Jan De Nul, a provider of complex offshore services and dredging works, has chosen to deploy GreenSteam’s machine learning technology on its fleet of dredgers to increase understanding of fouling to reduce fuel costs and emissions.
The companies have previously worked together on the GreenSteam Discover service, which uses a data only approach to analysis, and are now working together to ensure that high-traffic, nearshore areas and offshore areas can be analysed to the same degree of granularity.
“We were very interested to understand what machine learning and its associated insights could mean for our business. In many of our projects, this insight will help us to improve our environmental contribution and consequently differentiate Jan De Nul,” said Michel Deruyck of Jan De Nul.
“Working together with GreenSteam underlines the commitment that Jan De Nul makes to reduce the environmental impact of its activities and to be an example for others working in the same industry,” Mr Deruyck added.
“From the beginning we saw a granularity of data that we had not seen with legacy technologies. This enabled us to vary our operating procedures and costs in both directions – increasing cleaning cycles in some cases and reducing them in others, greatly assisting our opex and reducing our carbon footprint by less fuel consumption. We are currently working with GreenSteam to optimise our travel speed when moving between locations. In the future, we will gain real insight into other aspects of the performance of the hull, in particular the true performance of coatings and the deployment of energy saving devices.”