A review of ship fuel consumption models

Operational optimisation is one of the most important methods for reducing energy consumption and emissions related to ships. In this study, a speed-trim joint optimisation method is proposed. First, the propulsion power for various operating conditions was estimated by numerical simulation, two-dimensional method, and ship-engine-propeller principle. Second, the obtained data, consisting of ship power, draft, trim angle, and speed, were further expanded to form a joint optimisation decision database using the artificial neural network method. Subsequently, a dynamic programming algorithm was used to calculate the optimal speed for each segment of the voyage. The optimal trim angle corresponding to the speed was obtained based on the joint optimisation decision database. Finally, a 7500 ton inland bulk carrier was chosen as the case ship to validate the proposed method. The results showed that the method could reduce the total ship power consumption by up to 7.64%. 

Joint optimisation for improving ship energy efficiency considering speed and trim control

This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient. 

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

The maritime sector has diligently endeavored to mitigate fuel consumption to curtail emissions and expenditures within the sustainable development framework. To comprehensively analyze ships’ fuel consumption, considering the combined influence of multiple factors, we develop an integrated optimization approach encompassing speed, trim, and speed–trim adjustments under different loading conditions. Firstly, we employ the fuel consumption prediction models established before, conduct a detailed analysis of the ship’s route in distinct segments, and formulate optimization models by scrutinizing the ship’s actual voyage. Secondly, we conducted single-parameter optimization for speed and trim to achieve the minimum fuel consumption for the entire route. We also implemented a joint optimization approach for simultaneously optimizing speed and trim to enhance fuel efficiency further. Lastly, we applied a smoothing method to the model’s prediction results to solve the step problem; and compared the optimization results before and after smoothing to assess the approach’s effectiveness. The results show that the joint optimization in the ballast condition yielded fuel consumption savings of 12.30% and 11.70% before and after smoothing, respectively; The fuel savings achieved under full load conditions were 10.18% and 9.47%. Demonstrate that joint optimization can overcome the limitations of single parameter optimization and enhance the fuel-saving rate. 

Joint optimization of ship speed and trim based on machine learning method under consideration of load

Abstract Maritime transport forms the backbone of international logistics, as it allows for the transfer of bulk and long-haul products. The sophisticated planning required for this form of transportation frequently involves challenges such as unpredictable weather, diverse types of cargo kinds, and changes in port conditions, all of which can raise operational expenses. As a result, the accurate projection of a ship’s total time spent in port, and the anticipation of potential delays, have become critical for effective port activity planning and management. In this work, we aim to develop a port management system based on enhanced prediction and classification algorithms that are capable of precisely forecasting the lengths of ship stays and delays. On both the training and testing datasets, the XGBoost model was found to consistently outperform the alternative approaches in terms of RMSE, MAE, and R2 values for both the turnaround time and waiting period models. When used in the turnaround time model, the XGBoost model had the lowest RMSE of 1.29 during training and 0.5019 during testing, and also achieved the lowest MAE of 0.802 for training and 0.391 for testing. It also had the highest R2 values of 0.9788 during training and 0.9933 during testing. Similarly, in the waiting period model, the XGBoost model outperformed the random forest and decision tree models, with the lowest RMSE, MAE, and greatest R2 values in both the training and testing phases. 

Leveraging Artificial Intelligence to Enhance Port Operation Efficiency

Optimum trim prediction for container ships based on machine learning

太空台风是极盖区内一种新发现的大尺度亮斑状极光结构，直观表征了地磁平静期的一种堪比磁暴的太阳风能量注入现象，这更新了人们对太阳风–磁层–电离层耦合过程的认识，如何从海量星载极光数据中准确髙效识别出太空台风事件具有重要的科学意义。采用深度学习的方法，通过六种网络模型的对比，最终基于迁移学习和EfficientNetB2网络提出了一种太空台风自动识别方法。在2005－2021年美国国防气象卫星（Defense Meteorological Satellite Program，DMSP）上搭载的紫外光谱成像仪（Special Sensor Ultraviolet Spectrographic Imager，SSUSI）的观测数据中验证了该模型的有效性，识别准确率达到97.7%。研究结果表明，该方法可用于从海量星载极光观测数据中自动识别太空台风事件。 

Automatic Identification of Space Hurricane Based on Transfer Learning

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Optimum trim prediction for container ships based on machine learning

Automatic Identification of Space Hurricane Based on Transfer Learning