Energy Optimization of Ship and Maritime Structures

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: closed (20 October 2023) | Viewed by 16809

Special Issue Editor


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Guest Editor
INGEMAR Group, University of La Laguna, 38001 Tenerife, Spain
Interests: artificial intelligence; neuro-fuzzy systems; predictive maintenance; autonomous vehicles; desalination; marine renewable energy
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Special Issue Information

Dear Colleagues,

Energy is a key issue in maritime transport and the different maritime structures. The main targets are minimizing energy consumption, obtaining clean energy and managing the available energy in an efficient way.

This Special Issue is intended to collect studies aimed at improving these areas from a broad perspective. New approaches in the field of energy generation and efficient management will be well received.

Aspects such as new designs and optimization of energy-generation maritime structures, improvements in maintenance in order to run more efficient ships and maritime structures, artificial intelligence techniques applied to different installations of the ship achieving an efficient energy management, new propulsion systems in the ships or new schemes of control to improve energy generation maritime structures are also welcome. 

New proposals and cases studies related to the aforementioned aspects are encouraged for publication.

Prof. Dr. Graciliano Nicolás Marichal
Guest Editor

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

  • power system
  • power distribution
  • energy efficiency
  • guidance, navigation and control
  • maritime autonomous surface ships (MASS)
  • renewable energy
  • hybrid energy systems
  • offshore and tidal energy
  • marine turbines
  • smart maintenance

 

Published Papers (9 papers)

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14 pages, 3383 KiB  
Article
Optimal Design of Rotor Sails Based on Environmental Conditions and Cost
by Cem Guzelbulut and Katsuyuki Suzuki
J. Mar. Sci. Eng. 2024, 12(1), 31; https://doi.org/10.3390/jmse12010031 - 22 Dec 2023
Viewed by 1182
Abstract
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 [...] Read more.
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%. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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0 pages, 4383 KiB  
Article
Deep Reinforcement Learning-Based Energy Management for Liquid Hydrogen-Fueled Hybrid Electric Ship Propulsion System
by Wongwan Jung and Daejun Chang
J. Mar. Sci. Eng. 2023, 11(10), 2007; https://doi.org/10.3390/jmse11102007 - 18 Oct 2023
Cited by 4 | Viewed by 1531 | Correction
Abstract
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane fuel cell (PEMFC) and lithium-ion battery systems. [...] Read more.
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane fuel cell (PEMFC) and lithium-ion battery systems. This study analyzed the optimized performance of the DRL-EMS and the operational strategy of the LH2-HSPS. To train the proposed DRL-EMS, a reward function was defined based on fuel consumption and degradation of power sources during operation. Fuel consumption for ship propulsion was estimated with the power for balance of plant (BOP) of the LH2 FGSS and PEMFC system. DRL-EMS demonstrated superior global and real-time optimality compared to benchmark algorithms, namely dynamic programming (DP) and sequential quadratic programming (SQP)-based EMS. For various operation cases not used in training, DRL-EMS resulted in 0.7% to 9.2% higher operating expenditure compared to DP-EMS. Additionally, DRL-EMS was trained to operate 60% of the total operation time in the maximum efficiency range of the PEMFC system. Different hydrogen fuel costs did not affect the optimized operational strategy although the operating expenditure (OPEX) was dependent on the hydrogen fuel cost. Different capacities of the battery system did not considerably change the OPEX. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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19 pages, 10429 KiB  
Article
Speed Optimization in Bulk Carriers: A Weather-Sensitive Approach for Reducing Fuel Consumption
by You-Chen Shih, Yu-An Tzeng, Chih-Wen Cheng and Chien-Hua Huang
J. Mar. Sci. Eng. 2023, 11(10), 2000; https://doi.org/10.3390/jmse11102000 - 17 Oct 2023
Viewed by 1292
Abstract
The maritime industry faces the critical challenge of achieving net-zero greenhouse gas emissions by 2050, as mandated by the International Maritime Organization. This study introduces a novel speed optimization model, designed specifically for bulk carriers operating between two ports. Unlike conventional models that [...] Read more.
The maritime industry faces the critical challenge of achieving net-zero greenhouse gas emissions by 2050, as mandated by the International Maritime Organization. This study introduces a novel speed optimization model, designed specifically for bulk carriers operating between two ports. Unlike conventional models that often assume static weather conditions, the proposed model incorporated variable weather conditions at different times of arrivals, as quantified by the Beaufort number (BN) and weather direction, for each leg of the voyage. Fuel consumption was estimated by applying regression to historical voyage data. This study employed a genetic algorithm (GA) to optimize vessel speed and thereby minimize fuel consumption. The model was tested by using different fuel consumption response curves relative to different BNs and weather directions. The results indicated that the proposed method could effectively reduce fuel consumption compared with the historical sailing mode by around 3%. The optimal speed recommendation indicated that the vessel should operate at a higher speed in circumstances associated with relatively low fuel consumption, such as lower BN and following sea conditions. Nonetheless, if it is possible to attain relatively low fuel consumption by adjusting the speed, the GA assesses the viability of this course of action. The study suggests that the predictive accuracy could be further enhanced by incorporating more granular, validated voyage data in future research. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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14 pages, 3778 KiB  
Article
A Comparison of Intelligent Models for Collision Avoidance Path Planning on Environmentally Propelled Unmanned Surface Vehicles
by Carlos Barrera, Mustapha Maarouf, Francisco Campuzano, Octavio Llinas and Graciliano Nicolas Marichal
J. Mar. Sci. Eng. 2023, 11(4), 692; https://doi.org/10.3390/jmse11040692 - 24 Mar 2023
Cited by 3 | Viewed by 1829
Abstract
Unmanned surface vehicles (USVs) are increasingly used for ocean missions and services aimed for safer, more efficient, and sustainable routine operations. Path planning is a key component of autonomy addressed to obstacle detection and avoidance. As a multi-optimization nonlinear problem, it should include [...] Read more.
Unmanned surface vehicles (USVs) are increasingly used for ocean missions and services aimed for safer, more efficient, and sustainable routine operations. Path planning is a key component of autonomy addressed to obstacle detection and avoidance. As a multi-optimization nonlinear problem, it should include computational time, optimal path, and maritime traffic standard procedures. This becomes even more challenging for USV technologies propelled by harvesting ocean energy from waves and wind. Sea current state and wind conditions significantly affect the USV energy consumption becoming the path planning approach key for navigation performance and endurance. To improve both aspects, an energy-efficient new path planning algorithm approach based on AI techniques for computing feasible paths in compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) rules and taking energy consumption into account according to wind and sea current data is proposed. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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21 pages, 12481 KiB  
Article
An Improved Ship Weather Routing Framework for CII Reduction Accounting for Wind-Assisted Rotors
by Wenyu Sun, Siyu Tang, Xiyang Liu, Shinan Zhou and Jinfang Wei
J. Mar. Sci. Eng. 2022, 10(12), 1979; https://doi.org/10.3390/jmse10121979 - 12 Dec 2022
Cited by 7 | Viewed by 1520
Abstract
With the increasingly strict regulations for the energy-saving and emission-reduction technology of ships, minimizing fuel cost and thus reducing the carbon intensity index (CII) is one of the most critical issues in the design and operation of merchant ships. More recently, many wind-assisted [...] Read more.
With the increasingly strict regulations for the energy-saving and emission-reduction technology of ships, minimizing fuel cost and thus reducing the carbon intensity index (CII) is one of the most critical issues in the design and operation of merchant ships. More recently, many wind-assisted devices, such as rotors, wind sails, etc., have been investigated and designed to utilize renewable wind energy. With the equipment of wind-assisted rotors, the optimization of ship routes becomes more important because the effect of this wind-assisted device highly depends on the local wind field along the shipping route. In this paper, an improved ship weather routing framework based on the A* algorithm has been proposed to determine the optimal ship route and ship operations with wind-assisted rotors. The proposed framework effectively utilizes different sources of data, including ship design, weather forecasting and historical sailing information, to produce a better estimation of fuel consumption under the effect of sea states. Several improvements on the classic A* algorithm, including directed searching and three-dimensional extension, are proposed to improve the routing effect and efficiency. Finally, the proposed method was applied to test cases of a VLCC operating from China to the Middle East and the results show that the total fuel consumption could be reduced compared to the minimum distance route. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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12 pages, 2071 KiB  
Article
Data Preprocessing for Vibration Analysis: Application in Indirect Monitoring of ‘Ship Centrifuge Lube Oil Separation Systems’
by Marta Zamorano, Deivis Avila, Graciliano Nicolás Marichal and Cristina Castejon
J. Mar. Sci. Eng. 2022, 10(9), 1199; https://doi.org/10.3390/jmse10091199 - 26 Aug 2022
Cited by 3 | Viewed by 1714
Abstract
Air quality can be affected by merchant ships, so it is important to regulate emissions that are produced, as well as to use energy efficiently. In this sense, the cleanliness of the oil used in lubrication is essential to achieve a better use [...] Read more.
Air quality can be affected by merchant ships, so it is important to regulate emissions that are produced, as well as to use energy efficiently. In this sense, the cleanliness of the oil used in lubrication is essential to achieve a better use of energy and reduce losses in marine engines. For that, it is vital to carry out good maintenance strategies. Therefore, it is important to develop techniques that allow condition monitoring during engine operation. In order to detect potential problems as soon as possible, it is common to analyze vibratory signals, since sustainable changes in the rotating frequency and its harmonics can be detected, which was the objective of this work, by analyzing the time-frequency domain using wavelet packet transform. A methodology to select the optimal function (mother wavelet) and the best patterns to monitor, in order to determine the state of the purifiers of the marine lube oils, was carried out, including intelligent classification systems. Specifically, this document considers centrifugal oil lubricant separators systems, since the monitoring of these systems can determine the condition of different mechanical systems. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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18 pages, 3233 KiB  
Article
Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance
by Montserrat Sacie, Matilde Santos, Rafael López and Ravi Pandit
J. Mar. Sci. Eng. 2022, 10(7), 938; https://doi.org/10.3390/jmse10070938 - 8 Jul 2022
Cited by 9 | Viewed by 2327
Abstract
One of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural [...] Read more.
One of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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16 pages, 1395 KiB  
Article
Improvement of Regasification Process Efficiency for Floating Storage Regasification Unit
by Vigaile Semaskaite, Marijonas Bogdevicius, Tatjana Paulauskiene, Jochen Uebe and Ludmila Filina-Dawidowicz
J. Mar. Sci. Eng. 2022, 10(7), 897; https://doi.org/10.3390/jmse10070897 - 28 Jun 2022
Cited by 8 | Viewed by 2749
Abstract
Natural gas plays a vital role in the economically and environmentally sustainable future of energy. Its reliable deliveries are required, especially nowadays, when the energy market is so volatile and unstable. The conversion of natural gas to its liquefied form (LNG) allows its [...] Read more.
Natural gas plays a vital role in the economically and environmentally sustainable future of energy. Its reliable deliveries are required, especially nowadays, when the energy market is so volatile and unstable. The conversion of natural gas to its liquefied form (LNG) allows its transport in greater quantities. Affordability and reliability of clean energy is a key issue even for developed markets. Therefore, natural gas usage enables to implement green solutions into countries’ economies. However, the LNG-production process consumes a considerable amount of energy. This energy is stored in LNG as cold energy. After LNG unloading into storage tanks at receiving terminals, it is vaporised and compressed for transmission to a natural gas pipeline system. During the regasification process, the large part of the energy stored in LNG may be recovered and used for electricity generation, seawater desalination, cryogenic air separation, hydrogen liquefaction, material freezing, carbon dioxide capture, as well as for combined LNG cold energy utilization systems. Moreover, increased efficiency of LNG terminals may attract potential clients. In the presented paper, a mathematical model is performed to determine the influence of LNG composition and regasification process parameters on the quantity of released LNG cold energy in a large-scale floating storage and regasification units (FSRU)-type terminal “Independence” (Lithuania). Flow rate of LNG regasification, pressure, and boil-off gas recondensation have been considered. Possibilities to reduce the energy losses were investigated to find the ways to improve the regasification process efficiency for real FSRU. The results analysis revealed that potential of LNG cold energy at FSRU could vary from 20 to 25 MW. A utilisation of industrial and urban waste heat for the heat sink FSRU is recommended to increase the energy efficiency of the whole regasification process. Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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4 pages, 1539 KiB  
Correction
Correction: Jung, W.; Chang, D. Deep Reinforcement Learning-Based Energy Management for Liquid Hydrogen-Fueled Hybrid Electric Ship Propulsion System. J. Mar. Sci. Eng. 2023, 11, 2007
by Wongwan Jung and Daejun Chang
J. Mar. Sci. Eng. 2024, 12(3), 484; https://doi.org/10.3390/jmse12030484 - 14 Mar 2024
Viewed by 495
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Energy Optimization of Ship and Maritime Structures)
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