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Prediction Of The Kinematic Viscosity Of Petroleum Fractions

BROWSE_DETAIL_CREATION_DATE: 09-03-2018

BROWSE_DETAIL_IDENTIFIER_SECTION

BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Chemical engineering,

BROWSE_DETAIL_CREATORS: Ahmed, Hawa Ahmed Ali (Author),

BROWSE_DETAIL_CONTRIBUTERS: KAYI, Hakan (Advisor),

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Kinematic viscosity, petroleum fractions, predictive model, experimental data.


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Petroleum fractions are very complex mixtures. In engineering design, it is important to identify the viscosity of fluids, as it is used in calculations. Furthermore, the physical properties of the liquid, as well as its composition, are factors that help to form an understanding of the fluid’s behavior. There are two main methodologies that are used to get the viscosity of the fluid; an experimental approach, which requires measurements, and using a mathematical model for estimation using the physical properties. Because experimental viscosity measurements are expensive and time consuming, it is not always practical to perform these measurements in the lack of experimental data. Hence, reliable and accurate mathematical models are required. This study is concerned with the prediction of the viscosities of some Libyan petroleum products by evaluating the performance of various mathematical models available in the literature. Initially an experimental database was established for this purpose. The database consists of kinematic viscosity and density data for several Libyan petroleum products in kerosene and gas oil ranges. Among all the models evaluated, the modified group-contribution viscosity-thermodynamics model (GC-UNIMOD) was proposed for kinematic viscosities’ prediction at investigated temperature levels (30, 35, 40, 50 °C) using various Libyan petroleum fractions. The model proposed was tested using the collected data and compared with the existing models. It was concluded that utilizing the GC-UNIMODvmodel to predict results is more accurate and precise than other methods’ predictions, which are used in literature like the investigated empirical models in this work.


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