Enabling digital twins in the maritime sector through the lens of AI and industry 4.0

Abstract

Sustainability and environmental compliance in ship operations is a prominent research topic as the waterborne sector is obliged to adopt ”green” mitigation strategies towards a low emissions operational blueprint. Fuel-Oil-Consumption (FOC) estimation, constitutes one of the key components in maritime transport information systems for efficiency and environmental compliance. This paper deals with FOC estimation in a more novel way than methods proposed in literature, by utilizing a reduced-sized feature set, which allows predicting vessel’s Main-Engine rotational speed (RPM). Furthermore, this work aims to place the deployment of such models in the broader context of a cutting-edge information system, to improve efficiency and regulatory adherence. Specifically, we integrate B-Splines in the context of two Deep Learning architectures and compare their performance against state-of-the-art regression techniques. Finally, we estimate FOC by combining velocity measurements and the predicted RPM with vessel-specific characteristics and illustrate the performance of our estimators against actual FOC data.

Keywords

• Fuel oil consumption estimation
• Digital twin
• Splines
• Quadratic estimators
• Delaunay triangulation
• Time-series forecasting