Быстрая молекулярная реконструкция химического состава сложных углеводородных смесей

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Abstract

Предложен новый эвристический подход для проведения стохастической молекулярной реконструкции значительно быстрее. За основу взят двухступенчатый метод, объединяющий стохастическую реконструкцию и реконструкцию максимизацией энтропии. В предложенном методе поиск оптимальных параметров распределений осуществляется при решении нескольких сравнительно простых оптимизационных задач. Предложенный метод позволил реконструировать состав образца вакуумного газойля как минимум в 100 раз быстрее классического подхода с генетическими алгоритмами.

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About the authors

Н. А. Глазов

Институт катализа СО РАН

Author for correspondence.
Email: glazov@catalysis.ru
Russian Federation, Новосибирск

А. Н. Загоруйко

Институт катализа СО РАН

Email: glazov@catalysis.ru
Russian Federation, Новосибирск

References

  1. De Oliveria L., Hudebine D., Guillaume D. A review of kinetic modeling methodologies for Complex Processes // Oil Gas Sci. Technol. 2016. V. 71. P. 45.
  2. Ren Y., Liao Z., Sun J. Molecular reconstruction: Recent progress toward composition modeling of petroleum fractions // J. Chem. Eng. 2019. P. 761.
  3. Neurock M., Nigam A., Trauth D. Molecular representation of complex hydrocarbon feedstocks through efficient characterization and stochastic algorithms // Chem. Eng. Sci. 1994. V. 49. № 24А. P. 4153.
  4. Hudebine D., Verstrate J. Molecular reconstruction of LCO gasoils from overall petroleum analyses// Chem. Eng. Sci. 2004. V. 59. P. 4755.
  5. Hudebine D., Verstraete J. Reconstruction of Petroleum Feedstocks by Entropy Maximization. Application to FCC Gasolines // Oil Gas Sci. Technol. 2011. V. 66. P. 437.
  6. De Oliveria L., Vazquez Trujillo A., Verstrate J. Molecular Reconstruction of Petroleum Fraction: Application to Vacuum Residues from Different Origins // Energy & Fuels. 2013. V. 27. P. 3622.
  7. Alvarez-Majmutov A., Chen J., Gieleciak R. Molecular-Level Modeling and Simulation of Vacuum Gas Oil Hydrocracking // Energy & Fuels. 2016. V. 30. P. 138.
  8. Zhao G., Yang M., Du W. A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha // Chin. J. Chem. Eng. 2022. V. 51. P. 153.
  9. Dantas T., Noriler D., Huziwara K. A multi-populating particle swarm optimization algorithm with adaptive patterns of movement for the stochastic reconstruction of petroleum fractions // Comput. Chem. Eng. 2023. P. 174.
  10. Deniz C.U., Yasar M., Klein M.T. Stochastic Reconstruction of Complex Heavy Oil Molecules using an Artificial Neural Network// Energy & Fuels 2017. V. 31. № 11. P. 11932.
  11. Alvarez-Majmutov A., Gieleciak R., Chen Jinwen. Deriving the Molecular Composition of Vacuum Distillates by Integrating Statistical Modeling and Detailed Hydrocarbon Characterization// Energy & Fuels. 2015. V. 29. № 12. P. 7931.
  12. Skander N., Chitour C.E. A new Group-contribution method for the estimation of Physical properties of Hydrocarbons// Oil Gas Sci. Technol. 2002. V. 57. № 4. P. 369.

Supplementary files

Supplementary Files
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2. Fig. 1. Simulated distillation (ASTM D2887-97a) of the sample.

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3. Fig. 2. Examples of the obtained curves of simulated distillation for reconstructed compositions based on the “non-stochastic” method. Dots – experiment, line – calculation. Each graph corresponds to a different initial approximation.

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4. Fig. 3. Calculated curves of simulated distillation. a – initial approximation, b – after the first iteration, c – after the fifth iteration. Points – experiment, line – calculation using stochastic reconstruction, dotted line – calculation result after entropy maximization.

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