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Energy integration of organic rankine cycle, exhaust gas recirculation and scrubber
Linnaeus University, Faculty of Technology, Kalmar Maritime Academy.ORCID iD: 0000-0003-0372-7195
Lund University.
Lund University.
Technical University of Denmark, Denmark.
2018 (English)In: Trends and challenges in maritime energy management / [ed] Aykut I. Ölçer, Momoko Kitada, Dimitrios Dalaklis & Fabio Ballini, Cham, Switzerland: Springer, 2018, p. 157-168Chapter in book (Refereed)
Abstract [en]

The vast majority of ships trafficking the oceans are fuelled by residual oil with high content of sulphur, which produces sulphur oxides (SOx) when combusted. Additionally, the high pressures and temperatures in modern diesel engines also produce nitrogen oxides (NOx). These emissions are both a hazard to health and the local environment, and regulations enforced by the International Maritime Organization (IMO) are driving the maritime sector towards the use of either distillate fuels containing less sulphur, or the use of exhaust gas cleaning devices.TwocommontechniquesforremovingSOx andlimitingNOx aretheopen loop wet scrubber and exhaust gas recirculation (EGR). A scrubber and EGR installation reduces the overall efficiency of the system as it needs significant pumping power, which means that the exhaust gases are cleaner but at the expense of higher CO2 emissions. In this paper we propose a method to integrate an exhaust gas cleaning device for both NOx and SOx with an organic Rankine cycle for waste heat recovery, thereby enhancing the system efficiency. We investigate three ORC configurations, integrated with the energy flows from both an existing state-of-the-art EGR system and an additional open loop wet scrubber.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2018. p. 157-168
Series
WMU Studies in Maritime Affairs, ISSN 2196-8772, E-ISSN 2196-8780 ; 6
Keywords [en]
Orc, Scrubber, Energy efficiency, Waste heat recovery, WHR, Ship emissions
National Category
Energy Engineering
Research subject
Shipping, Maritime Science
Identifiers
URN: urn:nbn:se:lnu:diva-78708DOI: 10.1007/978-3-319-74576-3_12ISBN: 9783319745756 (print)ISBN: 9783319745763 (electronic)OAI: oai:DiVA.org:lnu-78708DiVA, id: diva2:1261131
Available from: 2018-11-06 Created: 2018-11-06 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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