Abstract
Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data - â€'Noon Data' - which provides information on a ship's daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.
Original language | English |
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Pages (from-to) | 393-401 |
Number of pages | 9 |
Journal | Computers and Operations Research |
Volume | 66 |
DOIs | |
Publication status | Published - Feb 2016 |
Bibliographical note
Publisher Copyright:© 2015 Elsevier Ltd.
Funding
Elif BAL acknowledges Scientific and Technical Research Council of Turkey (TUBİTAK) for a 2214/A-International Research Fellowship Programme and BAP-ITU (Scientific and Projects-ITU). Elif BAL, Ozcan ARSLAN and I. Aykut OLCER acknowledges support from the International Association of Maritime Universities , under project IAMU FY 2014.
Funders | Funder number |
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BAP-ITU | |
TUBİTAK | |
International Association of Maritime Universities | IAMU FY 2014 |
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu |
Keywords
- Artificial neural networks
- Decision support system
- Operational measures
- Ship energy efficiency