Data collected by ships' sensors is used to train machine learning prediction models to identify the critical point for cleaning.
Cleaning biofouling improves the performance of the hull by decreasing both drag and fuel consumption.
Decreased fuel consumption leads to lower emissions and environmental impact.
SeaFlint is a University of Southampton project with support from Carisbrooke Shipping Ltd which aims to mitigate the harmful affects of biofouling on a ship's performance. When microorganisms such as plants, algae and small animals form a film on a ship’s hull, they negatively affect the ship by increasing the drag. The hull performance deteriorates, and fuel consumption grows substantially, increasing costs and contributing to emissions.
Cleaning fleets is costly, particularly due to lost income when ships are out of action. Through the use of machine learning, we can use data from ships' sensors to optimize the cleaning process and identifying the critical point for cleaning.