In the context of green shipping, this paper analyzes factors influencing carbon emissions during vessel navigation, including wind speed, wind direction, wave conditions, and ocean currents. Through qualitative analysis and data collection from various sources, the study identifies key factors with operational significance that impact vessel navigation speed. A predictive model for shaft power is proposed, outputting Energy Efficiency Existing Ship Index (EEDI) predictions. The research aligns with the trend of low-carbon green shipping, providing insights for reducing operational costs and emission expenses. The study holds significance in the field of artificial intelligence, particularly in machine learning and deep learning.


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    Titel :

    Study on Factors Affecting Carbon Emissions in Ship Navigation and Prediction of EEDI


    Weitere Titelangaben:

    Environ Sci Eng


    Beteiligte:
    Bashir, Mohammed J. K. (Herausgeber:in) / Ensafi, Aliasgahr (Herausgeber:in) / Daud, Zawawi Bin (Herausgeber:in) / Chehade, Fadi Hage (Herausgeber:in) / Zhang, Jingwen (Autor:in) / Peng, Zitong (Autor:in) / Liu, Jiaying (Autor:in) / Yang, Boxin (Autor:in)

    Kongress:

    International Conference on Advances in Energy Resources and Environment Engineering ; 2023 ; Sanya, China December 29, 2023 - December 31, 2023



    Erscheinungsdatum :

    16.04.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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