Analysis of the Boil-Off Phenomenon in Relation to Ambient Conditions

147 Pages·2017·6.68 MB·English

by  Erling Singstad Paulsen

Analysis of the Boil-Off Phenomenon in
Relation to Ambient Conditions
Erling Singstad Paulsen
Master of Science in Engineering and ICT
Submission date: June 2017
Supervisor: Eilif Pedersen, IMT
Co-supervisor: Nicolas Lefebvre, IMT
Norwegian University of Science and Technology
Department of Marine Technology

Preface
TheworkdescribedinthisMaster’sthesisispartofthestudyprogramEngineeringandICT
withaspecializationwithinMarineCyberneticsatNTNU.Thethesisiscarriedoutthespring
semesterof2017.WithabackgroundinbothICTandmarinetechnology,Iwantedtoexplore
theuseofstatisticallearningmethodsfordataanalysisusingreal-worlddatafromthemar-
itimeindustry. Thethesisisacontinuationofpreviousworkcarriedoutthefallsemesterof
2016.
ThethesisiswrittenforNTNUincooperationwithDNVGL,whoinitiallyprovidedthedata
togetherwithrelevantbackgroundinformationandinterestingproblemstoexplore.
Thereaderisassumedtohavebasicknowledgeofmarinevessels,linearalgebra,andstatis-
ticalanalysis.
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Acknowledgments
IwouldliketothanktheemployeesatDNVGLwhoprovidedthesensorydataandassisted
me throughout the project by providing necessary documentation. I would especially like
to thank Christos Chryssakis for providing valuable resources and for continuously being
involvedintheprocessthroughSkypemeetings.
It has been a great pleasure to have Nicolas Lefebvre as my co-supervisor. He supported
methroughweeklymeetingstodiscusstheworkdoneandtohowtobestproceed. Iwould
alsoliketothankEilifPedersenforbeingmymainsupervisor. Finally,Iwouldliketothank
Christian de Jonge, who has been working with the same data, for our collaboration and
numerousdiscussionsthroughoutthesemester.
Trondheim,June2017
ErlingSingstadPaulsen
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Summary and Conclusions
Therelationshipbetweenambientconditionsandtheboil-offofliquefiednaturalgasduring
marine transportation has been investigated using a data-driven approach. The data con-
sisted of sensory data collected from a vessel over a three year period coupled with global
measurementsofambientconditionssuchastemperatureandwaveheight.Statisticalmod-
elsweretrainedtopredictthechangeincargolevelfromambientconditions,asthechange
incargolevelisadirecteffectofboil-off. Themodelswerethenanalyzedtoassesstherela-
tiveimportanceofthedifferentambientconditions.
Therelevantdatawereextractedandputonasuitableformat. Thepreprocessingconsisted
ofoutlierremoval,noisereductionandsynchronizationofthedata. Fivedatasetswerecon-
structed;oneforeachofthefourcargotanksandoneforthecargolevelscombined. Prepro-
cessingofthecargolevelsreducedtheamountofdatabyabout95%.
Acrudepolynomialmodelwasusedtosimulateavirtualcargolevelasafunctionofthereal
ambient conditions. The simulated data were inspected using statistical learning methods
to verify that the employed methodology would uncover the relationships in the data. The
modelslearnedfromthedatashowedstrongsimilaritieswiththetruemodelandimproved
thepredictionerrorrateby87.29%. Bothlinearandnonlinearmodelsweretrained.
The same methodology was employed on the real-world data. The best linear and nonlin-
earmodelreducedthepredictionerrorrateby45.20%and68.45%respectively. Theresults
verifiedthatambientconditionscanbeusedtopredictboil-off, althoughitisunlikely that
truerelationshipislinearinnature. Theparametersensitivityofthebestlinearmodelswas
analyzed to assess the importance of the different ambient conditions. The results consis-
tently ranked the ambient temperature and waves as most important, as expected due to
heatleakageandsloshinginthetanks. Fromthebestlinearmodel,thechangeincargolevel
wasfoundtobevaryinglinearlywithambienttemperature, asshownintheliterature, and
nonlinearlywithwavesandwind.
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