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Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion

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Abstract

A methodology for kinetic modeling of conversion processes is presented. The proposed approach allows to overcome the lack of molecular detail of the petroleum fractions and to simulate the reactions of the process by means of a two-step procedure. In the first step, a synthetic mixture of molecules representing the feedstock is generated via a molecular reconstruction method, termed SR-REM molecular reconstruction. In the second step, a kinetic Monte Carlo method, termed stochastic simulation algorithm (SSA), is used to simulate the effect of the conversion reactions on the mixture of molecules. The resulting methodology is applied to the Athabasca vacuum residue hydrocracking. An adequate molecular representation of the vacuum residue is obtained using the SR-REM algorithm. The reaction simulations present a good agreement with the laboratory data for Athabasca vacuum residue conversion. In addition, the proposed methodology provides the molecular detail of the vacuum residue conversion throughout the reactions simulations.

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References

  1. Rana MS, Sámano V, Ancheyta J, Diaz JAI. A review of recent advances on process technologies for upgrading of heavy oils and residua. Fuel, 2007, 86: 1216–1231

    Article  CAS  Google Scholar 

  2. Boduszynski MM. Composition of heavy petroleums. 1. Molecular weight, hydrogen deficiency, and heteroatom concentration as a function of atmospheric equivalent boiling point up to 1400 F (760 °C). Energy Fuels, 1987, 1: 2–11

    Article  CAS  Google Scholar 

  3. Merdrignac I, Espinat D. Physicochemical characterization of petroleum fractions: The state of the art. Oil Gas Sci Technol, 2007, 62: 7–32

    Article  CAS  Google Scholar 

  4. McKenna AM, Blakney GT, Xian F, Glaser PB, Rodgers RP, Marshall AG. Heavy petroleum composition. 2. Progression of the Boduszynski model to the limit of distillation by ultrahigh-resolution FT-ICR mass spectrometry. Energy Fuels, 2010, 24: 2939–2946

    Article  CAS  Google Scholar 

  5. Liguras DK, Allen DT. Structural models for catalytic cracking. 2. Reactions of simulated oil mixtures. Ind Eng Chem Res, 1989, 28: 674–683

    Article  CAS  Google Scholar 

  6. Quann RJ, Jaffe SB. Structure-oriented lumping: Describing the chemistry of complex hydrocarbon mixtures. Ind Eng Chem Res, 1992, 31: 2483–2497

    Article  CAS  Google Scholar 

  7. Neurock M, Nigam A, Trauth DM, Klein MT. Molecular representation of complex hydrocarbon feedstocks through efficient characterization and stochastic algorithms. Chem Eng Sci, 1994, 49: 4153–4177

    Article  CAS  Google Scholar 

  8. Trauth DM, Stark SM, Petti TF, Neurock M, Klein MT. Representation of the molecular structure of petroleum resid through characterization and Monte Carlo modeling. Energy Fuels, 1994, 8: 576–580

    Article  CAS  Google Scholar 

  9. Quann RJ, Jaffe SB. Building useful models of complex reaction systems in petroleum refining. Chem Eng Sci, 1996, 51: 1615–1622

    Article  CAS  Google Scholar 

  10. Campbell DM, Klein MT. Construction of a molecular representation of a complex feedstock by Monte Carlo and quadrature methods. Appl Catal A, 1997, 160: 41–49

    Article  CAS  Google Scholar 

  11. Khorasheh F, Khaledi R, Gray MR. Computer generation of representative molecules for heavy hydrocarbon mixtures. Fuel, 1998, 77: 247–253

    Article  CAS  Google Scholar 

  12. Zhang Y. A Molecular Approach For Characterisation and Property Predictions of Petroleum Mixtures with Applications to Refinery Modeling. Doctor Dissertation. University of Manchester, 1999

    Google Scholar 

  13. Hudebine D, Vera C, Wahl F, Verstraete JJ. Molecular representation of hydrocarbon mixtures from overall petroleum analyses. In: AIChE Spring Meeting. New Orleans, USA, 2002

  14. Hudebine D, Verstraete JJ. Molecular reconstruction of LCO gas oils from overall petroleum analyses. Chem Eng Sci, 2004, 59: 4755–4763

    Article  CAS  Google Scholar 

  15. Verstraete JJ, Revellin N, Dulot H. Molecular reconstruction of vacuum gas oils. Prepr Pap-Am Chem Soc, Div Fuel Chem, 2004, 49: 20–21

    CAS  Google Scholar 

  16. van Geem KM, Hudebine D, Reyniers MF, Wahl F, Verstraete JJ, Marin GB. Molecular reconstruction of naphtha steam cracking feedstocks based on commercial indices. Comput Chem Eng, 2007, 31: 1020–1028

    Article  Google Scholar 

  17. Gomez-Prado J, Zhang N, Theodoropoulos C. Characterisation of heavy petroleum fractions using modified molecular-type homologous series (MTHS) representation. Energy, 2008, 33: 974–987

    Article  CAS  Google Scholar 

  18. Verstraete JJ, Schnongs P, Dulot H, Hudebine D. Molecular reconstruction of heavy petroleum residue fractions. Chem Eng Sci, 2010, 65: 304–312

    Article  CAS  Google Scholar 

  19. Lopéz-García C, Hudebine D, Schweitzer JM, Verstraete JJ, Ferré D. In-depth modeling of gas oil hydrotreating: From feedstock reconstruction to reactor stability analysis. Catal Today, 2010, 150: 279–299

    Article  Google Scholar 

  20. Ahmad MI, Zhang N, Jobson M. Molecular components-based representation of petroleum fractions. Chem Eng Res Des, 2011, 89: 410–420

    Article  CAS  Google Scholar 

  21. Hudebine D, Verstraete JJ. Reconstruction of petroleum feedstocks by entropy maximization. Application to FCC gasolines. Oil Gas Sci Technol, 2011, 66: 437–460

    Article  CAS  Google Scholar 

  22. Hudebine D, Verstraete JJ, Chapus T. Statistical reconstruction of gas oil cuts. Oil Gas Sci Technol, 2011, 66: 461–477

    Article  CAS  Google Scholar 

  23. Charon-Revellin N, Dulot H, Lopéz-García C, Jose J. Kinetic modeling of vacuum gas oil hydrotreatment using a molecular reconstruction approach. Oil Gas Sci Technol, 2011, 66: 479–490

    Article  CAS  Google Scholar 

  24. Pyl SP, Hou Z, van Geem KM, Reyniers MF, Marin GB, Klein MT. Modeling the composition of crude oil fractions using constrained homologous series. Ind Eng Chem Res, 2011, 50: 10850–10858

    Article  CAS  Google Scholar 

  25. Pereira de Oliveira L, Verstraete JJ, Kolb M. A Monte Carlo modeling methodology for the simulation of hydrotreating processes. Chem Eng J, 2012, 207-208: 94–102

    Article  CAS  Google Scholar 

  26. Pereira de Oliveira L, Trujillo Vazquez A, Verstraete JJ, Kolb M. Molecular reconstruction of petroleum fractions: Application to various vacuum residues. Energy Fuels, 2013, 27: 3622–3641

    Article  CAS  Google Scholar 

  27. Rodgers RP, McKenna AM. Petroleum analysis. Anal Chem, 2011, 83: 4665–87

    Article  CAS  Google Scholar 

  28. Shannon CE. A mathematical theory of communication. The Bell System Technical Journal. 1948, 27: 379–423, 623–656

    Article  Google Scholar 

  29. Gillespie DT. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys, 1976, 22: 403–434

    Article  CAS  Google Scholar 

  30. Gillespie DT. A rigorous derivation of the chemical master equation. Physica A, 1992, 188: 404–425

    Article  CAS  Google Scholar 

  31. McDermott JB, Libanati C, LaMarca C, Klein MT. Quantitative use of model compound information: Monte Carlo simulation of the reactions of complex macromolecules. Ind Eng Chem Res, 1990, 29: 22–29

    Article  CAS  Google Scholar 

  32. Boduszynski MM. Composition of heavy petroleums. 2. Molecular characterization. Energy Fuels, 1988, 2: 597–613

    Article  CAS  Google Scholar 

  33. Boduszynski MM, Altgelt KH. Composition of heavy petroleums. 4. Significance of the extended atmospheric equivalent boiling point (AEBP) scale. Energy Fuels, 1992, 6: 72–76

    Article  CAS  Google Scholar 

  34. Speight JG. The Chemistry and Technology of Petroleum. 2nd ed. New York: CRC Press, 1991

    Google Scholar 

  35. Sheu EY. Petroleum asphaltene properties, characterization, and issues. Energy Fuels, 2002, 16: 74–82

    Article  CAS  Google Scholar 

  36. Liu Y, Gao L, Wen L, Zong B. Recent advances in heavy oil hydroprocessing technologies. Recent Patents Chem Eng, 2010, 2: 22–36

    Article  Google Scholar 

  37. Verstraete JJ, Guillaume D, Roy Auberger M. Catalytic hydrotreatment and hydroconversion: Fixed bed, moving bed, ebullated bed and entrained bed. In: Huc AY. Heavy Crude Oil-From Geology to Upgrading an Overview. 1st ed. Paris: Editions Technip, 2011

    Google Scholar 

  38. Danial-Fortain P, Gauthier T, Merdrignac I, Budzinski H. Reactivity study of SARA fraction under deep hydroconversion conditions. In: 8th World Congress of Chemical Engineering. Montréal, Canada, 2009

    Google Scholar 

  39. Goldberg DE, Deb K, Clark JH. Genetic algorithms, noise, and the sizing of populations. Complex Syst, 1992, 6: 333–362

    Google Scholar 

  40. Parker JR. Genetic algorithms for continuous problems. In: Advances in Artificial Intelligence. Berlin Heidelberg: Springer, 2002. 176–184

    Google Scholar 

  41. API procedure 2B2.1 for estimating the molecular weight of a petroleum fraction. In: API Technical Handbook. New York: API, 1987

  42. Stanislaus A, Cooper BH. Aromatic hydrogenation catalysis: A review. Catal Rev, 1994, 36: 75–123

    Article  CAS  Google Scholar 

  43. Korre SC, Neurock M, Klein MT, Quann RJ. Hydrogenation of polynuclear aromatic hydrocarbons. 2. Quantitative structure/reactivity correlations. Chem Eng Sci, 1994, 49: 4191–4210

    Article  CAS  Google Scholar 

  44. Korre SC, Klein MT. Effect of temperature on quantitative structure/ reactivity relationships for hydrocracking polynuclear aromatics. Prepr Pap-Am Chem Soc, Div Fuel Chem, 1995: 956–961

    Google Scholar 

  45. Girgis MJ, Gates BC. Reactivities, reaction networks, and kinetics in high-pressure catalytic hydroprocessing. Ind Eng Chem Res, 1991, 30: 2021–2058

    Article  CAS  Google Scholar 

  46. Whitehurst DD, Isoda T, Mochida I. Present state of the art and future challenges in the hydrodesulfurization of polyaromatic sulfur compounds. Adv Catal, 1998, 42: 345–471

    Article  CAS  Google Scholar 

  47. Vrinat ML. The kinetics of the hydrodesulfurization process: A review. Appl Catal, 1983, 6: 137–158

    Article  CAS  Google Scholar 

  48. Verstraete JJ, Le Lannic K, Guibard I. Modeling fixed-bed residue hydrotreating processes. Chem Eng Sci, 2007, 62: 5402–5408

    Article  CAS  Google Scholar 

  49. Danial-Fortain P. Étude de la réactivité des résidus pétroliers en hydroconversion. Doctor Dissertation. Bordeaux: Université Bordeaux 1, 2010

    Google Scholar 

  50. López-García C. Analyse de la réactivité des composés soufrés dans les coupes pétrolières: Cinétique et modélisation de l’hydrotraitement. Doctor Dissertation. Lyon I: Université Claude Bernard, 2000

    Google Scholar 

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Authors and Affiliations

  1. IFP Energies nouvelles, Rond-point de l’échangeur de Solaize, BP 3, 69360, Solaize, France

    Luís Pereira de Oliveira & Jan J. Verstraete

  2. Laboratoire de Chimie, École Normale Supérieure, 69364, Lyon Cedex 07, France

    Luís Pereira de Oliveira & Max Kolb

Authors
  1. Luís Pereira de Oliveira
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  2. Jan J. Verstraete
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  3. Max Kolb
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Correspondence to Jan J. Verstraete.

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de Oliveira, L.P., Verstraete, J.J. & Kolb, M. Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion. Sci. China Chem. 56, 1608–1622 (2013). https://doi.org/10.1007/s11426-013-4989-3

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  • DOI: https://doi.org/10.1007/s11426-013-4989-3

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