AI-based project to optimize vessel performance forecasting concludes testingZoom Button

Credit: Yanmar, Informationen zu Creative Commons (CC) Lizenzen, für Pressemeldungen ist der Herausgeber verantwortlich, die Quelle ist der Herausgeber

AI-based project to optimize vessel performance forecasting concludes testing

AI-based project to optimize vessel performance forecasting concludes testing

  • Yara Marine Technologies, Molflow, and academics from Chalmers University of Technology, Halmstad University and Gothenburg University have concluded a 3-year project aimed at developing an AI-based semi-autonomous system to enable more energy efficient sea voyages.

July 4th, 2023

Maritime technology company Yara Marine Technologies, Artificial Intelligence (AI) application developers Molflow, and Chalmers University of Technology and social science specialists from Halmstad University and Gothenburg University have collaborated over 3 years to develop and trial an AI-based semi-autonomous voyage planning system. Initiated in August 2020, the Via Kaizen project explores how AI and machine learning can enable more energy-efficient voyage planning for ship operators.

Funded by the Swedish Transport Administration Trafikverket, the project utilized pre-existing tools, to enable a higher degree of digitalization and automation in vessel operations. These included Yara Marine’s propulsion optimization system FuelOpt and performance management and vessel data reporting tool Fleet Analytics, as well as Molflow’s vessel modelling system Slipstream. Existing work practices onboard and user needs were analyzed during the design process to ensure the technology facilitated processes and decisions with the greatest impact on energy efficiency.

The resulting system was trialed onboard two vessels, a PCTC car carrier operated by UECC and a Rederiet Stenersen product tanker. The wide-ranging results indicated successful energy efficiency optimization based on estimated time of arrival (ETA), with one of the two trial vessels opting to continue using the system.

Mikael Laurin, Head of Vessel Optimization at Yara Marine Technologies, said, “The Via Kaizen project speaks directly to where shipping is at the moment — where the intersections of digitalization, decarbonization and crewing determine our success in addressing climate change. The use of AI and machine learning to plan and predict energy-efficient voyages has significance for an industry looking to lower emissions while addressing rising fuel costs. Similarly, new technologies can streamline operations but require collaboration and buy-in from stakeholders across the board, necessitating crew familiarization and training, proactive design, and new corporate strategies. As a result, the insights and information gained from the project carry broader significance for our industry’s future.”

The Via Kaizen project demonstrated that incorporating machine-learning algorithms for improved predictive modelling of ship propulsion power can result in more accurate performance forecasting and optimization. It also evidenced the necessity of constructive collaboration between technology developers and users, as well as between ship operators and their customers.

Joakim Möller, CEO at Moflow, said, “The Via Kaizen project afforded an invaluable opportunity to explore and advance industry understandings of the role big data, data handling and model development can play in supporting lower emission strategies and maximized fuel efficiencies. Recent advances in vessel data tracking and analysis, weather information, and more can be used to gauge where operations have the potential to be streamlined. As the maritime industry seeks to utilize good data to inform decision-making, AI and machine learning can play a key role in processing and simplifying available data for clear, actionable outcomes.”

Throughout the trials, crew played a key role in determining the success of energy efficient voyages. This shows the necessity giving ship crews and management every opportunity to engage with, understand and embrace the value of AI-powered ship operation support technology in assisting daily operations onboard and ashore.

Martin Viktorelius of Halmstad University said, “Maritime’s ability to successfully decarbonize is dependent on its highly skilled workforce, and necessitates that we invest in creating seafarer support for digitalization and decarbonization. Clean technologies must prioritize intuitive, user-friendly interfaces and understand existing operations to maximize crew support and uptake of AI-powered solutions. The Via Kaizen project engaged with crew to explore and establish key parameters that crew indicated hindered their support of voyage efficiency.”

Simon Larsson from Gothenburg University said, “The Via Kaizen project documented potential challenges to implementing energy efficient voyages — notably, the impact of crew training and corporate processes that either facilitated or hindered the effective use of AI tools to improve efficiency. These findings are not specific to the project and have wider ramifications for an industry seeking advanced solutions to rapidly reduce emissions. While crew training will afford a much-needed bridge to build understanding and accelerate support for AI-powered voyage efficiency solutions among seafarers, it is just as important that we ensure effective channels of communication with management and corporate processes.”

Following the conclusion of this project, additional funding has been secured from the Swedish innovation agency Vinnova to further explore a selection of its findings. More

 
Gütsel
Termine und Events

Veranstaltungen
nicht nur in Gütersloh und Umgebung

September 2024
So Mo Di Mi Do Fr Sa
1234567
891011121314
15161718192021
22232425262728
2930
November 2024
So Mo Di Mi Do Fr Sa
12
3456789
10111213141516
17181920212223
24252627282930
Dezember 2024
So Mo Di Mi Do Fr Sa
1234567
891011121314
15161718192021
22232425262728
293031
Februar 2025
So Mo Di Mi Do Fr Sa
1
2345678
9101112131415
16171819202122
232425262728
September 2025
So Mo Di Mi Do Fr Sa
123456
78910111213
14151617181920
21222324252627
282930
Oktober 2025
So Mo Di Mi Do Fr Sa
1234
567891011
12131415161718
19202122232425
262728293031
Juli 2042
So Mo Di Mi Do Fr Sa
12345
6789101112
13141516171819
20212223242526
2728293031
August 3024
So Mo Di Mi Do Fr Sa
1234567
891011121314
15161718192021
22232425262728
293031

Für die Suche nach Inhalten geben Sie »Content:« vor den Suchbegriffen ein, für die Suche nach Orten geben Sie »Orte:« oder »Ort:« vor den Suchbegriffen ein. Wenn Sie nichts eingeben, wird in beiden Bereichen gesucht.