Jan De Nul to employ machine learning to improve performance

Offshore service company Jan De Nul is to deploy machine learning technology from GreenSteam on its fleet of dredgers, with the aim of reducing fuel costs and CO2 emissions through improved understanding of the impact of fouling.

The companies have previously worked together on the development of the GreenSteam Discover service, which uses a ‘data only’ approach to analysis, and have built on that progress to try and enable a similar level of granularity in analysing complex high-traffic nearshore areas and offshore areas.

“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. 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.”