Search Results (1,184)

Natural disasters disrupt maritime operations, yet their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework). Full article

To effectively leverage the role of green shipping corridors (GSCs) in promoting greenhouse gas emissions reduction in international shipping, this paper firstly examined the current status and challenges faced by GSCs with the aim of providing valuable solutions for future development. Then, a conceptual framework of multi-stakeholder partnerships (MSPs) for the international maritime industry that enables the construction and implementation of GSCs was proposed. Additionally, the inherent correlation mechanism between the “feasibility wall” of GSCs and the core elements as well as key principles in the MSP framework was also explored. The findings indicate that the GSC initiatives at the global, regional and local levels are advancing rapidly, yet very few have been truly implemented and effectively operationalized, with the fundamental cause lying in the lack of effective theoretical guidance and research support; based on the theory, mechanism and framework of MSPs, the existing GSCs are found to still have considerable deficiencies in partnership building, roles and responsibilities, governance structure, funding and resource support, as well as monitoring and accountability. Concept validation through case studies demonstrates that the conceptual framework proposed in this paper can serve as a practical diagnostic tool for GSC initiatives, which can help to identify the specific stage they are failing at and apply targeted principles to fix it. This paper is expected to contribute to a more effective advancement of the development of GSCs, thereby actively facilitating the achievement of net-zero emission targets for international shipping. Full article

Decarbonizing ocean-going shipping requires decision-grade environmental evidence for propulsion transitions, yet conventional LCA relies on static inventories that inadequately represent dynamic operations and route-dependent renewable generation. This study evaluates well-to-wake (WtW) Global Warming Potential (GWP) for two large container ships operated by a Korean company under four scenarios: conventional diesel main engine, diesel–electric with onboard generator, full battery-electric supplied by shore electricity from the Republic of Korea grid, and battery-electric with a route-resolved solar PV system. A Live-LCA (LLCA) framework couples LCI data with MATLAB/Simulink power and propulsion modeling driven by actual operating profiles and route environmental conditions to generate operational inventories for impact calculation. Diesel–electric operation increases annual WtW GWP by over 26% for both ships versus the baseline of a conventional diesel main engine, whereas shore-electric battery operation is able to reduce WtW GWP by around 40% versus diesel–electric. With limited PV installation, additional reductions are marginal. Depending on electricity profile, it can increase battery-electric GHG emissions by approximately 27%, highlighting sensitivity to electricity evolution. Overall, electric propulsion delivers climate benefits only when paired with low-carbon electricity, and LLCA enables operationally and route-grounded LCA for large container ships. Full article

To address the stringent emission regulations of the International Maritime Organization (IMO) and the growing demand for green port operations, this study proposes an innovative range-extended series hybrid powertrain system featuring a dedicated methanol engine as an Auxiliary Power Unit (APU) for harbor tugs. Based on an analysis of actual ship operational data, a core design paradigm of “battery-dominant, engine-as-range-extender” is established. A robust capacity parameter matching method is proposed, yielding a configuration comprising a 200 kW∙h/600 kW Lithium Iron Phosphate Battery Pack (LFPBP), a 250 kW methanol APU, and a 400/600 kW Permanent Magnet Synchronous Propulsion Motor (PMSM). A hierarchical intelligent energy management strategy (EMS), integrating state-machine coordination and real-time power allocation, is designed. High-fidelity simulations under a typical duty cycle demonstrate that the proposed system achieves an equivalent fuel-saving rate of 50.8% compared with a conventional diesel system, with the engine operating exclusively in its high-efficiency zone (>42% efficiency) for only 35% of the operational time. A full life-cycle techno-economic analysis reveals an incremental investment payback period (PBP) of approximately 3 months and a net present value (NPV) exceeding USD 9.69 million over a 10-year period. Quantitative environmental analysis shows an annual reduction of approximately 94.8% in CO₂ emissions (assuming the use of green methanol produced from renewable sources and captured CO₂), 95% in NO_x emissions, and the near-elimination of SO_x and particulate matter (PM). This study provides a systematic and economically attractive solution with promising engineering feasibility verified by simulation, which paves the way for further experimental validation and practical engineering implementation. Full article

Container vessels—characterized by high transport work and energy-demanding operating profiles—constitute one of the most emission-significant fleet segments and a strategically important area for implementing and assessing decarbonization initiatives. Responding to the persistent absence of integrated analytical approaches, this paper introduces a unified techno-economic and environmental assessment framework for evaluating green interventions on operating ships. The framework comprises a set of fuel-consumption, environmental performance, and techno-economic metrics and a transparent and globally applicable assessment procedure enabling the consistent comparison of heterogeneous intervention types towards sustainability. The framework is applied to a representative medium-size container vessel to demonstrate its analytical potential and practical relevance. The results of the specific application reveal the systematic trade-offs between environmental and economic performance of green interventions: operational optimization delivers the strongest carbon-intensity improvements and isolated technical retrofits provide favorable economic returns but limited environmental gains, while integrated technical–operational packages achieve the most balanced overall outcomes. Overall, the paper has both a methodological contribution by suggesting a coherent, regulation-aligned assessment structure, as well as a practical decision-support value for ship operators and policymakers. Full article

International shipping is indispensable to global commerce, yet it remains a significant contributor to greenhouse gas emissions. Although waste heat recovery has been applied in other industries, its performance and economic viability in shipping are not yet fully understood, particularly across different vessel sizes and engine loads. This study evaluates the technical, economic, and environmental potential of waste heat recovery (WHR) systems onboard ships with main engine power above and below 25,000 kW. Thermodynamic analysis and computational simulations were employed to estimate electricity generation, fuel savings, and emission reductions under optimistic and pessimistic scenarios, using operational data from four representative vessels. The results indicate that larger ships achieve the most significant benefits, with power ratios up to 10%, substantial CO₂ reductions, and viable payback periods. Smaller vessels, constrained by thermal and spatial limitations, show reduced efficiency and less favorable financial performance, although they still achieve meaningful environmental gains. The findings confirm that waste heat recovery is a mature and effective technology for improving ship energy efficiency and reducing emissions. The study contributes to scientific knowledge by quantifying performance differences between vessel types and providing a structured framework to support maritime decarbonization strategies. Full article

International shipping contributes approximately 3% of global carbon dioxide emissions while serving as the circulatory system of global commerce. The International Maritime Organization’s 2023 GHG Strategy mandates net-zero emissions by or around 2050, with indicative targets requiring a 20–30% reduction by 2030 and a 70–80% reduction by 2040. From a coastal and ocean conservation perspective, these targets represent more than climate mitigation—they offer an opportunity to reduce the maritime sector’s broader ecological footprint, including underwater noise pollution, chemical contamination from antifouling coatings, and the transfer of invasive species through biofouling. This article examines the role of artificial intelligence in supporting maritime decarbonization across multiple domains: voyage optimization, wind-assisted propulsion management, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull maintenance robotics. Critically, the analysis also addresses AI’s own environmental footprint—the substantial energy demands of data centers that power these technologies—and emphasizes the importance of transparent accounting of AI-related emissions. The article proposes research directions that advance both climate objectives and marine ecosystem protection. Full article

To meet the global climate change challenge and the International Maritime Organization’s (IMO) greenhouse gas emission reduction strategy, and promote the shipping industry’s transition to clean energy, this study focuses on the 6S35 2-stroke marine low-speed engine to explore hydrogen fuel combustion and emissions in the cylinder. A detailed chemical reaction kinetics model is constructed on the CONVERGE platform, coupling 42 components and 168 elementary reactions, integrating the SAGE combustion model with the extended Zeldovich NOx mechanism for refined numerical simulation of hydrogen combustion. Model validation shows the cylinder pressure peak simulation error is within 5%. Research results indicate hydrogen fuel has significant premixed combustion characteristics with a violent and concentrated heat release. Under simulation, the cylinder explosion pressure reaches about 28 MPa, and the max combustion temperature nears 3000 K, far exceeding traditional diesel engines. In terms of emissions, hydrogen’s carbon-free characteristic keeps CO₂ and CO emissions at extremely low levels (concentrations of approximately 0.02 and 0.085, respectively); whereas NOx emissions exhibit strong “high temperature dependence” and “expansion cooling effect,” with peak concentrations approaching 0.00042. This numerical model can effectively predict the combustion performance of hydrogen fuel, potentially providing a reference for optimizing fuel injection strategies and combustion chamber design to achieve efficient and clean combustion, and offering a theoretical basis for the development and commercial application of marine hydrogen fuel engines. Full article

The decarbonization of large cruise ships is challenged by their extreme and tightly coupled electrical, thermal, and cooling demands. This study investigates a liquid hydrogen (LH₂)-based tri-generation system for cruise ships that simultaneously supplies electricity, heat, and cooling. Key novelties include the use of LH₂ as the onboard energy carrier for large cruise ships, the recovery of cooling energy from LH₂, a dynamic control strategy that synergistically modulates PEM fuel cell utilization to regulate downstream catalytic burner heat generation and balance heat and electricity generation and demand, and the first full-scale cruise-ship model of such a system, including hydrogen consumption and onboard storage sizing. A dynamic system-level model is applied to a representative 7-day voyage of a large cruise ship. The results show that the proposed system can meet combined peak demands of approximately 61 MW while achieving overall system efficiencies approaching 75%. Compared to traditional marine diesel-based power plants, the LH₂-based tri-generation configuration improves system efficiency by more than 20 percentage points. Total hydrogen consumption is estimated at approximately 240 t, which can be reduced by about 20% through shore-to-ship power, yielding a system volume comparable to that of a conventional diesel-based power plant. These results demonstrate the technical feasibility and system-level advantages of LH₂-based tri-generation for zero-emission cruise ships. Full article

In response to the International Maritime Organization’s emission reduction targets, ship power systems are transitioning toward microgrid architectures with high renewable energy penetration. In islanded mode, the lack of main grid support and the low inertia of power electronic interfaces pose significant frequency stability challenges. Virtual Synchronous Generator (VSG) technology offers an effective solution, but conventional VSG control exhibits two inherent limitations: steady-state frequency deviation under load variations due to its primary regulation nature, and poor dynamic response characterized by large overshoot and prolonged settling time. This paper proposes an enhanced VSG control strategy integrating two key innovations: (i) a communication-free secondary frequency regulation loop that eliminates steady-state error, and (ii) an adaptive control scheme for virtual inertia and damping coefficients that dynamically responds to frequency deviations and their rate of change. The adaptive mechanism reduces overshoot by 57% (from 0.14 Hz to 0.06 Hz) and shortens settling time by 40% (from 0.38 s to 0.23 s) compared to non-adaptive secondary regulation, as demonstrated through MATLAB/Simulink simulations and 6 kW experimental prototype validation. The proposed strategy ensures both steady-state accuracy and enhanced transient performance, providing a reliable solution for improving power quality in islanded shipboard microgrids and contributing to maritime decarbonization goals. Full article

The decarbonization of shipping and the transformation towards digitally assisted or automated ship operation require new methods to analyze, predict, and optimize energy demand during maneuvering. The SimPleShip-SigMa sub-project of Hochschule Wismar developed and validated a comprehensive simulation-based framework combining real-time capable fast-time simulation of ship motion, detailed thermodynamic engine modeling, and hybrid data exchange via Functional Mock-up Units (FMU/FMI). The approach enables consistent coupling between navigation-related and machinery-related simulations and supports energy-optimized decision-making on the bridge. Operational relevance and validation of use cases were supported through collaboration with Carnival Maritime GmbH, providing practical feedback on large passenger-ship operations. The study presents the architecture of the simulation environment, the implementation of energy- and emission-prediction models, and the result of validation runs and simulator-based trials. The developed method was applied to a virtual cruise-ship scenario representing a confined coastal environment similar to the Geiranger Fjord. The work builds upon earlier research on simulation-augmented maneuvering and extends it toward a modular digital-twin concept linking hydrodynamic and thermodynamic models. The paper concludes with an outlook on applying the system for crew training, on-board support, and gradual automation of sustainable ship operations. Full article

Natural disasters disrupt maritime operations; yet, their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed a 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while the acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework). Full article

This study evaluates integrated shipboard freshwater production and fresh air pretreatment on a 20,000 TEU-class container vessel, addressing its freshwater demand and the inefficient recovery of exhaust waste heat from the main engine. The system integrates rotary dehumidification, seawater condensation, and water purification. A theoretical model was developed to evaluate the system performance, incorporating design, thermodynamic modeling, parameter optimization, and adaptability analyses under various operating conditions. The results indicate that under optimal conditions (seawater at 25 °C, outlet temperature difference of 2 °C), the single-stage system is predicted to produce approximately 1.45 m³ of freshwater per day, meeting 20.7% of the vessel’s freshwater requirement. The auxiliary electrical energy consumption, estimated based on standard engineering correlations, is 1–1.5 kWh/m³, representing a 70–80% reduction compared to conventional reverse osmosis systems (3–6 kWh/m³). The sensitivity coefficient for seawater temperature was −0.334, whereas that for output temperature was −0.167. A two-stage series configuration has the potential to further improve the demand satisfaction rate to 41–61%. Overall, the proposed system enables the cascade utilization of ship waste heat and functional integration of air pretreatment and freshwater production, offering a promising auxiliary engineering solution for energy conservation, emission reduction, and onboard freshwater self-sufficiency in marine applications. Full article

In the context of global efforts to promote energy conservation and emission reduction, geopolitical conflicts have intensified the challenges of mitigating marine climate change, posing increasingly severe economic and climatic pressures on the shipping industry worldwide. Research on multi-objective route optimization is of great significance for addressing climate challenges and enhancing economic efficiencies. This field focuses on constructing multi-objective optimization models that aim to reduce voyage time, fuel consumption, navigational risks, and carbon emissions and solving them using various algorithms. However, determining the optimal route and sailing speed under complex and variable meteorological conditions remains a significant challenge owing to the presence of numerous unevenly distributed feasible solutions within a vast solution space, making it difficult for traditional intelligent algorithms to effectively explore this space. To address this issue, this study proposes a hybrid algorithm named NRRT by integrating the Rapidly exploring Random Tree (RRT) algorithm with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By improving the sampling logic of the RRT algorithm and combining the vessel’s voluntary speed loss with the sampling step size, the algorithm efficiently explored the feasible route set, enhancing the quality and diversity of the solutions. Subsequently, the NSGA-III algorithm treats sailing speed and heading as direct decision variables to perform multi-objective optimization on the explored routes and generate Pareto-optimal solutions. The optimization results demonstrate that the proposed method excels at generating route plans that effectively reduce costs, minimize emissions, and mitigate risks compared with the 3D Dijkstra algorithm and the improved NSGA-III algorithm. Full article

To address the challenges of energy conservation and emission reduction in the shipping industry, this study proposes an innovative scheduling strategy for the ship integrated energy system (SIES) based on data-driven fuel consumption prediction and multi-objective optimization. A multi-feature dual-time scale Long Short-Term Memory (LSTM) network is developed, integrating Automatic Identification System (AIS) data with an average resolution of 6 min, meteorological conditions, and vessel state parameters, achieving fuel consumption prediction across dual time scales. The model outperforms other machine learning models (e.g., CNN, XGBoost) in terms of R², MAE, RMSE, and SMAPE. Dynamic simulation of annual cooling, heating, and power loads for crew accommodation areas, based on spatiotemporally matched customized meteorological data, reveals that the annual load is dominated by cooling demand, with significant seasonal fluctuations; summer loads are higher and more volatile than winter loads. A hybrid energy system integrating photovoltaic (PV) generation, energy storage, carbon capture and storage (CCS), and diesel engines is constructed. By treating the CCS load as a adjustable resource, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to solve the environmental–economic multi-objective optimization problem, simultaneously minimizing carbon emissions and present value of the total cost (PVC). Case studies conducted on a 79,970 DWT bulk carrier (Guangzhou–Qinhuangdao route) demonstrate the strategy’s effectiveness. The synergistic operation of solar energy and the energy storage system facilitates carbon emission reductions of 23.6% to 40.0% through fuel savings; during summer with abundant solar resources, over 95% of the CCS load can be covered. Economic analysis indicates that fuel savings from renewable energy can recover the investment in the PV and battery storage system within approximately 6 years. This integrated data-driven energy management framework mitigates CCS-induced parasitic loads and emissions, partially resolving the “carbon emissions vs. cost” dilemma, and provides a viable pathway for decarbonizing conventional diesel-powered ships, contributing to sustainable maritime operations. Full article