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A Review on Applications of Machine Learning in Shipping Sustainability

Pena, B; Huang, L; Ahlgren, F; (2020) A Review on Applications of Machine Learning in Shipping Sustainability. In: Proceedings of the SNAME Maritime Convention 2020. (pp. SNAME-SMC-2020-035). Society of Naval Architects and Marine Engineers (SNAME) Green open access

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Abstract

The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges.

Type: Proceedings paper
Title: A Review on Applications of Machine Learning in Shipping Sustainability
Event: Society of Naval Architects and Marine Engineers (SNAME)
Location: Houston (TX), USA
Dates: 29th September 2020 - 2nd October 2020
Open access status: An open access version is available from UCL Discovery
Publisher version: https://onepetro.org/SNAMESMC/proceedings-abstract...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: machine learning, freight & logistics services, upstream oil & gas, energy conservation, renewable energy, climate change, artificial intelligence, marine transportation, reinforcement learning, neural network
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Engineering Science Faculty Office
URI: https://discovery.ucl.ac.uk/id/eprint/10108404
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