Italian shipping company Grandi Navi Veloci (GNV) has opted for machine learning and predictive models from compatriot classification society RINA to optimize energy use and fuel consumption on its newbuild passenger/RoRo cargo ship.
GNV started testing RINA’s SERTICA Performance tool on the latest addition to its fleet, the 2024-built GNV Polaris, during its inaugural voyage from China to Italy.
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GNV said the system had enabled the identification of two optimal operating scenarios, allowing for the lowest specific fuel consumption, and a predictive model that serves as both a benchmark and a simulator for future operations.
As understood, this system records data collected in real-time through a network of sensors installed on board, including key parameters such as fuel consumption and the power of diesel generators and engines. The primary focus is on monitoring energy expenditure, but it also uses the collected data to calculate the ship’s actual efficiency,
During the GNV Polaris’ voyage, various operational scenarios were simulated at different speeds and configurations, such as alternating the use of diesel generators and shaft generators, to identify the most efficient solutions in terms of fuel consumption, the shipping company explained.
Subsequently, the operational setup for the Genoa-Palermo route was tested, verifying the consistency between the sea trial results and the forecasts.
The project also includes the development of predictive models using machine learning techniques.
According to Ivana Melillo , Energy Efficiency Director at GNV, GNV Polaris can achieve over 30% fuel savings, resulting in a significant reduction in CO2 emissions compared to the vessels currently in the fleet.
“GNV plans to continue modernizing its fleet with more eco-friendly ships. This includes the introduction of new vessels that meet higher environmental standards, reducing emissions and improving fuel efficiency. GNV is exploring the use of alternative fuels such as liquefied natural gas (LNG) and biofuels. These fuels produce fewer emissions compared to traditional marine fuels,” Melillo said.
“We are investing in energy management systems that leverage digital technologies to optimize energy use on board its vessels. This helps in reducing emissions and improving overall sustainability. These digitalization efforts are part of GNV’s broader strategy to enhance operational efficiency, reduce environmental impact, and contribute to a more sustainable future in maritime transport.”
10 days ago
about 1 year ago
Italian shipping company Grandi Navi Veloci (GNV) has opted for machine learning and predictive models from compatriot classification society RINA to optimize energy use and fuel consumption on its newbuild passenger/RoRo cargo ship.
