Prediction of physical-chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics

Prediction of physical-chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics

Copyright © 2012 John Wiley & Sons, Ltd.

Bibliographische Detailangaben
Veröffentlicht in:Magnetic resonance in chemistry : MRC. - 1985. - 50(2012), 11 vom: 01. Nov., Seite 729-38
1. Verfasser: Masili, Alice (VerfasserIn)
Weitere Verfasser: Puligheddu, Sonia, Sassu, Lorenzo, Scano, Paola, Lai, Adolfo
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2012
Zugriff auf das übergeordnete Werk:Magnetic resonance in chemistry : MRC
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Petroleum Protons
Beschreibung
Zusammenfassung:Copyright © 2012 John Wiley & Sons, Ltd.
In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300-MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil in a fast and cost-effective way. The main properties generally employed for evaluating crudes' quality and behavior during refining were measured and used for calibration and testing of the PLS models. Among these, the UOP characterization factor K (K(UOP)) used to classify crude oils in terms of composition, density (D), total acidity number (TAN), sulfur content (S), and true boiling point (TBP) distillation yields were investigated. Test set validation with an independent set of data was used to evaluate model performance on the basis of standard error of prediction (SEP) statistics. Model performances are particularly good for K(UOP) factor, TAN, and TPB distillation yields, whose standard error of calibration and SEP values match the analytical method precision, while the results obtained for D and S are less accurate but still useful for predictions. Furthermore, a strategy that reduces spectral data preprocessing and sample preparation procedures has been adopted. The models developed with such an ample crude oil set demonstrate that this methodology can be applied with success to modern refining process requirements
Beschreibung:Date Completed 05.08.2013
Date Revised 11.10.2012
published: Print-Electronic
Citation Status MEDLINE
ISSN:1097-458X
DOI:10.1002/mrc.3872