Projects per year
Abstract
Shipping is responsible for over 90% of global trade. Although it is generally considered a safe and clean mode of transportation, it still has a significant impact on the environment. Thus, state-of-the-art models that may contribute to the sustainable management of the life cycle of shipping operations without compromising safety standards are urgently needed. This paper discusses the potential of artificial intelligence (AI) based digital twin models to monitor ship safety and efficiency. A paradigm shift is introduced in the form of a model that can predict ship motions and fuel consumption under real operational conditions using deep learning models. A bi-directional Long Short-Term Memory (LSTM) network with attention mechanisms is used to predict ship fuel consumption and a transformer neural network is employed to capture ship motions in realistic hydrometeorological conditions. By comparing the predicted results with available sea trial data, it is suggested that following further testing and validation, these models could perform satisfactorily in real conditions. Accordingly, they could be integrated into a framework for safe and sustainable ship operations.
Original language | English |
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Number of pages | 1 |
Publication status | Published - 16 Nov 2023 |
MoE publication type | Not Eligible |
Event | Gulf of Finland Science Days - Tallinn, Estonia Duration: 16 Nov 2023 → 17 Nov 2023 |
Conference
Conference | Gulf of Finland Science Days |
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Country/Territory | Estonia |
City | Tallinn |
Period | 16/11/2023 → 17/11/2023 |
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RETROFIT55: Retrofit solutions to achieve 55% ghg reduction by 2030
Remes, H. (Principal investigator)
01/01/2023 → 31/12/2025
Project: EU: Framework programmes funding