Machine learning in sustainable ship design and operation: A review

Huang, Luofeng; Pena, Blanca; Liu, Yuanchang; Anderlini, Enrico

doi:10.1016/j.oceaneng.2022.112907

Cited by 37 publications

(9 citation statements)

References 81 publications

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“…When designing better control systems that enable autonomous vessels to negotiate challenging marine scenarios with precision and efficiency, machine learning is one area in which it excels [90]- [92]. It is possible to optimize control approaches for autonomous ships by utilizing machine learning algorithms that consider vessel dynamics, environmental factors, and operational limits [93], [94].…”

Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Sepheri et al [91] presented the use of Cyber-physical systems, Augmented Reality and Digitalization, Internet of Things, Big Data, and Cloud Computing to prevent navigational accidents. Based on the 4.0 technologies framework, concepts such as Shipping 4.0 [92,93], Ports 4.0 [94], Maritime 4.0 [95], and Machine Learning approaches to improve the sustainability of ship design and operations [96] are being considered in marine applications. Finally, the use of renewable energy sources to provide signalization or communication through autonomous floating, anchored, or fixed devices could reduce accident risks in navigation.…”

Section: Preventative Measures To Reduce Passenger Ship Accidents In ...mentioning

confidence: 99%

Marine Accidents in the Brazilian Amazon: The Problems and Challenges in the Initiatives for Their Prevention Focused on Passenger Ships

et al. 2022

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The Brazilian Amazon is part of one of the largest river systems in the world, in which the transport of cargo and passengers is commonplace. However, several accidents still occur to passenger ships, causing fatalities. Transportation occurs commonly in remote regions, where there are transport inequalities, and emergency assistance is hard to find. This can affect sustainability in communities with considerable levels of economic and social vulnerability. More information is needed about accidents involving inland transport in the Amazon, to identify the threats to ships and propose strategies for accident prevention. This paper addresses the main problems that long-distance passenger ships face in the Brazilian Amazon, presenting an integrated framework towards accident prevention. First, the present situation is characterized in terms of ship description, spatial distribution, and regulations that are applicable. Next, possible causes of passenger ship accidents are discussed, including topics of concern that should be considered in the Amazon waterways. Finally, measures to help minimize passenger ship accidents are proposed, and the social relevance is discussed. It was found that accidents in the Amazon are due to a combination of human and environmental factors. Stakeholders should strengthen the technical and legal training of ship operators. The use of new technologies for navigational aid and necessary maintenance of ships is suggested. Marine accident prevention initiatives should consider local conditions, such as environmental preservation, cultural respect, and difficulties related to navigation through the complex riverine system of the Amazon region.

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“…In the preliminary design phase, designers play a crucial role in defining a ship's major features based on implicit client requirements, including draft length, service speed range, and bollard pull capacity. Designers iteratively adjust dimensions by analyzing comparable ships or resort to empirical formulations and machine learning (ML) methods like neural networks (NN) to predict a ship's main particulars and to analyze dynamic systems, especially in early design stages [4][5][6][7]. During the early design stage of a ship, it could benefit from using an ML approach, Throughout these design stages, the focus is on optimizing the main particulars of the tugboat to ensure it can effectively fulfill its role in servicing ships in busy ports and harbors.…”

Section: Introductionmentioning

confidence: 99%

“…During the early design stage of a ship, it could benefit from using an ML approach, where a large number of configurations must be tested, which could be prohibitive to achieve using Computational Fluid Dynamics (CFD) or model experiments. It may provide fast predictions with non-linearities taken into account, overcoming the inaccuracies in linear analytical methods currently used in the early design stages [7].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Karaçay,

Karatuğ,

Uyanık

et al. 2024

Applied Sciences

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Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.

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Machine learning in sustainable ship design and operation: A review

Cited by 37 publications

References 81 publications

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Marine Accidents in the Brazilian Amazon: The Problems and Challenges in the Initiatives for Their Prevention Focused on Passenger Ships

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

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