Ship Emission Mitigation Strategies Choice Under Uncertainty

Various mitigation strategies have been proposed to reduce the CO 2 emissions from ships, which have become a major contributor to global emissions. The fuel consumption under different mitigation strategies can be evaluated based on two data sources, real data from the real ship systems and simulated data from the simulation models. In practice, the uncertainties in the obtained data may have non-negligible impacts on the evaluation of mitigation strategies. In this paper, a Gaussian process metamodel-based approach is proposed to evaluate the ship fuel consumption under different mitigation strategies. The proposed method not only can incorporate different data sources but also consider the uncertainties in the data to obtain a more reliable evaluation. A cost-effectiveness analysis based on the fuel consumption prediction is then applied to rank the mitigation strategies under uncertainty. The accuracy and efficiency of the proposed method is illustrated in a chemical tanker case study, and the results indicate that it is critical to consider the uncertainty, as they can lead to suboptimal decisions when ignored. Here, trim optimisation is ranked more effective than draft optimisation when the uncertainty is ignored, but the reverse is the case when the uncertainty in the estimations are fully accounted for.