DeepSea Technologies and G2 Ocean Roll Out AI Voyage Optimization Tool

DeepSea Technologies and G2 Ocean Roll Out AI Voyage Optimization Tool
(Image credit: DeepSea)

DeepSea Technologies, the award-winning artificial intelligence (AI) led maritime technology company and energy efficiency experts, announced the completion of a project supported by the EEA Grants under the Business Innovation Greece Program to develop an accessible AI solution for vessel performance routing. Working with G2 Ocean, the leading bulk shipping operator, and SINTEF, an independent research organization, DeepSea has run trials to develop a novel AI software solution that uses AIS and noon report data, together with high-frequency data from other vessels, to unlock voyage optimization.

Improvements in operational efficiency through optimized navigation help operators reduce fuel consumption and lower emissions. However, few companies in the shipping industry have data collection systems in place to support data-driven improvements. Together with G2 Ocean and SINTEF, DeepSea has developed an AI-based solution that can use the data available to every ship owner—AIS data and noon reports—to unlock the real benefits of performance routing. This approach is called transfer learning and allows high-frequency data models from hundreds of vessels already in DeepSea’s databases to be transferred over to low-frequency data vessels of the same type and further adapted to available data.

The new software solution, Pythia Augment, has been applied to the bulker market for the duration of this project; however, it has widespread applications for all types of vessels as well and provides route and speed guidance for individual vessels based on AI-generated energy performance modeling. DeepSea and G2 Ocean applied the solution to five Grieg Star vessels within the Norwegian company’s fleet to develop and pilot an AI model with the ability to produce accurate and optimized guidance that has been successfully applied to reduce fuel consumption and achieve the highest possible TCE for any given voyage. The ships used to test the solution provided an ideal training ground for developing the AI application, as the fleet features vessels operating noon reports as well as more modern live data acquisition systems.

During the project, DeepSea and G2 Ocean collaborated with SINTEF, a leading independent research organization based in Norway, to understand barriers to end-user compliance. Breaking down barriers to the adoption of AI recommendations by the crew is essential. The value of AI-driven optimization to sustainable shipping operations can only truly be realized if there is high compliance with the recommendations produced.

Dr. Konstantinos Kyriakopoulos, Co-Founder and CEO of DeepSea Technologies, commented: “Ship owners need to be taking action now to deliver on the maritime industry’s long-term goal to decarbonize. A mature market for the low-carbon fuels that will drive large-scale decarbonization of marine energy remains some way away. In the meantime, owners and operators should focus on reducing energy consumption through operational efficiencies. With the delivery of Pythia Augment using low-frequency data to optimize voyage planning, we are creating a new and innovative way for bulkers to effectively drive down carbon emissions in their operations.”

Arthur English, CEO of G2 Ocean, added: “G2 Ocean is committed to becoming a net-zero emissions company by 2050 and is enthusiastic about the potential benefits AI-powered technologies can unlock in our industry. The solution developed by DeepSea enables real-time analysis of a range of commercial, technical, and environmental factors resulting in a reduction of emissions as well as optimized earnings. With the wide-scale adoption of green fuels still some way off, it is vital that we grasp the opportunities that are currently economically viable to reduce emissions.”

DeepSea has used two critical AI technologies to unlock voyage optimization based on legacy data: Transfer Learning and Hybrid Modeling. Having access to a fleet with mixed data acquisition systems was paramount to the development of the AI tool.

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