Carbon Dioxide Emission Characteristics and Operation Condition Optimization for Slow-Speed and High-Speed Ship Engines

Abstract

Greenhouse gas emissions from ships are estimated to be approximately 1002 million tons per year; this is the largest carbon dioxide (CO₂) emission source among nonroad transportation. Previous studies have generally estimated CO₂ emissions using fuel- or power-based emission factors based on fuel consumption or engine power. In this study, CO₂ emissions from vessels were measured using a portable emission measurement system. Emission characteristics were analyzed according to the vessel’s operation conditions and compared with the results of other studies. Generally, the higher the rpm value, the more CO₂ is emitted, and the emissions at the maximum rpm differ depending on the type and size of the engine. In order to minimize the emissions by ships, those from high seas should be reduced rather than nearby ports. In addition, a method of establishing optimal operating conditions in consideration of economic and environmental perspectives was proposed. Fuel-based emission factors elicited in this study were constant regardless of engine rpm. The fuel-based emission factors of each engine were found to be similar at 3144.22 and 3150.58 kg-CO₂/tonne-fuel. Therefore, distinguishing CO₂ emission factors according to engine type is not necessary, and additional research is required to understand the emission factors of each fuel type.

1. Introduction

Fossil fuels have been widely used for industrial and technological development so the earth is currently facing a serious environmental crisis and ecosystem destruction due to greenhouse gases generated through their use [1]. Recently, interest in greenhouse gas emissions by ships has increased and many policies and regulations are being implemented and studies are being conducted to reduce these emissions and those from various industries [2,3]. Technologies such as E-fuel and ammonia fuel have emerged as strategies to reduce the dependence on greenhouse gases, but innovative technologies for their commercialization have not yet been developed.

In the transportation sector, the main sources of greenhouse gas emissions are passenger cars (41%), medium and heavy trucks (22%), shipping (11%), aviation (8%), and other modes of transport (18%). Most CO₂ is emitted by road mobile sources, such as passenger cars and trucks, whereas ships emit the most CO₂ in the field of nonroad mobile sources [4]. According to a recent IMO study, greenhouse gas emissions from ships, including CO₂, CH₄, and N₂O, increased by approximately 9.3% from 977 million tons in 2012 to 1076 million tons in 2018, of which around 1056 million tons were accounted for by CO₂ [5]. CO₂ emissions of ships are expected to decrease during 2020–2022 due to the recent decrease in international trade throughout COVID-19. However, as international trade has become active again post-COVID-19, CO₂ emissions from ships are also expected to increase.

There are both economic and time limitations when calculating total emissions by measuring air pollutants for a ship during operation; consequently, a method for calculating total emissions using ship activity data and an emission factor was used. Therefore, it is essential to ensure the reliability of the emission factor. Two types of emission factors were used to calculate the contents of air pollutants from ships: fuel- and power-based, whereby the fuel-based emission factor is constant regardless of the engine’s operating mode, whereas the power-based emission factor applies different emission factors depending on the engine modes used [6].

Most studies focus on calculating total or regional CO₂ emissions using existing emission factors (fuel-based or power-based) suggested by EEA, IMO, etc. H. N. Psaraftis et al. evaluated the relative impact on CO₂ emissions by ship types and sizes using fuel-based emission coefficients [7].

Tokuslu calculated the greenhouse gas emissions generated by ships at four ports in Georgia using ship activity data and emission factors. It was confirmed that greenhouse gas emissions differed depending on the type and size of ships considered. Accurate information concerning activity data, such as ship engine information, is necessary to minimize the uncertainty inherent to the emissions calculations [8].

Unlike previous studies, this study measured CO₂ emissions from actual operating ships using a portable emission measurement system (PEMS, Semtech DS+, Sensors Inc., Saline, MI, USA and Horiba Ltd., Horiba OBS-one, Horiba, Kyoto, Japan) used to measure air pollutant emissions from road mobile sources, such as passenger cars and trucks. A few studies used PEMS to measure air pollutants from ships. Jiang et al. compared measurement emission data using a SEMTECH-DS gaseous pollutant analyzer with emission data based on the Automatic Identification System (AIS) [9]. Wang et al. quantified fuel-based emission factors for cargo ships using a PEMS consisting of HORIBA OBS-2200 [10].

In this study, air pollutant emissions from two ocean-going vessels and three coastal vessels were measured using PEMS and compared. The CO₂ emission characteristics of slow-speed and high-speed engines were compared, and fuel-based emission factors were derived and compared with other studies. In addition, many studies are now addressing greenhouse gas reductions based on life cycle assessment (LCA) [11,12]. However, the development of marine engines is progressing more slowly than expected, making it difficult to switch to carbon-neutral fuels. Therefore, this study presented and compared methodologies for setting operating conditions to minimize CO₂ emissions for each ship on actual routes in order to present operating conditions for economic operation based on bunker A and diesel as commonly used fuels.

Many studies on pollutants emitted from vessels use AIS data because it is difficult to obtain operational data from ship engines like engine rpm, power, fuel consumption, etc. L. Yang et al. acquired operational data from AIS for ships operating in local Chinese waters to derive a power-based CO₂ emission factor and build an emission inventory [13]. J. Zhao et al. installed an energy efficiency management system (EEMS) on operating ships and used a PEMS to measure various pollutant emissions and derive a power-based CO₂ emission factor [14]. Most studies focus on power-based emission factors. For standardized vehicles such as cars and heavy equipment, using power-based emission factors is effective for compiling an overall emission inventory. However, for most ships, excluding those used in some studies, obtaining engine data to derive power-based emission factors is difficult. Thus, a fuel-based emission factor is considered more efficient. Unlike previous studies, this study did not use a ship’s data acquisition system but instead installed sensors on ships to measure fuel flow and used a PEMS to measure CO₂ emissions, thereby deriving a fuel-based emission factor.

This study characterizes CO₂ emissions from vessels in operation and presents a methodology for deriving fuel-based emission factors. In addition, we presented a method for setting optimal operating conditions to minimize CO₂ emissions. Based on this study, the results can be used as basic research data to present a CO₂ emission map from vessels in a particular region or worldwide and examine ways to minimize CO₂ emissions from vessels.

The structure of this paper is as follows: Section 2 describes the specifications of the vessel, engine, PEMS, and equipment used in the experiments in detail and presents a schematic of the emission measurement methodology. In Section 3, a correlation between the engine parameters and CO₂ emissions is identified, and the results of CO₂ emissions from ships operating at different engine rpm values are analyzed. The fuel-based CO₂ emissions of each ship according to these experimental results are calculated and compared with the results of other studies, and a method for setting optimal operating conditions for ships is presented. Section 4 presents the conclusions of this study and mentions directions and goals for further research.

2. Materials and Methods

2.1. Vessel and Engine Specifications

Ship engines can be divided into three main groups. An engine with a crankshaft rpm of less than 300 is classified as a slow-speed diesel engine (SSD), a value of 300–900 rpm is considered a medium-speed diesel engine (MSD), and more than 900 rpm is considered a high-speed diesel engine (HSD) [15]. The main propulsion engines of the five vessels used in this experiment can be classified as SSD and HSD.

As shown in

Table 1. Experimental vessel specifications.

Vessel ID	Vessel Type	Engine Type	Displacement (Ton)	Vessel size (L × B × H) (m)	Engine Specification	Fuel
SSD 1	Passenger vessel	Slow speed	9196	133 × 19.4 × 11.05	MAN B&W 6S40ME-B9.5 6618 kW/146 rpm	Bunker A
SSD 2	Passenger vessel	Slow speed	4701	103 × 15.6 × 9.9	MAN B&W 6S35MC 4440 kW/173 rpm	Bunker A
HSD 1	Passenger vessel	High speed	595	61.28 × 14 × 3	CUMMINS MARINE K38 690 kW/1900 rpm	Diesel
HSD 2	Passenger vessel	High speed	225	44.87 × 9 × 2.5	YANMAR 12LAAL-UTN 735 kW/1800 rpm	Diesel
HSD 3	Fishing boat	High speed	16	18.35 × 5.84 × 0.93	VOLVO D16-MI 401 kW/1800 rpm	Diesel

, five ships were considered when measuring CO₂ emissions from each main propulsion engine. Two passenger ships with SSD and two passenger ships and fishing boats with HSD differ in their type of engine (output, max rpm), size, use, and tonnage. Passenger ships are relatively large ships built for training apprentices, whereas passenger ships equipped with an HSD are used as a means of transportation to and from islands close to land. Ships with SSD use bunker A (diesel 70% + bunker C 30%) and ships with HSD use diesel as fuel.

2.2. Measurement System Specifications

Two types of PEMS were used to measure CO₂ concentrations in ship exhaust gas. Both types of equipment measure CO₂ concentrations using a nondispersive interpolated (NDIR) method, the detailed specifications of which are shown in

Table 2. Experimental CO₂ emission measurement system specifications.

Model	SEMTECH DS+	HORIBA OBS-ONE (HDV)
Measurement range	0–18% vol	0–20% vol
Measurement principle	NDIR	Heated NDIR
Zero drift	<±0.1% vol (over 4 h)	<±0.5% vol (over 4 h)
Span drift	≤2% of span value or ≤±0.1% vol	≤1% of span value or ≤±0.1% vol
Accuracy	<±2% of reading or ≤±0.3% of full scale	-
Linearity	|Xminx(a1–1) + a0| ≤ 0.5% of span Standard Error of estimates ≤ 1% of span	Intercept: |Xmin(a1–1) + a0| ± 0.5% of full scale Slope: 0.99 ≤ a1 ≤ 1.01
Repeatability	≤±2% of point or ≤±1% of span	≤±1% of full scale
Sample flow rate	3 LPM	2.5 LPM
Data rate	1–5 Hz	1–10 Hz
Vessel installed	SSD 1, SSD 2	HSD 1, HSD 2. HSD 3

. Due to the experimental schedule, SEMTECH DS+ was used to measure ships with SSD, and HORIBA OBS-ONE was used to measure CO₂ emission from ships with HSD; both sets of equipment collect one data point per second.

PEMS is mainly used to measure pollutants in road mobile sources and conduct the real driving emission (RDE) test; its suitability has been proven in many studies [16].

In addition, in order to minimize errors in the PEMS data, calibration was performed using a calibration gas (CO₂ 12.33%) before and after each experiment, and the filter inside the equipment was replaced for each test. The maximum measuring time was set within 4 h to minimize the span and zero drift according to the manufacturer’s recommendations.

The amount of fuel flow was measured by a Coriolis mass flowmeter (RHM 03L, Rheonik, Maisach, Germany). The fuel was modified to install a fuel flowmeter, and the device was calibrated before and after the experiment to ensure measurement accuracy. The detailed specifications of the fuel flowmeter are shown in

Table 3. Experimental fuel flowmeter specifications.

Model	RHM 03L
Accuracy	0.10%
Repeatability	0.05%
Responsibility	30 s
Pressure rating (dependent upon material)	Up to 1379 bar/20,000 psi
Operating temperature	−196~350 °C

.

2.3. Measuring and Analysis Methodology

The PEMS and fuel flowmeter were installed as shown in Figure 1 to measure CO₂ concentrations in exhaust gases and determine real-time fuel consumption. IMO NO_X TECHNICAL CODE 2008 recommends that exhaust gas measurement equipment be installed at least 0.5 m from the end of the exhaust pipe or three times the pipe diameter (3D) [17]. The PEMS connection was manufactured in the form of a tailpipe and installed on the straight part of the exhaust pipe to minimize the effects of turbulence.

Figure 2 shows a schematic of the PEMS and fuel flowmeter installed on the experimental ship. Most of the ship’s environment is inadequate and there is insufficient space to install each piece of equipment. Therefore, sufficient consideration is required for the installation of measuring equipment and each accessory, and it is essential to determine in advance whether or not the power supply of the ship can be used. In the case of fishing boats, it was impossible to supply power on its own, and power was supplied using a portable generator.

A PEMS is a device capable of measuring the CO₂ concentration (%) in exhaust gas using an NDIR method, and exhaust flow rate data are essential to convert the concentration (%) into gram (g) units. In general, since the engines installed on ships are larger than those in road mobile sources, it is impossible to use an exhaust flowmeter due to the high exhaust flow rate. Therefore, the U.S. Environmental Protection Agency (EPA) developed a carbon balance method to calculate the exhaust flow rate using the concentration of CO₂, carbon monoxide (CO), and total hydrocarbon (THC) in exhaust gas and fuel consumption [18,19]. In this study, that method was used to calculate the exhaust gas flow rate.

$$q_{exh}=\frac{F\times f_{fuel}}{f_{{CO}_2}\times u_{{CO}_2}\times c_{{CO}_2}+f_{CO}\times u_{CO}\times c_{CO}+f_{THC}\times u_{THC}\times c_{THC}}$$

(1)

where q_exh = exhaust flow rate(kg/s); F = fuel consumption (kg/h); f_fuel = carbon percentage in the fuel (mc/m_fuel); f_gas = carbon percentage of the pollutant according to the fuel (mc/m_gas); u_gas = pollutant density ratio to the total exhaust gas density; and c_gas = measured pollutant concentration (ppm).

The experimental design for data analysis of this paper is shown in Figure 3. The fuel consumption (kg/s) was measured with a Coriolis-type fuel flowmeter and the exhaust gas flow was calculated using the carbon balance method based on fuel consumption and fuel properties. Since the CO₂ emission was measured as % per volume by the NDIR method in the PEMS, it can be converted to gram units using the calculated exhaust gas flow value. The CO₂ emission value converted into mass units was classified by rpm to understand the CO₂ emission characteristics according to rpm variation, and the fuel consumption value was used to finally derive the fuel-based CO₂ emission factor.

For the experiments, any special operating conditions, variables for the external environment, and parameters were not set, and the vessels were operated by skilled experts based on their usual routes. The experimental conditions cannot be assumed to be representative of the operating environment of all ships, but the aim of this research is to explore a basic understanding of the CO₂ emission characteristics of ships with slow-speed and high-speed engines. In order to minimize the error caused by noise in the collected data, a statistical approach was mainly used for data analysis.

3. Result and Discussion

3.1. Correlation between CO₂ Emission and Engine Variables

Air pollutant emissions vary depending on the engine combustion condition, and that condition is greatly affected by the external environment of the vessel and engine operating conditions such as acceleration and deceleration. Pearson correlation analysis was performed on 12 pieces of data with approximately 12,000 rows to identify effective factors associated with CO₂ emissions. The data for analysis was collected from SSD 1, the only experimental vessel to acquire various engine operation data, and the data include speed, load, average MIP (mean indication pressure), average pmax (maximum pressure), scavenge air pressure and temperature, turbocharger in/outlet temperature, torque, power, rpm, and exhaust. Excluding the scavenge air temperature and turbocharger outlet temperature, the similarity was relatively high in nine items.

The data used in correlation analysis are dependent data that fluctuate together by increasing or decreasing rpm. Except for the scavenge air temperature, the correlation coefficient with rpm (yellow box) is over 0.7, showing a very high correlation. In the case of the scavenge air temperature, it appears to have a low correlation with rpm because it is mainly influenced by the surrounding air temperature and the cooling water temperature of the air cooler. As a result of the Pearson correlation analysis, as shown in Figure 4, the correlation coefficient between emissions and rpm is positive at 0.9, showing a very high correlation. Except for commercial ships such as container ships, oil tankers, and LNG carriers engaged in international trade, an engine operation data acquisition system is not installed on ships. However, rpm data are relatively easily accessible on most ships, and data can be acquired by configuring simple data acquisition (DAQ), so this study was conducted on CO₂ emission characteristics according to rpm variations.

In general, ship engine control is performed by setting the rpm and adjusting the fuel injection amount to reach the set rpm. The fuel injection quantity and the rpm variation have a linear relationship. Most of the fuel is composed of carbon compounds, whereas the carbon contained in the fuel is completely burned due to combustion in the engine and emitted as CO₂ and water, as shown in Equation (2). However, as the air–fuel ratio decreases, the carbon in the fuel is incompletely burned and included in its exhaust gas in the form of CO or THC. In other words, greenhouse gas emissions by combustion in internal combustion engines are related to the amount of fuel consumption, the conditions of combustion, and the amount of carbon within the fuel [20]:

$$C_a{H}_b+\left(a+\frac{b}{4}\right)\left(O_2+3.773N_2\right)={aCO}_2+\frac{b}{2}{H}_{2}O+3.773\left(a+\frac{b}{4}\right)N_2$$

(2)

In the case of a spark ignition engine using gasoline as fuel, the ratio of fuel injected is higher than that of air in the high-load section, resulting in high CO emissions due to incomplete combustion. However, in the case of a compression ignition engine that uses diesel as fuel, CO emissions are very low because the fuel is injected in an appropriate ratio. This study aims to identify emission characteristics and compare them with the results of various studies by measuring CO₂ emissions from the main propulsion engine installed on the ship [21,22,23].

3.2. CO₂ Emission According to Vessel’s Operation

To evaluate CO₂ emissions from diesel engines mounted on ships, exhaust gases from two large passenger ships, two small passenger ships, and one fishing boat sailing along the coast were measured during operations using a PEMS.

Figure 5 shows the real-time CO₂ emission measurement results according to rpm variations for five experimental ships in normal operation modes. With increasing rpm, the load on the engine increases; this causes the fuel consumption to increase, and the CO₂ emission (unit: g) also increases as shown in graphs (a)–(e) of Figure 5. In addition, the data show that CO₂ emissions change with rpm when the rpm of each ship changes, as shown in graphs (a) and (e) of Figure 5. In particular, CO₂ emissions react sensitively to instantaneous rpm fluctuations at a low rpm.

Overall, higher rpm values correlate with higher CO₂ emissions; CO₂ emissions at the highest rpm vary depending on the type and capacity of the engine used. This is because the fuel consumption required at the maximum output is different depending on the engine capacity (unit: cc). In international trade ports such as Shanghai, Busan, and Singapore, CO₂ emissions are expected to be higher than in other regions because large ships with relatively large capacity engines frequently stop at these ports. In particular, when applying a carbon tax policy to shipping companies, the size of the engine mounted on the ship should be used as a standard.

The rpm of a ship fluctuates depending on external influences, such as current and wind direction, and there is a difference in fuel consumption required by the engine to maintain the rpm and speed. As shown in part (a) of Figure 5, there is a difference in CO₂ emissions even in 140 rpm conditions, and it is assumed that this has an influence on the external environments. Since the current and wind direction change depending on the season, it is necessary to evaluate the impact on the external environment according to the season during which CO₂ emissions in the shipping sector are evaluated.

Marmer et al., Medina et al., and Well et al. evaluated the seasonal impact on air pollution emissions from ships [24,25,26], and when considered together with the results of this study, the development of optimal sailing routes for each season is considered essential to minimize greenhouse gas emissions.

Kecojevic et al. (2010) demonstrated that engine load (speed) and fuel consumption have a linear relationship; consequently, CO₂ emissions were also related [27]. Therefore, to reduce CO₂ emissions in port areas close to land, it is necessary to improve dock facilities to make it easier to dock and develop a docking method that is most suitable for the dock environment using navigation simulations; this can reduce CO₂ in the long term.

Generally, the emission factor used to calculate CO₂ emissions is fuel-based (kg-CO₂/tonne-fuel) or power-based (kg-CO₂/kWh). However, it is difficult to measure data on real-time fuel consumption or engine output on vessels other than commercial ships, and collecting data is even more difficult. Each ship’s engine has a different maximum rpm and CO₂ emissions depending on the capacity of each engine. Therefore, in order to achieve standardization, the rpm measured from SSD 1 to HSD 3 is converted into a dimensionless number as shown in Equation (3), and the dimension is reduced by using the Log function for CO₂ emissions in g units.

$${DN}_{RPM}=\frac{RPM}{Max.RPM}$$

(3)

CO₂ emissions according to the six stages of rpm are shown in Figure 6. Because the study was conducted on two SSD and three HSD vessels, it is difficult to conclude that the results are highly reliable. However, there is a tendency to be divided into HSD and SSD groups in general. For ships corresponding to SSD, the average CO₂ emissions according to each rpm stage are between 1.91 and 2.90, and for HSD, it is between 1.27 and 1.93. The difference in CO₂ emissions according to rpm is up to 16% for HSD and 19% for SSD, which is predicted by differences in the ship’s shape and tonnage. CO₂ emissions appear to decrease in the 50–55 section of SSD 1, but this is judged to be an error due to the small number of data in that section. In addition, as rpm increases, CO₂ emissions also increase linearly. Therefore, if various data on emissions are collected for each engine speed, it is expected that a statistical basis for emissions data for each engine rpm can be prepared.

Figure 7 shows the CO₂ emissions according to the operating routes of the SSD 1 vessel, which primarily sails the ocean, and the HSD 2 vessel, which sails to fisheries near land. Normally, an engine on each vessel is operated in a steady rpm range at high sea, but the difference in the CO₂ emission quantity is approximately 5 times.

There are two vessels that have the most significant difference in their sailing patterns. SSD1, with a slow-speed large engine, fluctuates its rpm frequently to berth. Geographical conditions may affect the differences, but it is suspected that slow-speed large engines, unlike high-speed engines, have a large change in speed according to rpm variations, so they steer the ship using momentary thrust. Even in the high sea, where the rpm is relatively constant, CO₂ emissions form a peak, which is expected to be under the influence of instantaneous changes in tides, wind direction, etc. In other words, CO₂ emissions are expected to be higher than in normal conditions when ships are sailing in reverse tide or headwind conditions.

The CO₂ emission characteristics in SSD and HSD show the same pattern where emissions increase as rpm increases, but there is a difference in quantities. Therefore, a standard is necessary to quantitatively evaluate CO₂ emissions for various ships, and fuel-based emission factors and power-based emission factors are commonly used. However, unlike cars, it is almost impossible to obtain real-time output data from ship engines because on-board diagnostics (OBD) cannot be connected. Therefore, in this study, a fuel flow sensor was installed to measure the fuel consumption to calculate the fuel-based CO₂ emission factor.

3.3. CO₂ Emission Factor for SSD and HSD

Figure 8 shows the fuel-based CO₂ emissions according to an rpm range of 1~6: 3117.53–3162.57 kg/tonne-fuel for SS1, 3044.75–3136.17 kg/tonne-fuel for SSD 2, 3134.98–3156.19 kg/tonne-fuel for HSD 1, 3100.04–3162.94 kg/tonne-fuel for HSD 2, and 3147.06–3162.61 kg/tonne-fuel for HSD 3. The CO₂ emissions from the combustion of 1 ton of fuel are found to not vary significantly depending on the rpm range.

The Range 2 section of SSD 2 ships is a critical rpm zone, which should be avoided by rapid acceleration and deceleration to minimize damage due to resonance. Therefore, the difference in trend is considered to be due to the fact that measured data for the SSD 2 Range 2 do not include enough data in a stable state for a sufficient period of time.

The amount of CO₂ emitted per 1 ton of fuel burned, normalized by the rpm of the five experimental vessels, is shown in Figure 9. The amount of CO₂ emitted per ton of fuel over the entire rpm range is mostly distributed between 3050 and 3163 kg, with a mean value of 3148.78 and a standard deviation of approximately 17.71, indicating that there is little difference in the amount of CO₂ kg/tonne-fuel depending on the type of engine. Due to the characteristics of the diesel engine, it was confirmed that there was almost no difference in CO₂ emissions per 1 ton of fuel burned between SSD and HSD due to perfect combustion over the entire rpm range tested [28].

Therefore, when calculating CO₂ emissions from ships, the impact of load is expected to be insignificant, and the results of calculating CO₂ emissions based on total fuel consumption are expected to be highly reliable. The CO₂ emissions per fuel amount vary depending on the carbon content in the fuel. Therefore, uncertainties prevail when applying this result to ships using gasoline or LNG.

Currently, various fuels, such as LNG and LPG, are used for the main propulsion engines of ships. Demirbas et al. confirmed that CO₂ emissions from engine exhaust gases vary depending on the carbon content in fuel, as shown in Equation (4) [29,30]. It is therefore necessary to study the CO₂ emissions for various fuels as follows:

$$C_a{H}_{2a+2}+\frac{3a+1}{2}{O}_2\to \mathrm{a}{CO}_2+(a+1)H_{2}O$$

(4)

The operation mode of the ship can be classified as either maneuvering mode (M) or cruising mode (C), except when docked on land. When in cruising mode, the ship operates at a relatively high rpm when sailing in the ocean and can change rpm immediately according to the external environment and traffic volume in the maneuvering mode. As shown in Figure 10, the CO₂ emissions consistent with both maneuvering and cruising modes are shown during the entire experimental period of SSD 1, SSD 2, HSD 1, HSD 2, and HSD 3.

In addition, the fuel-based emission factors for SSD and HSD are shown in

Table 4. Fuel-based CO₂ emission analysis results of this study (unit: kg/tonne-fuel).

	SSD 1		SSD 2		HSD 1		HSD 2		HSD 3
	C	M	C	M	C	C	M	C	M	C
Q1	3156.83	3148.50	3131.81	3132.07	3111.34	3154.53	3144.60	3147.08	3150.46	3158.67
Q3	3162.93	3151.04	3138.43	3136.94	3148.55	3158.86	3156.50	3157.73	3164.37	3162.12
Median	3160.36	3149.84	3135.56	3135.16	3139.24	3157.33	3150.45	3155.11	3159.73	3160.53
Average	3156.82	3149.86	3132.36	3134.44	3128.31	3155.55	3146.41	3147.34	3156.30	3159.85
Stand. DV	27.61	2.03	28.89	3.75	25.09	5.01	16.37	18.91	11.20	3.42

For SSD, the CO₂ emission factors are 3132.36–3156.82 kg/tonne-fuel in maneuvering mode and 3131.44–3149.86 kg/tonne-fuel in cruising mode. In the case of HSD, the corresponding values are 3128.31–3156.30 kg/tonne-fuel and 3147.34–3159.85 kg/tonne-fuel in cruising mode. These findings demonstrate that there is little difference in fuel-based emission factors between operation modes.

As such, it was confirmed that there is almost no difference in fuel-based CO₂ emission factors for each operation mode. In the case of SSD, the difference between maneuvering and cruising modes is approximately 0.08% and 0.33% in the case of HSD. In addition, the difference between SSD and HSD is 0.03% in maneuvering mode and 0.38% in cruising mode.

According to previous studies [31,32,33,34,35], the fuel-based emission factors for air pollutants excluding CO₂ differ depending on the type and size of the engine used. This study has demonstrated that the fuel-based emission factor for CO₂ is constant regardless of the engine type. Therefore, the type of engine is not expected to have a significant impact on the emission factor of the shipping sector; it is instead expected that the reliability of this study will increase through additional research on MSD (300–900 rpm) to supplement the research results.

Fuel-based CO₂ emission factors for SSD and HSD are shown in

Table 5. Comparison with other studies and criteria (unit: kg/tonne-fuel).

	This Study		EEA [37]		IMO [36]		Fan Zhang [31]			Williams et al. [38]
	SSD	HSD	LFO	MGO	LFO	MGO	HH	DFH	XYH	Commercial Vessel
Emission factor	3144.32	3150.58	3151	3206	3114	3206	3071	3153	3151	3170

. The CO₂ emission factor for SSD using bunker A as fuel is 3144.32 kg/tonne-fuel and 3150.58 kg/tonne-fuel for HSD using diesel. A significant difference in emission factors is observed between each institution and other studies including this result [31,36,37,38].

Although no significant differences have been observed in the emission factors through many studies, continued research and experimentation are necessary to develop emission factors for each fuel. In addition, in the results of this study, it is not necessary to classify emission factors according to low, medium, and high speeds when developing emission factors. Therefore, it is expected that reliable CO₂ emission factors can be obtained even if experiments are conducted on accessible engines in consideration of the economic costs.

3.4. Optimizing the Vessel’s Operation Condition

According to Kim et al. (2005), container ships sailing in the European–Far East sail approximately 23,692 miles one way and approximately 13,662 miles in the Far East–North America [39]. Most of the sailing sections of ships engaged in international trade operate their engine at a high rpm range, and when considering the operating time for one round trip, CO₂ emissions in the seas are known to be high in absolute volume terms when compared with sections close to land. i.e., to minimize CO₂ emissions due to the international shipping trade, it is more effective to intensively manage CO₂ emissions in the ocean rather than emissions in areas close to land.

Tokuslu et al. (2020) demonstrated that CO₂ emissions from a ship’s main propulsion can be reduced by controlling the speed of ships through the energy efficiency design index (EEDI) [40]; many studies have thus suggested that CO₂ emissions may be significantly reduced by applying the EEDI to Very Large Crude Carrier and cargo ships [41,42]. It is expected that CO₂ emissions from ships can be minimized by further expanding the widely implemented EEDI and modifying the energy efficiency existing ship index (EEXI).

Currently, systems such as the EEDI, EEXI, and ship energy efficiency management plan (SEEMP) are being implemented to minimize CO₂ emissions by maximizing energy efficiency. However, some experts suggest that these are not effective [43,44]. However, as shown in Figure 6 and Figure 7, there is a difference in CO₂ emissions depending on the rpm range. So, this study analyzed the correlation between a vessel’s speed and CO₂ emissions to derive the optimal operating speed.

A simulation was conducted by setting up a virtual route between the Far East (Ulsan, Republic of Korea) and the Middle East (Ras Tanura, Saudi Arabia), which is mainly used to transport crude oil, as shown in Figure 11. The sailing distance is approximately 6382 miles one way, and the rpm, speed, fuel consumption, and speed-specific fuel-based CO₂ emission factor of the five test vessels were set as determining factors. As shown in Figure 12, the rpm is divided into six levels, and the total CO₂ emissions are calculated considering the average speed, navigation period, and fuel consumption for each section. Based on actual measurement data, the speed, fuel consumption, and CO₂ emission factor are classified according to the rpm. The total sailing duration and the total CO₂ emissions for each rpm range can be calculated according to the amount of fuel consumed in the corresponding duration.

According to the calculation formula shown in Figure 12, the total operating time and CO₂ emissions according to the rpm range of the five experimental ships in the Far East (Ulsan, Republic of Korea) to the Middle East (Ras tanura, Saudi Arabia) operation section are shown in Figure 13. It is a general fact that the lower the rpm, the longer the operation time. But, as shown in Figure 13b, lower rpm does not mean that the total CO₂ emissions are lower. This appears to be because fuel consumption does not have a completely linear relationship with rpm changes. In other words, it means that lowering the speed unconditionally does not lower the total CO₂ emission.

The main purpose of shipping is to transport goods for profit. Therefore, shipping companies operate their vessels under optimal operating conditions, taking into account operating time and fuel consumption. However, as global warming is currently accelerating due to the indiscriminate use of fossil fuels, it is not considered appropriate to operate vessels by approaching only from an economic perspective. However, setting operating conditions considering only an environmental point of view is not suitable for the purpose of operating vessels. In other words, vessels must be operated taking into account both economic and environmental standpoints. This study presents a method to set optimal operating conditions as follows. First, based on constant speed operation conditions on the high sea, the ratio of the operating time and total CO₂ emissions corresponding to each rpm range is set as a factor. The lower the rpm, the longer the operation time, so the factor value becomes lower. As for the factor for total CO₂ emissions, the smaller the amount, the higher the factor value. As shown in Equation (5), an operation coefficient is derived by multiplying the two factors calculated for each rpm range, and the rpm range in which the highest operation coefficient is derived is proposed as the optimal operating condition.

$${CF}_{OP}=F_{SD}\times F_{CO2}$$

(5)

where FSD = sailing duration factor; FCO₂ = total CO₂ emission factor; and CFOP = operation coefficient. Figure 14 shows the operation coefficient results according to each rpm range change in the simulation operation section of experimental vessels SSD 1–2 and HSD 1–3. Range 3 for SSD 1, range 2 for SSD 2, range 2 for HSD 1, range 6 for HSD 2, and range 3 for HSD 3 were found to be the optimal operating conditions considering both economic and environmental perspectives.

The results shown in this study are the optimal operating conditions and are not applicable to all types of vessels. However, as in this study, it is necessary to suggest the optimal operating conditions for each vessel that can satisfy economic aspects and protect the environment under various conditions. As shown in Figure 14, the optimal operating conditions are expected to be different according to the shape, tonnage, engine type, and power of each ship. Therefore, it would be practically appropriate to calculate the optimal operating conditions of the vessels by approaching it from various perspectives rather than simply lowering the load and operating economically.

4. Conclusions

Prior studies on exhaust gas emissions from ships have primarily focused on gaseous substances, such as NO_X and SO_X, or particulate matter, such as PM10. In the case of CO₂ emissions, total emissions are calculated based on estimated fuel consumption. In this study, CO₂ emissions were measured using a PEMS and the results were compared with those of other studies. Using experimental ships (two passenger ships equipped with an SSD, two passenger ships equipped with an HSD, and one fishing boat equipped with an HSD), the CO₂ emissions from the main propulsion engine were measured in real time without setting any operational conditions for the experiment, and the following conclusions are drawn:

• Depending on the type of engine size and displacement, there is a difference in CO₂ emissions at the engine’s maximum output (maximum rpm), whereby the higher the displacement, the higher the CO₂ emissions. Therefore, a differentiated plan according to engine size should be prepared when establishing a policy for limiting CO₂ emissions by ships.
• Fuel-based emission factors are similar for both types of engine considered, and only a slight difference is observed in emission factors, even when both types of engines used bunker A and MGO. Therefore, when calculating the total CO₂ emitted from ships, using a fuel-based emission factor is highly desirable.
• According to the results of this study, it is found that there was little difference between the emission factors of SSD (3144.32 kg/tonne-fuel) using bunker A and HSD (3150.58 kg/tonne-fuel) using diesel. In addition, no difference in the fuel-based CO₂ emission factor according to engine rpm is observed, and there is no significant difference arising from the comparison with the results of other studies and the emission factor presented by EEA, IMO, and other studies. However, to develop the CO₂ emission factor for each fuel, continuous experiments are needed in the future.
• Ships spend most of their time sailing in the ocean using high rpm and emitting immense amounts of CO₂. Therefore, to minimize CO₂ emissions, it is necessary to establish a method to reduce CO₂ emissions in the ocean. To establish optimal operating conditions from economic and environmental perspectives, a method was proposed using the sailing duration and CO₂ emission data. The optimal operating conditions for each ship are different, and the optimal operating conditions for the ship should be evaluated by approaching them from several standpoints.
• This research method has the advantage of being able to measure ships for which it is impossible to acquire engine data from a data acquisition system, but it needs a lot of manpower and expertise such as fuel line modification. Additionally, it cannot be concluded that the vessels or engines used in this experiment are representative of all vessels. Therefore, we found that basic experiments on engine dynamo are absolutely necessary, like in the early stages of developing automobile emission factors. In future research, representative ships and engines will be selected through basic data on ship distribution, and basic experiments on various engines will be conducted on an engine dynamo. The raw data supporting the conclusions of this article will be made available by the authors on request.