Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

Ta, Zhou; Hu, Qinyou; Hu, Zhihui; Zhi, Jiamao

doi:10.1155/2022/5333171

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Te technical means are applicable in new ships; however, they are difcult to realize for existing ships. In terms of the operation mode, there are three main ways to optimize ship energy consumption: route optimization [21,22], speed optimization [23][24][25][26], and trim optimization [27,28]. Route optimization involves fnding the route with the minimum operating cost or minimum energy consumption by combining meteorological, sea conditions, and other information to ensure the shipping safety.…”

Section: Introductionmentioning

confidence: 99%

“…Bouman et al [23] summarized and analyzed various energy-saving and emission reduction measures for ships and found that the energysaving efect of ship speed optimization was between 1% and 60%, which is an efective energy-saving measure. Zhou et al [24] established a dynamic speed optimization model using a particle swarm optimization algorithm and obtained an energy-saving efect of 4.38% through a simulation experiment. To reduce the ship's energy consumption, Wang et al [25] and Du et al [26] optimized the ship's speed to minimize energy consumption and achieved good energy-saving results.…”

Section: Introductionmentioning

confidence: 99%

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Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Gao

Chi

2022

Mathematical Problems in Engineering

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Global warming, environmental pollution, and the soaring cost of energy consumption for ships have drawn the attention of the International Maritime Organization and the shipping industry. By reducing the energy consumption of ships, the greenhouse gas emissions and operating costs of ships can be effectively reduced simultaneously. However, current research on the ship energy consumption optimization based on operating mode is mainly focused on route and speed optimization and less on trim optimization, but ship trim is also an important factor affecting energy consumption. Therefore, this study proposed a ship trim optimization method based on operational data and ensemble learning to achieve energy savings and emission reductions for inland sea ships. First, data processing and feature selection of operational data from an inland ro-ro passenger ship were undertaken. Second, the energy consumption prediction models were established based on ensemble learning. Finally, the trim optimization model was developed by combining the energy consumption model with the best prediction performance and enumeration method. Experimental results show that compared with linear regression, neural networks, and support vector machines, ensemble learning methods have better prediction performance in energy consumption modeling, and the extra tree (ET) model has the best prediction performance. With the trim optimization, the energy consumption and carbon emissions of a ro-ro passenger ship can be reduced by 1.4641%, which is conducive to the green and low-carbon navigation of ships.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Gao

Chi

2022

Mathematical Problems in Engineering

Self Cite

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Speed Optimization of Container Ship Considering Route Segmentation and Weather Data Loading: Turning Point-Time Segmentation Method

Sun

Jin

et al. 2022

JMSE

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As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering the influence of weather conditions, route segmentation and weather data loading methods significantly affect the reliability of speed optimization results. Therefore, taking the ocean-going container ship as the research object, on the basis of constructing the main engine fuel consumption prediction model and shaft speed prediction model based on machine learning methods, a route segmentation and weather loading-speed optimization iterative algorithm is proposed in this study. Single-objective speed optimization research is then conducted based on the algorithm. The research results show that the proposed algorithm effectively reduces the difference between optimized fuel consumption and actual fuel consumption, and can achieve a fuel-saving rate between 2.1% and 5.2%. This study achieves an accurate and reliable prediction of ship fuel consumption and shaft speed, and solves the strong coupling problem between route segmentation, weather loading, and speed optimization by iterative optimization of ship speed. The proposed algorithm provides strong technical support for ships to achieve the goal of energy saving and emission reduction.

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A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

Zhou,

Wang,

et al. 2024

JMSE

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Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization.

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Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

Cited by 3 publications

References 40 publications

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Speed Optimization of Container Ship Considering Route Segmentation and Weather Data Loading: Turning Point-Time Segmentation Method

A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

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