Abstract
In shipping, the choice of the right routing and speed offers the opportunity to act more sustainably from both an economic and an ecological point of view. Reinforcement Learning (RL) agents could be suitable for this task. However, as a learning environment the agents require the most detailed, accurate, and fast representation of reality possible. This paper describes approaches to build such an environment using neural networks (NN) trained with both simulation and real-world data. It is shown that simple feed-forward networks can reproduce data created by 1D flow simulation sufficiently accurate. By examining the differences between simulation and measured data, the simulation could be improved. Since NNs trained with vessel data only are limited in their generality, approximating nets trained with simulation data to vessel data using Transfer Learning (TL) was investigated. Initial results for this approach show good quantitative results, but only in the data region where vessel and simulation data overlap. The paper provides an overview of the necessary steps towards Digital Twins for ship propulsion systems.
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References
CMA CGM Group. (2022). Sustainable Development Report. CMA CGM S.A., Marseille. https://cmacgm-group.com/api/sites/default/files/2022-06/CMACGM_Rapport_2021_Web_UK.pdf
Dinu, O., & Ilie, A. M. (2015). Maritime vessel obsolescence, life cycle cost and design service life. IOP Conference Series: Materials Science and Engineering, 95, 012067. https://doi.org/10.1088/1757-899X/95/1/012067
Hapag-Lloyd AG. (2022). Nachhaltigkeitsbericht 2021. Hapag-Lloyd AG, Hamburg. https://www.hapag-lloyd.com/sustainability-report-2021/de/assets/downloads/HAPAG-LLOYD%20AG_Nachhaltigkeitsbericht%202021.pdf
International Maritim Organization (IMO). (2020). Fourth IMO greenhouse gas study. International Maritim Organization, London. https://wwwcdn.imo.org/localresources/en/OurWork/Environment/Documents/Fourth%20IMO%20GHG%20Study%202020%20-%20Full%20report%20and%20annexes.pdf
Jungbluth, C. (2021). Challenge and opportunity: China inside the WTO and EU-China relations. Bertelsmann Stiftung, Gütersloh. https://doi.org/10.11586/2021122. https://www.bertelsmannstiftung.de/fileadmin/files/user_upload/EU-China-Relations_Policy_Brief.pdf
Milojevic, S., Bodza, S., Bargende, M., Rether, D., & Grill, M. (2021). Next generation data based models powered by AI. In 9th international symposium on development methodology, Wiesbaden. ISBN 978-3-9816971-7-9
Milojevic, S., Bodza, S., Cimniak, V., Angerbauer, M., Rether, D., Grill, M., & Bargende, M. (2022). Data-driven modeling: an AI Toolchain for the powertrain development process. SAE Technical Paper 2022-01-0158, WCX SAE World Congress Experience.
Moradi, M. H., Brutsche, M., Wenig, M., Wagner, U., & Koch, T. (2022). Marine route optimization using reinforcement learning approach to reduce fuel consumption and consequently minimize CO2 emissions. Ocean Engineering, 259, 111882. https://doi.org/10.1016/j.oceaneng.2022.111882
MSC Group. (2022). Sustainability report. Mediterranean Shipping Company SA, Genf. https://www.msc.com/-/media/files/sustainability/reports/msc_sustainability_report_2021.pdf
Naturschutzbund Deutschland e.V. (NABU). (2014). Luftschadstoffemissionen von Container schiffen. Naturschutzbund Deutschland e.V., Berlin. https://www.nabu.de/imperia/md/content/nabude/verkehr/140623-nabu-hintergrundpapier_containerschifftransporte.pdf
Schneiter, D., Goranov, S., Ott, M., & Printz, P. (2019). WinGD 12X92DF, the development of the most powerful Otto engine ever. In CIMAC Congress 2019, Paper 425, Vancouver.
Seum, S., Bahlke, C., Grasmeier, C., Gröger, J., Mottschall, M., & Schnegelsberg, S. (2011). Umweltschonender Schiffsbetrieb, PROSA Studie zum RAL Umweltzeichen UZ 110. Öko-Institut e.V., Berlin. https://www.oeko.de/oekodoc/1280/2011-412-de.pdf
United Nations Conference on Trade and Development (UNCTAD). (2021). Review of maritime transport 2021. ISBN: 978-92-1-113026-3. https://unctad.org/system/files/official-document/rmt2021_en_0.pdf
West, S., Stoll, O., Meierhofer, J., & Züst, S. (2021). Digital twin providing new opportunities for value co-creation through supporting decision-making. Applied Sciences, 11(9), 3750. https://doi.org/10.3390/app11093750
Winterthur Gas & Diesel Ltd. (2021). Marine Installation Manual X92DF
Zhang, Y., Chang, Y., Wang, C., Fung, J. C. H., & Lau, A. K. H. (2022). Life-cycle energy and environmental emissions of cargo ships. Journal of Industrial Ecology, 1–12.https://doi.org/10.1111/jiec.13293
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Rether, D., Brutsche, M., Sklias, I., Wenig, M. (2023). Towards ANN Based Digital Twins of Ship Propulsion Systems. In: Meierhofer, J., West, S., Buecheler, T. (eds) Smart Services Summit. SMSESU 2022. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-36698-7_19
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