Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference

Abstract Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chai...

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Autores principales:	Aleksandra Svalova, David Walshaw, Clement Lee, Vasily Demyanov, Nicholas G. Parker, Megan J. Povey, Geoffrey D. Abbott
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/68fa3d635db449389bfb4d1c12c7a1a0
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spelling	oai:doaj.org-article:68fa3d635db449389bfb4d1c12c7a1a02021-12-02T17:04:34ZEstimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference10.1038/s41598-021-85926-82045-2322https://doaj.org/article/68fa3d635db449389bfb4d1c12c7a1a02021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85926-8https://doaj.org/toc/2045-2322Abstract Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene “monomers” self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, making the water-in-oil emulsions extremely stable. Due to their molecular complexity, modelling the self-association of the asphaltenes can be a very computationally-intensive task and has mostly been approached by molecular dynamic simulations. Our approach allows the use of literature and experimental data to estimate the nanoaggregation and its credible intervals. It has a low computational cost and can also be used for other analytical/experimental methods probing a changepoint in the molecular association behaviour.Aleksandra SvalovaDavid WalshawClement LeeVasily DemyanovNicholas G. ParkerMegan J. PoveyGeoffrey D. AbbottNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aleksandra Svalova David Walshaw Clement Lee Vasily Demyanov Nicholas G. Parker Megan J. Povey Geoffrey D. Abbott Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
description	Abstract Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene “monomers” self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, making the water-in-oil emulsions extremely stable. Due to their molecular complexity, modelling the self-association of the asphaltenes can be a very computationally-intensive task and has mostly been approached by molecular dynamic simulations. Our approach allows the use of literature and experimental data to estimate the nanoaggregation and its credible intervals. It has a low computational cost and can also be used for other analytical/experimental methods probing a changepoint in the molecular association behaviour.
format	article
author	Aleksandra Svalova David Walshaw Clement Lee Vasily Demyanov Nicholas G. Parker Megan J. Povey Geoffrey D. Abbott
author_facet	Aleksandra Svalova David Walshaw Clement Lee Vasily Demyanov Nicholas G. Parker Megan J. Povey Geoffrey D. Abbott
author_sort	Aleksandra Svalova
title	Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
title_short	Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
title_full	Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
title_fullStr	Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
title_full_unstemmed	Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference
title_sort	estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and bayesian inference
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/68fa3d635db449389bfb4d1c12c7a1a0
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Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference

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