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In response to the Food and Agriculture Organization of the United Nations (FAO)'s "Blue Initiative" and the need for sustainable development of the marine economy, this study aims to reduce the energy consumption and operating costs of a 40 m-long ocean-going purse seine vessel throughout its entire operating cycle to enhance the energy efficiency management technology for ocean-going fishing vessels, this paper relies on the Digital Research and Development Platform of fishing vessels, based on Reynolds-averaged Navier-Stokes equations (RANS), using the computational fluid dynamics (CFD) analysis software STAR- CCM+, the resistance of an ocean-going purse seine fishing vessel under different draught and different yaw conditions was simulated and calculated, based on the analysis of the change law of the resistance and the search for the optimal pitch angle under different draught and speeds, the displacement and speed requirements of the purse seine fishing vessels at the key operating points in the complete operating cycle are discussed, the optimal pitch operation manual and the economical selection scheme of the main engine power are established, and the actual energy saving effect of the pitch optimization is verified by comparing the energy consumption of the ship in the positive floating state and the optimal pitch state. The research results provide theoretical support for implementing energy-saving technology of ocean-going fishing vessel pitch optimization. [ABSTRACT FROM AUTHOR]

Multi‐objective optimization of ship form can effectively reduce ship energy consumption, and is one of the important research topics of green ships. However, the computational cost of numerical simulation based on computational fluid dynamics (CFD) theory is relatively high, which affects the efficiency of optimization. Traditional subjective weighting methods mostly rely on expert's experience, which affects the scientificity of optimization. This paper effectively integrates the CFD method, the improved multi‐objective optimization algorithm and the objective weighting method to build a ship form multi‐objective optimization framework. Conduct multi‐objective optimization research on resistance and seakeeping performance of a very large crude oil carrier (KVLCC) ship. The improved bare‐bones multi‐objective particle swarm optimization (IBBMOPSO) algorithm is used to obtain the pareto front, and the kernel principal component analysis (KPCA) method is used to objectively assign the weight of each target. Finally, the optimal ship form scheme with high satisfaction was obtained. The multi‐objective optimization framework constructed in this paper can provide a certain theoretical basis and technical support for the development of ship greening and digital transformation. [ABSTRACT FROM AUTHOR]

The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems can be improved significantly through route optimization. However, finding the ship's optimal route is computationally expensive when the totality of possible weather conditions is taken into consideration. To determine the optimal route that minimizes energy consumption, an energy model based on the environmental conditions, ship route and ship speed was built using artificial neural networks. The energy consumed for given input data was calculated using a ship dynamics model and a database was generated to train the artificial neural networks, which predict how much energy is consumed depending on the route followed in given environmental conditions. Then, such networks were exploited to derive the optimal routes for all the relevant operational conditions. It was found that route optimization can reduce the overall ship energy consumption depending on the weather conditions of the environment by up to 9.7% without any increase in voyage time and by up to 35% with a 10% delay in voyage time. The proposed methodology can be applied to any ship by training real weather conditions and provides a framework for reducing energy consumption and greenhouse gas emissions during the service life of ships. [ABSTRACT FROM AUTHOR]

With the further establishment of relevant regulations on ship emissions by countries worldwide and the IMO, and the increasing frequency of severe sea conditions in shipping routes, optimizing ship energy efficiency under high wind and wave conditions has become an important research direction. This study establishes a grey-box model for optimizing ships' energy consumption under severe sea conditions, with wave heights above two meters and a Beaufort scale score above five, based on the principle of ship–engine–propeller matching and a non-dominated sorting optimization algorithm. Using historical navigation data from a case ship under severe sea conditions, a white-box model and a black-box model for ship fuel consumption were established. These models were combined to create a grey-box model for ship fuel consumption. The K-Medoids clustering algorithm was used to cluster severe sea conditions. The optimization variables were the main engine's speed, with the fuel consumption per nautical mile and the ship's speed being used as optimization objectives. The non-dominated sorting genetic algorithm was optimized for each sea condition, resulting in the best speed for each sea state. The results indicate that the model developed in this paper reduced the main engine's fuel consumption per nautical mile by 21.9% and increased the speed by 16.7% under the most severe sea conditions. Therefore, the proposed model effectively optimizes ship energy efficiency and reduces navigation time under severe sea conditions, providing an effective solution for operations in actual severe sea conditions. [ABSTRACT FROM AUTHOR]

A large amount of cargo is transported between European and Southeast Asian countries. Ships sometimes take different routes when sailing between ports due to the best commercial speed; navigational, economical, and hydrometeorological conditions; and political and military situations. Several routes are available for sailing between Europe and Southeast Asia: sailing the Suez Canal, sailing around the African continent, sailing the Panama Canal, as well as sailing the Northern Sea route. This article analyzes the possible sailing routes between Southeast Asia and Europe and presents a developed methodology for the evaluation of sailing routes. This sea route evaluation methodology is based on a comparative mathematical model that evaluates the main factors of cargo transportation by sea: transportation cost and time, possible maximum ship parameters, transportation energy (fuel) demand, and other possible factors, such as the probability of various restrictions. This paper presents a case study of cargo transportation between Rotterdam (The Netherlands) and Shanghai (China) using different possible sea routes. Assessments of various possible routes are presented; the main topics of discussion and conclusions are formulated. [ABSTRACT FROM AUTHOR]

Sailing speed is a critical factor affecting the ship's energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship's energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship's main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions. [ABSTRACT FROM AUTHOR]

Optimizing ship energy efficiency is a crucial measure for reducing fuel use and emissions in the shipping industry. Accurate prediction models of ship energy consumption are essential for achieving this optimization. However, external factors affecting ship fuel consumption have not been comprehensively investigated, and many existing studies still face efficiency and accuracy challenges. In this study, we propose a neural network model called TCN-GRU-MHSA (TGMA), which incorporates the temporal convolutional network (TCN), the gated recurrent unit (GRU), and multi-head self-attention mechanisms to predict ship energy consumption. Firstly, the characteristics of ship operation data are analyzed, and appropriate input features are selected. Then, the prediction model is established and validated through application analysis. Using the proposed model, the prediction accuracy of ship energy consumption can reach up to 96.04%. Comparative analysis results show that the TGMA model outperforms existing models, including those based on LSTM, GRU, SVR, TCN-GRU, and BP neural networks, in terms of accuracy. Therefore, the developed model can effectively predict ship fuel usage under various conditions, making it essential for optimizing and improving ship energy efficiency. [ABSTRACT FROM AUTHOR]

Ensuring the safety of Arctic shipping and preserving the Arctic ecological environment are emerging as key challenges in the shipping sector. Ship collisions and getting trapped in ice are frequently occurring under dynamic ice conditions due to the Arctic environment, making research on ship navigation in Arctic routes significant. Leveraging ship networking technology, we developed an intelligent microscopic model which considered factors such as the future motion trends of multi-ships in front and the influence of pack ice, and carried out a stability analysis of the model utilizing linear and nonlinear methods. Additionally, the accuracy of the theoretical results was further validated through simulation experiments with diverse scenarios. The conclusions manifest that the model can magnify the anti-disturbance ability of traffic flow. Additionally, the problem of energy consumption due to ship speed is explored, and it is determined that the model has a positive intention in reducing speed fluctuations and ship energy consumption. This paper highlights the potential of intelligent microscopic models in studying the safety and sustainability of Arctic shipping routes, providing targeted initiatives to improve safety, efficiency, and sustainability in Arctic shipping. [ABSTRACT FROM AUTHOR]

The new regulations on ship energy efficiency proposed by the International Maritime Organization have had a significant impact on the shipping and shipbuilding industries. Improving the speed performance of ships was an effective approach to meet the requirements of energy conservation and emission reduction, reduce ship energy consumption and operating costs, and strengthen the market competitiveness of the ship enterprises. One method to improve ship speed performance was by mainly reducing the ship's resistance. The main aim was to optimize the principal dimensions and hull lines of ships. Taking the research and development of a ship as an example, the application of the fully parametric optimization method in the optimization of ship hull lines was described in detail. The parent ship was selected from the ship database, which has excellent speed performance. The parent ship was simply transformed to meet the requirements of the target ship. The traditional optimization method was used to optimize the hull lines to reduce the hull resistance as much as possible, and a hull line with the best resistance performance was obtained. Based on the initial hull lines that met the requirements, the fully parametric model of the hull was established, and the fully parametric optimization method was used to optimize the hull with the best resistance performance. Experiments were carried out in the towing tank, and the resistance results of the two types of ships were found. The simulation of computational fluid dynamics (CFD) and model test had good consistency, and it was found that the hull lines optimized by the fully parameterization method had better performance than those optimized by the traditional method. The fully parametric hull line optimization method was effective in optimizing hull lines, and it could also reduce the excessive dependence of research personnel on the parent ship and design experience. [ABSTRACT FROM AUTHOR]

Given the need for ship emission reduction and the energy constraints of unmanned surface vehicles, it is imperative to develop an efficient ship path planning method that incorporates energy-saving considerations. In this paper, considering the challenges posed by the complexity of the marine environment, an adaptive ant colony system is proposed for ship energy-saving path planning. Firstly, a ship energy consumption estimation model is presented by analyzing the influence of wind, wave, and current on energy consumption. Secondly, two process evaluation indexes are designed for the quality and diversity of the paths in the optimization process. Based on these indexes, three improvements are presented to enhance the performance of the ant colony system. These improvements include (a) an adaptive state transition rule adjusted dynamically based on the information entropy, (b) the number of ants contributing to the global updating rule and the corresponding update value are controlled dynamically based on the quality of the path, and (c) an improved recombination mechanism. Besides, an improved turning point optimization method and B-spline curve are introduced for path post-processing. Finally, the effectiveness of the proposed method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Rotor sails are one of the promising solutions to reducing the energy consumption and CO2 emissions of ships. Previous studies focused on how rotor sails affect ship dynamics and energy consumption. In the present study, an optimization-based workflow was proposed to find the optimal design of a rotor sail based on given environmental conditions for a target ship. Since the performance of a rotor sail depends on both operational conditions and the design of the rotor sail, a two-level optimization problem was proposed to separate the optimization of operational conditions and rotor sail design. At the operational level, the spin ratio of a given rotor sail was optimized at each time step under different environmental conditions. Then, the design of the rotor sail was optimized on the design level considering the initial cost of rotor sails and the average operational cost of the ship depending on the environmental conditions and their probabilities. The reductions in energy consumption of ships having optimal rotor sail designs, considering 5-year, 10-year, 15-year, and 20-year investment plans were found to be 0.34%, 2.7%, 3.91%, and 4.29%, respectively. When more severe environmental conditions were assumed for the 10-year investment plan, the diameter of the rotor sail increased and the reduction in energy consumption increased from 2.7% to 4.06%. [ABSTRACT FROM AUTHOR]

Arctic navigation research will be more urgent as it is predicted that 4.7% of the shipping trade will be deployed in the Arctic region by 2030. This paper presents a multi-objective path optimization algorithm based on the field theory to investigate Arctic shipping route design and navigation strategies that take safety, economy and environmental protection as factors. The algorithm considers the ice conditions in Arctic waters throughout the year, the energy consumption of ships with different ice classes and carbon emissions. The sea environment is initially modeled using the potential field method, incorporating the ice condition field, fuel consumption, emission field and directional field. The navigable area is defined based on the ship navigability, and path optimization is conducted using the principle of gradient descent between the departure point and destination. The experimental results show that the cost of carbon emissions and fuel consumption varies for each class of ship on the planned path in different months. Therefore, by combining different classes of ships and months, we obtain an Arctic navigation strategy with a consideration of the best cost and environmental protection for each month. The path planning and navigation strategy decision method proposed in this paper can provide a reference for an Arctic route design, navigation month and vessel selection, which is beneficial to the sustainable development of Arctic shipping. [ABSTRACT FROM AUTHOR]

The most significant aspect of international shipping is sea transportation, and the developments to be made in maritime transport will inspire and predict all other fields. Therefore, determining a ship's main engine power has great importance in terms of both energy efficiency and environmental factors. The maritime transport and shipping industry has currently begun to understand the importance of artificial intelligence technology. This study uses an artificial neural network (ANN) model to predict the main engine power and pollutant emissions of container, cargo, and tanker ships over 14 parameters: maximum speed, average speed, breadth, year built, ship type, status, length overall (LOA), light displacement, summer displacement, fuel type, deadweight tonnage (DWT), gross tonnage, engine cylinder size, and engine stroke length. In order to provide accurate results, the ANN analysis was trained with data from 3,020 ships, which is quite high compared to the studies in the literature. Many ANN models have been developed and compared to achieve minimal errors and highest accuracy in the results. The regression values, which involve the training, validation, and test values for the different ship types, were obtained as 0.99773 for container ships, 0.98964 for cargo ships, and 0.97755 for tanker ships, with a value of 0.97189 for all ships. The ANN structure was tested using many variations for hidden neuron counts, with the ANN analysis made with 30 neurons obtaining the best results. The ANN analysis results were compared with real values, which showed that very accurate results had been obtained according to the mean squared error (MSE), regression, and mean absolute percentage error (MAPE) results. The MSE value had exceeded 20,000 in the two-input ANN model, but decreased to 0.03, 0.081, and 0.13 with the 14-input model for container, cargo, and tanker ships, respectively. In order to make accurate predictions with maximum precision in the ANN analyses, the study attempted to use different values for the numbers of hidden neurons and inputs and then presented the performance results. The developed model can be used in future studies to be done on fuel consumption and energy efficiency for ships in maritime transport. [ABSTRACT FROM AUTHOR]

Sail-assisted technology can reduce greenhouse-gas emissions by saving the energy consumption of ships with wind energy utilization. The distribution characteristics of marine wind resources are critical to the energy-saving effect of sail-assisted ships. However, due to the lack of effective energy-saving evaluation methods for improving the utilization rate of wind energy, a high potential for wind energy utilization still exists. A novel energy-saving evaluation method based on the wind energy resource analysis of typical ship routes is proposed in this paper. First, a three-degree-of-freedom motion model for sail-assisted ships considering the wing sail forces is constructed. Then, a wind resource acquisition and analysis method based on spatial–temporal interpolation is proposed. On this basis, the wind field probability matrix and wing sail force matrix are established. Ultimately, an energy-saving evaluation method for sail-assisted ships on typical routes is proposed by combining the sailing condition of ships. The case study results show that the energy-saving effect of a wing sail-assisted oil tanker that sailed on the China-to-Middle East route was more than 5.37% in 2021 and could reach 9.54% in a single voyage. It is of great significance to realize the popularization and application of sail-assisted technology, thus improving the greenization of the shipping industry. [ABSTRACT FROM AUTHOR]

The effective use of energy proposes using less energy to achieve the same goal. The International Maritime Organization started using criteria for ship's energy efficiency in 2013, when the new International Convention for the Prevention of Pollution from Ships, Annex VI, was adopted. The purpose of the Annex is to improve the ships' energy efficiency through the use of new technologies such as improved hull design, and new propulsion systems, including innovations in energy management. One of the topics for improving the car carriers' energy efficiency is the constantly working lights in the superstructure and on the car decks. Lights' energy consumption may be negligible on most ships, but on car carriers, the consumption is of greater magnitude. This article presents a survey of the improvement of energy consumption on board a car carrier during regular voyages as well as the cost-effectiveness of introducing light control automation. The authors of this article review an example of light automation set up to control the lights in a ship's superstructure and on the car decks. The implementation of such a system and the different types of automation are also reviewed. This research is towards new regulations, established by Maritime Environment Protection Committee and implemented in shipping since 1 November 2022. Conclusions for practical use are extracted. [ABSTRACT FROM AUTHOR]

Global warming, environmental pollution, and the soaring cost of energy consumption for ships have drawn the attention of the International Maritime Organization and the shipping industry. By reducing the energy consumption of ships, the greenhouse gas emissions and operating costs of ships can be effectively reduced simultaneously. However, current research on the ship energy consumption optimization based on operating mode is mainly focused on route and speed optimization and less on trim optimization, but ship trim is also an important factor affecting energy consumption. Therefore, this study proposed a ship trim optimization method based on operational data and ensemble learning to achieve energy savings and emission reductions for inland sea ships. First, data processing and feature selection of operational data from an inland ro-ro passenger ship were undertaken. Second, the energy consumption prediction models were established based on ensemble learning. Finally, the trim optimization model was developed by combining the energy consumption model with the best prediction performance and enumeration method. Experimental results show that compared with linear regression, neural networks, and support vector machines, ensemble learning methods have better prediction performance in energy consumption modeling, and the extra tree (ET) model has the best prediction performance. With the trim optimization, the energy consumption and carbon emissions of a ro-ro passenger ship can be reduced by 1.4641%, which is conducive to the green and low-carbon navigation of ships. [ABSTRACT FROM AUTHOR]

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency. The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency. [Display omitted] • An ISS is proposed to evaluate and select basic models for ship energy consumption prediction. • A Stacking-based fusion mode for ship energy consumption prediction is constructed. • The SPO is proposed to optimize the fusion model to achieve better prediction performance of ship energy consumption. • The proposed ISSH method can achieve high-precision and self-adaptive prediction of ship energy consumption. [ABSTRACT FROM AUTHOR]

Improving ship energy efficiency has been a crucial part for energy preservation and emission control of the shipping industry. However, the current optimization technologies mainly concentrate on a single optimization of navigation speed, route and trim. There is still a shortage of an effective collaborative optimization method to enhance the ship energy efficiency. In this regard, it is necessary to carry out more efficient optimization approach to further enhance the ship fuel efficiency. Therefore, a new collaborative optimization approach for energy efficiency optimization considering the coupling effects of navigation route, speed, trim and various environmental variables is proposed in this study. Firstly, a predictive model for ship energy consumption, which considers the sailing route, speed, trim and various environmental factors, is established by using Genetic Algorithm (GA) improved Long Short-Term Memory (LSTM) approach. On these bases, a collaborative optimization method based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed. The results of a case study show that the proposed collaborative optimization strategy can save fuel consumption by as much as 4.54%, compared with the original operational mode. Therefore, it holds significant importance for further enhancing ship fuel efficiency and promoting the advancement of low-carbon shipping. Improving ship energy efficiency has been a crucial part for energy preservation and emission control of the shipping industry. However, the current optimization technologies mainly concentrate on a single optimization of navigation speed, route and trim. There is still a shortage of an effective collaborative optimization method to enhance the ship energy efficiency. In this regard, it is necessary to carry out more efficient optimization approach to further enhance the ship fuel efficiency. Therefore, a new collaborative optimization approach for energy efficiency optimization considering the coupling effects of navigation route, speed, trim and various environmental variables is proposed in this study. Firstly, a predictive model for ship energy consumption, which considers the sailing route, speed, trim and various environmental factors, is established by using Genetic Algorithm (GA) improved Long Short-Term Memory (LSTM) approach. On these bases, a collaborative optimization method based on the Non-dominated Sorting Genetic Algorithm III is proposed. The results of a case study show that the proposed collaborative optimization strategy can save fuel consumption by as much as 4.54%, compared with the original operational mode. Therefore, it holds significant importance for further enhancing ship fuel efficiency and promoting the advancement of low-carbon shipping. [Display omitted] • A collaborative optimization model of sailing route, speed and trim based on the GA-LSTM and NSGA-III is established. • A collaborative optimization method for the ship energy efficiency improvement is proposed. • The proposed collaborative optimization method can reduce fuel usage as much as by 4.54%. • The proposed method can be regarded as an essential guidance for the ship energy efficiency improvement, thereby promoting the low-carbon shipping development. [ABSTRACT FROM AUTHOR]

Ship energy consumption (SEC) efficiency optimization is crucial to sustainable maritime transportation. Due to commercial confidentiality, shipping data cannot be freely shared. Federated learning (FL) addresses this by predicting fuel consumption while maintaining data privacy. Traditional FL struggles with accuracy due to statistical heterogeneity in shipping data. To address it, an FL framework incorporating adaptive regularization terms is proposed. Alongside, given the scarcity of maritime communication resources, a tailored convolutional neural network-gated recurrent unit (CNN-GRU) model is designed for ship clients. Furthermore, based on the prediction results, a Mixed Integer Non-Linear Programming (MINLP) model is formulated to derive optimal speeds and establish a joint database for trim strategy output. Through the prediction and optimization approach, three optimized SEC efficiency metrics are obtained. Experimental results on data from eight bulk carriers show that the proposed FL framework with adaptive regularization terms improves prediction accuracy over individual, FedAvg, and FL with classical regularization term models. The CNN-GRU model outperforms IGWO-LSTM, GRU, and CNN-LSTM models in RMSE and adjusted R-squared value. The optimization approach enhances SEC efficiency by 1.42%–3.94 %. These findings provide practical recommendations for maritime organizations to set efficient speeds and trim strategies for fleet management under secured data conditions. • A novel federated learning framework with adaptive regularization terms is designed for ship fleet fuel oil consumption prediction. • A CNN-GRU predictive model is developed for ship clients. • Three key metrics are selected and optimised to reflect the comprehensive environment protection. [ABSTRACT FROM AUTHOR]

The International Maritime Organization (IMO) had made effort to reduce the ship's energy consumption and carbon emission by optimizing the ship's operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship's heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report. [ABSTRACT FROM AUTHOR]

This study presents the real-time energy consumption of a container ship's generator engine on two round-trips from the West Coast of the US to the East Asian ports and analyzes the ship's PM10, PM2.5, NOx, SOx, CO, and HC emissions, shore power usage, and factors affecting energy consumption. The average total energy consumption and air emissions for the two round trips were 1.72 GWh and 42.1 tons, respectively. The transpacific crossing segment had the highest average energy consumption (2848 ± 361 kWh) and pollutant emission rate (78.9 ± 10.0 kg h−1). On the other hand, the West Coast of the US had the least energy consumption due to shore power adoption. Furthermore, switching from heavy fuel oil (HFO) to ultra-low-sulfur fuel oil (ULSFO) greatly reduced the emissions of PM and SOx by > 96% and NOx by 17.0%. However, CO and HC increased by 16.9% and 36.1%, respectively, implying incomplete combustion. In addition, the energy consumption was influenced by the number of reefers and wind. Therefore, this study recommends further research on energy-efficient reefers, generator engine optimization, and shore power adoption to reduce emissions from container ships. [ABSTRACT FROM AUTHOR]

Maritime shipping is one of the most important activities of the global economy, with outstanding advantages in terms of volume and distance. According to projections by the International Transport Forum, Global demand for marine transportation is expected to grow dramatically over the next three decades. Along with that, maritime activities emit large amounts of greenhouse gases and other harmful emissions, causing negative impacts on the environment. Energy consumption trends (ECT) are formed by the application of potential emission reduction solutions such as technical improvement, use of alternative fuels, efficient and economical use of fuel and optimal vessel operation. However, ECT is, an issue related to various criteria to be assessed. The goal of this study is to make science assessment of ECT which can be developed national ship emission reduction strategy. Availability of energy supply and use, emission reduction potential, economic efficiency were simultaneously considered to evaluating ECT to cut GHG emission from shipping by using Fuzzy Analytic Hierarchy Process combined with opinions of 46 experts. The results show that hydrogen fuel ranks highest in ECT as the future fuel of the maritime industry. The findings of this study are expected as an important platform to develop national ship emission reduction strategy planning. [ABSTRACT FROM AUTHOR]

In order to solve the problem of collecting necessary data affecting fuel consumption and building an intelligent energy efficiency system, this paper makes an in-depth study on the management of cross ship energy efficiency on the basis of program configuration generation. Firstly, this paper studies three methods: technology-driven analysis, support vector machine method, and energy efficiency evaluation method. In order to improve the functions of energy management, energy efficiency analysis, and auxiliary decision-making, this paper constructs a comprehensive intelligent scheme for the best ship navigation energy efficiency from the aspects of ship operation guidance, navigation performance evaluation suggestions, and shipping management decision-making, so as to meet the higher-level management needs of interested parties for ship energy efficiency. Among them, energy efficiency online monitoring can collect energy efficiency data and serve the other three functions, mainly monitoring ship energy consumption, environment, navigation, and other parameters. Energy efficiency evaluation is based on the data collected by online monitoring, and the results of ship energy consumption are obtained by using data mining technology. Through experiments and research, under the background of green, efficient, and intelligent ships, ship shore collaboration is an effective way to improve the ship's energy efficiency management ability. Intelligent ships provide a new development path for strengthening the application service of ship energy efficiency management system. [ABSTRACT FROM AUTHOR]

As a main force of ocean shipping, the energy consumption of container ships is considerably high due to the frequent load shifts of the main engines and other disturbances. In this study, for main engine power control systems consisted of several subsystems in small and medium sized container ships, a distributed scheme is proposed to relief input and output fluctuations caused by disturbances. First, a simplified interconnected model considering couplings among subsystems is established in a state space form. Second, a distributed model predictive control scheme based on Nash equilibrium is proposed to reduce influences from unknown and bounded disturbances by optimising in a negotiated manner. Finally, a contrast simulation is carried out between the proposed method and the decentralised model predictive control. The result shows that the proposed method works better than the decentralised one with less fluctuations. This study provides a theoretical basis for designs and reformations of main engine power control systems in small and medium sized container ships. [ABSTRACT FROM AUTHOR]

Recently, the ship energy consumption issue has been in the spotlight. Meanwhile, new conventions and standards of ship energy conservation as well as ship emission reduction have been continuously introduced by the International Maritime Organization and governments around the world. Energy conservation is a crucial factor in ship route planning. This paper proposes an improved ant colony algorithm that can handle a complicated ocean current environment and find an optimal energy-saving path. Firstly, this paper focuses on both the path length and energy consumption. To reduce energy consumption, a comprehensive energy cost function is introduced by a conventional ant colony algorithm. In addition, this paper analyzes the influence of the ocean current speed and setting on a marine vehicle and establishes an improved ant colony algorithm model based on the influence of the ocean current. Finally, simulation tests are carried out in Lianyungang and its surrounding areas to test the energy consumption performance of the improved ant colony algorithm. The simulation result shows that the improved ant colony algorithm can significantly reduce the energy consumption by 3.326% compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

The research on the dynamics analysis-based energy-saving technology is significant to reduce ship energy consumption and greenhouse gas emissions. The adoption of dynamics analysis theory and Computational Fluid Dynamics (CFD) approaches can achieve the optimal design and energy efficiency improvement of the ship. This research focuses on the ship energy efficiency improvement technology through CFD-based dynamics analysis, including the hull optimization design, drag reduction technology, navigation state optimization, efficient propulsion devices, energy-saving equipment, and the coupled dynamics analysis for comprehensive performance optimization. The current research and application status of ship performance optimization based on CFD approaches for energy-efficient shipping are systematically analyzed. On this basis, the challenges and problems in the application of the CFD-based energy-saving technology are discussed, and the future research works are proposed, aiming to provide references for the development of ship energy-saving technology based on CFD approaches. The analysis results show that the adoption of CFD-based dynamics analysis methods can effectively optimize the ship dynamics performance, thus reducing ship energy consumption and pollution gas emissions. In the future, the CFD-based coupled dynamics analysis should be further studied to achieve the overall performance optimization of the integrated ship-engine-propeller-appendages system under the influence of multiple complex factors, to continuously improve the ship energy efficiency, thus promoting the low-carbon development of the shipping industry. The research on the dynamics analysis-based energy-saving technology is significant to reduce ship energy consumption and greenhouse gas emissions. The adoption of dynamics analysis theory and Computational Fluid Dynamics (CFD) approaches can achieve the optimal design and energy efficiency improvement of the ship. This research focuses on the ship energy efficiency improvement technology through CFD-based dynamics analysis, including the hull optimization design, drag reduction technology, navigation state optimization, efficient propulsion devices, energy-saving equipment, and the coupled dynamics analysis for comprehensive performance optimization. The current research and application status of ship performance optimization based on CFD approaches for energy-efficient shipping are systematically analyzed. On this basis, the challenges and problems in the application of the CFD-based energy-saving technology are discussed, and the future research works are proposed, aiming to provide references for the development of ship energy-saving technology based on CFD approaches. The analysis results show that the adoption of CFD-based dynamics analysis methods can effectively optimize the ship dynamics performance, thus reducing ship energy consumption and pollution gas emissions. In the future, the CFD-based coupled dynamics analysis should be further studied to achieve the overall performance optimization of the integrated ship-engine-propeller-appendages system under the influence of multiple complex factors, to continuously improve the ship energy efficiency, thus promoting the low-carbon development of the shipping industry. [Display omitted] • The progress on the CFD-based energy-saving technologies has been summarized and discussed in detail. • The challenges faced in the development of CFD-based energy-saving technologies are comprehensively analyzed. • The future research work on the CFD approaches-based energy efficiency optimization is proposed. • This work can be regarded as an essential guidance for future research on the CFD-based ship energy efficiency optimization. [ABSTRACT FROM AUTHOR]

Maritime transportation is the backbone of global trade, as ships carry over 80% of trading goods worldwide. As the shipping industry is mainly powered by heavy fuel oil, it has an adverse environmental footprint due to the emissions of greenhouse gases and polluting substances. To comply with IMO emission regulations and optimally save on fuel costs (which can account up for 50% to 60% of the total cost of operating a ship), shipping companies are motivated to optimize energy consumption. In this study, we first develop am innovative and tailored artificial neural network-based fuel consumption prediction model. This model innovates in that it explicitly considers shipping domain knowledge by modifying and optimizing its structure and parameters, where such properties have rigorously been proven. Moreover, it considers a broad range of influence factors based on data fusion technology. Next, we optimize the ship sailing speed profile for a bulk carrier in two application scenarios using the predicted fuel consumption rates by the proposed neural network-based model as the input: one is a bi-objective model, and the other considers market-based measures. Numerical experiments show that the proposed fuel consumption prediction model outperforms other models and that the model we propose can help to improve ship energy efficiency by a considerable extent. The proposed model conforms more closely to common sense than existing models; thus, it will likely have a better potential for use in the maritime industry and other problems with similar domain knowledge possessed. • Use data fusion to combine a wide scope of data sources into a single dataset for artificial neural network (ANN) model construction • Develop a tailored ANN model for ship energy consumption prediction considering monotonicity and convexity • The mean absolute percentage error of the developed ANN model is 7.5% • Develop ship sailing speed optimization models in various application scenarios • The proposed ANN model improves ship energy efficiency by 2% to 2.5% [ABSTRACT FROM AUTHOR]

Estuarine ports account for more than 80% of world ports. Considering the large portion of ships depart and arrive at estuarine ports worldwide, using current and tide to departure and arrive at the estuarine ports can help to save energy and reduce fuel consumption and pollution emissions. To better understand to what extent ships take and against the tide when entering and leaving estuarine port for energy-saving purposes, this study collected AIS data of ships in the Pearl River Estuary region in 2022 and fused it with the simulated current data to unveil the ship behavior patterns. It is found that most of the ships sailed against tide among the 225,333 ships entered and left the harbor in 2022. 75–76% of the ships choose to enter the harbor against current, 9% choose to enter the harbor in transverse current, and only 15–16% choose to take tide. In terms of outbound ships, the proportions of ships sailed against tide equals to the proportion take tide, each took 41–45%, 11–15% sailed in the transverse current direction. Our result revealed that the outbound ships is better at take tide than inbound ships, especially at the tide with a speed higher than 0.6 m/s. The methods demonstrated in this study can be adapted and applied to other estuarine ports to understand the impact of the tide on shipping energy consumption. Future studies should focus on increasing the energy efficiency of ships by better planning the shipping schedule and taking advantage of the tide while leaving and entering the ports. • Spatio-temporal distribution of ships at the estuarine port was analyzed. • Tide cycles at the estuarine port was simulated and correlated with shipping data. • 75–76% and 41–45% of inbound and outbound ships sailed against tide. • Outbound ships is better at take tide than inbound ships, especially at current higher than 6 m/s. [ABSTRACT FROM AUTHOR]

Due to fuel price fluctuations and greenhouse gas emissions caused by international shipping, the reduction of ship fuel consumption is important. In this paper, a ship energy consumption prediction model is established based on the BP neural network optimized by the Levenberg-Marquardt (LM) algorithm to predict ship energy consumption. Firstly, the ship navigation data are analyzed. Ship speed, draught and current speed are selected as the main factors affecting ship energy consumption and their weights are determined by the spearman and regression analysis. Then, the ship energy consumption model is established based on the LM-BP algorithm. Finally, ship speed, draught and current velocity are used as the input and ship energy consumption is considered as output for the model to validate the model. The predicted results is compared with the real energy comsumption and the final RMSE is approximately 259.74 kW. For ships sailing with power ranging from 6000 kW to 8500 kW, the model prediction error is 3.06 %. The results of this study could be used to optimize ship operational plans to improve the economics of waterway transport. • The effect of factors are quantified on ship energy consumption through Spearman analysis and regression analysis. • A prediction model based on LM-BP algorithm is established to predict ship energy consumption. • The model is trained and verified using the real data from 40 voyages of the ship. • The strength of the model is shown by comparing the results with the BP and LSTM models. [ABSTRACT FROM AUTHOR]

Improving ship energy efficiency and intelligent optimization depend heavily on predictive maintenance of Marine diesel engine performance. For successful Condition-Based Maintenance, a multi-step fuel consumption prediction of ships that is accurate and stable is needed. However, existing methods mainly focus on current time or future single-step forecasts. Therefore, it is essential to investigate the optimum prediction model across various prediction time steps from the perspective of model accuracy and model generalization capability. Based on the 14-month sensor data of bulk carriers, high-quality ship energy consumption data is obtained using the local weighting method to establish a short-term multi-step prediction model of engine fuel consumption based on deep learning. Five real fuel consumption sample sets with different equilibrium levels were determined to evaluate the robustness and generalization of varying prediction models. According to the research, the ensemble empirical mode decomposition-based memory network (EEMD-LSTM) can maintain good stationarity and high accuracy in long-term trend prediction within 30 to 60 steps. In contrast, the bidirectional memory network (BiLSTM) has high accuracy in short-term volatility prediction within 30 steps. An efficient method for ship prediction maintenance and defect diagnosis can be found in a high-precision multi-step forecast method for Marine diesel engine fuel consumption. • The prediction models consider the impact of various dataset equilibrium levels. • Only using historical fuel data for consumption shows predictive solid capability. • We have innovatively identified more appropriate models for different time steps. • Confirmed LSTM and BiLSTM's prediction feasibility through model generalization. • Before step 30, BiLSTM's MAPE is 3.8%; EEMD-LSTM's MAPE is 1.9% from steps 30-60. [ABSTRACT FROM AUTHOR]

Painting is the largest energy consumption unit in ship repair enterprises, with annual electrical energy costs amounting to millions of dollars. The energy-saving model of painting business based on artificial intelligence, Internet of Vehicles (IoV) and big data technologies has become a current research hot spot. This paper takes a cargo ship of Youlian Shipyard as an example to improve the quality of special coating construction, reduce cost and increase efficiency. In the process of special coating construction, a comprehensive analysis of equipment usage in different compartments is carried out, and a compartment energy consumption analysis model based on combined weighting and Gray Fuzzy Comprehensive Evaluation is proposed. The model uses factors such as energy consumption, hours, and number of devices as evaluation indicators. Based on the fuzzy comprehensive evaluation, the gray correlation coefficients and comprehensive weights were weighted to obtain the final comprehensive evaluation results of each planning scheme, and then compare them to determine the optimal scheme. The results show that the grey fuzzy comprehensive evaluation model with combined weighting has better results than other models, and the evaluation results are scientific and reasonable. It has certain application value in multi-objective scheme optimization. [ABSTRACT FROM AUTHOR]

Aiming at the low efficiency and high energy consumption of unmanned ships traversing the entire area, a complete coverage path planning algorithm based on the improved A-star algorithm is proposed. The positioning and vision systems of unmanned ships are used to digitize the actual water information, and the grid method is used to convert the information into an environmental map that can be planned. Compared to the trapezoidal partition of unity method and the short-side reciprocating traversal algorithm in the traversal process, experiments show that path planning is more efficient with the boustrophedon partition of unity method and the long-side reciprocating traversal algorithm. Aiming at the "dead zone", an improved A-star algorithm is proposed on the basis of the traditional A-star algorithm, that it can shorten about 1/4 path using the proposed algorithm. Simulation shows that the improved A-star algorithm can shorten the traversal path to 40 steps but the traditional A-star algorithm needs 54 steps. Navigation test shows that the proposed algorithm can shorten the traversal path and improve traversal efficiency while ensuring the coverage of unmanned ships. [ABSTRACT FROM AUTHOR]

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Ship energy management has an effect both on cost efficiency and the environment due to the huge amount of CO2 emission caused by ship activities. Meanwhile, research regarding efforts to save energy consumption in the container terminal area is scarce. This paper aims to estimate CO2 emissions from ship activities in the container port. The influential variable of CO2 emissions is in consideration to Ship Energy Efficiency Management Plan (SEEMP). The estimation of CO2 emission starts from the ship activities when the ship approaches the port, which includes ship maneuvering and ship berthing. Ship's energy consumption and CO2 emission were analyzed using random forest regression (RF) at the default setting, and then the effectiveness was verified using k-folds cross-validation. The analysis result showed there are five influential variables to reduce the CO2 emission: (1) main engine power; (2) auxiliary engine power; (3) waiting time in a port basin; (4) maneuvering time; and (5) berthing time. Among those five variables, maneuvering, waiting in a port basin, and berthing have the same position at the top with the same amount of weight importance from the four attribute selection training results. The random forest model training and k-folds cross-validation confirmed that the model has 98.85% of accuracy. Finally, a fuel-efficient operation is discussed, and it can be concluded that by combining several voyage optimizations with a skilled operator and cold ironing when available, it is possible to reduce the CO2 emission by 20%. The findings and proposed plan in this paper can become a reference to develop Ship Energy Efficiency Management Plan. [ABSTRACT FROM AUTHOR]

In Ukraine, there has been an increase in agricultural production. The availability of river basins and seaports contributes to the use of inland shipping. However, there is a lack of understanding of how to reduce the energy consumption of inland shipping. We assume the hypothesis that the energy efficiency of inland shipping is much higher than it is technically possible. The purpose of this study is to identify perspective energy-saving technologies for IWT. We use statistical information to determine the export potential and to reveal the status of inland navigation. Traction efficiency, theoretical and actual specific fuel consumption were used as indicators to determine the energy efficiency. The main results are as follows. We have found the grain and sunflower seed production in the Dnieper Basin (Ukraine). Their shares range from 34.75 to 50.92% of national production. Therefore, there is a significant flow of products for inland shipping. The present status of inland waterways transport has been analyzed. The main novelty of this study is the finding of actual and theoretical technical specific energy consumption. It is revealed that actual specific energy consumption is five times higher compared to the theoretical one. Self-propelled vessels are inferior in energy efficiency to towing barges. The energy efficiency of inland waterways transport has been compared to railways and road freight transport. Transport management was identified as the more effective tool to improve the energy efficiency of inland shipping. [ABSTRACT FROM AUTHOR]

Global greenhouse gas emission attracts significant attentions across varied communities, and carbon emission (CE) reduction has become hot topic in the maritime field considering that appropriately 3% CE come from the field. The prerequisite for fulfilling the task is to accurately quantify the ship CE. To achieve the aim, the study utilizes indicators, such as carbon dioxide (CO 2) emission, CO 2 index, fuel consumption, energy efficiency operational indicator (EEOI), fleet energy efficiency management index (FEEMI), to analyze ship energy consumption. We employ ship voyage data from container, oil tanker, bulk carrier and liquefied natural gas (LNG) carrier to evaluate ship energy consumption. We have testified EEOI variation tendency under different ship cargo loading volume states (i.e., full/partial load) and speed deceleration scenario. Moreover, the FEEMI indicator is used to determine energy efficiency for different ship fleets (container ship fleet, oil tanker fleet, bulk carrier fleet, LNG fleet). Experimental results suggest that EEOI is proportional to ship energy consumption when the sailing distance and cargo volume are constant. The ship EEOI indicator calculated in full-loaded status is obviously smaller than the counterpart under partial-load status. The fleet energy consumption efficiency shows a slight increase (at least 1%) due to release of ship energy efficiency management plan. The research findings can help maritime policy-makers provide more reasonable regulations for the purpose of ship energy consumption enhancement. • Five indicators: CO 2 emissions, CO 2 index, fuel consumption, EEOI and FEEMI are selected to evaluate ship energy efficiency and environmental management. • EEOI is evaluated together with the CO 2 emission to analyze the variation of EEOI via spatial-temporal maritime data. • By quantitative analysis and designing different scenarios, the key factors affecting the change of EEOI are studied. [ABSTRACT FROM AUTHOR]

China aims to peak carbon dioxide (CO 2) emissions before 2030 and achieve carbon neutrality before 2060. Currently, 9% of China's CO 2 emissions come from the transportation sector. Transportation decarbonization is important for China to achieve carbon neutrality. By representing the transportation sector with nine segments and 20 modes in Global Change Analysis Model, this study explores China's transportation decarbonization and potential role of electricity and hydrogen at the segment-mode level under three illustrative scenarios – policy scenario (PS), 2060 carbon-neutrality scenario (CN60), and 2050 carbon-neutrality scenario (CN50). The PS reflects the continuation of current low-carbon policies and trends, while the CN60 and the CN50 represent China's pursuit of net-zero emissions before 2060 and 2050, respectively. Results in the PS show a gradual saturation of service demand and an increasingly efficient modal structure for China's future transportation. Compared to the PS, the two carbon neutrality scenarios specifically emphasize the importance of decarbonizing transportation fuel structure after 2030. In the CN60, China's transportation emissions peak in 2035 and fall to 0.36 GtCO 2 in 2050; electricity and hydrogen provide 43% and 12% of transportation energy in 2050, respectively. The CN50 features further penetration of low-carbon fuels to reduce transportation carbon intensity, with transportation emissions peaking in 2030 and declining to only 0.21 GtCO 2 in 2050. In 2050 of the CN50, the share of electricity and hydrogen in China's transportation energy increases to 53% and 16%, respectively, with near-zero emissions being achieved in urban, rural and business passenger segments, as well as in car and bus; hydrogen provides 17% and 26% of China's airplane and ship energy consumption, respectively. • A segment-mode analysis of China's transportation decarbonization is conducted. • Three scenarios inform transportation decarbonization effort for carbon neutrality. • Transportation emissions peak in 2030–2035 and fall to 0.21–0.36 GtCO 2 in 2050. • Car could be 94% electrified and achieve near-zero emissions in 2050. • Hydrogen could provide 17% and 26% of airplane and ship energy consumption in 2050. [ABSTRACT FROM AUTHOR]

This paper addresses the importance of the implementation and enforcement of the energy efficiency measures for ships. These measures are frequently referred to and used as a tool for carbon mitigation by reducing greenhouse gas emissions from ships in order to protect the Earth's climate. Moreover, these measures can also play an important role in climate adaptation. The purpose of this paper is to look briefly at the current and expected impact of the ships' energy efficiency measures developed under auspices of the International Maritime Organization, i.e.: the Energy Efficiency Design Index (EEDI) and the Ship Energy Efficiency Management Plan (SEEMP) on climate change mitigation. Both of them, EEDI and SEEMP became mandatory measures after the adoption of amendments to the Annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL), and their entry into force in 2013. Furthermore, those measures were also the first legally binding tools relating to climate change, adopted since the Kyoto Protocol. [ABSTRACT FROM AUTHOR]

Reducing friction with specialised hull coatings or air lubrication technologies has a potential reducing energy consumption and emissions in shipping. The EU project AIRCOAT combines both by developing a passive air lubrication technology inspired by nature that is implemented on a self-adhesive foil system. Besides validating the friction reduction it is of high interest to understand the underlying mechanism that causes the reduction. Therefore, a flow channel was designed, that creates a stationary turbulent flow within a square duct allowing for non-invasive measurements by laser doppler velocimetry. The high spatial resolution of the laser device makes recording velocity profiles within the boundary layer down to the viscous sublayer possible. Determination of the wall shear stress τ enables direct comparison of different friction reduction experiments. In this paper we validate the methodology by determining the velocity profile of the flat channel wall (without coatings). We further use the results to validate a CFD model in created in OpenFOAM. We find that velocities along the longitudinal axis are generally in good agreement between numerical and experimental investigations. [ABSTRACT FROM AUTHOR]

Machine learning models for predicting ship energy consumption are built and their influencing factors are investigated. First, data collected from a real ship is preprocessed. Six machine learning methods are used to establish the prediction models of ship fuel consumption, and the performance of models is evaluated by Mean Absolute Error, Coefficient of Determination and training time. Then, by analysing the correlation and importance of the features, it's studied whether the model established complies with the laws of physics. Finally, the factors affecting the prediction performance of machine learning models are analysed. The results show that Random Forest and Extreme Gradient Boosting are the most suitable algorithms for ship fuel consumption prediction. Data preprocessing, data normalisation, training sample size, model type, ship operating conditions, as well as the thermotechnical parameters of main engine have impact on the prediction performance. In particular, when taking the thermotechnical parameters into consideration, R2 is increased by 0.32%, MAE is reduced by 5.0%. • Shipboard sensor data are used to build machine learning models for fuel consumption. • Performance of machine learning algorithms are evaluated by MAE, R2 and training time. • Impact of data preprocessing, sample size, model type, operating condition, feature type is quantitatively investigated. • Main engine thermotechnical features contribute to predicting ship energy consumption. [ABSTRACT FROM AUTHOR]

The navigability of Arctic maritime passages has improved with the rapid retreat of sea ice in recent decades, and it is projected that the Northern Sea Route (NSR) will support further increases in shipping in the future. However, the opening of the NSR may bring potential environmental and climate risks to the Arctic and the rest of the world. This investigation assessed shipping emissions along the NSR and the climate impacts under global warming of 2 °C and 3 °C to support coordinated international decision-making. The results show that the magnitude of annual energy consumption of ships along the NSR is 109 kWh under global warming of 2 °C and 3 °C. The environmental impacts of the shipping decrease with fuel transition to clean, carbon-neutral fuel sources. Specifically, the maximum emission is CO 2 (106 t), followed by NO X (104−5 t), CO (103−4 t), SO X (103 t), CH 4 (102−3 t), organic carbon (102−3 t), N 2 O (101−2 t), and black carbon (BC, 101−2 t), in which CO 2 and BC have great difference under high and low loads. Total emission exacerbates Arctic and global warming, and it is more significant in the Arctic in the next twenty years and across the rest of the world in the next one hundred years. The greatest climate impact factor is CO 2 , followed by NO X and BC which are more important in global and Arctic warming, respectively. [Display omitted] • The maximum emission is CO 2 , followed by NO X and CO in the shipping along the NSR. • Shipping emissions have great impact on the Arctic climate in the next 20 years. • NO X and BC from shipping have greater impact on globe and the Arctic, respectively. [ABSTRACT FROM AUTHOR]

Using the systematic method of modern ship energy efficiency management, strict control and management of ship energy consumption, energy utilization and carbon dioxide emissions will be of great significance for achieving energy conservation and emission reduction of ships and promoting the green development of shipping. On the basis of summarizing the previous research results, this paper designs a ship energy efficiency management model based on data analysis through a large number of current industry data collection, manufacturing and operation data collection, collation and analysis. The model system can monitor and collect various dynamic and static parameters in real time, and calculate the ship energy efficiency design index (EEDI) and the ship energy efficiency operation index (EEOI). Combined with the ship's own and its energy consumption information, such as range, cargo capacity, speed, host power, the system can dynamically analyze the entire production and operation process of the ship. The final simulation experiment shows the feasibility and effectiveness of this model. [ABSTRACT FROM AUTHOR]

Emulsion fuels are one option currently being explored to reduce powerplant and maritime emissions. Emulsification enables hydrophilic, typically low-value, molecules to be incorporated into traditional hydrocarbon fuels. Energetic oxygenated molecules, such as glycerol, are biorenewable and have the potential to reduce refueling costs and carbon emissions. When properly formulated, emulsions improve diesel combustion characteristics and reduce particulate matter (PM) and oxides of nitrogen (NOx). This paper explores the utilisation of glycerol-biodiesel emulsion (23 wt% glycerol) in a one-megawatt, six-cylinder, medium-speed diesel engine at constant speed to determine impacts on combustion dynamics, emissions and overall suitability. Fuel performance is compared to ultra-low-sulphur diesel (ULSD) and 380 cSt. heavy fuel oil (RMG 380). Idle emissions are shown to be comparatively poor due to low combustion stability and longer combustion delay. As load is increased to 25% of peak output, reductions in carbon monoxide (CO) and PM are observed. The lower energy density, however, restricts peak engine power which achieved 90% of full load at maximum fuel consumption. Despite engine maximum power derating, glycerol emulsion fuels require no engine modification and show promise as a powerplant fuel with a low-carbon footprint. [ABSTRACT FROM AUTHOR]

The conventional marine vessel power systems typically have the potential to improve their fuel consumption and their emissions. This can be done by redesigning the system configuration, the machinery and the power management strategy. The addition of options in power management allows for the running of individual power sources closer to their optimal operating point. However, this immediately raises questions about how to redesign the system and how to operate it to maximise the benefits. The information needed to answer these questions is often scattered around separate sectors of the marine industry. The system integrator needs to be able to combine the complex dependencies of these individual sectors to formulate the big picture that describes the whole power system. Numerical optimisation algorithms provide solutions to develop methodologies to solve multi-variable and potentially multi-objective problems. This literature review presents the authors' findings of design and power management optimisation cases in marine vessel power systems. [ABSTRACT FROM AUTHOR]

Abstract Ships using auxiliary power generators when docking can cause serious air pollution and thus pose a threat to port cities. Alternative Maritime Power (AMP) technology is an effective means of pollution control, which has been used in many countries. However, a series of difficulties have surfaced during the demonstration and promotion of AMP in China. To solve the problem, this study first summarizes 12 restrictions for AMP promotion, and then analyzes to what degree the restrictions inhibit promotion and application and their interaction mechanism using the fuzzy DEMATEL model. In the final, the three key factors restricting AMP application and promotion in China, namely policies and support systems, AMP construction standard, and the laws and regulations, are explored. The study results should go a long way in helping government departments and policymakers address issues that hinder accelerated promotion and application of the AMP technology in China. Highlights • Alternative Maritime Power application is important for green port construction. • A comprehensive analytical framework of barriers in China is constructed. • A fuzzy DEMATEL analysis method is established. • Empirical research of China provides decision-making basis for stakeholders. [ABSTRACT FROM AUTHOR]

This paper presents a contribution in improving the earthing system of ship electric grids, which affects the phenomena developed in highly unbalanced operating conditions like the single - and two-phase faults. Furthering previous work, an alternative to ungrounded earthing scheme is introduced, namely the grounding via a conditionally varying resistance. Within this framework, the mathematical expressions with respect to zero sequence impedance, of voltages and currents under study are yielded. Furthermore, as a means to easily evaluate the quality of earthing, appropriate indices are introduced. Thus, it is shown that the effect of the earthing scheme in fault conditions can be adjusted based upon certain design limits. [ABSTRACT FROM AUTHOR]

Utilisation of renewable energy sources (RES) is increasing day by day to reduce greenhouse emissions. The toxic emission from ship is the main concern in marine sector. Here, utilisation of renewable energy for propulsion and electrification of accessories in a ship are proposed. Microgrid with AC and DC bus is developed using solar panels, wind mills, fuel cell, diesel generator, and energy storage devices. Energy management system with two fuzzy logic controllers (FLCs) is used to select and manage energy in the microgrid. Selection of source is decided by FLC1 based on the availability of RES. Generation of control pulses for inter-linking converters is decided by FLC2 based on variation in solar irradiance and wind velocity. The microgrid with RES is simulated using MATLAB/SIMULINK. The results show that uncertainty in RES can be handled by FLCs to provide a continuous power supply for transportation of ship and its accessories. [ABSTRACT FROM AUTHOR]

Abstract Due to the increasing demand for energy conservation and the reduction of emissions, renewable energy applications for ships have attracted a great deal of attention. In this paper, a 5000-vehicle space pure car and truck carrier (PCTC) is selected as the research object. Then, on the basis of the existing power system, a unified grid-tied/stand-alone solar system is designed with a built-in battery energy storage system. The system includes a solar energy generation unit, a battery storage system, a diesel generating set, grid-tied/stand-alone controlled inverters, a battery management system (BMS) and an energy management system. According to an analysis of the experimental data, it can be concluded that the use of solar energy hybrid power, in theory, can reduce fuel consumption by 4.02% and carbon dioxide (CO 2) emissions by 8.55% a year. Highlights • A large-scale hybrid PV/diesel ship power system is designed. • The energy saving and emission reduction effects are analyzed through an experimental test. • Ships using this PV hybrid power system can effectively reduce fuel consumption. • The PV hybrid power system design scheme can provide significant guidance for the same type of ships. [ABSTRACT FROM AUTHOR]