Predicting Optimal Trim Configuration of Marine Vessels with respect to Fuel Usage

• Main Author: Stefán Pétursson 1977-
• Other Authors: Háskóli Íslands
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: Reikniverkfræði; Skip; Líkön; Orkunýting; Rekstrarkostnaður; Olíunotkun; North Atlantic
• Online Access: http://hdl.handle.net/1946/3058

High fuel prices and environmental concerns are compelling shipping companies to consider how the fuel efficiency of vessels can be improved in order to reduce cost. Since the fuel cost is by far the largest portion of the operating cost of a vessel, a fractional savings in fuel usage can result in considerable savings in operational costs. Furthermore, fuel savings have environmental benefits in the reduction of greenhouse gas emissions. Many operational optimizations for marine vessels concentrate on minimizing the fuel consumption by optimizing the vessel speed. However, during a typical cruise, the captain of the ship must meet a predefined schedule which limits the scope for speed optimizations. Trim and displacement i.e. the difference between the draft at the bow and the stern and, the volume of sea displaced by the ship are, alternatively, controlled parameters worthy of attention with respect to fuel usage while the ship is cruising. Both can be controlled by arrangement of ballast. It has been shown that the power performance of vessels vary with different trim configurations. Often, the trim configuration is such that it is not operated at the optimal efficiency level. A substantial amount of money could be saved by trimming the vessel correctly. In this thesis, black box models are used to predict how the power consumption depends on the trim given various input parameters. The goal is to find the lowest power consumption with respect to the trim. The investigation is based on empirical data sampled at a passenger and freight vessel with a cruising schedule based in the North-Atlantic Ocean. The difficulty with such data, sampled under normal operation, is that the range of the numerous parameter values can be quite narrow, which may in turn limit the predictive accuracy of a regression model. Careful attention must also be given to the preprocessing of said data. It is shown how to deal with these aspects resulting in prediction models of trim configuration with potential fuel usage savings. The ...