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Quasi-steady state simulation of an organic Rankine cycle for waste heat recovery in a passenger vessel
Lund University.
Linnaeus University, Faculty of Technology, Kalmar Maritime Academy.ORCID iD: 0000-0003-0372-7195
Linnaeus University, Faculty of Technology, Kalmar Maritime Academy. Lund University.
Lund University.
2017 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 185, no Special Issue Part 2, p. 1324-1335Article in journal (Refereed) Published
Abstract [en]

In this work we present the quasi-steady state simulation of a regenerative organic Rankine cycle (ORC)integrated in a passenger vessel, over a standard round trip. The study case is the M/S Birka Stockholmcruise ship, which covers a daily route between Stockholm (Sweden) and Mariehamn (Finland).Experimental data of the exhaust gas temperatures, engine loads, and electricity demand on board werelogged over a period of four weeks. These data where used as inputs for a simulation model of an ORC forwaste heat recovery of the exhaust gases. A quasi-steady state simulation was carried out on an offdesignmodel, based on optimized design conditions, to estimate the average net power production ofthe ship over a round trip. The maximum net power production of the ORC during the round trip wasestimated to supply approximately 22% of the total power demand on board. The results showed apotential for ORC as a solution for the maritime transport sector to accomplish the new and morerestrictive regulations on emissions, and to reduce the total fuel consumption.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 185, no Special Issue Part 2, p. 1324-1335
Keywords [en]
cruise vessel, waste heat recovery, orc, organic rankine cycle, off-design, quasi steady simulation
National Category
Marine Engineering
Research subject
Shipping, Maritime Science
Identifiers
URN: urn:nbn:se:lnu:diva-56350DOI: 10.1016/j.apenergy.2016.03.024ISI: 000390494800035OAI: oai:DiVA.org:lnu-56350DiVA, id: diva2:957801
Available from: 2016-09-05 Created: 2016-09-05 Last updated: 2018-11-07Bibliographically 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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