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Modelling of the sediment content of heavy oil fractions

Silvia Gentile

Modelling of the sediment content of heavy oil fractions.

Rel. Stefania Specchia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Chimica E Dei Processi Sostenibili, 2020

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Abstract:

Modelling of the sediment contents of heavy oil fractions The of the study is to develop and to validate a universal model able to describe the sediment content in blends made by heavy fuel, called residuo, and light oil, known as cutter stocks. To find the model two different ways are possible, a thermodynamic or an empirical approach. The thermodynamic approach needs the knowledge of some properties which are very difficult to measured, for this reason an empirical approach has been preferred. To be able to follow the empirical approach, several data are necessary: some are collected from previous studies and some are new. The mixing rule to predict the sediment contents of the blends is an exponential model and to achieve this result more than four hundred binary mixtures (between light and heavy oils) were considered. It is important that the model is related to the properties both of the cutter stocks and of the residues. The equation parameter has been correlated to the density, hydrogen, carbon and sulfur content and surface tension for the residue, and to density, hydrogen content and temperature distillation for the cutter stocks. The developed model is not able to take well into account the cutter stocks that were not used for the calibration, in fact the sediment content of ternaries mixtures is very different from the prediction. Some improvements for the study could be the introduction of the time variable in the equation, because it is known that time plays an important role in sediment deposition. Sediment content it is not constant during time. Although the exponential equation seems nice to describe the content of the sediments, the model should be more sensitive to the properties of the cutter stocks and better explains the ternaries mixtures, maybe by adding new blends in the calibration database. Moreover, more properties can be taken into account.

Relatori: Stefania Specchia
Anno accademico: 2019/20
Tipo di pubblicazione: Elettronica
Numero di pagine: 112
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Chimica E Dei Processi Sostenibili
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-22 - INGEGNERIA CHIMICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/13694
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