Innovate UK Knowledge Transfer Partnership Grant Won by Silverstream Technologies to Advance Machine Learning in Maritime
The two-year partnership will see an Associate of the University of Southampton, secured under the programme, work with Silverstream’s Technical Team with the goal to advance machine learning and artificial intelligence within the Silverstream® System’s control and automation module.
The Silverstream® System uses air lubrication to reduce frictional resistance between a vessel’s hull and the water and delivers fuel savings of 5-10% depending on the vessel and its operating profile. The KTP will aim to increase this saving by analysing operational data taken from installed systems. This data, when combined with cutting edge machine learning techniques, will help to further increase Silverstream® System performance during a voyage, with the goal of gaining the theoretical maximum savings associated with the technology every time it is operating.
Ultimately, this project will serve as a testbed for advancing machine learning and artificial intelligence within maritime. With decarbonisation targets looming for the sector, optimising clean technologies will form a core part of the industry’s strategy to meet its obligations as they have been laid out by the International Maritime Organisation.
Speaking on the announcement, Noah Silberschmidt, CEO, Silverstream Technologies, said: “Today, there is an increasing amount of data coming from in-operation installations of the Silverstream® System that can be used for performance prediction and optimisation, requiring us, as clean technology leaders, to develop new expertise to master and understand this vast source of potential improvement.
“Previous work to understand how AI and machine learning can benefit the performance of our technology has aided us both in optimising each Silverstream® System, and in validating our performance claims to the market.
“This KTP grant marks the next step on this journey, allowing us to work with the world’s leading researchers at the University of Southampton and The Alan Turing Institute to accelerate machine learning within shipping, ultimately enabling us to bring about a more efficient and more sustainable maritime sector.”
Dr Adam Sobey, Associate Professor in the Maritime Engineering Group at the University of Southampton and co-lead of the marine and maritime group in the Data-Centric Engineering programme at The Alan Turing Institute will lead the project, supported by Professor Dominic Hudson, Shell Professor of Ship Safety and Efficiency at the University of Southampton.
Dr Adam Sobey added: “The potential for machine learning and artificial intelligence technologies to improve the performance and efficiency of the maritime sector is truly staggering. That is why this Knowledge Transfer Partnership with Silverstream is so important; it will allow us to build on fundamental research on Machine Learning, that was supported by our ongoing relationship with Shell Shipping and Maritime, and to test these exciting new technologies with the maritime industry’s clean technology leaders, ultimately accelerating the push for efficiency that the sector so desperately needs to meet its decarbonisation goals.”
Knowledge Transfer Partnerships aim to help businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK knowledge base.
KTPs are funded by UKRI through Innovate UK with the support of co-founders, including the Scottish Funding Council, Welsh Government, Invest Northern Ireland, Defra and BEIS. Innovate UK manages the KTP programme and facilitates its delivery through a range of partners including the Knowledge Transfer Network (KTN), Knowledge Bases and Businesses. Each partner plays a specific role in the support and delivery of the programme.
Adding his thoughts on the KTP, Terry Corner, Knowledge Transfer Adviser, Knowledge Transfer Network, commented: “This is an exciting KTP project that will greatly aid the international shipping industry to meet ambitious emission reduction targets of 40% and 70% by 2030 and 2050 respectively. Few technology offerings can offer savings like those generated by the Silverstream® System at a similar cost per tonne of CO2 abated.”