India-based maritime technology company Bluewater has launched its LNG voyage optimisation solution, which aims to tackle the challenge of limiting boil-off rate by optimising the tank pressure, whilst utilising the generated NBOG (natural boil off gas). By doing so, between five and seven per cent bunker savings can be achieved.
The optimisation solution incorporates a hybrid approach based on machine learning and thermodynamic principles, in order to optimally plan and monitor LNG voyages with efficient boil-off-gas management, speed scheduling, and weather routing.
Thermodynamic models are used to predict the liquid-gas equilibrium conditions for the given LNG composition, liquid temperature, and stowage. Machine learning models are used to derive empirical coefficients for predicting the boil-off-rate based on the deviation from the theoretical liquid-gas equilibrium. This hybrid approach accounts for the heat ingress coming into the tanks, sloshing caused by rough seas and chemical properties of the LNG grade to predict the boil-off-rate.
The optimum operating pressure for limiting the boil-off rate takes into account the liquid temperature, weather, and the composition of LNG; which also changes throughout the voyage.
BOSS NBOG model considers the initial LNG composition and predicts ageing as the voyage progresses. Finally, the algorithm considers the temperature and pressure requirements at the discharge port to plan a schedule that minimises the total LNG consumption while adhering to the discharge requirements.
Capt. Kumaresh Gupta, managing director at BlueWater, said: “The claimed annual savings, when translated into a dollar value for a fleet of 50 vessels, may well look illusionary and inflated. However, the numbers are accurate and derived by utilising a transparent and logical benchmarking system.”
