Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis
-
Liana F. Nurislamova
, Olga P. Stoyanovskaya
, Olga A. Stadnichenko , Irek M. Gubaidullin , Valeriy N. Snytnikov und Anastasia V. Novichkova
Abstract
A kinetic scheme of radical chain reactions in autocatalytic pyrolysis of ethane was studied using a sensitivity analysis method, bringing in the experimental data. In the gas-phase kinetic experiments, ethane pyrolysis was carried out in laboratory reactors with the reaction mixture heated by CO2 laser irradiation. It was shown that the scheme with autocatalytic routes includes as few steps as possible and adequately describes the ethane pyrolysis with high ethylene yield at 900–1,150 K. Admissible variation ranges of preexponential factors and activation energies for the kinetic model of the reactions were found using the Monte Carlo statistical method. Reducibility of the scheme was examined by means of the Sobol’s variance based strategy applied for the sensitivity analysis evaluation.
Funding statement: Research funding: Russian Foundation for Basic Research (Grant/Award Number: “Projects Nos. 12-07-31029, 12-07-00324, 12-08-0087”).
Acknowledgments
A part of this research was performed under the UNIHEAT project. The authors wish to acknowledge the Skolkovo Foundation and BP for financial support.
The study was also partially supported by the Russian Foundation for Basic Research (Project Nos 12-07-31029, 12-07-00324, 12-08-00871, and 12-08-31095) and RF President grant MK-2737.2013.3. The work is supported by the Ministry of Education and Science of the Russian Federation.
References
1. MatheuDM, GrendaJM. Pathways to the minor products. J Phys Chem A2005;109:5332–42.10.1021/jp0451804Suche in Google Scholar PubMed
2. MatheuDM, GrendaJM. Radical disproportionations, missing reaction families, and the consequences of pressure dependence. J Phys Chem A2005;109:5343–51.10.1021/jp0451647Suche in Google Scholar PubMed
3. PonomarevAV, TsivadzeAA. Gas-to-liquid conversion of alkanes by electron beam radiolysis. Dokl Phys Chem2006;411:345–51.10.1134/S0012501606120062Suche in Google Scholar
4. PushkarevAI, Ai-MinZ, Xiao-SongLi, SazonovRV. Methane conversion in low-temperature plasma. High Energy Chem2009;43:156–62.10.1134/S0018143909030023Suche in Google Scholar
5. ChavadejS, RueangjittN, SreethawongT. Reforming of CO-containing natural gas with partial oxidation using an AC gliding arc system. In: Proceedings of 18th international symposium on plasma chemistry, Kyoto, 2007;552–61.Suche in Google Scholar
6. MantashyanAA, KhachatryanLA, EduardAA. Slozhnyye gazofaznyye reaktsii prevrashcheniya uglevodorod-kislorodnykh smesey v usloviyakh lazernogo nagreva. Kinetika I Kataliz1989;30:272–6 (Russian).Suche in Google Scholar
7. KarlovNV, KirichenkoNA, Luk’yanchukBS. Laser thermochemistry: fundamentals and applications. Cambridge: Cambridge International Science Publishing, 2000:370.Suche in Google Scholar
8. SnytnikovVN, MishchenkoTI, SnytnikovVN, StoyanovskayaOP, ParmonVN. Autocatalytic gas-phase ethane dehydrogenation in a wall-less reactor. Kinet Catalysis2010;51:10–17.10.1134/S0023158410010039Suche in Google Scholar
9. SnytnikovVN, MischenkoTI, SnytnikovVN, ChernykhIG. A reactor for the study of homogeneous processes using laser radiation energy. Chem Eng J2009;150:231–6.10.1016/j.cej.2009.02.028Suche in Google Scholar
10. SnytnikovVN, MishchenkoTI, SnytnikovVN, MalykhinSE, AvdeevVI, ParmonVN. Autocatalytic gas-phase dehydrogenation of ethane. Res Chem Intermed2012;38:1133–47.10.1007/s11164-011-0449-xSuche in Google Scholar
11. SnytnikovPV, StoyanovskayaOP. Supercomputer simulation of laser-catalytic reactor of C2-C3 alkane pyrolysis. Proceedings of the International Conference “Advanced Mathematics, computations and applications – 2014”; 2014 June 8–11; Institute of Computational Mathematics and Mathematical Geophysics SBRAS; Akademgorodok, Novosibirsk, Russia. Novosibirsk: Academizdat, 2014:122.Suche in Google Scholar
12. NorinagaK, DeutschmannO. Detailed kinetic modeling of gas-phase reactions in the chemical vapor deposition of carbon from light hydrocarbons. Ind Eng Chem Res2007;46:3547–57.10.1021/ie061207pSuche in Google Scholar
13. TomlinAS, PillingMJ, MerkinJH, BrindleyJ, BurgessN, GoughA. Reduced mechanisms for propane pyrolysis. Ind Eng Chem Res1995;34:3749–60.10.1021/ie00038a010Suche in Google Scholar
14. TurányiT. Sensitivity analysis of complex kinetic systems. Tools and applications. J Math Chem1990;5:203–48.10.1007/BF01166355Suche in Google Scholar
15. SobolIM. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simulation2001;55:271–80.10.1016/S0378-4754(00)00270-6Suche in Google Scholar
16. MaselRI. Chemical kinetics and catalysis. New York: Wiley Interscience, 2001:952.Suche in Google Scholar
17. PolakLS, GoldenbergMY, LevitskiyAA. Computational methods in chemical kinetics. Moscow: Nauka, 1984:280 (Russian).Suche in Google Scholar
18. SaltelliA, RattoM, TarantolaS, CampolongoF. Sensitivity analysis for chemical models. Chem Rev2005;105:2811–28.10.1021/cr040659dSuche in Google Scholar
19. ZádorJ, ZsélyIG, TurányiT. Local and global uncertainty analysis of complex chemical kinetic systems. Rel Eng Sys Safety2006;91:1232–40.10.1016/j.ress.2005.11.020Suche in Google Scholar
20. HeltonJC. Uncertainty and sensitivity analysis for models of complex systems. Comput Methods Transp Verification Validatio2008;62:207–28.10.1007/978-3-540-77362-7_9Suche in Google Scholar
21. TomlinAS, ZiehnT. The use of global sensitivity methods for the analysis, evaluation and improvement of complex modelling systems. In: GorbanAN, RooseD, editors. Coping with complexity: model reduction and data analysis. Heidelberg (Berlin): Springer, 2011:355.Suche in Google Scholar
22. YouXQ, RussiT, PackardA, FrenklachM. The role of sensitivity and uncertainty analysis in combustion modeling. Proc Combust Inst2011;33:509–16.Suche in Google Scholar
23. FrenklachM, PackardA, SeilerP, FeeleyR. Collaborative data processing in developing predictive models of complex reaction systems. Int J Chem Kinet2004;36:57–66.10.1002/kin.10172Suche in Google Scholar
24. KantorovichLV. On some new approaches to computational methods and processing of observations. Sib Mat Zh1962;3:701–9.Suche in Google Scholar
25. TikhonovaMV, GarifullinaGG, GerchikovAY, SpivakSI. The kinetic model of n-decane oxidation in the presence of inhibitory composition. Int J Chem Kinet2014;46:220–30.10.1002/kin.20848Suche in Google Scholar
26. SaltelliA. Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun2002;145:280–97.10.1016/S0010-4655(02)00280-1Suche in Google Scholar
27. SobolIM. Uniformly distributed sequences with an additional uniform property. Zh Vych Mat Mat Fiz1976;16:1332–7 (Russian).10.1016/0041-5553(76)90154-3Suche in Google Scholar
28. SaisanaM, SaltelliA, UncertaintyTS. Sensitivity analysis techniques as tools for the quality assessment of composite indicators. J R Stat Soc A2011;168:307–23.10.1111/j.1467-985X.2005.00350.xSuche in Google Scholar
29. TuranyiT, NagyT, ZselyIG, CserhatiM, VargaT, SzaboBT, et al. Determination of rate parameters based on both direct and indirect measurements. Int J Chem Kinet2012;44:284–302.10.1002/kin.20717Suche in Google Scholar
©2014 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Prediction of Fischer–Tropsch Synthesis Kinetic Parameters Using Neural Networks
- Temperature Peak Analysis and Its Effect on Absorption Column for CO2 Capture Process at Different Operating Conditions
- Valorization of Glycerol into Polyhydroxyalkanoates by Sludge Isolated Bacillus sp. RER002: Experimental and Modeling Studies
- Parameter Estimation of Kinetic Model Equations for Chemical Leaching of Coal
- Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis
- A Mathematical Modeling and Experimental Study on Adsorptive Desulfurization of Model Gasoline Using Synthesized Ni–Y and Ce–Y Zeolites
- Inter-Communicative Decentralized Multi-Scale Control (ICD-MSC) Scheme: A New Approach to Overcome MIMO Process Interactions
- Technical Note
- Effect of Operating Parameters on Ethanol–Water Vacuum Separation in an Ethanol Dehydration Apparatus and Process Modeling with ANN
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Prediction of Fischer–Tropsch Synthesis Kinetic Parameters Using Neural Networks
- Temperature Peak Analysis and Its Effect on Absorption Column for CO2 Capture Process at Different Operating Conditions
- Valorization of Glycerol into Polyhydroxyalkanoates by Sludge Isolated Bacillus sp. RER002: Experimental and Modeling Studies
- Parameter Estimation of Kinetic Model Equations for Chemical Leaching of Coal
- Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis
- A Mathematical Modeling and Experimental Study on Adsorptive Desulfurization of Model Gasoline Using Synthesized Ni–Y and Ce–Y Zeolites
- Inter-Communicative Decentralized Multi-Scale Control (ICD-MSC) Scheme: A New Approach to Overcome MIMO Process Interactions
- Technical Note
- Effect of Operating Parameters on Ethanol–Water Vacuum Separation in an Ethanol Dehydration Apparatus and Process Modeling with ANN