Hybrid turbochargers can become an attractive solution for new built and retrofitted ship power plants, as their use can result in increasing the plant efficiency and reducing emissions. This study aims at computationally investigating the hybrid turbocharger effects on a large marine dual-fuel four-stroke engine performance and emissions characteristics as well as determining its electrical generator optimal size for the case of a ship power plant considering an actual operating profile. An existing model of a large marine four-stroke dual-fuel engine of the zero/one-dimensional type, which was developed in the commercial software GT-Power, is extended to include the hybrid turbocharger sub-model. This model is subsequently employed to carry out a parametric investigation considering a wide range for the hybrid turbocharger electric motor power. The derived results are analysed to identify the variations of the investigated dual fuel engine performance and emissions parameters in the whole engine operating envelope at both the diesel and gas modes, whilst taking into account the engine and its components operational limits. For the considered annual load profile, the results demonstrate that the optimal nominal size of the hybrid turbocharger electric motor power is 300 kW and leads to an annual energy surplus between 2% and 3% of the annually delivered engine mechanical energy. This study benefits the quantification of the hybrid turbocharger impact on large marine dual fuel four-stroke engines as well as the ship energy efficiency, thus providing useful decision support to facilitate the shipboard implementation of this technology.

Background: Maritime transportation accounts for around 80% of the world freight movements, remarkably contributing to the global environmental footprint. Dual fuel engines, running on both gaseous and liquid fuels, represent a viable way toward the reduction of emissions at the cost of additional complexity in monitoring activities. Motivation: Data-driven methods represent the frontier in research and in maritime industrial applications, and they usually require a large amount of labelled data, i.e., sensor measurements plus the associated engine status usually annotated by human operators, which are costly and seldomly available in the wild. Unlabelled samples, instead, are commonly, cheaply, and readily available. Hypothesis: The enabling technology for data-driven methods is the availability of a network of sensors and an automation system able to capture and store the associated stream of data. Methods: In this paper, we design and propose multiple alternatives toward the weakly supervised marine dual fuel engines data-driven monitoring. To this aim, we will rely on a Digital Twin of the dual fuel engine or on novelty detection algorithms and we will compare them against state-of-the-art fully supervised approaches. Results: Results on data generated from a real-data validated simulator of a marine dual fuel engine demonstrate that the proposed weakly supervised monitoring approaches lead to a negligible loss in accuracy compared to costly and often unfeasible fully supervised ones supporting the validity of the proposal for its application in the wild. Conclusion: The main outcome is a guideline for selecting the best data-driven dual fuel engine monitoring method according to the available data.