Voyage optimisation: prediction of ship specific fuel consumption for energy efficient shipping

Voyage optimization is a technology to predict the ship performance in various sea states and current conditions, and based on the performance of the ship to assist ship masters in route selection. The targets of increasing energy
efficiency and reducing Green House Gas (GHG) emission in the shipping industry can be achieved by voyage optimization. However, the practical and accurate prediction of ship operational performance is the prerequisite to
achieve targets. In this paper, empirical fuel consumption prediction approach based on Kwon’s added resistance modeling (Kwon, Y.J. 2008) with a specific application to Suez-Max oil tanker is proposed. By using this approach, an operational performance model can be created for each loading condition, speed and relative wave heading on each Suez-Max oil tanker. The accuracy of operational performance prediction for sea-going vessels can be further enhanced by utilizing noon report data of a specific vessel. The operational performance model enables the user to investigate the relation between fuel consumption and the various sea states that the ship may encounter in its voyage. The potential results of operational performance model are collected in the ship operational performance database. Based on the database and real time climatological information, the ships’ various courses can be evaluated according to a number of objectives including minimization of voyage time,
maximization of safety, and minimization of fuel consumption using single or multi-objective methodologies. By utilizing a decision support tool, the ship’s crew may now select the optimum course according to their preference.

Energy Efficiency of Operation (EEO) is defined as an indicator to illustrate the main engine fuel consumption efficiency in the study. The results of the two case studies indicate that the modified empirical approach for the Suez-Max oil tanker can predict the fuel consumption reasonably well considering the uncertainty factors in the ship actual onboard data recording process. In future work, the modified empirical approach will be applied to other vessel sizes, and extended to various other commercial ship categories.