Demonstrating Data-Based Digital Twin Modeling Use Cases Towards Maritime Net Zero

Important results from the project

Yes, the project´s objective has been largely met. We have demonstrated and validated data-driven digital twins for energy optimization, predictive maintenance, electrification analysis and simulation in an operational environment, and strengthened cloud and model automation. The project has also contributed to increased collaboration in the marine sector, strengthened AI competence, and created decision-making data that accelerates the transition to reduced emissions.

Expected long term effects

The project is expected to contribute to reduced emissions in the long term through data-driven energy optimization and support for electrification in shipping. It strengthens Swedish maritime competitiveness, enables scalable digitalization even for smaller vessels and establishes a model-based collaboration structure between industry and academia, which accelerates innovation and sustainable transition.

Approach and implementation

The project was implemented in six work packages around energy optimization, predictive maintenance, electrification, situation simulation, system optimization and development of cloud and model processes. Digital twins were developed from operational ship data, validated in real operation and integrated into simulation environments, in close collaboration between industry and research actors.