Abstract:

Fuel consumption in commercial ships constitutes the most important expense item in maritime enterprises. This issue, which is also closely related to energy efficiency, is extremely important for the maritime industry. Considering that the issue of emission reduction remains on the agenda in the maritime sector as per the International Maritime Organization rules, fuel consumption and the emissions on ships are followed seriously by the maritime authorities. In this study, the fuel consumption of a chemical tanker ship was modeled and estimated with the help of data-driven methods based on real voyage data. First of all, the voyage data taken from the ship was processed and made into a way that algorithms can work on. Algorithms were run on the data and fuel consumption prediction performances were examined. The success of some models established in the first stage was found to be inadequate. The estimation process was repeated by tuning the algorithm parameters with unsatisfactory estimation performance. Finally, the predictions were compared using error metrics. When the results are examined, it has been determined that the Multi-Layer Deep Neural Network method is more successful than the other methods discussed for the chemical tanker fuel consumption estimation problem.

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