IAA PortNews is not the author of this article and the editorial opinion can differ from that of the author.

IAA PortNews is not the author of this article and the editorial opinion can differ from that of the author.

  • Источник: https://www.offshore-energy.biz

    2022 August 16

    Maritime industry to spend $931 mln on AI solutions in 2022

    The maritime industry is forecast to spend $931 million on artificial intelligence (AI) solutions in 2022, according to a recent report published by Lloyd’s Register in cooperation with Thetius.

    That figure is expected to more than double in the next five years to $2.7 billion by 2027, a compound annual growth rate of 23%.

    The rapid growth is driven in part by investment into the sector. In the last 12 months, $331 million has been invested in startups and SMEs developing AI solutions for the maritime sector, with a further $43 million in grant funding being awarded to develop the technology for the maritime sector around the world, the report said.

    The adoption of AI solutions in the maritime industry is still at a nascent stage, however, they have an immense potential to unlock value in optimizing fleet efficiency.

    There is a wide range of AI use cases in the maritime industry, including support of autonomous navigation, voyage optimization, as well as systems for supporting maintenance and monitoring of vessels.

    The report shows that the emerging use cases for the technology can be seen in digital twins, machine learning, knowledge-driven AI, natural language processing (NLP), neural networks, and sensor fusion.

    “One area where Lloyd’s Register’s Maritime Performance Services through our subsidiary i4 Insight has developed vast experience is in the use of AI for vessel optimisation and helping to ultimately improve vessel performance. For example, we have found that traditional and legacy data analytics only look at 10% of vessel data, whereas our AI models can now look at close to 100% of vessel data and process this data instantaneously to create extremely accurate vessel performance insights around fuel consumption, speed, trim, hull fouling and power consumption,” Andy McKeran, Director of Maritime Performance Services, Lloyd’s Register, said.

    AI technology for voyage optimisation is primarily focused on reducing vessel fuel consumption, resulting in the reduction of CO2 emissions and running costs.

    Namely, digital systems onboard collect enormous amounts of data which AI tools can process almost instantaneously to create extremely accurate vessel performance insights. These insights are then used to improve fleet efficiency and cut costs. Maximizing fleet utilization is one of the key ways to decarbonize vessel operations, regardless of the future fuel mix.

    Aside to optimizing vessel’s speed, route and performance, AI models can also be used to manage environmental factors like hull fouling.

    “Hull fouling is the biggest preventable cause of excess fuel consumption and controllable GHG emissions in the worldwide shipping fleet. The AI technology creates a condition-based cleaning regime which optimises hull cleaning schedules to prevent over cleaning and damage to coatings, or under cleaning creating excess resistance. Optimum cleaning regimes ultimately cut costs and reduce emissions,” Lloyd’s Register said.

    The report further shows that applications of supervised learning in the maritime industry can be seen in the forecasting of port traffic density using AIS data, and the classification of the carbon emission levels of various ships using the noon report data and environmental data.

    One of the potentially most prominent uses at the moment are knowledge-based AI tools that allow operators to measure and monitor their carbon dioxide emissions to ensure compliance with impending environmental regulations.

    Nevertheless, the key to using AI effectively is to work with the right data.

    “Some data sets can be bought, such as weather, maritime traffic, or trade volumes. But data that is unique to a particular fleet, such as fuel consumption, will need to be collected, stored and made accessible. The quality of the insight generated or the decisions made by an AI system will be directly correlated to the quality of the data it has access to. Attempting to introduce AI anywhere in a ship’s operation without the right data will at best result in poor results. At worst it could be dangerous,” the report said.