Jan De Nul “cleans-up” with GreenSteam machine learning technology after extensive trials
GreenSteam (“GS”), the pioneer of machine learning for improved hull and efficiency performance in shipping, announces that Jan De Nul, a sector-leading provider of complex offshore services and dredging works, has chosen to deploy GreenSteam on its fleet of dredgers.
The project focused on reducing fuel costs and CO2 emissions through improved understanding of the impact of fouling. A difficult task, given the broad variation of both offshore and near-shore projects that are encountered by Jan De Nul across the globe, including the impact of seasonal conditions.
Having worked together on the development of the GreenSteam Discover service, which uses a dataonly approach to analysis, the two companies have worked together to ensure that high-traffic, nearshore areas and offshore areas can be analyzed to the same degree of granularity.
Shaun Gray of GreenSteam said “This is another first for GreenSteam – performance optimization technologies have traditionally failed to address the needs of specialist vessels, smaller vessels and those operating near-shore. The GreenSteam machine learning technology is agnostic to sector, vessel size or location and can identify vessel, fleet and unique sector-specific insights.
“We have worked with Jan De Nul diligently since the introduction of the “Discover program” and could not be more pleased that this knowledgeable company, so committed to high environmental standards, is now able to utilise the service in its day to day operations and we look forward to working with them to further develop the insights and offerings”
Michel DERUYCK of Jan De Nul said – “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. 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 optimize 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.”