Search Results (31)

The maritime sector is undergoing rapid transformation, driven by increasingly stringent international regulations targeting air pollution. While newly built vessels integrate advanced technologies for compliance, the global fleet averages 21.8 years of age and must meet emission requirements through retrofitting or operational changes. This study evaluates, at environmental and economic levels, two key sulphur abatement strategies for a 1998-built cruise vessel nearing the end of its service life: (i) the installation of open-loop scrubbers with fuel enhancement devices, and (ii) a switch to marine diesel oil as main fuel. The analysis was based on real operational data from a cruise vessel. For the environmental assessment, a Tier III hybrid emissions model was used. The results show that scrubbers reduce SO_x emissions by approximately 97% but increase fuel consumption by 3.6%, raising both CO₂ and NO_x emissions, while particulate matter decreases by only 6.7%. In contrast, switching to MDO achieves over 99% SO_x reduction, an 89% drop in particulate matter, and a nearly 5% reduction in CO₂ emissions. At an economic level, it was found that, despite a CAPEX of nearly USD 1.9 million, scrubber installation provides an average annual net saving exceeding USD 8.2 million. From the deterministic and probabilistic analyses performed, including Monte Carlo simulations under various fuel price correlation scenarios, scrubber installation consistently shows high profitability, with NPVs surpassing USD 70 million and payback periods under four months. Full article

The maritime sector faces escalating demands to minimize emissions and optimize operational efficiency under tightening environmental regulations. Although technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twins (DT) offer substantial potential, their deployment in real-time ship performance analytics is at an emerging state. This paper proposes a machine learning-driven framework for real-time ship performance management. The framework starts with data collected from onboard sensors and culminates in a decision support system that is easily interpretable, even by non-experts. It also provides a method to forecast vessel performance by extrapolating Key Performance Indicator (KPI) values. Furthermore, it offers a flexible methodology for defining KPIs for every crucial component or aspect of vessel performance, illustrated through a use case focusing on fuel oil consumption. Leveraging Artificial Neural Networks (ANNs), hybrid multivariate data fusion, and high-frequency sensor streams, the system facilitates continuous diagnostics, early fault detection, and data-driven decision-making. Unlike conventional static performance models, the framework employs dynamic KPIs that evolve with the vessel’s operational state, enabling advanced trend analysis, predictive maintenance scheduling, and compliance assurance. Experimental comparison against classical KPI models highlights superior predictive fidelity, robustness, and temporal consistency. Furthermore, the paper delineates AI and ML applications across core maritime operations and introduces a scalable, modular system architecture applicable to both commercial and naval platforms. This approach bridges advanced simulation ecosystems with in situ operational data, laying a robust foundation for digital transformation and sustainability in maritime domains. Full article

The Carbon Intensity Indicator (CII) plays a critical role in assessing vessel efficiency. This study examines the impact of cold ironing (CI) on CII performance by analyzing 183 voyages of container ships. The research evaluates the attained CII values, CII ratings, and a Levelized Cost of Energy (LCOE) under different voyage data of container ships between 2023 and 2030. Results show that while 90.7% of voyages met the CII reference value in 2023, this rate decreased to 68.9% and 19.7% by 2026 and 2030, underscoring the increasing challenge of regulatory compliance, if no energy efficiency measures can be taken. Seasonal variations significantly influenced the CII, especially in March and May. With the implementation of CI on container ships, 6441.95 tons of heavy fuel oil and 6101.77 tons of marine gas oil consumption have been eliminated during port stays based on voyage data. Economic analysis indicates that CI increases the LCOE by 13.76%–19.65%, with a discounted payback period ranging from 4.69 to 24 years. This study highlights CI as a viable short-term measure for reducing maritime emissions and enhancing CII compliance, emphasizing the need for optimized economic models. Full article

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

Offshore oil and gas activities are crucial in the petroleum industry. Offshore oil and gas installations require different cargo to operate. A heterogeneous fleet of platform supply vessels (PSVs) transports cargo supply to installations. The PSV fleet management in Brazil faces challenges such as the non-availability of the spot market, variations and uncertainties in delivery order demands and due dates, inspection and corrective vessel maintenance, and multiple time windows for service at installations. PSV fleet management aims to satisfy cargo delivery requests in time and quantity, avoid delays, and achieve a balance among delivery service levels, vessel costs, and greenhouse gas emissions. We develop several PSV fleet management policies with delivery service level or fuel consumption goals, composed of new fleet management procedures such as vessel control, vessel assignment to voyages including cargo selection, vessel routing, speed selection, and dynamic re-routing. The results of tests on a real Brazilian case demonstrate that the developed policies with the incorporated fleet management procedures improve fleet performance indicators. The comparative analysis of policies shows their different impacts on indicators, allowing managers to select the best fleet management policy by considering the trade-offs between delivery service level, costs, and emissions, depending on their goals. Full article

The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO). Full article

The FuelEU Maritime Regulation, part of the European Union’s (EU’s) Fit for 55 initiative, aims to achieve significant reductions in greenhouse gas (GHG) emissions within the maritime sector. This study assesses the feasibility of alternative fuels for the Estonian pilot fleet using a Well-to-Wake (WtW) life cycle assessment (LCA) methodology. Operational data from 18 vessels, sourced from the Estonian State Fleet’s records, were analyzed, including technical specifications, fuel consumption patterns, and operational scenarios. The study focused on marine diesel oil (MDO), biomethane, hydrogen, biodiesel, ammonia, and hydrotreated vegetable oil (HVO), each presenting distinct trade-offs. Biomethane achieved a 59% GHG emissions reduction but required a volumetric storage capacity up to 353% higher compared to MDO. Biodiesel reduced GHG emissions by 41.2%, offering moderate compatibility with existing systems while requiring up to 23% larger storage volumes. HVO demonstrated a 43.6% emissions reduction with seamless integration into existing marine engines. Ammonia showed strong potential for long-term decarbonization, but its adoption is hindered by low energy density and complex storage requirements. This research underscores the importance of a holistic evaluation of alternative fuels, taking into account technical, economic, and environmental factors specific to regional and operational contexts. The findings offer a quantitative basis for policymakers and maritime stakeholders to develop effective decarbonization strategies for the Baltic Sea region. Full article

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

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

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles. Full article

This paper addresses the scheduling problem of a mixed fleet passing through a river bottleneck in multiple ways, considering the impact of streamflow velocity, the fuel cost with different sailing speeds, and the potential opportunity cost of various types and sizes of vessels. From the perspective of centralized management by river bottleneck authorities, a unified scheduling approach is proposed, and a nonlinear model is constructed, where the total fuel cost and potential opportunity cost of the fleet are minimized. To handle the nonlinear terms in the model, an outer approximation technique is applied to linearize the model while ensuring the approximation error remains controlled. The optimal value range of the nonlinear variables is also proven to ensure solution speed. Furthermore, the applicability and effectiveness of the model and solution method are validated through a real-world case study on the Yangtze River. The results show the following: (1) Unified collaborative scheduling by bottleneck authorities can ensure the optimal total cost of the fleet is effectively met and that the vessels passing through the river bottleneck are arranged under rational ways. (2) When fuel consumption is the same as that of traditional oil-fuelled vessels, giving priority to liquefied natural gas (LNG)-fuelled vessels to pass through the river bottleneck can reduce the potential opportunity cost and the total cost of the fleet reasonably. (3) In accordance with changes in the fuel price, streamflow velocity, and proportion of LNG-fuelled vessels, timely adjusting the opportunity cost expectations, vessel arrival time, and service times of bottleneck passing ways is crucial for shipowners and authorities to reduce fleet waiting times at the bottleneck, delay time, and the total cost. Full article

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship’s carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO₂ emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships. Full article

In this study, the life cycle assessment (LCA) was used to compare the environmental performances of a conventional bulk carrier (baseline vessel) and a wind-energy-optimised bulk carrier equipped with modern on-board technologies working in synergy (future vessel). Fossil fuels was used for the baseline vessels, whereas the future vessel used liquefied biogas (LBG) and hydrotreated vegetable oil (HVO) as marine fuels. The entire life cycle phases of the vessels, namely, construction, operation, maintenance, and end-of-life, were included. The results showed that the future vessel could reduce 31.23% energy consumption, compared to the baseline model. Furthermore, the significant reduction in CO₂ (48.6%), NO_X (88.6%), SO_X (100.0%), and black carbon (94.0%) in the tank-to-wake phase was achieved owing to energy-saving technologies working in synergy and alternative fuels. This study emphasizes the vital role of energy efficiency, technologies, and alternative fuels to achieve the zero-emission ambition of the maritime industry. Furthermore, the impacts of ship construction, maintenance, and end-of-life need to be fully considered in order to decarbonize vessel from a life cycle perspective. Full article

Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO₂, NO₂, SO₂, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33. Full article

Thin-walled structures are a significant and growing portion of engineering construction, with a wide range of applications, including storage vessels, industrial buildings, warehouses, aircraft, automobiles, bridges, ships, and oil rigs. Thin-walled components with minimum thickness without compromising strength and other quality characteristics are the desire of modern industry. Reducing wall thickness not only aids in lowering the cost of production. It also improves the effectiveness of engineering systems, resulting in lower fuel consumption and lower emissions of hazardous gases to the environment. Nowadays, even though thin-walled parts are demanded, the constraints of the production process, quality, and reliability are the concerns of current research and development. The ability to produce parts with intricate geometries and tight dimensional tolerances are important criteria for advanced manufacturing processes. In the early days of society, investment casting was used to produce jewelry, weapons, and statues. In modern industry, investment casting is still used to produce thin-walled and intricate parts such as turbine blades. The current advancements in SLM, which has the capacity to produce thin-walled and intricate parts, have recently attracted attention due to several benefits, such as the supreme degree of design freedom and the viability of tool-free production directly from CAD data. However, the current technological applications of SLM and investment casting are crucial for producing parts at the desired quality and reliability. This review article focuses on comparative studies of SLM and investment casting at the current technology level. The basis of comparison via systematic approach is mechanical characterization; quality in terms of porosity, microstructure, surface roughness and dimensional accuracy; and residual stress. Therefore, the latest open scientific sources published are considered to obtain sufficient literature coverage. Better tensile strength and fine microstructure are found in SLM, while better surface quality, fatigue load resistance, ductility, and residual stress are found in investment casting. The research gap for further investigation is indicated. Full article