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

Hudebine, Damien; Verstraete, J.

doi:10.2516/ogst/2011110

Cited by 38 publications

(46 citation statements)

References 14 publications

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“…Generally, N is in the order of 10 2 to 10 6 while J + K + 1 is maximum 13 [11], the number of variables thus is greatly decreased with this method.…”

Section: Optimization Of Mole Fractions Of the Structural Increments mentioning

confidence: 97%

“…However, numerous sampling and a very long simulation time are needed by using this combined MC method to obtain a satisfactory result because of the uncertainties of the stochastic sampling involved. Furthermore, the probability distribution function was determined by hypotheses, which may cause that some molecules are favored over others and also introduce some deviations [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feedstock molecular reconstruction for secondary reactions of fluid catalytic cracking gasoline by maximum information entropy method

Pan

Yang

Zhou

2015

Chemical Engineering Journal

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“…Generally, N is in the order of 10 2 to 10 6 while J + K + 1 is maximum 13 [11], the number of variables thus is greatly decreased with this method.…”

Section: Optimization Of Mole Fractions Of the Structural Increments mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Feedstock molecular reconstruction for secondary reactions of fluid catalytic cracking gasoline by maximum information entropy method

Pan

Yang

Zhou

2015

Chemical Engineering Journal

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“…These deviations may be caused by the group contribution method used to estimate the boiling point. This group contribution method has been developed on the basis of a large database of boiling points, but most of the data available pertain to molecules with less than 42 carbon atoms [24,28]. Therefore, extrapolation to very large compounds may increase the uncertainty of the method.…”

Section: Application To the Athabasca Vacuum Residuementioning

confidence: 99%

“…For this purpose, a molecular reconstruction algorithm, termed SR-REM, has been developed during previous works [21][22][23][24][25]. This algorithm results from the coupling of two methods, Stochastic Reconstruction (SR) [21,26,27] and Reconstruction by Entropy Maximisation (REM) [21,[28][29][30].…”

Section: Molecular Representation Of Vacuum Residuesmentioning

confidence: 99%

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Development of a General Modelling Methodology for Vacuum Residue Hydroconversion

Oliveira

Verstraete

KoIb

2013

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

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Re´sume´-De´veloppement d'une me´thodologie ge´ne´rale de mode´lisation pour l'hydroconversion de re´sidu sous vide -Ce travail porte sur le de´veloppement d'une me´thodologie de mode´lisation cine´tique de proce´de´s de raffinage, et plus particulie`rement de proce´de´s pour la conversion de re´sidus sous vide. L'approche propose´e permet de surmonter le manque de de´tail mole´culaire des fractions pe´trolie`res et de simuler la transformation des mole´cules de la charge en mole´cules de l'effluent au moyen d'une proce´dure en deux e´tapes. Dans la premie`re e´tape, un me´lange synthe´tique de mole´cules repre´sentant la charge du proce´de´est ge´ne´re´par une me´thode de reconstruction mole´culaire appele´e SR-REM. Dans la seconde e´tape, une me´thode de Monte-Carlo cine´tique est utilise´e pour simuler les re´actions de conversion sur ce me´lange de mole´cules. La reconstruction mole´culaire a e´te´applique´e a`plusieurs re´sidus pe´troliers et est illustre´e pour un re´sidu sous vide Athabasca (Canada). La me´thode de Monte-Carlo cine´tique est ensuite de´crite en de´tail. Afin de valider cette approche stochastique, un mode`le de´terministe regroupe´pour la conversion de re´sidu sous vide a e´te´simule´en utilisant l'algorithme de simulation stochastique de Gillespie. Malgre´le fait que les deux approches sont fonde´es sur des hypothe`ses tre`s diffe´rentes, l'algorithme de simulation stochastique simule les re´actions de conversion avec la meˆme pre´cision que l'approche de´terministe. En utilisant des voies re´actionnelles au niveau mole´culaire, l'approche de simulation stochastique fournit ainsi des informations de´taille´es sur la composition de l'effluent et est brie`vement illustre´e pour l'hydrocraquage du re´sidu sous vide d'Athabasca.Abstract -Development of a General Modelling Methodology for Vacuum Residue Hydroconversion -This work concerns the development of a methodology for kinetic modelling of refining processes, and more specifically for vacuum residue conversion. The proposed approach allows to overcome the lack of molecular detail of the petroleum fractions and to simulate the transformation of the feedstock molecules into effluent molecules by means of a two-step procedure. In the first step, a synthetic mixture of molecules representing the feedstock for the process is generated via a molecular reconstruction method, termed SR-REM molecular reconstruction. In the second step, a kinetic Monte-Carlo method (kMC) is used to simulate the conversion reactions on this mixture of molecules. The molecular reconstruction was applied to several petroleum residues and is illustrated for an Athabasca (Canada) vacuum residue. The kinetic Monte-Carlo method is then described in detail. In order to validate this stochastic approach, a lumped deterministic model for vacuum residue conversion was simulated using Gillespie's Stochastic Simulation Algorithm. Despite the fact that both approaches are based on very different hypotheses, the stochastic simulation algorithm simulates

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Mathematical approach to compute the molecular composition of hydrothermal liquefaction‐derived renewable crude oil

et al. 2021

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Hydrothermal liquefaction (HTL), a thermo‐chemical conversion process, uses water as a reaction medium at elevated pressure and temperature, to convert biomass to renewable liquid fuel and recovers fertilizer‐rich water. To assess the techno‐economic screening of HTL oils from various feedstock, it is crucial to have information on molecular composition of the feed and products. There are limitations of existing analytical methods to identify and quantify all the molecules present in the bio‐fuel. Therefore, there is a need to find alternate ways to quantify the molecular composition of feed and expected products. The modeling work on bio‐oil is developed and validated on mathematical approach using simple analytical results like CHNO along with structural analysis of oil like Fourier‐transform infrared, nuclear magnetic resonance analysis for HTL derived oil from microalgae. This mathematical framework is further extended to predict the molecular composition of oil obtained from HTL of feedstocks like mixed plastic waste, sludge, and so on. A multi‐dimensional molecular matrix is developed based on the distributions of side chains, aromatic rings, and olefinic carbon on top of core molecules. The parameters of the distributions are estimated computationally using global optimization algorithm (genetic algorithm) and local optimization algorithm to predict a mixture composition that matches closely with bulk properties of the product.

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

Cited by 38 publications

References 14 publications

Feedstock molecular reconstruction for secondary reactions of fluid catalytic cracking gasoline by maximum information entropy method

Feedstock molecular reconstruction for secondary reactions of fluid catalytic cracking gasoline by maximum information entropy method

Development of a General Modelling Methodology for Vacuum Residue Hydroconversion

Mathematical approach to compute the molecular composition of hydrothermal liquefaction‐derived renewable crude oil

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