Highlights Thoroughly review and compare 83 papers on vessel fuel consumption prediction and optimization in recent 13 years. Present features and sources of main datasets used in current ship fuel consumption prediction models. Classify ship fuel consumption prediction models into white-box models (WBMs), black-box models (BBMs), and grey-box models (GBMs) combining WBMs and BBMs. Analyze fuel consumption optimization models from the perspectives of decision variables and objective functions. Review technical reports on ship fuel consumption prediction as a typical type of “grey literature”. Propose and discuss research challenges and promising research questions on ship fuel management.

    Abstract The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more cost-effective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil [HFO], liquified natural gas [LNG]) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports as a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data analytics for fuel consumption management in maritime transportation: Status and perspectives


    Contributors:


    Publication date :

    2021-09-18




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Decision analytics and trade ideology: New perspectives of maritime logistics

    Wong, Eugene Yin Cheung / Lam, Jasmine Siu Lee / Ng, Adolf K.Y. et al. | Elsevier | 2022


    PRE-CONSUMPTION FUEL ANALYTICS SYSTEM

    MOORE BUDDY | European Patent Office | 2019

    Free access

    Reducing Fuel Consumption: A Maritime Energy Portfolio Management Approach

    K. Schneck / T. Levac / G. Sturtevant | NTIS | 2011


    Spatiotemporal Data Analytics for the Maritime Industry

    Schmitt, Patrick / Bartosiak, Marcin Lukasz / Rydbergh, Torbjörn | Springer Verlag | 2020


    Maritime transportation

    International Conference on Maritime Transport | TIBKAT | 2020