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.
Keeping ships’ hulls free from just a thin layer of slime can reduce a ship’s GHG emissions by up to 25 percent, according to the preliminary findings of a new IMO study released last week.
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.
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 of how model-based monitoring can help, is to analyze the net performance before and after a hull cleaning.
It is good to understand that our Digital Twin is representing a clean vessel, with no fouling and no aging. When feeding the Digital Twin with real-time operational data, it calculates a baseline consumption, taking into account actual speed through water, draft, and weather impact. Every 15 minutes it calculates the expected consumption. If the reported consumption comes in (either by noon reports or sensors), you can calculate the difference between the expected performance and the reported performance. The difference is inefficiencies like hull fouling.
By reporting the difference and the trend over time, you can see a difference. Below, the difference is about 25%. By repeating the exercise after a cleaning, you can check the effect of the intervention. In this case, the difference dropped about 20% to just 5%. You can now calculate the improvement (in MT per day saved) and the business case for cleaning the hull.
Data of one of our clients showed that the noon-reported consumption had a 10% difference with the Digital Twin model-based data. Our customer decided to perform a hull-cleaning.
After the hull 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 the 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 analyze the effect of a hull cleaning on one of your vessels!