The enginei system uses volumetric and mass flow measurement for enhanced fuel data analysis and engine reporting options that give vessel owners and operators detailed performance data, fuel optimisation rates and mission-critical information. In the conventional enginei installation, the specific operational mode of the vessel is indicated by manual notification into the system by a crew member. Some modes, such as ‘standby’ and ‘transit’, are common to all vessels, whilst others are specific to certain types of vessel, such as ‘dynamic positioning’ (DP) with offshore support vessels, ‘towing’ by tugs and ‘loading’ for container vessels.
Operational modes are defined by different activities being undertaken by the vessel at different times and stages in a journey, with fuel consumption and emissions levels being influenced by the specific type of activity, speed and weather conditions. The accurate monitoring of performance during different modes can therefore have a significant impact on the economic operation of the vessel. To meet this need, working with marine engineering specialists from Newcastle University, Royston developed an upgraded version of the enginei fuel management system that utilises sophisticated data processing and statistical analysis to automatically identify the vessel’s operational mode. By identifying individual operational modes automatically, the auto-mode capability removes the risk of human error introduced by the manual intervention of crew members and avoids the consequent risk of misinterpretation of engine and voyage data. In this way, the automatic detection of operational modes enables more reliable vessel and engine performance data to be produced. This means that onboard engineers and offshore fleet management staff have the ability to make more informed and accurate decisions based on trusted information on fuel consumption.
Development of the auto-mode system included trials undertaken in partnership with offshore vessel owner GulfMark using the platform supply vessel Highland Prince, which has a diesel-electric propulsion system with four Caterpillar engines and two auxiliary engines. In tests undertaken on the vessel, engine and fuel data was gathered by the enginei system to enable performance comparisons to be made between crew-pressed operational mode and the automatic predicted mode. Engine and other sensor data was collected and analysed by the system to develop control limits for different operational modes. These profiles were used to automatically identify changes in the operational behaviour of the vessel as they occurred.
Jim Bradford, general manager of operations for GulfMark, said, “The tests we have undertaken on the new enginei auto-mode detection capability have been very successful. The auto-mode identification was very accurate, enabling close correlation between the different types of vessel operational activities with specific fuel consumption rates. The automatic logging of vessel activity type will mean that the crew and onshore staff can identify not only the mode of operation but the time spent in each mode.”
On Highland Prince, voyage data showed that 52 per cent of time is spent in transit, 5 per cent in port, 23 per cent in DP mode and 20 per cent in standby mode waiting to access the offshore installation. Mr Bradford said, “Auto mode will allow better voyage planning with optimum speeds and fuel consumptions achieved during transit. By arriving on time at eco speeds, this will ultimately contribute to reducing not only the transit consumption but also the standby time at the installation and consequently the fuel burnt when in standby mode. In addition, the conversion of the fuel consumption data will also enable accurate CO2 and other emissions levels to be calculated and operational adjustments to be made. Importantly, having more accurate performance data will also enable us to look at the actual working hours of individual engines, enabling us to more effectively balance their use at optimal levels of power output and to prioritise service and condition-based maintenance requirements.”