India-based maritime technology company BlueWater has launched LNG voyage optimization solution, which aims to tackle the challenge to limit the boil-off rate by optimising the tank pressure, whilst maximally utilizing the generated NBOG.

This development follows a year of research and development into the LNG voyage optimization technology, which has the potential to save 5-7% proven bunker savings while ensuring timely arrival.

The optimization 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, the composition of LNG; which also changes throughout the voyage.

BOSS NBOG model considers the initial LNG composition and predicts the ageing as the voyage progresses. Finally, the algorithm considers the temperature and pressure requirements at the discharge port to plan a schedule that minimizes 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 & inflated. However, the numbers are accurate and derived by utilising a transparent and logical benchmarking system.”
Source: BlueWater