This paper presents the construction of a neural network-based model to predict fuel consumption and sailing time using a feedforward neural network trained by the backpropagation method. Instead of using in-service data collected from real ships like most previous studies, it often does not have all the necessary data and takes a long time to get a large enough dataset. We use a hardware-in-the-loop (HIL) simulator to create a dataset with many scenarios covering the most common ship operations at sea. Experimental results show that the proposed model gives high-accuracy results, which can be applied to find optimal routes and minimum main engine speeds for cargo ships before each voyage in the future.


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    Title :

    A Neural Network-Based Model to Predict Fuel Consumption and Sailing Time for Cargo Ships


    Contributors:


    Publication date :

    2023-09-10


    Size :

    1358432 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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