Kriging Modeling to Predict Viscosity Index of Base Oils

Da Costa, Jean-Jerôme; Chainet, Fabien; Celse, Benoit; Lacoue-Nègre, Marion; Ruckebusch, Cyril; Caillol, Noémie; Espinat, Didier

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1021/acs.energyfuels.7b03266

URL permanente :

http://hdl.handle.net/20.500.12210/57773

Titre :

Kriging Modeling to Predict Viscosity Index of Base Oils

Auteur(s) :

Da Costa, Jean-Jerôme [Auteur]
IFP Energies nouvelles [IFPEN]
Chainet, Fabien [Auteur]
IFP Energies nouvelles [IFPEN]
Celse, Benoit [Auteur]
Lacoue-Nègre, Marion [Auteur]
IFP Energies nouvelles [IFPEN]
Ruckebusch, Cyril [Auteur]

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Caillol, Noémie [Auteur]
IFP Energies nouvelles [IFPEN]
Espinat, Didier [Auteur]
IFP Energies nouvelles [IFPEN]

Titre de la revue :

Energy & Fuels

Numéro :

Pagination :

2588-2597

Date de publication :

2018-02

Discipline(s) HAL :

Chimie/Chimie théorique et/ou physique

Résumé en anglais : [en]

Predicting petroleum products’ properties, such as the viscosity index (VI) of base oils, is an important challenge for refiners because the production always requires more time-consuming and costly experiments. Base oils ...
Lire la suite >Predicting petroleum products’ properties, such as the viscosity index (VI) of base oils, is an important challenge for refiners because the production always requires more time-consuming and costly experiments. Base oils can have very different characteristics depending on which production process they have undergone. In this work, kriging is proposed to predict the VI of base oils obtained from hydrotreatment or/and hydrocracking processes using global properties (density, refractive index, distillation curve, etc.) of feedstock and/or total liquid effluent, with conversion rate of the 370+ cut. Kriging is an interpolation method that predicts the value at a given point by computing a weighted average of the observations in the neighborhood of this point. As kriging is closely related to regression analysis, the results obtained were compared with multilinear regression (MLR). Results show that kriging and MLR have very close performances for hydrotreatment data (base oil with viscosity indices ranging from 9 to 113) for which 63% of the validation samples are predicted within the confidence interval of the standard measure. For hydrocracking data (base oils with viscosity indices ranging from 85 to 126), kriging provides better results as 62% of the validation samples are predicted in the confidence interval of the standard measure against 46% for MLR. In light of these results, we discuss the potential of kriging to deal with both linear and nonlinear situations.Lire moins >

Langue :

Anglais

Audience :

Non spécifiée

Vulgarisation :

Non

Établissement(s) :

CNRS
ENSCL
Université de Lille

Collections :

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Équipe(s) de recherche :

Dynamics, Nanoscopy & Chemometrics (DyNaChem)

Date de dépôt :

2021-11-16T08:23:28Z
2024-02-14T07:43:53Z

Numéro de version	Lien	Date de modification
2	20.500.12210/57773*	2024-02-14T07:39:34Z
1	20.500.12210/57773.1	2021-11-16T08:23:28Z

Kriging Modeling to Predict Viscosity Index ...

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Kriging Modeling to Predict Viscosity Index ... BibTeX CSV Excel RIS

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Kriging Modeling to Predict Viscosity Index ...

BibTeX

CSV

Excel

RIS