Search Results (9)

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

This study investigates energy efficiency indicators including the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Index (EEOI), Ship Energy Efficiency Management Plan (SEEMP), Energy Efficiency Existing Ship Index (EEXI), and Carbon Intensity Indicator (CII) by providing a comprehensive scientometric analysis. The specified indices are scrutinized using papers from WoS and Scopus databases by applying the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) method to select the most appropriate papers in the related literature. Keyword and citation analysis of papers were performed using the VOSviewer software program to reveal the current research trends. The analysis addresses several critical aspects. Firstly, it focuses on identifying which indicators are employed more frequently in the literature, and secondly, it classifies the research according to whether a calculation was made, and the yearly distribution was determined. The results pinpoint that the EEDI and EEOI were examined in 42.55% and 22.49% of the reviewed studies, respectively. Furthermore, it is evident that the EEXI and CII percentages have increased drastically over the past three years, with the figures standing at 20.01% and 18.59%, respectively. Consequently, in alignment with the findings, the theoretical and managerial implications are highlighted for the private sector, academia, and maritime stakeholders. Full article

Maritime transport is the most widely used means of transporting goods, and forecasts indicate that it will continue to grow in the coming years, which is why the IMO is regulating energy efficiency and emissions from maritime transport with exhaustive monitoring. The most widely used measure of energy efficiency during operation on a ship is the Energy Efficiency Operational Indicator (EEOI); however, the difficulty in obtaining references for this indicator, together with the great variability that exists between shipowners when choosing terms such as “cargo transported”, among others, means that this operational indicator of energy efficiency in ships does not have the expected results. This work develops a two-phase procedure for the selection of representative EEOI values for ships, taking into account parameters such as ship speed, cargo, mode of operation and the subsequent determination of a suitable control system that allows the continuous and real-time implementation of measures to improve energy and environmental efficiency on the ship. The proposed final procedure is effective in terms of improving energy efficiency and emissions, on top of being simple, easily implementable, working in real time and adapting to navigation circumstances while keeping emissions under control. Full article

This article presents an analysis of the influence of engine types, fuel types and selected methods of fuel treatment before injection on reductions in fuel consumption and exhaust emission components. This is the first of such studies, the continuation of which will allow a comprehensive assessment of the impact of cutter operations on environmental pollution. For the selected type of cutter, EEOIs (Energy Efficiency Operational Indicators) were determined to be a measure that takes into account both fuel consumption and harmful gas compound emissions depending on the type of engine used and the type of fuel. The data necessary to prepare this analysis were obtained during operational tests carried out on selected cutters with various types of engines fuelled with liquid fuel and a new catalytic fuel treatment method developed by the authors, while for the case of using gas fuel and a common rail engine, possible effects were forecasted. The effects of engine type, years of manufacture and fuel type (liquid and gas) on fuel consumption and emissions of selected exhaust components are demonstrated. The positive effects on fuel consumption and emissions of harmful exhaust constituents obtained, under laboratory test conditions, for an engine fuelled with catalytically treated fuel have allowed further research directions to be set for cutter engines equipped with injection equipment with applied catalytic coatings. Full article

Optimizing the operational performance of green ships can further improve the energy saving and emission reduction effect of ships, and speed optimization is one of the more widely used and effective measures. It is a new challenge for the shipping industry to achieve speed optimization that simultaneously saves energy, reduces emissions and meets transportation requirements, while considering changes in the navigation environment. In this paper, a hybrid electric ship energy efficiency optimization strategy based on working condition prediction is proposed to solve the problem of navigation condition at a future moment, by making a time series prediction of energy efficiency influencing factors, such as wind speed and current speed. Further, on the basis of establishing the sailing speed prediction model and the real-time energy efficiency operation index (EEOI) model, the real-time EEOI deviation and the sailing speed deviation are adopted as the comprehensive objective function to establish a dynamic optimization model of hybrid electric ship energy efficiency, considering the time-varying environmental factors. Then, the fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied to solve the bi-objective optimization problem and obtain the optimal ship engine speed in real time. Finally, experimental studies show that the proposed optimization model can improve the energy-saving and emission-reduction effect of the ship under the given speed limit requirements and working environment conditions, which can provide theoretical support for the optimal navigation of hybrid electric ships. Full article

Container shipping industries are highly capital intensive. If shipping carriers want to execute international shipping financing, they must follow the IMO emission reduction targets and meet the decarbonization trajectory of the Poseidon Principle (PP). This article used an activity-based model to calculate container shipping industry carbon emissions. It was found that the carbon intensity per unit for each ship was decreased because of the upsizing of container vessels and route deployment based on the alliance strategy. On the Asia–Europe (A/E) trunk route, as the ship size increased from 11,300 to 24,000 TEU, the results showed that the carbon intensity ranged from 6.48 to 3.06 g/ton-nm. It is also proven that the mega-container deployment on the A/E trunk route followed the decarbonization trajectory proposed by PP, while the Asia–Pacific trunk route was not fully in line with the trajectory of EEOI/AER. It is worth noting that starting from 2020, due to the COVID-19 pandemic, shipping companies deployed a higher number of small-size vessels to boost revenues, resulting in more pollutants produced and a mismatch of the trajectory proposed by PP. Full article

Nowadays, marine propulsion systems based on thermal machines that operate under the diesel cycle have positioned themselves as one of the main options for this type of applications. The main comparative advantages of diesel engines, compared to other propulsion systems based on thermal cycle engines, are the low specific fuel consumption of residual fuels, and their higher thermal efficiency. However, its main disadvantage lies in the emissions produced by the combustion of the residual fuels, such as carbon dioxide (CO₂), sulfur oxide (SO_x), and nitrogen oxide (NO_x). These emissions are directly related to the operating conditions of the propulsion system. Over the last decade, the International Maritime Organization (IMO) has adopted a series of regulations to reduce CO₂ emissions based on the introduction of an Energy Efficiency Design Index (EEDI) and an Energy Efficiency Operational Indicator (EEOI). In this context, adding a Shaft Generator (SG) to the propulsion system favoring lower EEDI and EEOI values. The present work proposes a selective control system and optimization scheme that allows operating the shaft generator in Power Take Off (PTO) or Power Take In (PTI) mode, ensuring that the main engine operates, always, at the optimum fuel efficiency point, thus ensuring minimum CO₂ emissions. Full article

Limitation of CO₂ emission is one of the main goals and regulations introduced by the international institutions’ rules. In the case of ships using oil-related and gas fuels this problem is dealt with by the International Maritime Organization (IMO) introducing the methodology of Energy Efficiency Operational Indicator (EEOI) determining for ships being under exploitation. The methodology allows for determining EEOI for seven types of ships, for which the value of this index depends on the amount of transported cargo or number of passengers, type of and amount of fuel used, as well as distance travelled by the ship. Such a methodology cannot be used for the specialized ships, whose exploitation tasks are different to the ships of the trade fleet that transport the cargo or the passengers. The methodology allows for determining EEOI for seven types of ships and it does not include specialized ships. The article presents a new methodology of determining EEOI for specialized ships that takes the characteristics of their exploitation into consideration. The way of its use has been presented taking into account the results of exploitation studies carried out on the chosen research and training ship. Obtained results and their analysis allowed for energy efficiency assessment of research and training ships depending on exploitation tasks, voyage time, type of fuel used, distance travelled and ship’s speed. EEOI index value determines energy efficiency of the vessel power system that is directly connected to the amount of the liquid or gas fuel used and the amount of emitted CO₂. The aim should be to minimalize the value of EEOI index through planning of the exploitation tasks realization order and adjusting the speed of the ship as well as realization time of particular exploitation tasks, in the case of specialized ships. The analysis results can also be used when managing energy efficiency of these types of ships. Full article

The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship’s main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship “YUMING” are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed. Full article