Representation of the Molecular Structure of Petroleum Resid through Characterization and Monte Carlo Modeling

Trauth, Daniel M.; Stark, Scott M.; Petti, Thomas F.; Neurock, Matthew; Yaşar, Muzaffer; Klein, Michael T.

doi:10.1021/ef00045a010

Cited by 64 publications

(81 citation statements)

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“…This more detailed methodology has been used by the petrochemical industry to characterize feedstock impacts on product properties derived through chemical processing [8][9][10][11][12]. The structural groups of interest can be readily determined using a number of analytical tools.…”

Section: Overview Of Gasoline Compositionmentioning

confidence: 99%

Development of an Experimental Database and Chemical Kinetic Models for Surrogate Gasoline Fuels

Pitz

Cernansky

Dryer

et al. 2007

SAE Technical Paper Series

320

243

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The development of surrogate mixtures that represent gasoline combustion behavior is reviewed.1 Combustion chemistry behavioral targets that a surrogate should accurately reproduce, particularly for emulating homogeneous charge compression ignition (HCCI) operation, are carefully identified. Both short and long term research needs to support development of more robust surrogate fuel compositions are described. Candidate component species are identified and the status of present chemical kinetic models for these components and their interactions are discussed. Recommendations are made for the initial components to be included in gasoline surrogates for near term development. Components that can be added to refine predictions and to include additional behavioral targets are identified as well. Thermodynamic, thermochemical and transport properties that require further investigation are discussed.

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Section: Overview Of Gasoline Compositionmentioning

confidence: 99%

Development of an Experimental Database and Chemical Kinetic Models for Surrogate Gasoline Fuels

Pitz

Cernansky

Dryer

et al. 2007

SAE Technical Paper Series

320

243

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“…Using Equations (27) and (28) finally allows to obtain the molar fractions of the compounds for each of three examples (Tab. 4 and Fig. 3).…”

Section: Entropy Maximization With Linear Constraintsmentioning

confidence: 85%

“…They are based on different strategies that depend on the type of feedstocks to rebuild. For the heaviest petroleum fractions (vacuum gas oils, atmospheric residues, vacuum residues), the main approach consists in sampling randomly different statistical distributions of structural blocks which are the basic elements of the molecules present in the mixture after reconstruction [1][2][3][4][5][6][7][8]. For the lightest petroleum cuts (gasolines, kerosenes, gas oils), the majority of the methods is based on a predefined set of compounds which is supposed to be representative of the molecules that are actually present in the fraction under investigation.…”

Section: Introductionmentioning

confidence: 99%

“…1.6). In such a case, the mixing rules of Equation (3) can be transformed into J exact linear constraints, which are grouped together into a matrix system: (4) where f j Equality term for the constraint j f i,j Parameter of the compound i for the constraint j x i Molar fraction of the compound i N Number of compounds in the database after filtering J Total number of constraints In the simplest example of a linear mixing rule as Equation (4), the equality term f j for constraint j equals the experimental value of property (P j ) for the petroleum fraction, while f i,j corresponds to the value of the pure component property j for compound (P i,,j ). In order to account for the conditional expressions in some mixing rules (distillation curve or a PIONA analysis) and in order to improve the numerical stability, the terms f j and f i,j may be transformations of these values P j and P i,,j , respectively.…”

Section: Introduction Of the Petroleum Analysesmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of Petroleum Feedstocks by Entropy Maximization. Application to FCC Gasolines

Hudebine

Verstraete

2011

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Résumé -Reconstruction de coupes pétrolières par maximisation d'entropie. Application aux essences de FCC -Dans le domaine pétrolier, les coupes sont généralement des mélanges complexes de plusieurs centaines à plusieurs millions d'espèces chimiques différentes. De ce fait, les outils analytiques, même les plus performants, ne permettent pas de séparer et d'identifier l'ensemble des composés présents. Les fractions pétrolières sont donc actuellement caractérisées soit via des descripteurs macroscopiques moyens (densité, analyse élémentaire, résonance magnétique nucléaire, etc.), soit à l'aide de techniques séparatives (distillation, chromatographie gaz ou liquide, spectrométrie de masse, etc.) qui ne quantifient cependant que quelques grandes familles de molécules. Les méthodes de reconstruction de coupes pétrolières sont des approches informatiques qui permettent d'évoluer vers un détail plus moléculaire en se basant sur le principe suivant : définir des mélanges simplifiés mais cohérents de composés chimiques à partir de données analytiques parcellaires et de connaissances expertes du procédé étudié. Ainsi, la méthode de reconstruction par maximisation d'entropie, proposée dans cet article, est une technique récente et puissante permettant de déterminer les fractions molaires d'un mélange préétabli de composés chimiques en maximisant un critère entropique et en respectant les contraintes analytiques fixées par le modélisateur. L'utilisation de cette méthodologie permet de réduire le nombre de degrés de liberté du système de quelques milliers (correspondant aux fractions molaires des composés) à quelques dizaines (correspondant aux paramètres de Lagrange associés aux contraintes analytiques) et ainsi de diminuer fortement le temps de calcul nécessaire à la résolution du problème. Cette approche a été appliquée avec succès à la reconstruction d'essences de FCC en prédisant précisément la composition moléculaire de ce type de coupes pétrolières à partir d'une distillation simulée et d'une analyse PIONA globale (Paraffines, Isoparaffines, Oléfines, Naphtènes et Aromatiques). L'extension à d'autres types de naphtas (naphtas Straight Run, naphtas de Coker, naphtas hydrotraités, etc.) est très aisée. Abstract

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“…To appreciate the potential of this modeling approach, it is helpful to understand its historical development. Reaction network modeling in the fields of chemical and petroleum engineering has progressed greatly over the past 25 years, including developments in the areas of group contribution methods (10), graph theory (11,12), Monte Carlo techniques (13)(14)(15), and quantum chemistry (16,17).…”

Section: A Ray Of Hope For Complex Chemical Mixtures: Reaction Networmentioning

confidence: 99%

Application of biologically based computer modeling to simple or complex mixtures.

Liao

Dobrev

Dennison

et al. 2002

Environ Health Perspect

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Representation of the Molecular Structure of Petroleum Resid through Characterization and Monte Carlo Modeling

Cited by 64 publications

References 0 publications

Development of an Experimental Database and Chemical Kinetic Models for Surrogate Gasoline Fuels

Development of an Experimental Database and Chemical Kinetic Models for Surrogate Gasoline Fuels

Reconstruction of Petroleum Feedstocks by Entropy Maximization. Application to FCC Gasolines

Application of biologically based computer modeling to simple or complex mixtures.

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