09 December 2022
The maritime industry will expectedly invest $931 million in artificial intelligence (AI) solutions in 2022. This according to a recent report, published by Lloyd’s Register in cooperation with Thetius.
That figure will expectedly elevate to more than double in the next five years to $2.7 billion by 2027, a compound annual growth rate of 23%.
Note that investment in the sector drives rapid growth. In the last 12 months, $331 million, awarded to startups and SMEs (Small and Medium Enterprises). Particularly, in order to develop AI solutions for the maritime sector. Additionally, $43 million in grant funding is being awarded to develop the technology for the maritime sector around the world.
AI in Maritime Industry
Unfortunately, the adoption of AI solutions in the maritime industry is still at a nascent stage. However, there is an immense potential to unlock value in optimizing fleet efficiency.
There is a wide range of AI use cases in the maritime industry.
- Support for autonomous navigation
- Voyage optimization
- 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.
“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
AI technology for voyage optimization primarily focuses on reducing vessel fuel consumption, resulting in the reduction of CO2 emissions and running costs.
Moreover, digital systems onboard collect enormous amounts of data which AI tools can process almost instantaneously to create extremely accurate vessel performance insights. Hence, these insights contribute to improving fleet efficiency and cutting costs. Maximizing fleet utilization is one of the key ways to decarbonize vessel operations, regardless of the future fuel mix.
Aside from optimizing a vessel’s speed, route and performance, AI models can also assist in managing 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
The report further shows that applications of supervised learning in the maritime industry are available 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,”Lloyd’s Register
How understanding port congestion can improve shipping efficiency. The new tool by MarineTraffic which will take shipping closer to Just In Time concept.