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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/18072
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dc.contributor.advisorMahalec, Vladimir-
dc.contributor.authorFu, Gang-
dc.date.accessioned2015-09-24T15:01:40Z-
dc.date.available2015-09-24T15:01:40Z-
dc.date.issued2015-11-
dc.identifier.urihttp://hdl.handle.net/11375/18072-
dc.description.abstractPlanning, scheduling and real time optimization (RTO) are currently implemented by using different types of models, which causes discrepancies between their results. This work presents a single model of a crude distillation unit (preflash, atmospheric, and vacuum towers) suitable for all of these applications, thereby eliminating discrepancies between models used in these decision processes. Hybrid model consists of volumetric and energy balances and partial least squares model for predicting product properties. Product TBP curves are predicted from feed TBP curve, operating conditions (flows, pumparound heat duties, furnace coil outlet temperatures). Simulated plant data and model testing have been based on a rigorous distillation model, with 0.5% RMSE over a wide range of conditions. Unlike previous works, we do not assume that (i) midpoint of a product TBP curve lies on the crude distillation curve, and (ii) midpoint between the back-end and front-end of the adjacent products lies on the crude distillation curves, since these assumptions do not hold in practice. Associated properties (e.g. gravity, sulfur) are computed for each product based on its distillation curve. Model structure makes it particularly amenable for development from plant data. High model accuracy and its linearity make it suitable for optimization of production plans or schedules.en_US
dc.language.isoen_USen_US
dc.subjectLinear hybrid modelen_US
dc.subjectCrude distillation unitsen_US
dc.subjectRefinery planning and schedulingen_US
dc.subjectSwing cut methoden_US
dc.subjectFractions property predictionen_US
dc.subjectRTOen_US
dc.titleHybrid Model for Optimization Of Crude Distillation Unitsen_US
dc.typeThesisen_US
dc.contributor.departmentChemical Engineeringen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Applied Science (MASc)en_US
Appears in Collections:Open Access Dissertations and Theses

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Master Thesis_(Gang FU, 1352809).pdf
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Gang Fu (1352809) master thesis1.96 MBAdobe PDFView/Open
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