverse of the storage time. When multiplied, these two factors form the storage factor [tm‐2y‐1]. The higher the storage factor, the more material can flow through the storage yard in a given time.

3.5 S

UMMARY

In this chapter, a description of the terminal and its processes was presented. The concept of process efficiency was elaborated on. Furthermore, a preliminary set of factors relevant to both the unloading process and the storage yard were derived. The parameters chosen are the product of limitations to available datasets and theoretical knowledge of the systems mentioned. The provided parameter list can therefore not be seen as conclusive or comprehensive in its own.

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24

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Dr the ul w sy qu un un de of un cr co a pr

4

ry bulk termina e EMO termina The unload timately det aiting times ystem, name ueuing syste nloading syst The servic nloading rate epends natu f different d nloading rate The unload ranes and t ommodity an role. A list resented bel  Nu  Co  Shi  Nu  Nu  Am

A

N UN

4

l efficiency und al ding proces termines the and demurr ed quayside em, the (de tem, by mea e rate, or ho e [thr‐1] and rally on the densities fo es [thr‐1] diffe ding process the commo nd number a of factors ow. This list umber and ty mmodity typ ips size [dwt umber of bat umber of hat mount to be u

NLOADI

der changing inp

s is a key e maximum rage costs. T system in t eep‐sea) an ns of the qu ow much tim the amount number and r coal and er per comm s service ra

dity of the nd type (50 that might was establis ype of cranes pe [Steam co ] ches ches unloaded

ING RAT

put factors; an process for throughput The unloadin this thesis. chorage an ay cranes, is me the unload t of materia d type of cra ore, differe modity (Naut

te thus dep e ship serve

tonnes or 8 affect the shed in parag s assigned oal, iron ore,

TE ESTIM

historical analy a dry bulk t of the ter ng process is When view d quay are the server. ding of a ship l per ship. T anes assigne ent grabs a ic Expo, sd). pends on th ed. Howeve 5 tonnes) of unloading r graph 3.4.1. coking coal]

MATION

ysis and modelli k terminal. I minal, as w part of a lar ed as an an e the queue

p takes, depe he unloading d to that sh re used an he number o er, more fa crane alone rate of a giv

]

N MODEL

ling study of Its capacity ell as ships rger service nalogy of a e, and the

ends on the g rate itself ip. Because d specified of assigned actors than e might play ven ship is