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Journal of Marine Science: Research & Development
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Optimization of the Energy Efficiency Operational Indicator for M/V NSU JUSTICE 250,000 DWT by Grey Relational Analysis Method in Vietnam

Tien Anh Tran1,2,*

1School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, P.R. China

2Faculty of Marine Engineering, Vietnam Maritime University, Haiphong, Vietnam

*Corresponding Author:
Tien Anh Tran
Faculty of Marine Engineering, Vietnam Maritime University
Haiphong, Vietnam
Tel: +86 13986027618
E-mail: [email protected]

Received date: June 26, 2017; Accepted date: July 13, 2017; Published date: July 17, 2017

Citation: Tran TA (2017) Optimization of the Energy Efficiency Operational Indicator for M/V NSU JUSTICE 250,000 DWT by Grey Relational Analysis Method in Vietnam. J Marine Sci Res Dev 7:232. doi: 10.4172/2155-9910.1000232

Copyright: © 2017 Tran TA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

“The International Convention for the Prevention of Pollution from Ships (MARPOL 73/78), Annex VI” with Chapter IV: “Regulations on Energy Efficiency for Ships” has made effectively then enforcing all ships must follow regulations in aims with the energy efficiency in operating ships and reducing the environmental pollution. In this article, the calculation of the “Energy Efficiency Operational Indicator (EEOI)” has been conducted under the guidelines for using of the “ship energy efficiency operational indicator” (EEOI) of the “International Maritime Organization” (IMO), MEPC.1/Circ.684 adopted on 17th August 2009. The Grey Relational Analysis (GRA) method has been studied and applied in this research in order to optimize the energy efficiency for M/V NSU JUSTICE 250,000 DWT in the shipping transportation company, Vietnam. These results collected will be identified that the energy efficiency of ships achieves then the decreasing fuel consumption of engine and optimizing the routes of a certain ship. Besides that, this research is fundamental to these next studies about the energy efficiency of ships.

Keywords

Energy efficiency of ships; Grey relational analysis; MARPOL 73/78; EEOI; Bulk carriers

Introduction

The increase of carbon dioxide CO2 emission into the environment has led to the big problem of the environmental pollution especially the greenhouse gas effects. The greenhouse gas emissions make the global warming potential, melting glaciers, or rising sea levels nowadays. This phenomenon has affected negatively to human and creature in the planet. So, they need to restrict and reduce the carbon dioxide CO2 emission from actives and economical development at each nation.

The “International Maritime Organization” (IMO) with “Marine Environment Protection Committee” (MEPC 67) has proved the document of the third IMO GHG Study 2014. In where, it provides the updated emission estimates about greenhouse gas emissions from ship operational activities. This research also indicates the certain statistic about carbon dioxide CO2 emissions from shipping transportation industries. In particular, the international shipping transportation (Figure 1) [1] “generated 796 million tonnes of carbon dioxide CO2 in 2012 corresponding to 2.2% of the total carbon dioxide emissions from other industries. On the other hand, the global economic downturn, the international shipping transportation has counted with emitting 885 million tonnes of carbon dioxide CO2 that was 2.8% of the total global carbon dioxide emissions for that year.

image

Figure 1: Carbon dioxide CO2 emissions from shipping transportation with total global emissions [1].

On the other hand, the “Marine Environment Protection Committee (MEPC)” has been considered to reduce the green house gas emissions for all ships at present. By method of revealing the energy efficiency measurement tool includes the operational measure and technical measure. In particular, the operational measure of energy efficiency is the “Energy Efficiency Operational Indicator” (EEOI) and the technical measure of energy efficiency is the “Energy Efficiency Design Index” (EEDI). Hence, the “Ship Energy Efficiency Management Plan” (SEEMP) is a tool in order to establish a mechanism in the field of development of the energy efficiency of ships nowadays.

The Grey Relation Analysis (GRA) method was formed by Ju-long Deng in 1982. In where, the internal message including architecture, operation mechanism, system characteristics and parameters is determined by a white system. On the other hand, a white system cannot get any information and characteristics about the system then it is called a black system. Grey relational analysis is adopted between the white system and black system. In a result, the minimal database requirement, simplicity of use and reasonably expected results are the advantages of the grey system over the traditional regression analysis. The Grey Relation Analysis (GRA) has been applied to various fields that relating to the energy efficiency. For instance, [2] the grey relational analysis model was used to examine the load of power system in Japan as well as was carried out by using the long-term load forecasting problem with a significance insteading of using the linear single regression model. Morita et al. [2] had proposed an interval prediction of the both upper and the lower bound values of load demand by using the grey dynamic model. Moreover, the interval prediction has been proposed by using grey dynamic model and the research results of Morita et al. had been compared with the actual cases from a linear single regression model. Besides that the relationship between energy consumption with relating carbon dioxide emissions [3]. Chang et al. had used grey relation analysis in order to analyze how energy-induced CO2 emissions from 34 industries in Taiwan that caused by elements including: production, energy consumption, coal, oil, gas and electricity. On the other hand, the industrial production had the strongest relationship with CO2 emissions and the economical development in Taiwan had made intensive industry development. Based on grey relational analysis method, the total energy consumption had a smaller and negative relational grade along with CO2 gas emissions. Liang [4] also used this method to catch the preliminary schedules for the short-term hydroelectric. The aims of hydroelectric generation scheduling will indicate the optimal amount of generated power for the hydro in the system that used in the future. Shi et al. [5] has applied the Grey Relational Analysis in field of energy efficiency assessment for pump station. The core of Energy Efficiency Assessment (EEA) of energy consumers has been proposed in order to design an integrated assessment model. The grey relational analysis method has been used for applying to the assessment method along with using a motor driving pump system like as a case of study. Furthermore, the operating data of one pump station will be calculated and assessed by using the grey relational analysis method. In addition, the EEA methodology of energy system will be considered as a fresh idea for evaluating the energy efficiency.

In recent time, the methodological and theoretical in grey relational analysis have been improved through researches. Deng [6] has been proposed the information of synthesizing kenning mode and the theory of grey hazy set that consists of embryo, growing, mature and evidence subsets. Hsu and Chen were proposed for the combination between residual modification model and Artificial Neural Networks (ANNs) [7] in aims with enhancing the predictive reliability and accuracy of the original GM (1.1) model. Mao and Chirwa [8] used grey relational analysis for applying to survey motor vehicle fatality in USA and UK. Lin et al. [9] used grey relational analysis to determine a dynamic network in aims with analyzing the correlations and performances of the industrial productivity along with assumpting the energy consumption and combinational uses between fuel and carbon dioxide emissions. The aim of this research has indicated that the increase in electricity generation during the past 10 years was the reason for CO2 gas emission in Taiwan.

The grey relational analysis can be provided a solution of a system that uses a model of the uncertain information [10]. Besides that the Grey theory system can be provided an optimal method to solve the multi-input, discrete data and uncertain information. On the other hand, the relationship between machining parameters and performance can be found in the Grey Relational Analysis (GRA) theory. In a result, the grey relational grade will utilize the discrete measurement method to measure the distance.

In general, the advantage of the Grey Relational Analysis (GRA) method is that it is designed to study uncertainty. Grey Relational Analysis theory is superior to other methods in field of theoretical analysis of systems with uncertain information and incomplete data experimental. Furthermore, it can be used if the experimental data collected is not supplemental or in case of the researchers does not sure that their experimental data collected is representative. On the other hand, it can be used in early effective factor assessment. However, having a limited number of researches referred in field of shipping transportation especially applying the Grey Relational Analysis (GRA) theory for energy efficiency of ships. Fong-Yuan Ma [11] has researched about the energy efficiency for bulk carrier that using grey relation method but in his research still had some drawbacks about analyzing the energy efficiency of ships. So, in this article, the author has conducted to research about energy efficiency operational indicator, EEOI by applying the Grey Relational Analysis (GRA) for M/V NSU JUSTICE 250,000 DWT of the shipping transportation company in Vietnam. This research included these sections in where section 1, Introduction; section 2, Energy Efficiency Operational Indicator; Section 3, Grey Relational Analysis; section 4, Case study; section 5, Results and discussion, and section 6, Conclusion.

Energy Efficiency Operational Indicator

“Energy Efficiency Operational Indicator”, EEOI is operational measure that is established by International Maritime Organization – IMO following the “International Convention for the Prevention of Pollution from Ships (MARPOL 73/78)” in particular Annex VI for Prevention of Air Pollution from Ships. The Conference of Parties to the “International Convention for the Prevention of Pollution from Ships” was happened in 1973 and modified by the Protocol in 1978 (MARPOL 73/78). This conference held from 15th to 26th September 1997 that it has been debated the contents concerning about the sea marine environment protection in aims with responding the convention.

“International Maritime Organization” has adopted resolution A.963 (23) in order to reduce greenhouse gas emissions by meaning of “Marine Environment Protection Committee”. MEPC is aims with identifying the mechanism to get the limitation of greenhouse has emission level for shipping. The establishment of carbon dioxide emission along with determing regulations and conventions of sea marine environment protection [1].

The EEOI index is presented for the energy efficiency of the ship operation over a consistent period which describes trading pattern of the vessel. On the other hand, IMO (International Maritime Organization) also indicated that in order to establish the EEOI (Energy Efficiency Operational Indicator) needs following steps below:

- “Define the period for which the EEOI is calculated*”

- “Define data sources for data collection”;

- “Collect data”;

- “Convert data to appropriate format” and

- “Calculate EEOI”.

(*”Ballast voyages, as well as voyages which are not used for transport of cargo, such as voyage for docking service, should also be included. Voyages for the purpose of securing the safety of a ship or saving life at sea should be excluded”).

Furthermore, the data recording method is used so that experimental data is collected and analyzed to manage the extraction of forced information. The experimental data must include: distance traveled, quantity of fuel used; kind of fuel used. All fuel used information may affect the amount of carbon dioxide emitted into the environment. For instance, the fuel information is provided on the bunker delivery notes that regulated under Regulation 18 of MARPOL 73/78 Annex VI. On the other hand, the unit used for distance traveled and quantity of fuel should be expressed in nautical miles and metric tonnes. It is necessary to provide the significant information that is collected from experimental data of vessel along with regarding to the voyage characteristics like as fuel used amount, type of fuel, traveled distance, and amount of cargo carried. The distance traveled should be computed following the actual voyages and it is saved on Ship’s Log-Book.

The calculation of EEOI index following Equation 1 below:

image (1)

Rolling average of EEOI equation following Equation 2:

image (2)

Where:

j: is the fuel type;

i: is the voyage number;

FCij is the mass of consumed fuel j at voyage i;

CFj : is the fuel mass to CO2 mass conversion factor for fuel j;

mcargo: is the cargo carried (tonnes) or work done (number of TEU or Passengers) or gross tonnes for passenger ships; and

D: is the voyage distance (unit: nautical mile).

The value of EEOI concerns about navigation characteristics, for example: tonnes CO2/(tonnes.nautical miles), tonnes CO2/(TEU. nautical miles), tonnes CO2/(person.nautical miles), etc.

The rolling average EEOI is used in the field of calculation of EEOI index in a suitable time period. For instance, six or ten voyages which are agreed as statistically relevant to the initial averaging period. The Equation (2) shows the rolling average EEOI formula with the certain elements.

On the another side, the fuel mass to CO2 mass conversion factor CF is a non-dimensional conversion factor between fuel consumption measured in gram and CO2 emission also measured in gram based on carbon content (Table 1). The value of CF is as follow:

Type of Fuel Reference Carbon Content CF
(t-CO2/t-Fuel)
1.Diesel/Gas Oil ISO 8217 Grades DMX through DMC 0.875 3.206000
2.Light Fuel Oil (LFO) ISO 8217 Grades RMA through RMD 0.86 3.151040
3.Heavy Fuel Oil (HFO) ISO 8217 Grades RME through RMK 0.85 3.114400
4.Liquified Petroleum Gas (LPG) Propane
Butane
0.819
0.827
3.000000
3.030000
5.Liquified Natural Gas (LNG)   0.75 2.750000

Table 1: Carbon dioxide conversion factor (CF).

It is possible to realize that the EEOI value is as smaller as better in the field of energy efficiency of ships at each certain voyage when considering operational condition on sea. On the other hand, the equations (1) and (2) can be established by adapting for a multiple numbers of voyages and an average EEOI value obtains during a period of time.

Energy Efficiency Operational Indicator itself is not mandatory. However, it is a monitoring tool for energy efficiency that implemented measures due to the International Maritime Organization reveals. It is a reason that to perform EEOI calculations would enable for the operators to measure the energy efficiency of ship and to analyze the effect of any modifies in operation such as voyage planning optimization, hull and propeller cleaning, trim optimization.

Grey Relational Analysis

Data preprocessing

The data processing is applied to give the data sequence into dimensional data sequence as well as transforming the original sequence to a comparable sequence. On the other hand, the original sequence and comparability sequence are represented as x0(k) and xi(k), i = 1,2,3, ..., m; k = 1,2,3, ..., n, respectively, where m is the total number of experiment to be considered, and n is the total number of observation data. Data preprocessing converts the original sequence to a comparable sequence. Several methodologies of preprocessing data can be used in Grey Relation Analysis, depending on the characteristics of the original sequence [12-14]. For the original sequence “the larger the better”, the original sequence is normalized as follows [12]:

image (3)

For “the smaller the better” characteristics of the original sequence, the original sequence is normalized as follows [12]:

image (4)

In case, if a defined target value, OB, exists, then the original sequence is normalized as follows [12]:

image (5)

The original sequence is normalized by using an alternative simple method which is separated from the first value of the sequence i.e. xi(1) as follows:

image (6)

where, xi(k) us the original sequence, xi(k) is the sequence after the data preprocessing, max(xi(k)) is the largest value of xi(k) and min(xi(k)) is the smallest value of xi(k).

Grey relational analysis coefficients and grey relational analysis grades

After the data preprocessing, a grey relational analysis coefficient is calculated using the preprocessed sequences. The grey relational analysis coefficient can be calculated as [12]:

image (7)

and, image

where Δoi (k) is the deviation sequence of the reference sequence x0(k) and comparability sequence xi(k);

image (8)

image (9)

image (10)

with ζ is the distinguishing coefficient, ζ ∈ [0,1].

After computing the grey relational analysis coefficients, grey relational analysis grade is calculated using the following relationship [14,15].

image

image (11)

The grey relational analysis grade (xo,xi) represents the degree of correlation between the reference and comparability sequences. In case of grey relational analysis grade is equal to 1 then the reference and comparability sequences are identical. In a result, the comparability sequence is more important than other comparability sequence due to the grey relational analysis will be applied into this research by using grey relational analysis grade. On the other hand, the comparability sequence and the reference sequence will be overcome to compare the other grey relational analysis grades. The results of grey relational analysis will be compared to the absolute value of data including the sequence data along with the correlation of sequence data.

Case Study

M/V NSU JUSTICE 250,000 DWT

In Vietnam, the shipping transportation companies have a numerous number of ships with variety of sizes and types. There are a lot of shipping transportation companies where provide vessels and officers responding to develop the transportation business in domestic and international such as VINIC shipping transportation company associates with Vietnam Maritime University, Vietnam; VOSCO shipping transportation company, NISSHO shipping transportation company, etc. In this study, the target ship is used with a certain name M/V NSU JUSTICE 250,000 DWT of VINIC shipping transportation company. This is the biggest bulk carrier of company that operating on international routes. The experimental data has been collected from its operational activities. This vessel operates on the fixed routes between Japan – Australia – Brazil. Table 2 presents these specification parameters of M/V NSU JUSTICE 250,000 DWT.

Item Category Parameter
1 Vessel name NSU JUSTICE
2 IMO Number 9441922
3 MMSI 373072000
4 Vessel type Bulk carrier
5 Gross tonnage 132,868
6 Dead Weight Tonnage (DWT) 250,000
7 Flag Panama
8 Draught 18 m
9 Speed Recorded (Max/Average) 13.3/10/4 knots
10 Length x Breadth (L x B) 329.95 m × 57 m
11 Year built 2012

Table 2: Specification parameters of M/V NSU JUSTICE 250,000 DWT.

In Figure 2 describes general of M/V NSU JUSTICE 250,000 DWT with design parameters show in Table 2 below.

image

Figure 2: M/V NSU JUSTICE 250,000 DWT.

M/V NSU JUSTICE 250,000 DWT is powered by two-stroke main engine, single acting, crosshead, exhaust gas turbo-charged marine diesel engine with engine type of MAN B&W 7S80MC-C (Mark 7). Its maximum continues rating (MCR): 21,910 kW at the shaft rotational speed of 74.5 rpm. Furthermore, this vessel is powered by three main diesel generators with engine type of 4-stroke, single acting, turbocharged diesel engine (6N21AL-SV). The Maximum Continuous Rating (MCR) is 880 kW at the respective shaft rotational speed of 900 rpm. On the other hand, this vessel is fitted the Fixed Pith Propeller (FPP) diameter 9.60 m with 4 blades. Especially, the vessel is equipped with a data acquisition system for collecting the experimental data including the performance and navigation data. The performance and navigation data of this vessel with respect to the fuel consumption rates are considered in this study along with navigation conditions impact on ship.

Calculation of energy efficiency operational indicator

The EEOI calculation has an important position in enhancing the energy efficiency operation index of ships. In this case of study, the target ship is a bulk carrier with certain name M/V NSU JUSTICE 250,000 DWT. The author conducted to choose this type of ship because it is a popular ship that carries a numerous cargo in shipping transportation industry in the world and in particular, Vietnam. On the other hand, it has been chosen completely based on research situation and operational condition of shipping transportation companies in Vietnam.

Throughout the Figure 3 above, the Energy Efficiency Operational Indicator (EEOI) at each certain voyage is different. In this study, the recent voyages are used in calculation of EEOI index for M/V NSU JUSTICE 250,000 DWT that this vessel operating. These voyages almost carry bulk ore so there is nothing harmful good affects the carbon dioxide (CO2) emission to the environment. In addition, it will not impact on the calculation of EEOI index following Equation 1. So, the calculation results above of EEOI index for M/V NSU JUSTICE 250,000 DWT completely depend on the fuel consumption level of main engine.

image

Figure 3: EEOI values for M/V NSU JUSTICE 250,000 DWT.

However, the name of voyage like as Voyage No16A, No16B, No17A, No17B, No18A, No18B, No19A, No19B, No20A, and No20B is separated from Voyage No16, No17, No18, No19, and No20 due to its operational characteristics of routes, time on sea-going and at each port are individual. In fact, the voyage No16 is total of voyage No16A and voyage No16B. In addition, these other voyages are similar like this. So, the value of the Energy Efficiency Operational Indicator (EEOI) at each certain voyage will be represented in Figure 4.

image

Figure 4: The total of EEOI value at each voyage No16, No17, No18, No19, No20.

In Figure 4, the maximum EEOI index among these voyages is on Voyage No16 with 9.83E-04, and the minimum EEOI index is on Voyage No20 with 1.05E-05. Besides that, these left voyages have also the low values. i.g. EEOI of voyage No17, No18, No19 in turns 1.08E- 05, 1.13E-05, 1.14E-05. From these results calculated for M/V NSU JUSTICE 250,000 DWT will be fundamental background to carry out this research.

Optimization of energy efficiency of M/V NSU JUSTICE 250,000 DWT

In this study, the determination of grey relational analysis grades of experimental data for M/V NSU JUSTICE 250,000 DWT conducted in this section. This is the important working, which is analyzed from the data of voyages. The fundamental calculation is based completely from researching the theory before and referred into section 2.

In the experimental data was collected from the actual voyages of M/V NSU JUSTICE 250,000 DWT when this vessel carried a big amount of cargo traveled around routes between Japan-Australia-Brazil with certain name of voyages. The data of voyages has been taken from Noon-log Record on M/V NSU JUSTICE 250,000 DWT (Table 3). For instance, the Noon-log Record of a certain Voyage No16 is showed in Figure 5 below.

marine-science-research-development-Noon-log-Record

Figure 5: Noon-log Record of Voyage No16, M/V NSU JUSTICE 250,000 DWT.

Item Voyage No EEOI
1 16A 4.1616E-04
16B 5.6719E-04
2 17A 4.9504E-06
17B 5.8613E-06
3 18A 5.0109E-06
18B 6.2809E-06
4 19A 5.1751E-06
19B 6.2269E-06
5 20A 5.0054E-06
20B 5.509E-06

Table 3: EEOI value of M/V NSU JUSTICE 250,000 DWT.

In reality, there are five voyages that collected from the experimental data throughout actual travel with names Voyage No16, No17, No18, No19, No20 due to n=5. On the other hand, the proof is based on the calculation of EEOI values for voyages as well as aims of this research that reduce the fuel consumption of engine on M/V NSU JUSTICE 250,000 DWT in order to increase the energy efficiency of ship in particular the Energy Efficiency Operational Indicator (EEOI). So, these parameters need to consider including the fuel consumption level (HFO + DO), distance traveled, and cargo carried among these studied voyages. From then, the indication of k is equal 4 (k=4);

In Table 4, the experimental data of M/V NSU JUSTICE 250,000 DWT has been taken out the Noon-log Record for Voyage No16, No17, No18, No19, and No20. These parameters include the fuel consumption rate of engine corresponding to each certain voyage, types of fuel used both HFO (Heavy Fuel Oil) and DO (Diesel Oil), distance traveled, and amount of cargo carried. All of them were represented in Table 4 with these comparability sequences xi(k).

Item Voyage HFO (T) DO (T) Distance (NM) Cargo (T)
1 No16 1485.773 15.3 7588 247400
2 No17 1663.1 13.3 7837 247500
3 No18 5434.67 27.6 24469 246200
4 No19 1680.941 17.1 7466 246876
5 No20 1479.477 13.2 7156 246800

Table 4: These comparability sequences xi(k).

In where: Voyage No16= No16A+No16B, voyage No17=No17A+No17B, voyage No18=No18A+No18B, voyage No19=No19A+No19B, voyage No20=No20A+No20B;

These comparability sequences have been taken from the actual experimental data of M/V NSU JUSTICE 250, DWT researched. These parameters considered in this study concern about the energy efficiency operation index including the fuel consumption rates of engine (HFO+DO), the distance traveled (unit: NM=Nautical Mile), and a weight of cargo carried (unit: T=Ton). Especially, the optimization of these parameters hase been conducted in this research in aims with increasing the energy efficiency of ships. So, these comparability sequences xi(k) have been studied concerning about these parameters above and described in Table 4 corresponding to each certain voyage.

In Table 5, the reference sequences x0(k) were indicated that under the certain parameters then applied the Grey Relational Analysis (GRA) method to optimize the experimental data of M/V NSU JUSTICE 250,000 DWT. The reference sequences x0(k) are the operational parameters that M/V NSU JUSTICE 250,000 DWT wants to achieve these values. This means that the reference sequences x0(k) are desirable values and they are established completely based on the Grey Relational Analysis (GRA) method associating with the operational experience on M/V NSU JUSTICE 250,000 DWT.

Item HFO (T) DO (T) Distance (NM) Cargo (T)
x0(k) 1479.477 13.2 24469 247500

Table 5: The reference sequence x0(k).

The reference sequence was designed to base on the actual experimental operation plus using the Grey Relational Analysis method. The reference sequence is corresponding to the desired sequence that the ship-owners and operators who want to gain these values. To optimize the energy efficiency operational indicator through the operational parameters then the fuel consumption rates of engine need to reduce along with ensuring the cargo carrying abilities of ships (distance traveled and amount of cargo carried). From the Grey Relational Analysis method, the fuel consumption level both HFO and DO (unit: Ton) is “the smaller the better” following the Equation (4) above. In contrast, the distance traveled (unit: Knot or NM – Nautical Mile) and cargo carried (unit: Ton) are “the larger the better” applying the Equation (3).

The difference sequences Δoi between xo(k) and xi(k) have been computed based on Equation (8) in section 3.2 (Table 6). This calculation of the difference sequences Δoi of these parameters concerning about the energy efficiency operational index corresponding at each voyage separated. These voyages have been studied total of five voyages with the certain name: No 16, No17, No18, No19, and No20 (n=5).

Doi Voyage HFO (T) DO (T) Distance (NM) Cargo (T)
Δo1 No16 6.296 2.1 16881 100
Δo2 No17 183.623 0.1 16632 0
Δo3 No18 3955.193 14.4 0 1300
Δo4 No19 201.464 3.9 17003 624
Δo5 No20 0 0 17313 700

Table 6: The difference sequences Δoi between xo(k) and xi(k).

The grey relational analysis coefficient γ(k) has been calculated following the Equation (7) in section 3.2 above. These values have been recorded in Table 7 along with these values of grey relational analysis grade γ. The grey relational analysis coefficient of each voyage is a range of between 0 and 1 corresponding the each parameter needs to optimize in this study. In reality, there is a difference from these values collected and they will be compared and analyzed in section 5. On another side, the grey relational analysis grade will be indicated out at same Table 7 responding to each grey relational analysis coefficient γ(k). In where, there are four grey relational analysis coefficients γ1(k), γ2(k), γ3(k), γ4(k) corresponding to each parameter that needs to optimize. The analysis of the grey relational analysis coefficient and grey relational analysis grade will be carried out in section 5 of this article. From then, the optimization of the experimental data of M/V NSU JUSTICE 250,000 DWT will be represented.

γi(k) Voyage No16 Voyage No17 Voyage No18 Voyage No19 Voyage No20 g
γ1(k) 0.9968 0.9150 0.3333 0.9075 1.0000 0.8305
γ2(k) 0.7741 0.9863 0.3333 0.6486 1.0000 0.7485
γ3(k) 0.3389 0.3423 1.0000 0.3374 0.3333 0.4704
γ4(k) 0.8667 1.0000 0.3333 0.5102 0.4815 0.6383

Table 7: The Grey Relational Coefficient γi(k) and grey relational grade γ.

Results and Discussion

The energy efficiency of ships plays a significant role in the shipping transportation industry. Especially, the energy efficiency measures have been established through the International Maritime Organization (IMO) that regulated in MARPOL 73/78, Annex VI about the Prevention of Air Pollution from Ships. The Energy Efficiency Operational Indicator (EEOI) is an operational measure, which is a target objective in this research in aims with reduction of the Energy Efficiency Operational Indicator associating with the sea environmental protection. These operational parameters have been concentrated on this research including the fuel consumption rates of engine, types of fuel used, distance traveled of certain vessel, amount of cargo carried responding to each certain voyage. Especially, the novel of this research has applied to the Grey Relational Analysis method to solve the issue in field of the energy efficiency of ships. Since these previous researches have just referred to apply and optimize the process parameters at some different fields. However, the use of Grey Relational Analysis (GRA) method for the shipping transportation industries still limits. So, this research has also indicated out the innovative trend in applying the Grey Relational Analysis method in field of optimizing the experimental data of vessels. In particular, this article has been represented throughout these results below then based on the Grey Relational Analysis (GRA) method.

The combination of calculating the Energy Efficiency Operational Indicator (EEOI) along with the Grey Relational Analysis coefficients and the Grey Relational Analysis Grade is initial background to conduct the optimize the energy efficiency operational index for a certain vessel, M/V NSU JUSTICE 250,000 DWT of VINIC shipping transportation company in Vietnam. Especially, this vessel has been operated by the author and Officers of VINIC shipping transportation company in Vietnam that some of them who work as Lecturers at Vietnam Maritime University, Haiphong, Vietnam. So, the optimization of experimental data for M/V NSU JUSTICE 250,000 DWT has been carried out in this research beside the Grey Relational Analysis (GRA) method applied.

In Figure 6, the grey relational analysis coefficient γ1(k) is described for voyages including voyage No16, No17, No18, No19, and No20 about using the heavy fuel oil (HFO) on ship. In general, the maximum values of grey relational analysis coefficients are voyage No16 and No20 with 0.9968 and 1.0000. In contrast, the lowest grey relational analysis coefficient is voyage No18 with 0.3333. These left values for voyage No17 and No19 are in turns 0.9150 and 0.9075. In then, the grey relational analysis coefficient 1(k) is represented for consuming the heavy fuel oil of engine on M/V NSU JUSTICE 250,000 DWT with the design value before then applied the Grey Relational Analysis method. The grey relational analysis coefficient is as higher as better. So, the voyage No16, No17, No19, and No20 have used effectively about the heavy fuel oil on ship reflecting to voyage No18.

image

Figure 6: Grey relational coefficient γ1(k) for Heavy Fuel Oil (HFO).

Similarly, the grey relational coefficient γ2(k) is also represented for all voyages in Figure 7. Throughout these results, the voyage No17 and No20 have the hight values corresponding to 0.9863 and 1.0000. In contrast, the similarity of grey relational analysis coefficient 1(k) then the voyage No18 reaches the minimal value with 0.3333. These left voyages include voyage No16 and No19 in turn 0.774 and 0.6486.

image

Figure 7: Grey relational coefficient γ2(k) for Diesel Oil (DO).

The grey relational analysis coefficient γ3(k) is represented in Figure 8 below corresponding to each different voyage.

image

Figure 8: Grey relational coefficient γ3(k) for distance traveled.

The grey relational analysis coefficient γ3(k) also indicates that the highest value of it is voyage No18 with 1.0000. These left voyages have these values similarly under 0.5000. In turns, the voyage No16, No17, No19, and No20 is 0.3389, 0.3423, 0.3374, and 0.3333.

The grey relational analysis coefficient γ4(k) for amount of cargo carried on M/V NSU JUSTICE 250,000 DWT also indicates in Figure 9. However, the highest value of grey relational analysis coefficient is voyage No17 (1.0000). These left voyages are in turn 0.8667 for voyage No16, 0.3333 for voyage No18, 0.5102 for voyage No19, and 0.4815 for voyage No20.

image

Figure 9: Grey relational coefficient γ4(k) for cargo carried.

Furthermore, the compound diagram between these gray relational analysis coefficients is represented in Figure 10 responding to each different voyage.

image

Figure 10: Grey relational analysis coefficients.

From the combination of the grey relational analysis coefficients each other also indicates that the distance traveled by ship has an important position in field of the energy efficiency of ships. Since it has been identified that voyage No18 achieved low value of the Energy Efficiency Operational Indicator (EEOI index=1.13E-05) due to the grey coefficient analysis coefficient of this voyage gains the highest value (γ3(k)=1.0000). Moreover, the travel route optimization method is a starting point to carry out the energy efficiency of ships. This result has been performing throughout Figure 10. The different aspect of Figure 10 also indicates that the travel route optimization of ship will be the best method for energy efficiency management based on EEOI index associating with the grey relational analysis method in this research. In operational working, the significant selection of travel route will decide the energy efficiency of ship. The reduction of travel distance is a fundamental point in order to reduce the carbon dioxide emissions, fuel consumption of ship then enhancing the energy efficiency management of ship.

In Figure 11, the grey relational analysis grade γ at each different element of the energy efficiency operation index is identified. The response of experimental data is separate at each voyage. The grey relational analysis grade of the heavy fuel oil consumption is the highest with 0.8305 after that in turn the diesel oil fuel oil consumption, amount of cargo carrier, and distance traveled with these values are 0.7485, 0.6383, 0.4704.

image

Figure 11: Grey relational analysis grades.

In a result, the optimization of energy efficiency of ships through these operational parameters needs to consider under these external factors impact. Moreover, to gain the optimal operation in field of operating and managing the energy used on board must consider about the fuel consumption rates including the heavy fuel oil and diesel oil with the distance traveled of ship.

Conclusion

This article has referred to the use of energy efficiency of engine on ship by applying the Grey Relational Analysis method in research. The use of effective energy on ship will be reduced the fuel consumption rate of engine nowadays as well as protection of sea environment following the International Convention for Prevention of Pollution from Ships (MARPOL 73/78). Besides that, these results also indicated that the optimization of energy efficiency operation index through these operational parameters is able to do completely when considering about the distance traveled of ships. Moreover, the distance traveled can be conducted by Masters of their ships. This task can be ensured that the energy efficiency of ships increases along with remaining the ship’s safety in case of operation. In this research, the author has applied effectively the Grey Relational Analysis method in optimizing the experimental data of M/V NSU JUSTICE 250,000 DWT in field of the energy efficiency of ships. This is a new trend in the energy efficiency of ships especially using the energy economically on ships as well as restricting the exhaust gas emissions in the environment. In addition, the mass of consumed fuel oil on ships and cargo carried depend on different type of ships including passenger ship, container ship, Ro-Ro, etc. So, the use of energy efficiency operational indicator (EEOI) and grey relational analysis methodology can be applied into other cases of ship type not only bulk carriers like as M/V NSU JUSTICE 250,000 DWT but also various type of ship in the field of energy efficiency management of ships nowadays.

Acknowledgement

The author wants to give a kindly thank to Key Laboratory of Marine Power Engineering & Technology (Ministry of Transportation), School of Energy and Power Engineering, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, P.R. China to support in complement of this research.

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