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The Challenge

Help Euro Marine Logistics to monitor the impact of hull fouling on fuel consumption before and after a hull cleaning.

The impact

A  fuel cost saving of $150,000 in 6 months’ time.

“About $150,000 for bunkers were saved in six months’ time. It is now clear to anyone that monitoring and taking informed decisions based on data is a no-brainer.”  – Bastian Schwarz – Head of Marine & Technical Department, EML

The project

Regular hull cleaning has the potential to save fuel consumption, but comes at a cost. The aim was to demonstrate the effect of a hull cleaning on fuel consumption and establish the return of investment. This to make informed decisions on the best timing for these cleanings in the future.

The customer

Euro Marine Logistics NV (EML) is a leading short-sea car carrier. EML has a modern fleet, designed to operate on short sea and can be used according to market expectations. EML provides safe transportation of cars, commercial vehicles and offers solutions for the transport of high and heavy and breakbulk cargo.

Fuel costs are a considerable part of total voyage costs for EML. Also, a drive to reduce emissions made EML look for fuel monitoring solutions in the market.

The challenges

The system of daily noon-reports in place at EML was simply not good enough, due to the short 1 to 2 day voyages and limited data that was collected.  Also the impact of weather and currents on the North Sea trades can be significant and needed to be taken into account. Last, for comparison of the performance of sister vessels, data needed to be normalised for weather impact.

The main challenge was that EML is not the owner of the vessels, but the charterer. Therfore, installation of sensor systems was not an option.

After looking around in the market, We4Sea was selected as they had a readily available solution, using a Digital Twin. Although this new technology was unknown, EML decided to start a pilot project on one of their vessels, the Hoegh Inchon. This 1997 build vessel has a capacity of 4300 cars, and was sailing on a regular schedule between  Northern Europe and the Mediterrenean.

The solution

We4Sea tracked the vessel, using satellite data and a small tracking device that was installed on board.  This ensured a position interval of maximum 3 minutes. Based on the position, weather data was collected on the same interval.

We4Sea also designed a vessel specific Digital Twin model. This Digital Twin is the digital representation of the vessel, including all specifications such as hull shape, engine, dimensions and propellor. By using the actual sailing conditions, such as speed, heading, draught and weather conditions into the model, we calculated the so-called achievable performance: the performance the vessel has when she is in perfect condition. This exercise is repeated each time we had a new vessel position, so typically every 3 minutes.

The Digital Twin was used to compare achievable performance with reported consumption, using the noon-reports.

The outcome

Data showed that the vessel clearly was under-performing. In October 2019, a hull cleaning was performed. After analysing the data, it was clear that the hull cleaning has had a positive effect on the performance of the vessel.

Before the hull cleaning, the vessel was overconsuming about 10%, which translates to 2.2 MT/day, after the hull cleaning this was reduced to just over 0.2%. This clearly indicates that the performance loss before the hull cleaning could be fully attributed to hull fouling.

We4Sea EML working on fuel efficiency EML will now roll out the Digital Twin based monitoring solution over their fleet, to collect performance data. This data will be used to make informed decisions on the optimal cleaning intervals.

“The Digital Twin proved to be essential for this analysis, as it could normalize the data for weather, speed and draught. Using only noon-reported data, this analysis could not have been done”  Bastian Schwarz – Head of Marine & Technical Department, EML

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Schieweg 93
2627 AT Delft
The Netherlands

EU H2020
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 877766.
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