Reducing ships' fuel consumption and emissions by learning from data

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Fältnamn	Indikatorer	Metadata
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001		oai:DiVA.org:lnu-78709
003		SwePub
008		181107s2018 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
020		a 9789188898227q print
020		a 9789188898234q electronic
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040		a (SwePub)lnu
041		a engb eng
042		9 SwePub
072	7	a vet2 swepub-contenttype
072	7	a dok2 swepub-publicationtype
100	1	a Ahlgren, Fredrik,d 1980-u Linnéuniversitetet,Sjöfartshögskolan (SJÖ)4 aut0 (Swepub:lnu)frahaa
245	1 0	a Reducing ships' fuel consumption and emissions by learning from data
264	1	a Växjö :b Linnaeus University Press,c 2018
300		a 204 s.
338		a electronic2 rdacarrier
490	0	a Linnaeus University Dissertations ;v 339
520		a 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.
650	7	a TEKNIK OCH TEKNOLOGIERx Maskinteknikx Energiteknik0 (SwePub)203042 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Mechanical Engineeringx Energy Engineering0 (SwePub)203042 hsv//eng
653		a shipping
653		a energy efficiency
653		a orc
653		a machine learning
653		a emissions
653		a Sjöfartsvetenskap
653		a Maritime Science
700	1	a Thern, Marcus,c Docentu Lunds Tekniska Högskola4 ths
700	1	a Mondejar, Maria E.,c Associate Professoru Technical University of Denmark4 ths
700	1	a Österman, Cecilia,d 1971-u Linnéuniversitetet,Sjöfartshögskolan (SJÖ)4 ths0 (Swepub:lnu)ceosaa
700	1	a Larsson, Ann-Charlotteu Linnéuniversitetet,Institutionen för byggd miljö och energiteknik (BET),Växjö universitet, Institutionen för teknik och design4 ths0 (Swepub:lnu)anlaab
700	1	a Dahlquist, Erik,c Professoru Mälardalens Högskola4 opn
710	2	a Linnéuniversitetetb Sjöfartshögskolan (SJÖ)4 org
856	4	u mailto:lnupress@lnu.se?subject=Order: Ahlgren, Reducing ships' fuel consumption and emissions by learning from data&body=Publication: Reducing ships' fuel consumption and emissions by learning from data%0D%0AAuthor: Ahlgren, Fredrik%0D%0APrice per copy: SEK 250 + VAT and postage%0D%0A%0D%0ANo of Copies: %0D%0ACustomer name: %0D%0ABilling address: %0D%0APostal address:y Buy Book (SEK 250 + VAT and postage) lnupress@lnu.se
856	4	u https://lnu.diva-portal.org/smash/get/diva2:1261368/FULLTEXT01.pdfx primaryx Raw objecty fulltext
856	4	u https://lnu.diva-portal.org/smash/get/diva2:1261368/PREVIEW01.jpgx Previewy preview image
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78709

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Av författaren/redakt...: Ahlgren, Fredrik ...; Thern, Marcus, D ...; Mondejar, Maria ...; Österman, Cecili ...; Larsson, Ann-Cha ...; Dahlquist, Erik, ...

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Maskinteknik; och Energiteknik

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