Keyword: Sustainable shipping


Offshore Energy Hubs

Spaniol, Matt

This report provides an assessment on the prospects for offshore energy hubs. Four use cases have been developed and evaluated by respondents in a survey instrument for their forecasted time horizon to implementation and their business potential as opportunities for the maritime and offshore
industries. The report is produced by the PERISCOPE Group at Aarhus University for the PERISCOPE network.

Periscope Report / 2020
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A genetic algorithm-based grey-box model for ship fuel consumption prediction towards sustainable shipping

Yang, Liqian; Chen, Gang; Rytter, Niels Gorm MalĂ˝; Zhao, Jinlou; Yang, Dong

In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.

S.I.: OR for Sustainability in Supply Chain Management / 2019
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