With the International Maritime Organisation announcing a goals-based approach to emissions reductions for sulphur, nitrogen and carbon oxides over the next two decades, shipping owners and operators must use vessel data to build a fuller picture of unnecessary fuel consumption. This is according to GreenSteam, a marine data intelligence business that helps operators cut fuel wastage and reduce emissions by optimizing vessel performance.
While the shipping industry has been preparing for the sulphur emissions regulations coming into force on January 1st 2020, these are just the beginning of greenhouse gas emissions regulations with reductions targets of 85% by 2050. With the IMO favouring a goals-based approach, owners and operators are free to choose the most effective technology strategy to reduce carbon emissions by 40% in line with 2030 targets.
As these target deadlines approach and "hardware" based technologies to cut emissions, such as Flettner rotors, electric drivetrains and air lubrication are considered, the industry is expected to widely adopt machine learning as a means of accurately quantifying each operational lever that can cut fuel consumption and reduce emissions as part of a multi-solution approach. GreenSteam believes the shipping industry cannot ignore any area of operational fuel wastag
e, as it considers each area of new technology.
Data collected from the ship together with metocean and AIS data indicates where and why the vessel uses excess fuel. These insights allow the crew to adjust operations, minimizing fuel wastage, reducing cost and cutting emissions. On average, fuel savings of 5% are typical and
, in some cases, 20% fuel savings have been observed.
Traditional or legacy methods of vessel data analysis are still very common across the industry despite the fact that some of these methods exclude a staggering 90% of vessel data. This is because analysts don't have access to the methods or processing capacity to consider all the multi-dimensional factors affecting performance and therefore need to use quite drastic sampling to simplify their task. Critical data that might highlight worrying symptoms of excess fuel consumption, can take longer to become apparent or might be overlooked entirely under this data sampling regime.
Machine learning enables data analysis of almost all vessel data, with powerful computing technology "connecting the dots" and identifying the relationship between each of the 13+ factors affecting vessel fuel efficiency. This inclusive and accurate approach can highlight fuel savings opportunities 2-4x higher than traditional or legacy data analysis.
As well as playing its own part to reduce GHG emissions through vessel optimisation, GreenSteam's machine learning platform also measures the contribution of other complementary emissions reduction technologies. GreenSteam's machine learning platform is being continuously updated and in turn the model of each vessel is continually refreshed and improved with the benefit of each new day's data. In this sense GreenSteam is set to become a key, future-proofed element of the shipping industry's GHG emissions reduction strategy.
Simon Whitford, COO of GreenSteam, comments, "Vessel performance is highly complex involving multiple and often inter-related factors. In order to identify and measure the true level of fuel wastage it is vital that all data is used and analysed. Vessel owners and operators who are not using data analytics informed by machine learning may only be working from 10% of the critical information they need to make decisions on their vessel and fleet operations.
"Each tonne of HFO fuel wasted enters the atmosphere as three tonnes of carbon dioxide. Machine learning provides a solid foundation for clear, actionable advice empowering ship owners and operators to cut fleet-wide GHG emissions"
GreenSteam | http://www.greensteam.com