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A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil

Equations of state are powerful tools for modeling thermophysical properties; however, so far, these have not been developed for shale oil due to a lack of experimental data. Recently, new experimental data were published on the properties of Kukersite shale oil, and here we present a method for mod...

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Autores principales: Mozaffari, Parsa, Baird, Zachariah Steven, Järvik, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228787/
https://www.ncbi.nlm.nih.gov/pubmed/35744282
http://dx.doi.org/10.3390/ma15124221
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author Mozaffari, Parsa
Baird, Zachariah Steven
Järvik, Oliver
author_facet Mozaffari, Parsa
Baird, Zachariah Steven
Järvik, Oliver
author_sort Mozaffari, Parsa
collection PubMed
description Equations of state are powerful tools for modeling thermophysical properties; however, so far, these have not been developed for shale oil due to a lack of experimental data. Recently, new experimental data were published on the properties of Kukersite shale oil, and here we present a method for modeling the properties of the gasoline fraction of shale oil using the PC-SAFT equation of state. First, using measured property data, correlations were developed to estimate the composition of narrow-boiling-range Kukersite shale gasoline samples based on the boiling point and density. These correlations, along with several PC-SAFT equations of the states of various classes of compounds, were used to predict the PC-SAFT parameters of aromatic compounds present in unconventional oil-containing oxygen compounds with average boiling points up to 180 °C. Developed PC-SAFT equations of state were applied to calculate the temperature-dependent properties (vapor pressure and density) of shale gasoline. The root mean square percentage error of the residuals was 13.2%. The average absolute relative deviation percentages for all vapor pressure and density data were 16.9 and 1.6%, respectively. The utility of this model was shown by predicting the vapor pressure of various portions of the shale gasoline. The validity of this model could be assessed for oil fractions from different deposits. However, the procedure used here to model shale oil gasoline could also be used as an example to derive and develop similar models for oil samples with different origins.
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spelling pubmed-92287872022-06-25 A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil Mozaffari, Parsa Baird, Zachariah Steven Järvik, Oliver Materials (Basel) Article Equations of state are powerful tools for modeling thermophysical properties; however, so far, these have not been developed for shale oil due to a lack of experimental data. Recently, new experimental data were published on the properties of Kukersite shale oil, and here we present a method for modeling the properties of the gasoline fraction of shale oil using the PC-SAFT equation of state. First, using measured property data, correlations were developed to estimate the composition of narrow-boiling-range Kukersite shale gasoline samples based on the boiling point and density. These correlations, along with several PC-SAFT equations of the states of various classes of compounds, were used to predict the PC-SAFT parameters of aromatic compounds present in unconventional oil-containing oxygen compounds with average boiling points up to 180 °C. Developed PC-SAFT equations of state were applied to calculate the temperature-dependent properties (vapor pressure and density) of shale gasoline. The root mean square percentage error of the residuals was 13.2%. The average absolute relative deviation percentages for all vapor pressure and density data were 16.9 and 1.6%, respectively. The utility of this model was shown by predicting the vapor pressure of various portions of the shale gasoline. The validity of this model could be assessed for oil fractions from different deposits. However, the procedure used here to model shale oil gasoline could also be used as an example to derive and develop similar models for oil samples with different origins. MDPI 2022-06-14 /pmc/articles/PMC9228787/ /pubmed/35744282 http://dx.doi.org/10.3390/ma15124221 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mozaffari, Parsa
Baird, Zachariah Steven
Järvik, Oliver
A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title_full A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title_fullStr A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title_full_unstemmed A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title_short A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil
title_sort predictive approach towards using pc-saft for modeling the properties of shale oil
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228787/
https://www.ncbi.nlm.nih.gov/pubmed/35744282
http://dx.doi.org/10.3390/ma15124221
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