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dc.contributor.advisorElbashir, Nimir O.
dc.contributor.advisorEl-Halwagi, Mahmoud M.
dc.creatorYueh, Andrew
dc.date.accessioned2016-05-04T13:23:44Z
dc.date.available2017-12-01T06:36:19Z
dc.date.created2015-12
dc.date.issued2015-12-09
dc.date.submittedDecember 2015
dc.identifier.urihttp://hdl.handle.net/1969.1/156522
dc.description.abstractAn energy pathway of great interest is gas-to-liquid (GTL) technologies, which converts natural gas to valuable chemicals and fuels. Three powerful regression methods were implemented, to create models for accurate predictions of physical properties– density, freezing point, flash point, and heat content. With the use of experimental training data, three distinct techniques were performed and analyzed: artificial neural networks, support vector machine (SVM) and Kriging modeling. For further accuracy, optimal simulation settings were elucidated through repeated runs and rigorous testing, with substantial increases in performance in low performing models. Most models generated were accurate with good trends, except freezing point. A formulation package called DataMine, coded in R, was created for current work and future endeavors.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectData Mine Optimizationen
dc.subjectHydrocarbonen
dc.subjectMonte Carloen
dc.titleBlack Box Modeling of Hydrocarbon Physiochemical Properties and its Approach to Fuel Optimizationen
dc.typeThesisen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineChemical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberButenko, Sergiy
dc.contributor.committeeMemberMannan, Mahboobul
dc.type.materialtexten
dc.date.updated2016-05-04T13:23:44Z
local.embargo.terms2017-12-01
local.etdauthor.orcid0000-0002-0618-0386


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