AI-enhanced energy efficiency measures for optimal ship operations to reduce GHG emissions
Research Project
, 2021
– 2024
Ship operation-related energy efficiency measures (EEMs) can help reach the IMO 2030 emission goals. However, today, their benefits have not been fully realized. Through the digital transformation in shipping, vast amounts of vessel data are being collected, enabling activation of AI-powered solutions to further improve vessel performance. Our purpose is to support the shipping industry to achieve greener ship operations. Our goal is to develop an operational support solution strengthened by AI on taking EEMs into account for enhanced energy efficiency.
Participants
Wengang Mao (contact)
Professor at Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology
Zhang Daiyong
Doctoral Student at Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology
Xiao Lang
Doctoral Student at Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology
Collaborations
DNV Advisory Services
Goteborg, Sweden
Lean Marine AB
Göteborg, Sweden
Molflow AB
Gråbo, Sweden
Möller Data Workflow Systems
Gråbo, Sweden
Yara Marine Technologies AB
Göteborg, Sweden
Funding
VINNOVA
Project ID: 2021-02768
Funding Chalmers participation during 2021–2024
Related Areas of Advance and Infrastructure
Sustainable development
Driving Forces
Transport
Areas of Advance