An artificial neural network based decision support system for energy efficient ship operations. (February 2016)

• Record Type: Journal Article
• Authors: Bal Beşikçi, E.; Arslan, O.; Turan, O.; Ölçer, A.I.
• Is Part Of: Computers & operations research. Volume 66(2016)
• Page Start: 393
• Page End: 401
• Publication Date: 2016-02
• Subjects: Ship energy efficiency -- Operational measures -- Decision support system -- Artificial neural networks; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2015.04.004
• Languages: English
• ISSNs: 0305-0548

Abstract

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 – '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. Highlights: An efficient DSS is developed concerning energy efficient ship operations. An artificial neural network (ANN) is the fundamental tool of the proposed DSS. Ship fuel consumption is predicted using Noon Data. ANN predicts the ship fuel consumption better than MR. The operating parameters, which influence the fuel consumptions, are examined.