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Predicting dynamic fuel oil consumption on ships with automated machine learning
Linnaeus University, Faculty of Technology, Kalmar Maritime Academy.ORCID iD: 0000-0003-0372-7195
Technical University of Denmark, Denmark.
Lund University, Sweden.
2019 (English)In: Innovative Solutions for Energy Transitions: Proceedings of the 10th International Conference on Applied Energy (ICAE2018) / [ed] Prof. J.Yanab, Prof. H.Yang, cDr. H.Lid, Dr. X.Chene, Elsevier, 2019, Vol. 158, p. 6126-6131Conference paper, Published paper (Refereed)
Abstract [en]

This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 158, p. 6126-6131
Series
Energy Procedia, E-ISSN 1876-6102
Keywords [en]
Shipping, Auto machine learning, Energy efficiency, Predicting fuel consumption
National Category
Energy Engineering
Research subject
Shipping, Maritime Science
Identifiers
URN: urn:nbn:se:lnu:diva-78705DOI: 10.1016/j.egypro.2019.01.499ISI: 000471031706075Scopus ID: 2-s2.0-85063916104OAI: oai:DiVA.org:lnu-78705DiVA, id: diva2:1261129
Conference
10th International Conference on Applied Energy (ICAE2018), Hong Kong, China, August 22-25, 2018
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2019-08-29Bibliographically approved
In thesis
1. Reducing ships' fuel consumption and emissions by learning from data
Open this publication in new window or tab >>Reducing ships' fuel consumption and emissions by learning from data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In the context of reducing both greenhouse gases and hazardous emissions, the shipping sector faces a major challenge as it is currently responsible for 11% of the transport sector’s anthropogenic greenhouse gas emissions. Even as emissions reductions are needed, the demand for the transport sector rises exponentially every year. This thesis aims to investigate the potential to use ships’ existing internal energy systems more efficiently. The thesis focusses on making existing ships in real operating conditions more efficient based logged machinery data. This dissertation presents results that can make ship more energy efficient by utilising waste heat recovery and machine learning tools. A significant part of this thesis is based on data from a cruise ship in the Baltic Sea, and an extensive analysis of the ship’s internal energy system was made from over a year’s worth of data. The analysis included an exergy analysis, which also considers the usability of each energy flow. In three studies, the feasibility of using the waste heat from the engines was investigated, and the results indicate that significant measures can be undertaken with organic Rankine cycle devices. The organic Rankine cycle was simulated with data from the ship operations and optimised for off-design conditions, both regarding system design and organic fluid selection. The analysis demonstrates that there are considerable differences between the real operation of a ship and what it was initially designed for. In addition, a large two-stroke marine diesel was integrated into a simulation with an organic Rankine cycle, resulting in an energy efficiency improvement of 5%. This thesis also presents new methods of employing machine learning to predict energy consumption. Machine learning algorithms are readily available and free to use, and by using only a small subset of data points from the engines and existing fuel flow meters, the fuel consumption could be predicted with good accuracy. These results demonstrate a potential to improve operational efficiency without installing additional fuel meters. The thesis presents results concerning how data from ships can be used to further analyse and improve their efficiency, by using both add-on technologies for waste heat recovery and machine learning applications.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2018. p. 204
Series
Linnaeus University Dissertations ; 339
Keywords
shipping, energy efficiency, orc, machine learning, emissions
National Category
Energy Engineering
Research subject
Shipping, Maritime Science
Identifiers
urn:nbn:se:lnu:diva-78709 (URN)978-91-88898-22-7 (ISBN)978-91-88898-23-4 (ISBN)
Public defence
2018-12-13, B135, Landgången 4, Sjöfartshögskolan, Kalmar, 10:00 (English)
Opponent
Supervisors
Available from: 2018-11-12 Created: 2018-11-07 Last updated: 2018-12-06Bibliographically approved

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Ahlgren, Fredrik

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