Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information cri...

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Bibliographic Details
Main Authors: Dicho Stratiev, Svetoslav Nenov, Dimitar Nedanovski, Ivelina Shishkova, Rosen Dinkov, Danail D. Stratiev, Denis D. Stratiev, Sotir Sotirov, Evdokia Sotirova, Vassia Atanassova, Krassimir Atanassov, Dobromir Yordanov, Nora A. Angelova, Simeon Ribagin, Liliana Todorova-Yankova
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Resources
Subjects:
vacuum gas oil
gas oil
viscosity
empirical modeling
sensitivity analysis
Akaike information criterion
Online Access:https://www.mdpi.com/2079-9276/10/10/99
Description
Summary:Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.
ISSN:2079-9276

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