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A Review of Kinetic Modeling Methodologies for Complex Processes Luís Pereira De Oliveira, Damien Hudebine, Denis Guillaume, Jan Verstraete

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A Review of Kinetic Modeling Methodologies for Complex Processes Luís Pereira De Oliveira, Damien Hudebine, Denis Guillaume, Jan Verstraete A Review of Kinetic Modeling Methodologies for Complex Processes Luís Pereira de Oliveira, Damien Hudebine, Denis Guillaume, Jan Verstraete To cite this version: Luís Pereira de Oliveira, Damien Hudebine, Denis Guillaume, Jan Verstraete. A Review of Kinetic Modeling Methodologies for Complex Processes. Oil & Gas Science and Technology - Revue d’IFP Energies nouvelles, Institut Français du Pétrole, 2016, 71 (3), pp.45. 10.2516/ogst/2016011. hal- 01395195 HAL Id: hal-01395195 https://hal.archives-ouvertes.fr/hal-01395195 Submitted on 10 Nov 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Oil & Gas Science and Technology – Rev. IFP Energies nouvelles (2016) 71,45 Ó L.P. de Oliveira et al., published by IFP Energies nouvelles, 2016 DOI: 10.2516/ogst/2016011 Dossier Methodology for Process Development at IFP Energies nouvelles Méthodologies pour le développement de procédés à IFP Energies nouvelles A Review of Kinetic Modeling Methodologies for Complex Processes Luís P. de Oliveira, Damien Hudebine, Denis Guillaume and Jan J. Verstraete* IFP Energies nouvelles, Rond-point de l’échangeur de Solaize, BP 3, 69360 Solaize - France e-mail: [email protected] * Corresponding author Abstract — In this paper, kinetic modeling techniques for complex chemical processes are reviewed. After a brief historical overview of chemical kinetics, an overview is given of the theoretical background of kinetic modeling of elementary steps and of multistep reactions. Classic lumping techniques are introduced and analyzed. Two examples of lumped kinetic models (atmospheric gasoil hydrotreating and residue hydroprocessing) developed at IFP Energies nouvelles (IFPEN) are presented. The largest part of this review describes advanced kinetic modeling strategies, in which the molecular detail is retained, i.e. the reactions are represented between molecules or even subdivided into elementary steps. To be able to retain this molecular level throughout the kinetic model and the reactor simulations, several hurdles have to be cleared first: (i) the feedstock needs to be described in terms of molecules, (ii) large reaction networks need to be automatically generated, and (iii) a large number of rate equations with their rate parameters need to be derived. For these three obstacles, molecular reconstruction techniques, deterministic or stochastic network generation programs, and single-event micro-kinetics and/or linear free energy relationships have been applied at IFPEN, as illustrated by several examples of kinetic models for industrial refining processes. Résumé — Une revue de méthodes de modélisation cinétique pour des procédés complexes — Dans cet article, les techniques de modélisation cinétique des processus chimiques complexes sont examinées. Après un bref aperçu historique de la cinétique chimique, un aperçu des bases théoriques de la modélisation cinétique d’étapes élémentaires et de réactions globales est présenté. Les techniques classiques de regroupement (lumping) sont ensuite présentées et analysées. Deux exemples de modèles cinétiques regroupés (pour l’hydrotraitement de gazole atmosphérique et pour l’hydrotraitement de résidus) développés à IFP Energies nouvelles (IFPEN) sont présentés. La plus grande partie de cette revue décrit des stratégies avancées de modélisation cinétique, dans lesquelles le détail moléculaire est retenu : les réactions entre les molécules sont représentées ou même subdivisées en étapes élémentaires. Pour être en mesure de conserver ce niveau moléculaire à la fois dans le modèle cinétique et dans les simulations de réacteurs, plusieurs obstacles doivent d’abord être éliminés : (i) la charge doit être décrite en termes de molécules, (ii) les grands réseaux réactionnels doivent être générés automatiquement et (iii) un grand nombre d’équations de vitesse avec leurs paramètres de vitesse doit être dérivé. Pour ces trois obstacles, des techniques de reconstruction moléculaire, des programmes de génération de réseaux déterministes ou stochastiques, et des modèles microcinétiques basés sur des événements constitutifs (single events) et/ou des This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 2 of 49 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles (2016) 71,45 relations linéaires d’énergie libre (Linear Free Energy Relationships) ont été utilisés à IFPEN, comme illustré par plusieurs exemples de modèles cinétiques pour des procédés de raffinage industriels. LIST OF ABBREVIATIONS NIST National Institute of Standards and Technology NMR Nuclear Magnetic Resonance AEBP Atmospheric Equivalent Boiling Point ODE Ordinary Differential Equation AED Atomic Emission Detector PCP Protonated CycloPropane AGO Atmospheric Gas Oil PCB Protonated CycloButane ARO Aromatics PDE Partial Differential Equation ASP Asphaltenes PDF Probability Distribution Function ASTM American Society for Testing Materials PES Potential Energy Surface BT BenzoThiophene(s) Pr Protonation CARB Carbazoles QFER Quadratic Free-Energy Relationship CME Chemical Master Equation QSAR Quantitative Structure-Activity Relationships DBT DiBenzoThiophene(s) QSRC Quantitative Structure-Reactivity Correlations DFRN Deterministic Full Reaction Network QSS Quasi-Steady State DFT Density Functional Theory RDS Rate Determining Step DH Dehydrogenation REM Reconstruction by Entropy Maximization DI Di-aromatics RES Resins DIPPR Design Institute for Physical PRoperties RRK Rice–Ramsperger–Kassel DLRN Deterministic Limited Reaction Network SARA Saturates/Aromatics/Resins/Asphaltenes FCC Fluid Catalytic Cracking SAT Saturates FID Flame Ionization Detector SCD Sulfur Chemiluminescence Detector FT-ICR-MS Fourier Transform – Ion Cyclotron Resonance SEC Size Exclusion Chromatography – Mass Spectrometry SEK Single Event Kinetics GC Gas Chromatography SLRN Stochastically Limited Reaction Network HDS HydroDeSulfurization SOL Structure Oriented Lumping HPLC High-Performance Liquid Chromatography SR Stochastic Reconstruction HS Hydride Shift SSA Stochastic Simulation Algorithm IUPAC International Union of Pure and Applied TRC Thermodynamics Research Center Chemistry TRI Tri-aromatics kMC Kinetic Monte-Carlo TS Transition State LC Liquid Chromatography TST Transition State Theory LCO Light Cycle Oil VR Vacuum Residue LFER Linear Free-Energy Relationships LHSV Liquid Hourly Space Velocity LSODE Livermore Solver for Ordinary Differential LIST OF SYMBOLS Equations MARI Most Abundant Reaction Intermediate MASI Most Abundant Surface Intermediate Ai Pre-exponential factor for the rate constant of MC Monte-Carlo reaction i MONO Mono-aromatics c0 Unit concentration MS Mass Spectrometry ci Stochastic rate constant for reaction i MTHS Molecular Type and Homologous Series Cj Concentration of species j approach E Shannon’s information entropy criterion NCD Nitrogen Chemiluminescence Detector Ea Activation energy NES Nearly Empty Surface Ei Activation energy for reaction i Oil & Gas Science and Technology – Rev. IFP Energies nouvelles (2016) 71,45 Page 3 of 49 Fk Mixing rule for analytical property k Dn Difference in molecularity between the reverse h Planck’s constant (6.626068 9 10À34 m2 kg sÀ1) and the forward reaction D 6¼ – Ik Intermediate k n 1 molecularity of the reaction that forms the k Rate constant activated complex (TST) ~ D k Intrinsic single-event rate coefficient of the SR Entropy of reaction elementary step DS6¼ Activation entropy (Transition State Theory) k ki Rate constant for reaction i Reorganization energy (Quadratic Free-Energy kB Boltzmann’s constant Relationship) À23 2 À2 À1 (1.3806503 9 10 m kg s K ) mj Stoichiometric coefficient for component j K Number of analytical properties or constraints mij Stoichiometric coefficient for component j in Ka Ionization constant reaction i q ’ Kc Equilibrium constant (concentration basis) H Hammett s sensitivity constant r Keq Overall equilibrium constant i Stoichiometric number for reaction/elementary step i Ki Equilibrium constant for reaction / elementary r step i AB Reaction cross-section (collision theory) r ’ Mj Molecular weight of component j H Hammett s substitution constant rr MAB Reduced molecular weight of components A and Symmetry number for the reactant B (collision theory) r6¼ Symmetry number for the activated complex h ne Number of single events i Reactivity index of molecule i N Number of components hij Reactivity index j of molecule i NA Avogadro constant (6.0221408577 9 1023 molÀ1) NAlkyl Number of alkyl substituents in b position SUBSCRIPTS of a sulfur atom c Concentration basis NAR Number of aromatic rings eq At equilibrium conditions NSR Number of saturated rings glob For global symmetry and chirality NTR Number of thiophene rings i For reaction i P Pressure int Intrinsic Pk Value of analytical property k j For component j r Reaction rate overall For the overall reaction ri Reaction rate of reaction
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