Shchekinova, Elena¹ , Rath, W.¹ , Biastoch, A.¹ , Amann, N.² , Hennemann, F.³ , Kraus, P.³ , Myagotin, A.³ , Renz, M.² , van der Wulp, S.³

Introduction

Existing marine technology introduces the capability for every marine vehicle to integrate in a timely manner a large collection of global ship positioning data along with instructed higher safety and reduced emission intensity routes. New AI-assisted navigational devices could quickly integrate unsupervised routing predictions based on assimilated and forecasted global ocean and weather.

Description of goals

Here we design a route optimization algorithm for taking advantage of current predictions from ocean circulation models. We develop validation scenarios for a marine vehicle and show how it can propagate in a safer environment. Our optimization is designed to fulfill emission goals by achieving a lower fuel use.

Results and Achievements

The weather data from the European Observational Marine Copernicus Center allows leveraging both satellite observations and high resolution wind, wave and current predictions at any position in the global ocean. We propose a low fuel consumption, carbon emission ship routing optimization that employs these real time high resolution data in a stochastic optimization algorithm.

The proposed optimization method is based on local continuous random modifications subsequently applied to an initial shortest-distance route between two points. It is parallelised using a genetic approach. The model is validated using both vessel noon reports and data from a global automated identification system records.

We show that for the performed routing scenarios across the north Atlantic the achieved fuel could save about 10% of fuel for slow-steaming scenarios and hence lead to an additional reduction of carbon emissions even for already fuel-optimized operation.