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.
Lowering fuel consumption of ships has gained a great deal of attention in maritime industry with regards to both environmental and economic concerns. The potential for fuel economy in shipping ranging between 25% to 75% is possible by using existing technology and practices and technical improvements in the design of new ship. Despite the existence of many technology and design-based approaches, limitations of emerging these measures has led to discussions about the potential energy savings through operational changes. In this study, operational measures were examined within the scope of Ship Energy Efficiency Management Plan (SEEMP) adopted by International Maritime Organization (IMO). We applied the Analytic Hierarchy Process (Fuzzy-AHP) approach, one of multi-criteria decision making (MCDM) techniques, to prioritize the weight of each measure. Fuzzy AHP effectively reflects the vagueness of human thinking with interval values, and shows the relative importance of operational measures - which can be the fundamental decision making data for decision makers (ships' masters, operating companies and ship owners) - by providing a strategic approach to identify energy efficient solutions
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