Probabilistic analysis/estimation on fuel consumption and ship emissions - Μπουραζάνης, Αναστάσιος (Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Μελέτης Πλοίου και Θαλάσσιων Μεταφορών. Εργαστήριο Θαλασσίων Μεταφορών, 2015)
Global concerns about environmental pollution, regulatory framework, the ever increasing fuel costs and the competitive container carriers industry are driving the quest for ever improved ships with higher performance efficiency, lower emissions and more attractive financially. In order to meet all these requirements, researchers make efforts on setting up methods in order to predict the effects of operational changes in vessels. The present study has analyzed fuel consumption and emissions of a Container ship’s Main Engine from an operational perspective. Fuel consumption is essentially detailed in the following random variables: Loading Factor, Specific Fuel Oil Consumption and Time. The method used for this analysis is the Monte Carlo simulation, which is a well-known probabilistic methodology. The studied ship is a 2,824 TEU Sub-Panamax class Container ship and the emissions analyzed are: CO2, NOx, SOx and PM. The operating condition of the specific ship is slow steaming. The study has developed probabilistic distributions for fuel consumption from two different input categories: real recordings and model constructed in this thesis. In the first occasion, data were provided by real Noon Reports of the specific Container ship, whereas a large amount of data from Noon Reports have been used in combination with data from literature for the modelling process. The software tool used in this probabilistic analysis is Oracle Crystal Ball, which works as an add-in in an Excel spreadsheet. This study has also analyzed the impact of the three random input variables in the estimation of fuel consumption. Going further, in order to evaluate the importance of specific parameters to the results, a robustness analysis has been applied for fuel consumption. The amount of emissions depends on the type of Main Engine (Tier I, II, III), the operation load, the type of fuel used and its sulphur percentage. Estimation of emissions is conducted with updated emission factors from literature. A generic conclusion from this study is that the MC methodology may be applied to estimate fuel consumption and emissions in a satisfactory way, however it is very uncertain what happens in higher loads of operation. An essential feature for future research would be the creation of a probabilistic inventory based on a the whole range of load operation. This would make the probabilistic methodology more safe and easy for use in the shipping sector.