Power allocation influence on energy consumption of a double-ended ferry
Paper in proceeding, 2023

Fuel is one of the highest cost items while operating a ship, and its combustion results in air emissions polluting environments. Finding ways to increase shipping operations efficiency without compromising the provided service quality is necessary for economic and environmental reasons. This study first used data analysis to find hidden information in one-year navigation data of a double-ended ferry operated along the Swedish coast. The case study ferry was operated using both bow and stern engines partly loaded. A new feature of the power ratio is defined to describe the influence of engine power allocation on total fuel consumption. Then, different machine learning methods are used to establish the ship’s total fuel consumption model due to influences of external factors such as wind and sea currents, etc., together with the power ratio. The established machine learning model is used to find the most efficient operation of allocating power to different engines. It shows that, in theory, up to 35% fuel savings can be achieved for the case study vessel. These findings can further aid with the operational planning for the scope of Eco-driving.

machine learning

double-ended ferry

exploratory data analysis

XGBoost

Energy efficiency

Author

Daniel Vergara

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Martin Alexandersson

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Xiao Lang

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Proceedings of the International Offshore and Polar Engineering Conference

10986189 (ISSN) 15551792 (eISSN)

The 33rd (2023) International Ocean and Polar Engineering Conference
Ottawa, Canada,

DEMOPS - Machine learning based speed-power performance modelling to reduce fuel cost and emissions from shipping

Swedish Transport Administration, 2020-01-01 -- 2024-12-31.

Lighthouse, 2020-01-01 -- 2022-12-31.

Swedish Transport Administration, 2020-01-01 -- 2022-12-31.

AI-enhanced energy efficiency measures for optimal ship operations to reduce GHG emissions

VINNOVA (2021-02768), 2021-10-15 -- 2024-06-30.

Subject Categories

Other Mechanical Engineering

Vehicle Engineering

Marine Engineering

Areas of Advance

Information and Communication Technology

Transport

Energy

Driving Forces

Sustainable development

Innovation and entrepreneurship

More information

Latest update

6/18/2024