How to realize the most energy-efficient ship voyage in practice?

Projekt:

Hur realiserar man den mest energieffektiva fartygsresan i praktiken?

Sammanfattning:
The ongoing digitalization of society has reached the maritime sector, and an increasing amount of data is being made available digitally. All this data and information implies a lot of new possibilities but also many difficulties. The ability of humans to see patterns in large data sets and draw conclusions is constrained. Indeed, research has long pointed out that organizations and humans tend to choose what is good enough rather than what is optimal (Simon, 1957). Earlier research on shipping has shown that making large information sets available related to energy does not automatically lead to optimization of energy use, because
interpretation and understanding of the data poses a lot of demands on the land organization and ship crew (Viktorelius and Lundh, 2019).
Artificial Intelligence (AI) and machine learning comes with great possibilities to revolutionize decision-making and planning in shipping. By training neural networks with large data sets, instead of having humans try to draw conclusions from them directly, for example about the factors that affects energy efficiency for a ship, optimal operational modes may be identified in real time. Such an aid would be of great use for planning ashore as well as decision-making onboard. Technical and mathematical research is rapidly growing. Access to reliable data sources and means of actual testing developed algorithms in practice require rare collaborations between shipowners, data measurement suppliers, which can be a barrier to this research. Also, studies using social science methods about the actual use of AI-based tools and technologies are scarcer for the same reason.

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Författare: Via Kaizen
Utgivare: Yara Marine Technologies AB
Utgivningsdatum: 2023-03-20
Diarienummer: TRV 2019/119989
Antal sidor: 36
Språk: Engelska
Kontaktperson: Charlott Andersson, US