Methods for Modeling the Process of High-Temperature Conversion of Natural Gas

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Abstract

This review article describes modern methods for modeling the kinetics of high-temperature oxygen-free conversion of methane to acetylene. Published studies on both fundamental aspects of the reaction mechanism and applied methods of computer modeling are analyzed. Particular attention is paid to modern computational methods, such as numerical methods for solving systems of differential equations, quantum chemical methods, and machine learning methods. The influence of key process parameters on the yield of the target product and the selectivity of the reaction is considered. It is shown that the combination of traditional methods of chemical kinetics with modern computational technologies offers opportunities for optimizing laboratory and, in the future, industrial processes for obtaining acetylene from natural gas.

About the authors

D. A Lgotina

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Email: lgotinada@ihcp.ru
Omsk, Russia

R. A Sukhachev

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

A. A Chubarova

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

D. A Sergeicheva

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

L. N Stepanova

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

Yu. G Malinovsky

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

P. V Prudnikov

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

A. V Lavrenov

Center for New Chemical Technologies, IC SB RAS, Institute of Catalysis, SB RAS

Omsk, Russia

References

  1. https://www.iea.org/reports/gas-market-report-q1-2025#overview (дата обращения: 06.08.2025)
  2. https://www.iea.org/reports/gas-market-report-q1-2025#overview (accessed August 6, 2025)
  3. Аршинов И.С., Жагфаров Ф.Г., Мамаев А.В., Мирошниченко Д.А. Выбор наиболее эффективного пути переработки метана в условиях отечественного рынка // НефтеГазоХимия. 2023. № 1. С. 5.
  4. Arshinov I.S., Zhagfarov F.G., Mamaev A.V., and Miroshnichenko D.A. Choosing the most effective way to process methane in the domestic market. NefteGazKhimiya. 2023, no. 1, p. 5.
  5. Беденко С.П., Дементьев К.И., Максимов А.Л. Современные процессы получения продуктов нефтехимии из ацетилена (обзор) // Нефтехимия. 2022. Т. 62. № 6. С. 727.
  6. Bedenko S.P., Dementiev K.I., and Maximov A.L. Modern processes for obtaining petrochemical products from acetylene (review). Petrochemistry. 2022, vol. 62, no. 6, p. 727.
  7. Wang B., He L., Yuan X-C., Sun Z-M., Liu P. Carbon emissions of coal supply chain: An innovative perspective from physical to economic // Journal of Cleaner Production. 2021. V. 295. Р. 126377.
  8. Zhang Q., Wang J., Wang T. Enhancing the Acetylene Yield from Methane by Decoupling Oxidation and Pyrolysis Reactions: A Comparison with the Partial Oxidation Process // Ind. Eng. Chem. Res. 2016. V. 55. N 30. P. 8383.
  9. Литуновский В.Н., Карпов Д.А. Способ и мобильное устройство для утилизации метана из неконтролируемых источников: Патент 2646607 C1 РФ. 2018.
  10. Litunovsky V.N. and Karpov D.A. Method and mobile device for utilizing methane from uncontrolled sources: RF patent 2646607 C1. 2018.
  11. Голинский Д.В., Виниченко Н.В., Затолокина Е.В., Пашков В.В., Паукштис Е.А., Гуляева Т.И., Павлюченко П.Е., Кроль О.В., Белый А.С. Современные катализаторы и способы неокислительного превращения метана // Рос. хим. ж. (Ж. Рос. хим. об-ва им. Д.И. Менделеева). 2018. Т. LXII. № 1-2. С. 55.
  12. Golinsky D.V., Vinichenko N.V., Zatolokina E.V., Pashkov V.V., Paukshtis E.A., Gulyayeva T.I., Pavlyuchenko P.E., Krol O.V., and Belyi A.S. Modern catalysts and methods for non-oxidative conversion of methane. Russian Chemical Journal (Journal of the Russian Chemical Society named after D.I. Mendeleev). 2018, vol. LXII, no. 1–2, p. 55.
  13. Khrabry A., Kaganovich I.D., Barsukov Y., Raman S., Turkoz E., Graves D. Compact and accurate chemical mechanism for methane pyrolysis with PAH growth // Int. J. Hydrogen Energy. 2024. V. 56. N 1. P. 1340.
  14. Шляпин Д.А., Афонасенко Т.Н., Глыздова Д.В., Леонтьева Н.Н., Лавренов А.В. Технологии производства ацетилена в XXI веке. Основные тенденции их развития в парадигме низкоуглеродной экономики будущего // Катализ в промышленности. 2022. Т. 22. № 1. С. 20.
  15. Shlyapin D.A., Afonasenko T.N., Glyzdova D.V., Leontieva N.N., and Lavrenov A.V. Acetylene production technologies in the 21st century. Main trends in their development in the paradigm of a low-carbon economy of the future. Catalysis in Industry. 2022, vol. 22, no. 1, p. 20.
  16. Арсентьев И.В., Кобцев В.Д., Козлов Д.Н., Смирнов В.В., Гелиев А.В., Варюхин А.Н. Бескислородная конверсия метана для получения водорода: исследования и разработки - современное состояние // Газовая промышленность. 2023. № 2(845). С. 62.
  17. Arsentiev I.V., Kobtsev V.D., Kozlov D.N., Smirnov V.V., Geliev A.V., and Varyukhin A.N. Oxygen-free conversion of methane to produce hydrogen: research and development – current status. Gas Industry. 2023, no. 2(845). p. 62.
  18. Zhang T. Recent advances in heterogeneous catalysis for the nonoxidative conversion of methane // Chem. Sci. 2021. V. 12. P. 12529.
  19. Westbrook C.K., Pitz W.J., Curran H.J., Mehl M. The Role of Comprehensive Detailed Chemical Kinetic Reaction Mechanisms in Combustion Research: Tech. Rep. LLNL-BOOK-405431. Lawrence Livermore National Laboratory. 2008.
  20. Xu R., Meisner J., Chang A.M., Thompson K.C., Martínez T.J. First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis // Chem. Sci. 2023. V. 14. N 27. P. 7447.
  21. Bilera I., Lebedev Yu.A., Titov A., Epstein I.L. Simulation of Acetylene Formation from Methane in a Plasma Jet // Химия высоких энергий. 2024. Т. 58. № 3. С. 221.
  22. Dutta S.K., Ghosh S., Metiu H., Agarwal V. Nascent Decomposition Pathways of CH4 Pyrolysis in Gas-Phase Metal Halides // J. Phys. Chem. A. 2022. V. 126. N 35. P. 5900.
  23. Hohenberg P., Kohn W. Inhomogeneous electron gas // Phys. Rev. 1964. V. 136. N 3B. P. B864.
  24. Kohn W., Sham L.J. Self-Consistent equations including exchange and correlation effects // Phys. Rev. 1965. V. 140. N 4A. P. A1133.
  25. Hidaka Y., Nakamura T., Tanaka H., Inami K., Kawano H. High temperature pyrolysis of methane in shock waves. Rates for dissociative recombination reactions of methyl radicals and for propyne formation reaction // Int. J. Chem. Kinet. 1990. V. 22. N 7. P. 701.
  26. Li K., Li H., Yan N., Wang T., Li W., Song Q. Conversion of methane to benzene in CVI by density functional theory study // Sci. Rep. 2019. V. 9. P. 19496.
  27. An H., Cheng Y., Li T., Li Y. Numerical analysis of methane pyrolysis in thermal plasma for selective synthesis of acetylene // Fuel Process. Technol. 2018. V. 172. P. 195.
  28. Scapinello M., Delikonstantis E., Stefanidis G.D. A study on the reaction mechanism of non-oxidative methane coupling in a nanosecond pulsed discharge reactor using isotope analysis // Chem. Eng. J. 2019. V. 360. P. 64.
  29. Гартман Т.Н., Советин Ф.С., Новикова Д.К. Опыт применения программы CHEMCAD для моделирования реакторных процессов // Теоретические основы химической технологии. 2009. Т. 43. № 6. С. 702.
  30. Gartman T.N., Sovetin F.S., and Novikova D.K. Experience in using the CHEMCAD program for modeling reactor processes. Theoretical Foundations of Chemical Technology. 2009, vol. 43, no. 6, p. 702.
  31. Zhang Q., Wang J., Wang T. Enhancing the Acetylene Yield from Methane by Decoupling Oxidation and Pyrolysis Reactions: A Comparison with the Partial Oxidation Process // Ind. Eng. Chem. Res. 2016. V. 55. N 30. P. 8383.
  32. Wang H., Frenklach M. A detailed kinetic modeling study of aromatics formation in laminar premixed acetylene and ethylene flames // Combust. Flame. 1997. V. 110. N 1-2. P. 173.
  33. Ягодницына А.А., Ковалев А.В., Бильский А.В. Экспериментальное исследование гидродинамики и массообмена в микроканалах для проектирования микрореакторов и микроэкстракторов // Теоретические основы химической технологии. 2024. Т. 58. № 3. C. 278.
  34. Yagodnitsyna A.A., Kovalev A.V., and Bilsky A.V. Experimental study of hydrodynamics and mass transfer in microchannels for the design of microreactors and microextractors. Theoretical Foundations of Chemical Technology. 2024, vol. 58, no. 3, p. 278.
  35. Ibrahim S.M., Najmi M.I. Computational Fluid Dynamics (CFD) Optimization in Smart Factories: AI-Based Predictive Modelling // Journal of Technology Informatics and Engineering. 2025. V. 4. N 1. P. 56.
  36. Keith J.A., Carter-Fenk R.J., Stevenson T.S. Machine learning inquantum chemistry: Scope and opportunities // Chemical Reviews. 2021. V. 121. N 16. P. 9816.
  37. Gaussian 09, Revision D.01, Frisch M.J. et al., Gaussian, Inc., Wallingford CT, 2013.
  38. Jaguar, version 7.9, Schrödinger, LLC, New York, NY, 2012
  39. ADF2017, SCM, Theoretical Chemistry, Vrije Universiteit, Amsterdam, The Netherlands, http://www.scm.com (дата обращения: 06.08.2025)
  40. Gordon M.S., Schmidt M.W. Advances in electronic structure theory: GAMESS a decade later. In Dykstra C.E., Frenking G., Kim K.S., Scuseria G.E. (Eds.), Theory and Applications of Computational Chemistry. 2005. P. 1167–1189.
  41. Laikov D.N., Ustynyuk Y.A. PRIRODA-04: a quantum-chemical program suite. New possibilities in the study of molecular systems with the application of parallel computing // Russ Chem Bull. 2005. V. 54. P. 820.
  42. Atanasov M., Ganyushin D., Pantazis D.A., Sivalingam K., Neese, F. Detailed Ab Initio First-Principles Study of the Magnetic Anisotropy in a Family of Trigonal Pyramidal Iron(II) Pyrrolide Complexes // Inorg. Chem. 2011. V. 50. P. 7460.
  43. https://www.vasp.at/ (дата обращения: 06.08.2025)
  44. https://www.vasp.at/ (accessed August 6, 2025)
  45. https://www.cp2k.org/ (дата обращения: 06.08.2025)
  46. https://www.cp2k.org/ (accessed August 6, 2025)
  47. Faizan M., Pawar R.P. Pentazole (N5H) as a possible catalyst for CO2 activation: Density functional theory (DFT) and ab initio molecular dynamics (AIMD) studies // J. Phys. Org. Chem. 2021. V. 35. N 3. P. e4303.
  48. Wang T., He X., Li M., Li Y., Bi R., Wang Y., Cheng C., Shen X., Meng J., Zhang H., Liu H., Wang Z., Li S., Shao B., Liu T.-Y. Ab initio characterization of protein molecular dynamics with AIBMD // Nature. 2024. V. 635. P. 1019.
  49. Jonsson H., Mills G., Jacobsen K.W. Nudged elastic band method for finding minimum energy paths of transitions. Classical and Quantum Dynamics in Condensed Phase Simulations. 1998. P. 385.
  50. https://www.lammps.org (дата обращения: 06.08.2025)
  51. Yi J., Bai J., Zhang H., Kang L., Zhang Z. Development of a ReaxFF force field for simulation of coal molecular structures // AIP Advances. 2025. V. 15. N 3. P.035154.
  52. Chenoweth K., Van Duin A.C.T., Goddard W.A.I. ReaxFF Reactive Force Field for Molecular Dynamics Simulations of Hydrocarbon Oxidation // J. Phys. Chem. A. 2008. V. 112. N 5. P. 1040.
  53. https://manual.gromacs.org/current/index.html (дата обращения: 06.08.2025)
  54. https://manual.gromacs.org/current/index.html (accessed August 6, 2025)
  55. https://cantera.org/ (дата обращения: 06.08.2025)
  56. https://cantera.org/ (accessed August 6, 2025)
  57. Chemkin-Pro, Reaction Design, San Diego, 2009. https://cae-expert.ru/product/ansys-fluent (дата обращения: 06.08.2025)
  58. Chemkin-Pro, Reaction Design, San Diego, 2009. https://cae-expert.ru/product/ansys-fluent (accessed August 6, 2025)
  59. http://www.comsol.com (дата обращения: 06.08.2025)
  60. http://www.comsol.com (accessed August 6, 2025)
  61. Abadi M., Agarwal A., Barham P., et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015, tensorflow.org. (дата обращения: 06.08.2025)
  62. https://pytorch.org/ (дата обращения: 06.08.2025)
  63. https://pytorch.org/ (accessed August 6, 2025)
  64. https://scikit-learn.ru/stable/ (дата обращения: 06.08.2025)
  65. https://scikit-learn.ru/stable/ (accessed August 6, 2025)
  66. Kang Y., Tan Y., Zhao J., Yang K., Liu W., Yue S. End-point carbon content correction in converter steelmaking based on the Kalman filter and CBR model // Ironmak. Steelmak. 2024.
  67. Lafifi I., Khatmi D. Theoretical Investigation of the Intramolecular H-Bonding on Tautomerism // Adv. Quantum Chem. 2014. V. 68. P. 257.
  68. Zhao Y., Truhlar D.G. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals // Theor. Chem. Acc. 2008. V. 120. N 1-3. P. 215.
  69. Sun J., Ruzsinszky A., Perdew J.P. Strongly Constrained and Appropriately Normed Semilocal Density Functional // Phys. Rev. Lett. 2015. V. 115. N 3. P. 036402.
  70. Zhang Y., Kitchaev D.A., Yang J., Chen T., Dacek S.T., Sarmiento-Pérez R.A., Marques M.A.L., Peng H., Ceder G., Perdew J.P., Sun J. Efficient first-principles prediction of solid stability: Towards chemical accuracy // npj Comput. Mater. 2018. V. 4. P. 9.
  71. Ермаков А.И., Белоусов В.В. Релаксация функций базисных наборов STO-3G и 6-31G* в ряду изоэлектронных LiF молекул элементов второго периода // Журнал структурной химии. 2007. Т. 48. № 1. С. 11.
  72. Ermakov A.I., Belousov V.V. Relaxation of STO-3G and 6-31G* basis sets in a series of isoelectronic LiF molecules of second period elements. Journal of Structural Chemistry. 2007, vol. 48, no. 1, p. 11.
  73. Стариков А.Г., Старикова А.А., Минкин В.И. Квантово-химическое изучение строения и устойчивости димеров бис-дикетонатов меди(II) // Координационная химия. 2021. Т. 47. № 3. С. 147.
  74. Starikov A.G., Starikova A.A., and Minkin V.I. Quantum chemical study of the structure and stability of copper(II) bis-diketone dimers. Coordination Chemistry. 2021, vol. 47, no. 3, p. 147.
  75. Chiodo S.G., Russo N., Sicilia E. LANL2DZ basis sets recontracted in the framework of density functional theory // J. Chem. Phys. 2006. V. 125. N 10. P. 104107.
  76. Grimme S., Ehrlich S., Goerigk L. Effect of the Damping Function in Dispersion Corrected Density Functional Theory // J. Comput. Chem. 2011. V. 32. N 7. P. 1456.
  77. Park S.K., Varghese S., Kim J.H., Yoon S.-J., Kwon O.K., An B.K., Gierschner J., Park S.Y. Tailor-made highly luminescent and ambipolar transporting organic mixed stacked charge-transfer crystals: an isometric donor-acceptor approach // J. Am. Chem. Soc. 2013. V. 135. N 12. P. 4757.
  78. Cramer C. J. Essentials of Computational Chemistry: Theories and Models. 2nd ed. Hoboken: John Wiley & Sons. 2004. P. 607.
  79. Jensen F. Introduction to Computational Chemistry. 2nd ed. John Wiley & Sons. 1999. P. 620.
  80. O'Carroll D., English N.J. A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle // Appl. Sci. 2022. V. 12. N 2. P. 780.
  81. Lambie S., Kats D., Usvyat D., Alavi A. On the applicability of CCSD(T) for dispersion interactions in large conjugated systems // J. Chem. Phys. 2025. V. 162. N 11. P. 114112.
  82. Giussani A., Segarra-Martí J. XMS-CASPT2//XMS-CASPT2 and XMS-CASPT2//CASSCF at comparison: The impact of dynamic correlation in the excited state optimization of nitronaphthalene // J. Chem. Phys. 2024. V. 161. N 1. P. 011103.
  83. Melo M.C.R., Bernardi R.C., Rudack T., Scheurer M., Riplinger C., Phillips J.C., Maia J.D.C., Rocha G.D., Ribeiro J.V., Stone J.E., Neese F., Schulten K., Luthey-Schulten Z. NAMD goes quantum: an integrative suite for hybrid simulations // Nat. Methods. 2018. V. 15. N 5. P. 351.
  84. Li K., Li H., Yan N., Wang T., Li W., Song Q. Conversion of methane to benzene in CVI by density functional theory study // Sci. Rep. 2019. V. 9. P. 19496.
  85. Canneaux S., Bohr F., Henon E. KiSThelP: A program to predict thermodynamic properties and rate constants from quantum chemistry results // J. Comput. Chem. 2014. V. 35. N 1. P. 82.
  86. Truhlar D.G., Garrett B.C., Klippenstein S.J. Current status of transition-state theory // J. Phys. Chem. 1996. V. 100. N 31. P. 12771.
  87. Guo X., Fang G., Li G., Ma H., Fan H., Yu L., Ma C., Wu X., Deng D., Wei M., Tan D., Si R., Zhang S., Li J., Sun L., Tang Z., Pan X., Bao X. Direct, Nonoxidative Conversion of Methane to Ethylene, Aromatics, and Hydrogen // Science. 2014. V. 344. N 6184. P. 616.
  88. Helsel N., Chowdhury S., Choudhury P. Nonprecious Single Atom Catalyst for Methane Pyrolysis // Molecules. 2024. V. 29. N19. P .4541.
  89. Kresse G., Joubert D. From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method // Phys. Rev. B. 1999. V. 59. N 3. P. 1758.
  90. Heyden A., Bell A.T., Keil F.J. Efficient Methods for Finding Transition States in Chemical Reactions: Comparison of Improved Dimer Method and Partitioned Rational Function Optimization Method // J. Chem. Phys. 2005. V. 123. N 22. P. 224101.
  91. Marcinkowski M.D., Darby M.T., Liu J., Wimble J.M., Lucci F.R., Lee S., Michaelides A., Flytzani-Stephanopoulos M., Stamatakis M., Sykes E.C.H. Pt/Cu Single-Atom Alloys as Coke-Resistant Catalysts for Efficient C-H Activation // Nat. Chem. 2018. V. 10. N 3. P. 325.
  92. Еремин А.В., Дракон А.В., Куликов С.В. Комплексное прямое Монте-Карло и кинетическое моделирование неравновесных процессов во фронте ударной волны // Физико-химическая кинетика в газовой динамике. 2008. Т. 7. С. 7.
  93. Eremin A.V., Drakon A.V., and Kulikov S.V. Comprehensive direct Monte Carlo and kinetic modeling of non-equilibrium processes in shock wave fronts. Physicochemical kinetics in gas dynamics. 2008, vol. 7, p. 7.
  94. Hess F., Over H. Kinetic Monte Carlo simulations of heterogeneously catalyzed oxidation reactions // Catal. Sci. Technol. 2014. V. 4. N 3. P. 583.
  95. Andersen M., Panosetti C., Reuter K. A practical guide to surface kinetic Monte Carlo simulations // Front. Chem. 2019. V. 7. P. 202.
  96. Schulze T.P. Efficient kinetic Monte Carlo simulation // J. Comput. Phys. 2008. V. 227. N 4. P. 2455.
  97. Jansen A.P.J., Lukkien J.J. Dynamic Monte-Carlo simulations of reactions in heterogeneous catalysis // Catal. Today. 1999. V. 53. N 2. P. 259.
  98. Unruean P., Plianwong T., Pruksawan S., Kitiyanan B., Ziff R.M. Kinetic Monte-Carlo Simulation of Methane Steam Reforming over a Nickel Surface // Catalysts. 2019. V. 9. N 11. P. 946.
  99. Sprung C., Arstad B., Olsbye U. Methane steam reforming over a Ni/NiAl2O4 model catalyst-Kinetics // ChemCatChem. 2014. V. 6. N 9. P. 1969.
  100. Chen D., Lødeng R., Svendsen H., Holmen A. Hierarchical multiscale modeling of methane steam reforming reactions // Ind. Eng. Chem. Res. 2011. V. 50. N 5. P. 2600.
  101. Schuetz C.A., Frenklach M. Nucleation of Soot: Molecular Dynamics Simulations of Pyrene Dimerization // Proc. Combust. Inst. 2002. V. 29. P. 2307.
  102. Violi A., Kubota A., Truong T.N., Pitz W.J., Westbrook C.K., Sarofim A.F. A Fully Integrated Kinetic Monte Carlo/Molecular Dynamics Approach for the Simulation of Soot Precursor Growth // Proc. Combust. Inst. 2002. V. 29. P. 2343.
  103. Violi A., Sarofim A.F., Voth G.A. Kinetic Monte Carlo-Molecular Dynamics Approach to Model Soot Inception // Combust. Sci. Technol. 2004. V. 176. P. 991.
  104. Агафонов Г.Л., Власов П.А., Рябиков О.Б., Смирнов В.Н. Детальное кинетическое моделирование процесса сажеобразования: сравнение результатов расчетов методами моментов, секционными методами и дискретным методом Галеркина // Горение и взрыв. 2019. Т. 12. № 4. С. 33.
  105. Agafonov G.L., Vlasov P.A., Ryabikov O.B., and Smirnov V.N. Detailed Kinetic Modeling of the Soot Formation Process: Comparison of Calculation Results Using Moment Methods, Sectional Methods, and Galerkin's Discrete Method. Combustion and Explosion. 2019, vol. 12, no. 4, p. 33.
  106. Frenklach M., Wang H. Detailed Mechanism and Modeling of Soot Particle Formation // Soot Formation in Combustion: Mechanisms and Models / Ed. H. Bockhorn. Berlin: Springer, 1994. V. 59. P. 165.
  107. Harris S.J., Weiner A.M. Surface Growth of Soot Particles in Premixed Ethylene/Air Flames // Combust. Sci. Technol. 1983. V. 31. P. 155.
  108. Lummen N. REAX FF-molecular Dynamics Simulations of Non-oxidative and Non-catalysed Thermal Decomposition of Methane at High Temperatures // Phys. Chem. Chem. Phys. 2010. V. 12. N 28. P. 7873.
  109. Lummen N. Aggregation of Carbon in an Atmosphere of Molecular Hydrogen Investigated by REAXFF-molecular Dynamics Simulations // Comput. Mater. Sci. 2010. V. 49. N 2. P. 243.
  110. Кудинов А.В., Губин С.А., Богданова Ю.А. Моделирование термического разложения метана при постоянных значениях объема и температуры методами молекулярной динамики и термодинамики // Теплофизика высоких температур. 2023. Т. 61. № 4. С. 549.
  111. Kudinov A.V., Gubin S.A., and Bogdanova Yu.A. Modeling of methane thermal decomposition at constant volume and temperature using molecular dynamics and thermodynamics methods. High Temperature Thermophysics. 2023, vol. 61, no. 4, p. 549.
  112. Victorov S.B., El-Rabii H., Gubin S.A., Maklashova I.V., Bogdanova Yu.A. An Accurate Equation-of-state Model for Thermodynamic Calculations of Chemically Reactive Carbon-containing Systems // J. Energ. Mater. 2010. V. 28. N 1. P. 35.
  113. Kang H.S., Lee C.S., Ree T., Ree F.H. A Perturbation Theory of Classical Equilibrium Fluids // J. Chem. Phys. 1985. V. 82. N 1. P. 414.
  114. Liu L., Liu Y., Zybin S.V., Sun H., Goddard III W.A. ReaxFF-lg: Correction of the REAXFF Reactive Force Field for London Dispersion, with Applications to the Equations of State for Energetic Materials // J. Phys. Chem. A. 2011. V. 115. N 40. P. 11016.
  115. Mao Q., Ren Y., Luo K.H., van Duin A. Dynamics and Kinetics of Reversible Homo-molecular Dimerization of Polycyclic Aromatic Hydrocarbons // J. Chem. Phys. 2017. V. 147. P. 244305.
  116. Товбин Ю.К. Методы моделирования химических процессов при повышенных давлениях и теория неидеальных реакционных систем // Теоретические основы химической технологии. 2023. Т. 57. № 6. С. 763.
  117. Tovbin Yu.K. Methods of modeling chemical processes at elevated pressures and the theory of non-ideal reaction systems. Theoretical Foundations of Chemical Technology. 2023, vol. 57, no. 6, p. 763.
  118. Duangdangchote S., Seferos D.S., Voznyy O., Stability and transferability of machine learning force fields for molecular dynamics applications // Digital Discovery. 2024. V. 3. P. 2177.
  119. Köfinger J., Hummer G. Computational Methods Empirical optimization of molecular simulation force fields by Bayesian inference // Eur. Phys. J. B. 2021. V. 94. P. 245.
  120. Pototschnig U., Matas M., Scheiblehner D., Neuschitzer D., Obenaus-Emler R., Antrekowitsch H., Holec D. Predictive Model for Catalytic Methane Pyrolysis // J. Phys. Chem. C. 2024. V. 128. N 22. P. 9034.
  121. Holmen A., Olsvik O., Rokstad O.A. Pyrolysis of natural gas: chemistry and process concepts // Fuel Process. Technol. 1995. V. 42. N 2-3. P. 249.
  122. Лысенко Н.А., Коледина К.Ф. Автоматизация формирования математического описания кинетики для многостадийных химических реакций и численное решение прямой задачи // Advanced Engineering Research. 2023. Т. 23. № 4. С. 398.
  123. Lysenko N.A. and Kaledina K.F. Automation of the formation of a mathematical description of kinetics for multistage chemical reactions and numerical solution of a direct problem. Advanced Engineering Research. 2023, vol. 23, no. 4, p. 398.
  124. Тропин А.В., Спивак С.И. Приближенное аналитическое интегрирование прямой кинетической задачи // Сибирский журнал индустриальной математики. 2007. Т.10. № 4(32). С.135.
  125. Tropin A.V. and Spivak S.I. Approximate analytical integration of a direct kinetic problem. Siberian Journal of Industrial Mathematics. 2007, vol. 10, no. 4(32). p. 135.
  126. Calise F., Cappiello F.L., Cimmino L., Napolitano M., Vicidomini M. Analysis of the Influence of Temperature on the Anaerobic Digestion Process in a Plug Flow Reactor // Thermo. 2022. V. 2. N 2. P. 92.
  127. Yevgenieva Y., Zuyev A., Benner P., Seidel-Morgenstern A. Periodic Optimal Control of a Plug Flow Reactor Model with an Isoperimetric Constraint // J. Optim. Theory Appl. 2024. V. 202. N 2. P. 582.
  128. Власкин М.С., Зайченко В.М., Белов П.В., Григоренко А.В., Курбатова А.И., Еремин А.В., Фортов В.Е. Разложение ацетилена на водород и углерод: опыты с ДВС и эксперименты с проточным реактором // Теоретические основы химической технологии. 2021. Т. 55. № 2. С. 251. https://kinetics.nist.gov/kinetics/index.jsp (дата обращения: 06.08.2025)
  129. Vlaskin M.S., Zaychenko V.M., Belov P.V., Grigorenko A.V., Kurbatova A.I., Eremin A.V., and Fortov V.E. Decomposition of acetylene into hydrogen and carbon: experiments with internal combustion engines and experiments with a flow reactor. Theoretical Foundations of Chemical Technology. 2021, vol. 55, no. 2, p. 251. https://kinetics.nist.gov/kinetics/index.jsp (accessed August 6, 2025)
  130. Новиков А.Е. Расчет дифференциальных уравнений химической кинетики модифицированным методом Ческино // Молодой ученый. 2015. № 11 (91). С. 92.
  131. Novikov A.E. Calculation of differential equations of chemical kinetics using the modified Cheschino method. Young Scientist. 2015, no. 11, (91). p. 92.
  132. Ceschino F., Kuntzman G. Numerical solution of initial value problems. New Jersey: Prentice-Hall, 1966. P. 347.
  133. Merson R.H. An Operational Method for the Study of Integration Processes // Proc. Symp. Data Processing. Weapons Res. Establ. Salisbury. 1957. P. 110.
  134. Хайрер Э., Ваннер Г. Решение обыкновенных дифференциальных уравнений. Жесткие и дифференциально-алгебраические задачи. М.: Мир, 1999. С. 685.
  135. Hayerer E. and Wanner G. Solution of Ordinary Differential Equations. Rigid and Differential-Algebraic Problems. Moscow: Mir, 1999, p. 685.
  136. Билера И.В., Лебедев Ю.А., Титов А.Ю., Эпштейн И.Л. Моделирование образование ацетилена и водорода при плазмохимическом пиролизе метана // Сборник тезисов докладов LI Международной Звенигородской конференции по физике плазмы и УТС. 2024. С. 172.
  137. Biler I.V., Lebedev Yu.A., Titov A.Yu., and Epstein I.L. Modeling of acetylene and hydrogen formation during plasma chemical pyrolysis of methane. Collection of abstracts of reports of the LI International Zvenigorod Conference on Plasma Physics and UTS. 2024, p. 172.
  138. Bharanidharan R. Nonlinear Analysis in Gaseous Dielectric Using Finite Element Method (FEM) // Commun. Appl. Nonlinear Anal. 2025. V. 32. N 6s. P. 250.
  139. Scapinello M., Delikonstantis E., Stefanidis G.D. A study on the reaction mechanism of non-oxidative methane coupling in a nanosecond pulsed discharge reactor using isotope analysis // Chem. Eng. J. 2019. V. 360. P. 64.
  140. Epstein I.L., Lebedev Yu.A., Tatarinov A.V., Bilera I.V. Kinetics of methane conversion in non-equilibrium plasma // J. Phys. D: Appl. Phys. 2018. V. 51. N 21. P. 214007.
  141. Lebedev Yu.A., Tatarinov A.V., Epstein I.L. Plasma-chemical conversion of methane in non-equilibrium conditions // Plasma Chem. Plasma Process. 2019. V. 39. N 4. P. 787.
  142. Zhang H., Wang W., Li X., Han L., Yan M., Zhong Y., Tu X. Plasma activation of methane for hydrogen production in a N2 rotating gliding arc warm plasma: A chemical kinetics study // Chem. Eng. J. 2018. V. 345. P. 67.
  143. Hagelaar G., Pitchford L. Solving the Boltzmann equation to obtain electron transport coefficients and rate coefficients for fluid models // Plasma Sources Sci. Technol. 2005. V. 14. N 4. P. 722.
  144. Amores D.D., Lema E.P., Guerrero L.D., Andaluz V.H., García B.A., Guanopatín A.V., Varela-Aldás J. Virtual control of a perfectly stirred reactor for cyclopentene production // Lect. Notes Comput. Sci. 2021. V. 12980. P. 680.
  145. Дубинин А.М., Тупоногов В.Г., Иконников И.С. Моделирование процесса производства водорода из метана // Теоретические основы химической технологии. 2013. Т. 47. № 6. С. 634.
  146. Dubinin A.M., Tuponogov V.G., and Ikonnikov I.S. Modeling the process of hydrogen production from methane. Theoretical Foundations of Chemical Technology. 2013, vol. 47, no. 6, p. 634.
  147. Kassel L.I. The thermal decomposition of methane // J. Am. Chem. Soc. 1932. V. 54. N 9. P. 3949.
  148. Dors M., Nowakowska H., Jasinski J., Mizeraczyk J. Chemical kinetics of methane pyrolysis in microwave plasma at atmospheric pressure // Plasma Chem. Plasma Process. 2013. V. 34. N 2. P. 313.
  149. Punith N., Avaneesh A.V., Prasad B., Ravikrishna R.V., Rao L. Unveiling the Impact of Operating Current on Active Species Generation in Pin-To-Water Plasma Activated Water System // Plasma Process. Polym. 2025. V. 22. N 3. P. 2400190. https://cs-gr.ru/soft/chemical-workbench.html (дата обращения: 06.08.2025)
  150. Kang Y., Tan Y., Zhao J., Yang K., Liu W., Yue S. End-point carbon content correction in converter steelmaking based on the Kalman filter and CBR model // Ironmak. Steelmak. 2024.
  151. Тамме Э., Шерман Э. Численное решение уравнения Больцмана для функции распределения электронов по энергии в газовом разряде // Proc. Acad. Sci. Estonian SSR. Phys. Math. 1986. V. 35. N 3. P. 302.
  152. Tamme E. and Sherman E. Numerical solution of the Boltzmann equation for the energy distribution function of electrons in a gas discharge. Proc. Acad. Sci. Estonian SSR. Phys. Math. 1986, vol. 35, no. 3, p. 302.
  153. Сафиуллина Л.Ф., Губайдуллин И.М. Анализ идентифицируемости математической модели пиролиза пропана // Компьютерные исследования и моделирование. 2021. Т. 13. № 5. С. 1045.
  154. Safiullina L.F. and Gubaidullin I.M. Analysis of the identifiability of a mathematical model of propane pyrolysis. Computer Research and Modeling. 2021, vol. 13, no. 5, p. 1045.
  155. Atsi K., Adiku L., Yarima N., Kumlen G.M. A second derivative block method derived from a family of modified backward differentiation formula (BDF) for solving stiff ordinary differential equations // i-manager's J. Math. 2023. V. 11. N 2. P. 8. https://www.mathworks.com/products/matlab.html (дата обращения: 06.08.2025)
  156. Пескова Е.Е., Снытников В.Н. Численное исследование конверсии метановых смесей под воздействием лазерного излучения // Журнал Средневолжского математического общества. 2023. Т. 25. № 3. С. 159.
  157. Peskova E.E. and Snytnikov V.N. Numerical study of methane mixture conversion under the influence of laser radiation. Journal of the Middle Volga Mathematical Society. 2023, vol. 25, no. 3, p. 159.
  158. Губайдуллин И.М., Пескова Е.Е., Язовцева О.С. Математическая модель динамики многокомпонентного газа на примере брутто-реакции пиролиза этана // Огарев-Online. 2016. № 20(85). С. 7.
  159. Gubaidullin I.M., Peskova E.E., and Yazovtseva O.S. Mathematical model of the dynamics of a multicomponent gas using the example of the gross reaction of ethane pyrolysis. Ogarev-Online. 2016, no. 20(85). p. 7.
  160. Nurislamova L.F., Gubaidullin I.M., Novichkova A.V., Stoyanovskaya O.P., Stadnichenko O.A., Snytnikov V.N. Few-step kinetic model of gaseous autocatalytic ethane pyrolysis and its evaluation by means of uncertainty and sensitivity analysis // Chem. Prod. Process Model. 2014. V. 9. N 2. P. 143.
  161. Нигматуллин В.Р., Руднев Н.А. Использование методов машинного обучения и искусственного интеллекта в химической технологии. Часть I // Нефтегазовое дело. 2019. № 4. С. 243.
  162. Nigmatullin V.R. and Rudnev N.A. The use of machine learning and artificial intelligence methods in chemical technology. Part I. Oil and Gas Business. 2019, no. 4, p. 243.
  163. Нигматуллин В.Р., Руднев Н.А. Использование методов машинного обучения и искусственного интеллекта в химической технологии. Часть II // Нефтегазовое дело. 2019. № 5. С. 202.
  164. Nigmatullin V.R. and Rudnev N.A. The use of machine learning and artificial intelligence methods in chemical technology. Part II. Oil and Gas Business. 2019, no. 5, p. 202.
  165. Mowbray M., Vallerio М., Perez-Galvan C., et al. Industrial data science – a review of machine learning applications for chemical and process industries // Reaction Chemistry & Engineering. 2022. V. 7(6). P. 1471.
  166. Sinha A. Application of machine learning for detecting network threats in chemical industry // Eur. Chem. Bull. 2023. V. 12. P. 116.
  167. Galushko A.S., Boiko D.A., Pentsak E.O., Eremin D.B., Ananikov V.P. Time-Resolved Formation and Operation Maps of Pd Catalysts Suggest a Key Role of Single Atom Centers in Cross-Coupling // J. Am. Chem. Soc. 2023. V. 145. N 17. P. 9092.
  168. Eremin D.B., Galushko A.S., Boiko D.A., Pentsak E.O., Chistyakov I.V., Ananikov V.P. Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particles // J. Am. Chem. Soc. 2022. V. 144. N 13. P. 6071.
  169. Boiko D.A., Kozlov K.S., Burykina Yu.V., Ilyushenkova V.V., Ananikov V.P. Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning // J. Am. Chem. Soc. 2022. V. 144. N 32. P. 14590.
  170. Kashin A.S., Prima D.O., Arkhipova D.M., Ananikov V.P. An unusual Microdomain Factor Controls Interaction of Organic Halides with the Palladium Phase and Influences Catalytic Activity in the Mizoroki-Heck Reaction // Small. 2023. P. 2302999.
  171. Ghosh I., Shlapakov N.S., Karl T.A., Düker J., Nikitin M., Burykina J.V., Ananikov V.P., König B. General cross-coupling reactions with adaptive dynamic homogeneous catalysis // Nature. 2023. V. 619. P. 87.
  172. Granda J.M., Donina L., Dragone V., Long D-L., Cronin L. Publisher Correction: Controlling an organic synthesis robot with machine learning to search for new reactivity // Nature. 2018. V. 562. P. E26.
  173. Wu Z. B., Christofides P. D., Wu W., Wang Y., Abdullah Fahim, Alnajdi A., Kadakia Y. A tutorial review of machine learning-based model predictive control methods // Reviews in Chemical Engineering. 2024. V. 41. P. 359.
  174. Wu Z., Albalawi F., Zhang Z., Zhang J., Durand H., Christofides P.D. Control Lyapunov-barrier function-based model predictive control of nonlinear systems // Automatica. 2019. V. 109. P. 108508.
  175. Hirtreiter E., Schulze Balhorn L., Schweidtmann A.M. Toward automatic generation of control structures for process flow diagrams with large language models // AIChE J. 2024. V. 70. P. e18259.
  176. Wu Z., Li M., He Ch., Zhang B., Ren J., Yu H., Chen Q. Physics‐informed learning of chemical reactor systems using decoupling-coupling training framework // AIChE Journal. 2024. V. 70. N 7. P. E18436.
  177. Susits M., Orosz Á., Szilagyi B. PyPINNChem: an Open-source Tool for Chemical Reaction Modeling Using Physics-informed Neural Networks // Elsevier, Forthcoming. 2023. P. 9.
  178. Guo Y., Cao X., Liu B., Gao M. Solving Partial Differential Equations Using Deep Learning and Physical Constraints // Applied Sciences. 2020. V. 10. P. 5917.
  179. Mao Z., Jagtap A.D., Karniadakis G.E. Physics-Informed Neural Networks for High-Speed Flows // Computer Methods in Applied Mechanics and Engineering. 2020. V. 360. P. 112789.
  180. Cai S., Mao Z., Wang Z., Yin M., Karniadakis G.E. Physics-Informed Neural Networks (PINNs) for Fluid Mechanics: A Review // Acta Mechanica Sinica. 2021. V. 37. N 12. P. 1727.
  181. Zhang E., Dao M., Karniadakis G.E., Suresh S. Analyses of internal structures and defects in materials using phy-sics informed neural networks. Science Advances. 2022. V. 8. N 7. P. eabk0644.
  182. Антонов В.Н., Лапидус А.С. Производство ацетилена. – Москва: Химия. 1970. С. 415.
  183. Antonov V.N. and Lapidus A.S. Acetylene production. M.: Chemistry. 1970, p. 415.
  184. Котельников А.А., Долганов И.М. Моделирование процесса пиролиза метана // Химия и химическая технология в XXI веке: материалы XXIII Международной научно-практической конференции студентов и молодых ученых имени выдающихся химиков Л. П. Кулева и Н. М. Кижнера. Томск: Изд-во ТПУ, 2022. Т. 2. С. 64.
  185. Kotelnikov A.A. and Dolganov I.M. Modeling of the methane pyrolysis process. Chemistry and chemical technology in the 21st century: materials of the XXIII International scientific and practical conference of students and young scientists named after the outstanding chemists L.P. Kulev and N.M. Kizner. Tomsk: TPU Publishing House, 2022, vol. 2, p. 64.
  186. Bedarev I.A., Parmon V.N., Fedorov A.V., Fedorova N.N., Fomin V.M. Numerical Study of Methane Pyrolysis in Shock Waves // Combustion, Explosion, and Shock Waves. 2004. V. 40. N 5. P. 580.

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