The maritime industry is expected 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.
This figure is expected to more than double over the next five years to reach $2.7 billion by 2027, a compound annual growth rate of 23%.
The rapid growth is partly due to investments in the sector. Over the past 12 months, $331 million has been invested in startups and SMEs developing AI solutions for the maritime sector, and an additional $43 million in grants have been awarded to develop the technology for the maritime sector worldwide, according to the report.
The adoption of AI solutions in the maritime industry is still in its infancy, however, they have immense potential to unlock value in optimizing fleet efficiency.
There is a wide range of use cases for AI in the maritime industry, including supporting autonomous navigation, optimizing voyages, as well as supporting systems for maintenance and ship monitoring.
The report shows that emerging use cases for the technology can be seen in digital twins, machine learning, knowledge-driven AI, natural language processing (NLP), neural networks and fusion of sensors.
“An area where Lloyd’s Register Maritime Performance Services, through our subsidiary i4 Insight, have has developed extensive experience in using AI for ship optimization and to help improve ship performance. For example, we found that traditional and legacy data analytics only examine 10% of vessel data, whereas our AI models can now examine nearly 100% of vessel data and process that data instantly to create extremely accurate information about the fuel performance of ships. fuel consumption, speed, trim, hull fouling and energy consumption”, Andy McKeran, Director of Maritime Performance Services, Lloyd’s Register, said.
AI technology for voyage optimization mainly focuses on reducing the fuel consumption of ships, which leads to lower CO2 emissions and lower running costs.
Namely, onboard digital systems collect massive amounts of data that AI tools can process almost instantaneously to create extremely accurate insights into vessel performance. This information is then used to improve fleet efficiency and reduce costs. Maximizing fleet utilization is one of the primary ways to decarbonize vessel operations, regardless of future fuel mix.
Besides optimizing vessel speed, route and performance, AI models can also be used to manage environmental factors such as hull fouling.
“Hull fouling is the leading preventable cause of excess fuel consumption and controllable GHG emissions in the global maritime fleet. AI technology creates a condition-based cleaning regime that optimizes hull cleaning schedules to prevent over-cleaning and damage to coatings, or under-cleaning creating excessive resistance. Optimal cleaning regimes ultimately reduce costs and emissions,” says Lloyd’s Register.
The report further shows that the applications of supervised learning in the maritime industry can be seen in predicting port traffic density using AIS data and in classifying carbon emission levels of various vessels using data from the noon report and environmental data.
One of the potentially most important uses today are knowledge-based AI tools that allow operators to measure and monitor their carbon dioxide emissions to ensure compliance with impending environmental regulations.
Nonetheless, the key to using AI effectively is working with the right data.
“Some datasets can be purchased, such as weather, maritime traffic or trade volumes. But data specific to a particular fleet, such as fuel consumption, will need to be collected, stored and made accessible. The quality of the information generated or 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 operations without the right data will lead to poor results at best. At worst, it could be dangerous,“, says the report.