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Advanced Research on the Sustainable Maritime Transportation (2nd Edition)
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Advanced Research on the Sustainable Maritime Transportation (2nd Edition)

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 6860

Special Issue Editors

Prof. Dr. Xianhua Wu

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: maritime disaster risk analysis; big data analysis; applied statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Wu

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: decision sciences; shipping assessment; big data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Lang Xu

E-Mail Website
Guest Editor
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
Interests: port and shipping governance; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform where researchers can share their latest findings and insights regarding sustainable maritime transportation. This Special Issue covers a wide range of topics related to sustainable maritime transportation, including the following:

  1. Green shipping technologies: It explores the latest developments in green shipping technologies, such as alternative fuels, energy-efficient propulsion systems, and emission reduction technologies.
  2. Maritime safety: It refers to the measures and practices that are put in place to prevent accidents and incidents at sea. One of the key areas is the use of autonomous vessels. Another area includes terrorist attacks (such as the Red Sea event).
  3. Sustainable port operations: This Special Issue also covers sustainable port operations, including the use of renewable energy sources, waste management, and sustainable logistics.
  4. Environmental impact assessment: This Special Issue includes studies concerning the environmental impact of maritime transportation, such as the impact of shipping on marine ecosystems and the effects of climate change on shipping.
  5. The evaluation of sustainable development: For example, the reduction capacity performance evaluation of intermodal transport emission and feasibility studies of green and low-carbon technologies for ships, ports, or maritime transportation.
  6. Maritime Transportation: this Special Issue also includes studies concerning industrial development and sustainable environment relating to maritime transportation.

In light of the success of this Special Issue and the pressing issue, we would like to announce the 2nd Edition.

Prof. Dr. Xianhua Wu
Prof. Dr. Jian Wu
Dr. Lang Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable maritime transportation
  • green shipping
  • energy efficiency
  • alternative fuels
  • emission reduction
  • maritime safety
  • maritime accident
  • port
  • environmental impact
  • feasibility study

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

16 pages, 4180 KiB  
Article
Integrating Bayesian Network and Cloud Model to Probabilistic Risk Assessment of Maritime Collision Accidents in China’s Coastal Port Waters
by Zhuang Li, Xiaoming Zhu, Shiguan Liao, Jianchuan Yin, Kaixian Gao and Xinliang Liu
J. Mar. Sci. Eng. 2024, 12(12), 2113; https://doi.org/10.3390/jmse12122113 - 21 Nov 2024
Viewed by 202
Abstract
Ship collision accidents have a greatly adverse impact on the development of the shipping industry. Due to the uncertainty relating to these accidents, maritime risk is often difficult to accurately quantify. This study innovatively proposes a comprehensive method combining qualitative and quantitative methods [...] Read more.
Ship collision accidents have a greatly adverse impact on the development of the shipping industry. Due to the uncertainty relating to these accidents, maritime risk is often difficult to accurately quantify. This study innovatively proposes a comprehensive method combining qualitative and quantitative methods to predict the risk of ship collision accidents. First, in view of the uncertain impact of risk factors, the Bayesian network analysis method was used to characterize the correlations between risk factors, and a collision accident risk assessment network model was established. Secondly, in view of the uncertainty relating to the information about risk factors, a subjective data quantification method based on the cloud model was adopted, and the quantitative reasoning of collision accident risk was determined based on multi-source data fusion. The proposed method was applied to the spatiotemporal analysis of ship collision accident risk in China’s coastal port waters. The results show that there is a higher risk of collision accidents in Guangzhou Port and Ningbo Port in China, the potential for ship collision accidents in southern China is greater, and the occurrence of ship collision accidents is most affected by the environment and operations of operators. Combining the Bayesian network and cloud model and integrating multi-source data information to conduct an accident risk assessment, this innovative analysis method has significance for improving the prevention of and response to risks of ship navigation operations in China’s coastal ports. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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16 pages, 2184 KiB  
Article
Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments
by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin and Yi Han
J. Mar. Sci. Eng. 2024, 12(10), 1754; https://doi.org/10.3390/jmse12101754 - 4 Oct 2024
Viewed by 998
Abstract
Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion [...] Read more.
Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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18 pages, 3731 KiB  
Article
Algorithm Design for an Online Berth Allocation Problem
by Cong Chen, Fanxin Wang, Jiayin Pan, Lang Xu and Hongming Gao
J. Mar. Sci. Eng. 2024, 12(10), 1722; https://doi.org/10.3390/jmse12101722 - 30 Sep 2024
Viewed by 503
Abstract
In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that [...] Read more.
In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that the widely used Greedy algorithm has a very poor theoretical guarantee; specifically, the competitive ratio of the Greedy algorithm for this problem is lower bounded by Ω(log⁡m/log⁡log⁡m), which increases with the number of berths m. On account of this, we borrow an idea from algorithms for the online strip packing problem and provide a comprehensive theoretical analysis of the Revised Shelf (RS) algorithm as applied to our berth allocation problem. We prove that the competitive ratio of RS for our problem is 5, improving on the original competitive ratio of 6.66 for the online strip packing problem. Through numerical studies, we examine the RS algorithm and Greedy algorithm in an average case. The numerical simulation of competitive ratios reveals distinct advantages for different algorithms depending on job size. For smaller job sizes, the Greedy algorithm emerges as the most efficient, while for medium-sized jobs, the RS algorithm proves to be the most effective. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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24 pages, 3881 KiB  
Article
Methodological Solutions for Predicting Energy Efficiency of Organic Rankine Cycle Waste Heat Recovery Systems Considering Technological Constraints
by Sergejus Lebedevas and Tomas Čepaitis
J. Mar. Sci. Eng. 2024, 12(8), 1303; https://doi.org/10.3390/jmse12081303 - 1 Aug 2024
Cited by 3 | Viewed by 1051
Abstract
Solving strategic IMO tasks for the decarbonization of maritime transport and the dynamics of its controlling indicators (EEDI, EEXI, CII) involves the comprehensive use of renewable and low-carbon fuels (LNG, biodiesel, methanol in the mid-term perspective of 2030, ammonia, and hydrogen to achieve [...] Read more.
Solving strategic IMO tasks for the decarbonization of maritime transport and the dynamics of its controlling indicators (EEDI, EEXI, CII) involves the comprehensive use of renewable and low-carbon fuels (LNG, biodiesel, methanol in the mid-term perspective of 2030, ammonia, and hydrogen to achieve zero emissions by 2050) and energy-saving technologies. The technology of regenerating secondary heat sources of the ship’s power plant WHR in the form of an Organic Rankine Cycle (ORC) is considered one of the most promising solutions. The attractiveness of the ORC is justified by the share of the energy potential of WHR at 45–50%, almost half of which are low-temperature WHR (80–90 °C and below). However, according to DNV GL, the widespread adoption of WHR-ORC technologies, especially on operating ships, is hindered by the statistical lack of system prototypes combined with the high cost of implementation. Developing methodological tools for justifying the energy efficiency indicators of WHR–ORC cycle implementation is relevant at all stages of design. The methodological solutions proposed in this article are focused on the initial stages of comparative evaluation of alternative structural solutions (without the need to use detailed technical data of the ship’s systems, power plant, and ORC nodes), expected indicators of energy efficiency, and cycle performance. The development is based on generalized results of variation studies of the ORC in the structure of the widely used main marine medium-speed diesel engine Wärtsilä 12V46F (14,400 kW, 500 min−1) in the operational load cycle range of 25–100% of nominal power. The algorithm of the proposed solutions is based on the established interrelationship of the components of the ORC energy balance in the P-h diagram field of thermodynamic indicators of the cycle working fluid (R134a was used). The implemented strategy does allow, in graphical form, for justifying the choice of working fluid and evaluating the energy performance and efficiency of alternative WHR sources for the main engine, taking into account the design solutions of the power turbine and the technological constraints of the ORC condensation system. The verification of the developed methodological solutions is served by the results of comprehensive variation studies of the ORC performed by the authors using the professionally oriented thermoengineering tool “Thermoflow” and the specification data of Wärtsilä 12V46F with an achieved increase in energy efficiency indicators by 21.4–7%. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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30 pages, 4155 KiB  
Article
Thermo-Economic Comparison between Three Different Electrolysis Technologies Powered by a Conventional Organic Rankine Cycle for the Green Hydrogen Production Onboard Liquefied Natural Gas Carriers
by Doha Elrhoul, Manuel Naveiro and Manuel Romero Gómez
J. Mar. Sci. Eng. 2024, 12(8), 1287; https://doi.org/10.3390/jmse12081287 - 31 Jul 2024
Cited by 1 | Viewed by 1296
Abstract
The high demand for natural gas (NG) worldwide has led to an increase in the size of the LNG carrier fleet. However, the heat losses from this type of ship’s engines are not properly managed, nor is the excess boil-off gas (BOG) effectively [...] Read more.
The high demand for natural gas (NG) worldwide has led to an increase in the size of the LNG carrier fleet. However, the heat losses from this type of ship’s engines are not properly managed, nor is the excess boil-off gas (BOG) effectively utilised when generation exceeds the ship’s power demand, resulting in significant energy losses dissipated into the environment. This article suggests storing the lost energy into green H2 for subsequent use. This work compares three different electrolysis technologies: solid oxide (SOEC), proton exchange membrane (PEME), and alkaline (AE). The energy required by the electrolysis processes is supplied by both the LNG’s excess BOG and engine waste heat through an organic Rankine cycle (ORC). The results show that the SOEC consumes (743.53 kW) less energy while producing more gH2 (21.94 kg/h) compared to PEME (796.25 kW, 13.96 kg/h) and AE (797.69 kW, 10.74 kg/h). In addition, both the overall system and SOEC stack efficiencies are greater than those of PEME and AE, respectively. Although the investment cost required for AE (with and without H2 compression consideration) is cheaper than SOEC and PEME in both scenarios, the cost of the H2 produced by the SOEC is cheaper by more than 2 USD/kgH2 compared to both other technologies. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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17 pages, 6308 KiB  
Article
Method of Plotting Fundamental Diagrams of Waterway Traffic Flow—Shipping-Lane Subdivision
by Siqing Zhuang, Yihua Liu and Zhiyuan Xu
J. Mar. Sci. Eng. 2024, 12(7), 1163; https://doi.org/10.3390/jmse12071163 - 10 Jul 2024
Viewed by 707
Abstract
The difference between waterway traffic and road traffic in terms of lane lines leads to the direct application of the method of plotting the fundamental diagram of road traffic flow to waterway traffic, and it is difficult to reveal the mechanism of waterway [...] Read more.
The difference between waterway traffic and road traffic in terms of lane lines leads to the direct application of the method of plotting the fundamental diagram of road traffic flow to waterway traffic, and it is difficult to reveal the mechanism of waterway traffic flow operations. This study proposes a shipping-lane-subdivision approach to tackle this problem. Additionally, it introduces a more suitable fundamental diagram-plotting method for waterway traffic based on the aforementioned method. The southern channel in the estuary of the Yangtze River was taken as the research water, and the fundamental diagram of traffic flow in this water was plotted to verify the similarities between the fundamental diagram of waterway traffic flow and the fundamental diagram of road traffic flow. Upon evaluating the plotted fundamental diagram, it was determined that the blockage density of the subdivided shipping lane is around 6.5 vessels per nautical mile. This method has significant potential for its application in the theory of waterway traffic flow. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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19 pages, 8829 KiB  
Article
Detection and Analysis of Corrosion on Coated Metal Surfaces Using Enhanced YOLO v5 Algorithm for Anti-Corrosion Performance Evaluation
by Qifeng Yu, Yudong Han, Wuguang Lin and Xinjia Gao
J. Mar. Sci. Eng. 2024, 12(7), 1090; https://doi.org/10.3390/jmse12071090 - 27 Jun 2024
Cited by 4 | Viewed by 1200
Abstract
This study addresses the severe corrosion issues in the coastal regions of southern China by proposing an improved YOLO v5-GOLD-NWD model. Utilizing corrosion data from the National Center for Materials Corrosion and Protection Science of China, a dataset was constructed for metal-surface corrosion [...] Read more.
This study addresses the severe corrosion issues in the coastal regions of southern China by proposing an improved YOLO v5-GOLD-NWD model. Utilizing corrosion data from the National Center for Materials Corrosion and Protection Science of China, a dataset was constructed for metal-surface corrosion under different protective coatings. This dataset was used for model training, testing, and comparison. Model accuracy was validated using precision, recall, F1 score, and prediction probability. The results demonstrate that the proposed improved model exhibits better identification precision in metal corrosion detection, achieving 78%, a 4% improvement compared to traditional YOLO v5 models. Additionally, through identification and statistical analysis of corrosion image datasets from five types of coated metal specimens, it was found that powder epoxy coating, fluorocarbon coating, epoxy coating, and chlorinated rubber coating showed good corrosion resistance after 24 months of exposure. Conversely, Wuxi anti-fouling coating exhibited poor corrosion resistance. After 60 months of natural exposure, the powder epoxy coating specimens had the highest corrosion occurrence probability, followed by chlorinated rubber coating and epoxy coating, with fluorocarbon coating showing relatively lower probability. The fluorocarbon coating demonstrated relatively good corrosion resistance at both 24 and 60 months of exposure. The findings of this study provide a theoretical basis for enhancing the corrosion protection effectiveness of steel structures in coastal areas. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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