Search Results (69)

This study investigates the environmental and operational performance of X-DF and ME-GI propulsion systems in large LNG carriers, focusing on key emission and transport efficiency metrics—CO₂, the EEOI, and the CII—and their relationship with operational factors such as shaft power, vessel speed, propeller slip, and specific fuel oil consumption. Statistical methods including correlation analysis, regression modeling, outlier detection, and clustering are employed to evaluate engine behavior across the ship’s fuel gas steaming envelope and to identify critical efficiency trends. The results show that ME-GI engines deliver lower CO₂ emissions and consistent efficiency under steady-load conditions, due to their higher thermal efficiency and precise control characteristics. In contrast, X-DF engines demonstrate greater adaptability, leveraging LNG combustion to achieve cleaner emissions and optimal performance in specific operational clusters. Clustering analysis highlights distinct patterns: ME-GI engines excel with optimized shaft power and RPM, while X-DF engines achieve peak efficiency through adaptive load and fuel management. These findings provide actionable insights for integrating performance indicators into SEEMP strategies, enabling targeted emission reductions and fuel optimization across diverse operating scenarios—thus supporting more sustainable maritime transport. Full article

The analysis conducted in this paper observes long-term trends in the LNG transportation market with a tendency to predict its future development by applying market patterns and variables that follow changes in the historical period. The basis of this paper is a previous study that provided an analysis of this sector until the end of 2024 using linear regression. A comparison of the predictions with the results obtained showed that these predictions were mostly accurate, with a small deviation in LNG trade volume (10.9%), LNG fleet size (12.7%), and the number of countries exporting LNG (5%). The largest deviation was in the propulsion systems for new ships, where a new system (ME-GA) was introduced but later abandoned. Based on previous studies and current data, a forecast was made until the end of 2029, which shows that the LNG market will continue its growth and the LNG trade volume will exceed 450 MT by the end of this decade. As a result, the LNG fleet will grow to over 900 vessels. The data on the propulsion types of the LNG fleet show that the trend shown in previous studies will continue, namely that this LNG fleet will be powered by XDF and MEGI plants. Full article

In order to analyze the explosion risk of the engine room, this paper uses CFD software to simulate the LNG leakage process in the engine room of the ship, and uses the combustible gas cloud obtained from the leakage simulation to simulate the explosion, analyzing its combustion and explosion dynamics. On the basis of previous studies, this paper studies the coupling of leakage and explosion simulation to ensure that it conforms to the real situation. At the same time, taking explosion overpressure, explosion temperature, and the mass fraction of combustion products as the breakthrough point, this paper studies the harm of explosion to human body and the influence of ignition source location on the propagation characteristics of LNG explosion shock wave in the engine room, and discusses the influence of obstacles on gas diffusion and accumulation. The results show that the LNG leakage reaches the maximum concentration in the injection direction, and the obstacles in the cabin have a significant effect on the diffusion and accumulation of gas. In the explosion simulation based on the leakage results, it can be determined that the shape of the pressure field generated by the explosion is irregular, and the pressure field at the obstacle side has obvious accumulation. Finally, in order to reduce the explosion hazard, the collaborative strategy of modular layout, directional ventilation, and gas detection is proposed, which provides ideas for the explosion-proof design of the cabin. Full article

An increasing demand can be observed in ship-to-ship (STS) liquefied natural gas (LNG) bunkering operations, and the failures involved may lead to considerable casualties or environmental damage. For this purpose, a comprehensive methodology is proposed in this study to identify and assess these failure modes. In detail, the STS LNG bunkering process is first decomposed to develop a hierarchical structure according to systems-theoretic process analysis (STPA), the results of which serve to identify potential failure modes and their causes. Then, all the failure modes are evaluated by experts in terms of occurrence, severity, and detectability to develop a fuzzy confidential matrix, which is then transferred as an explicit confidential matrix to be weighted and normalized. Finally, the risk levels of these failure modes are analyzed by relative closeness obtained from the technique for order preference by similarity to an ideal solution (TOPSIS). This study determines nine failure modes, all of which are ranked in terms of risk level. “High pressure in vapor return line”, and “High flow rate and leakage of LNG” are determined as the top two failure modes, with risk closeness values of 0.5791 and 0.5728, respectively. “Power failure for emergency valves” is ranked as the last one, with the risk closeness value being 0.5444. Finally, suggestions are proposed according to bunkering operation guidelines to prevent or control these failure modes. Full article

At present, shipping companies are aiming to meet better energy and environmental requirements when designing large cruise ships, thus decreasing emissions, increasing efficiency and reliability and greatly reducing maintenance time and costs. This paper provides a technical–economic comparison for a real case study, including a complete feasibility study regarding the sizing of a generation system to supply base hotel loads, between two power plant architectures focused on fuel cells and diesel generators for a cruise ship. The paper describes, in detail, an innovative solid oxide fuel cell (SOFC) generation system, which offers high efficiency and low emissions, assessed for its technical, economic and environmental performance. This study examines generators for hotels, requiring continuous service at constant load and a 1 MW power supply. The work relates to ships with a tonnage of more than 100,000 tons. Subsequently, considering that, in the case study, the diesel generators are powered by LNG (liquefied natural gas), there will also be a comparison with a case where both systems are simply powered by LNG. The main technical specifications required by shipbuilders for choosing the most suitable system for on-board generation (weight, volume, maintenance intervals and operations, as well as investment and operational expenses) are analyzed and described. The economic comparison is based on two extreme assumptions of the purchase and operating costs of the fuel cell system and returns a different result depending on the assumption adopted. The usefulness of the proposed solution based on fuel cells is demonstrated on the basis of an accurate technical, energetic and economic comparison with the conventional technologies based on diesel generators. The work is completed by evaluating the overall power-generating reliability improvement achievable with the new technology, in comparison with the traditional system. The comparison between the fuel cell system and the diesel system shows that the former has a higher weight (+40%), volume (+75%) and initial investment cost (3–6 times higher). However, the lower LNG consumption reduces the annual operating cost and the size and weight of the on-board tanks or, with the same tank capacity, increases the system’s range. The overall reliability of the fuel cell system is significantly higher than that of the traditional system. Full article

In response to climate change caused by shipping, the maritime emission trading system (METS) is used to reduce ship carbon emissions, and the METS also imposes additional costs on shipping carriers through emission permit trading. This paper focuses on the deployment of liquefied natural gas-powered (LNG-powered) ships for shipping alliances to comply with the METS. From the perspective of a liner alliance, we investigate how to determine the deployment of LNG-powered ships and how ship emissions will be affected. To investigate these problems, we propose an LNG-powered fleet deployment problem, which integrates slot co-chartering and emission permit trading, to determine the fleet deployment of LNG-powered and oil-powered ships, ship speeds and container shipment. To formulate our proposed problem, we develop a mixed-integer linear programming model, which can be solved effectively by CPLEX. Numerical experiments are provided to assess the effectiveness of our proposed model. Full article

The increasing emphasis on environmental sustainability and stricter gas emissions regulations has made the optimization of fuel and emissions a crucial factor for marine propulsion systems. This paper investigates the potential to improve fuel efficiency and reduce emissions of LNG ship propulsion systems by using different load sharing strategies in Dual-Fuel Diesel-Electric (DFDE) propulsion systems. Using data collected from on-board cyclic measurements and an optimization model, the effects of different load sharing strategies for various types of fuel, such as HFO, MDO, and LNG, under different engine load conditions were investigated. The results of these strategies are compared with those of on-board power management systems (PMS), which evenly allocate power among the engines, irrespective of fuel usage and emission levels. The results show that load adjustments according to the optimization model can considerably increase fuel economy and contribute to the reduction of CO₂ and NO_x compared to standard practice at the equal load in different ship operating modes. Our approach introduces an innovative optimization concept that has been proven to improve fuel efficiency and reduce emissions beyond standard practices. This paper demonstrates the robustness of the model in balancing environmental and operational objectives and presents an effective approach for more sustainable and efficient ship operations. The results are in line with global sustainability efforts and provide valuable insights for future innovations in energy optimization and ship emission control. Full article

International shipping is responsible for approximately 2.7% of the global greenhouse gas emissions, a share expected to rise by as much as 250% by 2050. In response, the International Maritime Organization (IMO) has set ambitious targets to reduce these emissions to near-zero by 2050, focusing on alternative fuels like LNG. This study examines the energy consumption patterns of dual-fuel engines powered by LNG and develops machine learning models using LightGBM to predict fuel usage for both fuel oil (FO) and gas (GAS) modes. The methodology involved analyzing operational data to identify patterns in fuel usage across different voyage conditions. The FO mode was found to be predominantly used for rapid propulsion during speed changes or directional shifts, while the GAS mode was optimized for stable conditions to maximize fuel efficiency. Additionally, a mixed mode of FO and GAS was occasionally applied on complex routes to balance safety and efficiency. Using these insights, LightGBM models were trained to predict fuel consumption in each mode, achieving high accuracy with R² scores of 0.94 for the GAS mode and 0.98 for the FO mode. This model enables ship operators to optimize fuel decisions in response to varying voyage conditions, resulting in reduced overall fuel consumption and lower CO₂ emissions. By applying the predictive model, operators can adjust fuel usage strategies to match operational demands, potentially achieving notable cost savings and meeting stricter environmental regulations. Furthermore, the accurate estimation of fuel usage supports CO₂ emissions management, aligning with the Carbon Intensity Indicator (CII) and providing ship operators with actionable data for fleet management optimization. This research provides essential data to support carbon emission compliance, improves fuel efficiency, and offers practical insights into fuel management strategies. The predictive model serves as a valuable resource for ship operators to optimize fuel use and aligns with the IMO’s environmental targets, aiding the maritime industry’s transition toward carbon neutrality. Full article

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships, car carriers, oil tankers, bulk carriers, and container ships, under six different operational scenarios, such as varying cargo loads and weather conditions. Traditional fuels, like marine gas oil (MGO) and intermediate fuel oil (IFO), challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm, which dynamically adjusts engine parameters, demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL, fuel efficiency increased by up to 10%, while EEXI values decreased by 8% to 15%, and CII ratings improved by 10% to 30% across different scenarios. Specifically, under heavy cargo loads, the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally, the DRL approach consistently outperformed traditional optimization methods, demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels, integrating additional machine learning techniques to enhance optimization. Full article

This research paper presents an effective approach to reducing marine pollution and costs by determining the optimal marine alternative fuels framework for short-sea shipping vessels, with a focus on energy efficiency. Employing mathematical models in a Python environment, the analyses are tailored specifically for conventional and fully autonomous high-speed passenger ferries (HSPFs) and tugboats, utilizing bottom-up methodologies, ship operating phases, and the global warming potential approach. The study aims to identify the optimal marine fuel that offers the highest Net Present Value (NPV) and minimal emissions, aligning with International Maritime Organization (IMO) regulations and environmental objectives. Data from the ship’s Automatic Identification System (AIS), along with specifications and port information, were integrated to assess power, energy, and fuel consumption, incorporating parameters of proposed marine alternative fuels. This study examines key performance indicators (KPIs) for marine alternative fuels used in both conventional and autonomous vessels, specifically analyzing total mass emission rate (TMER), total global warming potential (TGWP), total environmental impact (TEI), total environmental damage cost (TEDC), and NPV. The results show that hydrogen (H2-Ren, H2-F) fuels and electric options produce zero emissions, while traditional fuels like HFO and MDO exhibit the highest TMER. Sensitivity and stochastic analyses identify critical input variables affecting NPV, such as fuel costs, emission costs, and vessel speed. Findings indicate that LNG consistently yields the highest NPV, particularly for autonomous vessels, suggesting economic advantages and reduced emissions. These insights are crucial for optimizing fuel selection and operational strategies in marine transportation and offer valuable guidance for decision-making and investment in the marine sector, ensuring regulatory compliance and environmental sustainability. Full article

The study introduces an innovative three-stage nested power generation system that enables the cascading utilization of LNG cold energy. It makes the most of wasted energy by using ship jacket cooling water (JCW) and exhaust gas (EG) as heat sources, a trans-critical carbon dioxide cycle as internal circulation, and utilizing the pressure exergy of LNG. We choose two azeotrope mixing fluids that match the requirements and create four cases for the outer and middle cycle working fluids in the three-stage nested system. To discover the ideal system performance from the perspectives of exergy (E), economy (E), and environment (E), four cases were subjected to multi-objective optimization using the multi-objective particle swarm optimization technique (MOPSO). Finally, the optimal solution was found by applying the TOPSIS decision-making method. Through comparative analysis, the optimal system is selected among the four optimization results. R170 (22.66%) and R1150 (77.34%) are used as the outer circulating working medium, while R170 (90.86%) and R1270 (9.14%) are utilized as the inter-cycle working fluid. The net output work is 575.75 kW, the optimal exergy efficiency is 46.09%, the optimal electricity production cost is $0.04009 per kWh, the carbon dioxide emissions can be reduced by 36,910 tons, and the payback period is 2.548 years. After optimization, a more energy-efficient and environmentally friendly power generation system is obtained. Full article

This study was aimed at improving the energy efficiency of the distribution of liquefied natural gas (LNG) as shipping fuel in the southern Baltic Sea. The objective of this study was to determine the demand for LNG for maritime shipping by analyzing the distribution of the resource from the water side (ship-to-ship). LNG was chosen due to the location of the LNG terminal in Świnoujście within the analyzed water area, where a problem has arisen in the southern part of the Baltic Sea regarding fuel supply for vessels due to the lack of developed infrastructure along the coast. An analysis was conducted to optimize the size of the LNG fleet and infrastructure facilities. Seeking compliance with Annex VI to the MARPOL 73/78 Convention, adopted by the International Maritime Organization (IMO), shipowners see potential in the switch from conventional fuels to LNG. As one of the alternative solutions, it will contribute to reducing harmful emissions. Determination of the LNG distribution volume requires the identification of LNG storage facility locations, specifying the number of LNG-powered ships (broken down by type) and the number of LNG bunkering ships. The first part of this study contains a detailed analysis of the number of sea-going ships that provide services in the southern part of the Baltic Sea and the world’s number of LNG bunkering ships. The database contains a set of the characteristics required to determine the optimal demand for LNG, where LNG bunkering vessels are capable of supplying fuel within the shortest possible time and covering the shortest possible distance to LNG-powered ships. The characteristics include the type of ship, requested LNG volume, the speed of LNG bunkering ships, the distance between LNG facilities, and the loading rate (the volume of fuel received per time unit). Based on the collected data, the volume of LNG distribution was determined using MATLAB R2019a software. The remainder of this study contains a description of the conducted research and results of an analysis of the traffic density in the Baltic Sea. The results were obtained on the basis of data from the Statistical Yearbook of Maritime Economy and IALA IWRAP Mk2 2020 software. The number of LNG-powered ships and number of LNG bunkering ships were specified, and the demand for LNG for the area under analysis was determined. Full article

Solving strategic IMO tasks for the decarbonization of maritime transport and the dynamics of its controlling indicators (EEDI, EEXI, CII) involves the comprehensive use of renewable and low-carbon fuels (LNG, biodiesel, methanol in the mid-term perspective of 2030, ammonia, and hydrogen to achieve zero emissions by 2050) and energy-saving technologies. The technology of regenerating secondary heat sources of the ship’s power plant WHR in the form of an Organic Rankine Cycle (ORC) is considered one of the most promising solutions. The attractiveness of the ORC is justified by the share of the energy potential of WHR at 45–50%, almost half of which are low-temperature WHR (80–90 °C and below). However, according to DNV GL, the widespread adoption of WHR-ORC technologies, especially on operating ships, is hindered by the statistical lack of system prototypes combined with the high cost of implementation. Developing methodological tools for justifying the energy efficiency indicators of WHR–ORC cycle implementation is relevant at all stages of design. The methodological solutions proposed in this article are focused on the initial stages of comparative evaluation of alternative structural solutions (without the need to use detailed technical data of the ship’s systems, power plant, and ORC nodes), expected indicators of energy efficiency, and cycle performance. The development is based on generalized results of variation studies of the ORC in the structure of the widely used main marine medium-speed diesel engine Wärtsilä 12V46F (14,400 kW, 500 min⁻¹) in the operational load cycle range of 25–100% of nominal power. The algorithm of the proposed solutions is based on the established interrelationship of the components of the ORC energy balance in the P-h diagram field of thermodynamic indicators of the cycle working fluid (R134a was used). The implemented strategy does allow, in graphical form, for justifying the choice of working fluid and evaluating the energy performance and efficiency of alternative WHR sources for the main engine, taking into account the design solutions of the power turbine and the technological constraints of the ORC condensation system. The verification of the developed methodological solutions is served by the results of comprehensive variation studies of the ORC performed by the authors using the professionally oriented thermoengineering tool “Thermoflow” and the specification data of Wärtsilä 12V46F with an achieved increase in energy efficiency indicators by 21.4–7%. Full article

The high demand for natural gas (NG) worldwide has led to an increase in the size of the LNG carrier fleet. However, the heat losses from this type of ship’s engines are not properly managed, nor is the excess boil-off gas (BOG) effectively utilised when generation exceeds the ship’s power demand, resulting in significant energy losses dissipated into the environment. This article suggests storing the lost energy into green H₂ for subsequent use. This work compares three different electrolysis technologies: solid oxide (SOEC), proton exchange membrane (PEME), and alkaline (AE). The energy required by the electrolysis processes is supplied by both the LNG’s excess BOG and engine waste heat through an organic Rankine cycle (ORC). The results show that the SOEC consumes (743.53 kW) less energy while producing more gH₂ (21.94 kg/h) compared to PEME (796.25 kW, 13.96 kg/h) and AE (797.69 kW, 10.74 kg/h). In addition, both the overall system and SOEC stack efficiencies are greater than those of PEME and AE, respectively. Although the investment cost required for AE (with and without H₂ compression consideration) is cheaper than SOEC and PEME in both scenarios, the cost of the H₂ produced by the SOEC is cheaper by more than 2 USD/kgH₂ compared to both other technologies. Full article

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction. Full article