One of the biggest performance killers that all shipping companies face is hull fouling. From the first day the vessel touches the water, fouling starts to build up and increases the hull resistance - resulting in higher fuel consumption.
Even minor biofilms affect the hydrodynamics of a ship's hull by increasing drag and, therefore, the required propulsion power. Fouling conditions can grow worse if the vessel has long idle periods or low activity such as frequent stays in port. The rate of growth increases with rising seawater temperature.
Typically, ship owners clean their vessels at regular intervals, but it would be better when the interval is based on the actual hull condition. But hull condition monitoring is a very complex task due to its many variables, like weather impact, different load conditions, varying service speeds, and drafts, currents, etc.
Both sensor data and noon-data will not show the impact of these effects. You need to combine a model-based approach with noon-reports or sensor-based measurement data to correct for the effects mentioned earlier. This is exactly what we do with our Digital Twin.
As there are virtually no upfront costs and no need for installation and maintenance of sensors, the model-based approach combined with noon-reports reduces investment costs and monitoring costs significantly.
By comparing the fuel consumption before and after a hull cleaning, while taking into account changes in speed, draft, and weather, the effect and the business case of a hull cleaning can be calculated.
An example how model-based monitoring can help, is to analyse the net performance before and after a hull cleaning.
Data of one of our clients showed that the noon-reported consumption had a 10% difference with the model-based data. Our customer decided to perform a hull-cleaning.
After the cleaning, the difference dropped to 0,2% for the first month, a saving of 2,1MT per day. We also saw the effect of fouling building up again, as shown in below graph. After 7 months, the difference decreased to 5,3%. You can use this trend line to decide on the next cleaning. Read the full customer case here.
We believe that real-time, accurate data with full transparency on vessel performance is essential. We have developed new, model-based tools to help in the real-time performance monitoring of vessels.It’s time for a new approach to performance monitoring of vessels. And the good thing: you can start today!
Get in touch with us for a pilot, where we analyse the effect of a hull cleaning on one of your vessels!