How can AI support ship operators hoping to achieve their most energy efficient voyages?
Making big vessel data available for performance management does not automatically lead to the optimization of energy/fuel efficiency, as understanding the data gathered requires a great deal of effort both onboard and ashore. This is where we come in.
As a group of Swedish technology entrepreneurs and academics, we are aware of the potential for Artificial Intelligence (AI) and machine learning to unlock great possibilities when it comes to transforming decision-making and planning in shipping. We have therefore joined forces to accelerate the research and development of an AI-based, semi-autonomous system for planning and executing more energy efficient sea voyages.
Lean Marine is leading this ground-breaking research and development project which commenced its work in August 2020. Titled Via Kaizen, the project derives its name from the combination of the Latin word “via” meaning ‘way’ and the Japanese word “kaizen” which is used in situations striving for continuous improvement.
Lean Marine has been collaborating with Molflow, Swedish academics from Chalmers University of Technology, Gothenburg University and Linnaeus University on this groundbreaking project funded by the Swedish Transport Administration. The Swedish Shipowners’ Association is also participating in the project, providing vital insights and input from the Swedish shipping industry and will assist in the dissemination of research findings and development information to the Swedish maritime industry.
In addition to the main project partners, ship owners and operators are also involved in the Via Kaizen project, including chemical/product tanker owner and operator, Rederiet Stenersen and pure car and truck carrier (PCTC) owner and operator, UECC. By offering their vessels for technology and product trials, they will enable on-board testing, the results of which will be directly evaluated within the scope of the project.
AI-powered ship operation support system
Lean Marine’s FuelOpt™ and Fleet Analytics™ technology and Molflow’s Slipstream technology enable a higher degree of digitalization and automation in vessel operations.
FuelOpt™, a propulsion automation system that sits ‘on top’ of the existing control system and other systems on the bridge, optimizes the propulsion line dynamically in real-time. This is based on orders given by the AI system that has been developed within the scope of the Via Kaizen project. In addition, the FuelOpt™ system gathers data from the AI system and other signals onboard. The vast amount of vessel data collected is then fed into Lean Marine’s cloud-based performance management platform Fleet Analytics™ where it will be shared with Molflow’s vessel modelling system, Slipstream.
Slipstream will be trained on the ship data made available from Fleet Analytics™ and will then predict the vessel’s performance in different conditions via deep learning technologies. Accordingly, the Slipstream system will be able to determine the most energy efficient voyage possible, given the constraints of the route and the ship, and calculate the commands that need to be set to reach the destination with the minimal amount of fuel consumed.
Once the perfect simulated journey is determined, FuelOpt™ will step in and create an interface between the captain and the AI-based voyage planning solution, empowering them to cooperate and execute the voyage accordingly.
The entire project is overseen from an academic perspective, as naval architect researchers at the Chalmers University of Technology are working in close collaboration with Lean Marine and Molflow on the development of new methods, models, and algorithms.
Adoption of AI technology by shipping organizations
An increasing number of shipping companies are installing advanced systems to help them achieve more energy-efficient ship operations. However, previous research on shipping has shown that making large information sets related to energy available is unlikely to automatically lead to the optimization of energy use as interpretation and understanding the data involved often poses an insurmountable challenge to the shore-based organization and ship crew (Viktorelius and Lundh, 2019).
Humans have a limited ability to see patterns in large amounts of data and evaluate several factors at the same time, which is why organizations and individuals tend to choose what is considered good enough rather than optimal. Therefore, to facilitate the work on decision-making through big data, an AI-based solution is the best way forward.
Understanding existing working methods, requirements, and conditions and adapting the AI-based support system to these contributes to new optimized working methods and routines.
Currently there is limited research exploring the possibilities of AI being used in a meaningful way within existing routines and working methods to support the decision-making process for a shipping company. Another key point currently left to be identified is the manner in which an AI-based system should be designed such that decision-makers onboard and ashore can feel confident about the increased degree of automation. These are concerns that this project will tackle.
In addition to the technology development, implementation, and evaluation of AI-based, semi-autonomous support system, the Via Kaizen project integrates social science into its analysis. Researchers from Gothenburg Research Institute (GRI) and the Maritime Academy at Linnaeus University are conducting comprehensive research on the evolution of practices onboard and ashore as new technology is implemented.
In this way, the implementation of AI-based optimization technologies can play a role in realizing the most energy-efficient voyage in practice.