GreenSteam Dynamic provides the ultimate machine learning advantage through real-time on-board advice and decision support. It takes machine learning technology to its full potential by delivering advice that maximises the potential outcomes of the operational inefficiencies identified by Discover.
DYNAMIC TRIM OPTIMIZER
The ultimate machine learning advantage through real-time on-board advice and decision support
WHY GREENSTEAM DYNAMIC?
Combining the historical baseline performance model from Discover with real-time data from on-board sensors, the machine learning-based GreenSteam Dynamic provides advice with the highest levels of accuracy to maximise operational efficiency. The crew get immediate real-time, dynamic advice so that they can make operational adjustments before fuel is wasted unnecessarily. With each voyage, GreenSteam Dynamic provides ever more input to the Discover performance model – increasing the accuracy of future advice even further.
HOW DOES IT WORK?
During voyages, GreenSteam Dynamic continuously collects and stores data from its sensors to get a real-time and highly detailed view of the vessel’s performance. Using machine learning technology, the real-time data is used to train a dynamic performance model of the vessel that enables Dynamic to understand and predict vessel performance under a range of conditions. As the model is updated in real-time with data gathered from the GreenSteam on-board sensors, it provides the crew with dynamic advice that when implemented, maximises operational efficiency based on the current, local conditions of the vessel.
ON-BOARD REAL-TIME ADVICE FOR YOUR VESSEL'S CREW
Using data from GreenSteam’s on-board sensors, along with data from vessel systems and weather and sea-state information, the Dynamic Trim Optimiser calculates, in real-time, optimal vessel trim settings that will maximise operational efficiency.
Machine learning provides the mechanism by which the crew is provided with accurate decision support information.