Skip to main content
Log in

Obtaining a reduced kinetic mechanism for Methyl Butanoate

  • Original Paper
  • Published:
Journal of Mathematical Chemistry Aims and scope Submit manuscript

Abstract

The computational treatment of detailed kinetic reaction mechanisms for combustion is expensive, especially in the case of biodiesel fuels. In this way, great efforts in the search of techniques for the development of reduced kinetic mechanisms have been observed. As Methyl Butanoate (MB, \(C_3H_7COOCH_3\)) is an essential model frequently used to represent the ester group of reactions in saturated methyl esters of large chain, this paper proposes a reduction strategy and uses it to obtain a reduced kinetic mechanism for the MB. The reduction strategy consists in the use of artificial intelligence to define the main chain and produce a skeletal mechanism, apply the traditional hypotheses of steady-state and partial equilibrium, and justify these assumptions through an asymptotic analysis. The main advantage of the strategy employed here is to reduce the work required to solve the system of chemical equations by two orders of magnitude for MB, since the number of reactions is decreased in the same order.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. J.F. Griffiths, Reduced kinetic models and their application to practical combustion systems. Prog. Energy Combust. Sci. 21(1), 25–107 (1995)

    Article  CAS  Google Scholar 

  2. F.A. Vaz, A.L. De Bortoli, A new reduced kinetic mechanism for turbulent jet diffusion flames of bioethanol. Appl. Math. Comput. 247, 918–929 (2014)

    Google Scholar 

  3. A. Demirbas, Biofuels securing the planet’s future energy needs. Energy Convers. Manag. 50(9), 2239–2249 (2009)

    Article  CAS  Google Scholar 

  4. R. Grana, A. Frassoldati, A. Cuoci, T. Faravelli, E. Ranzi, A wide range kinetic modeling study of pyrolysis and oxidation of methyl butanoate and methyl decanoate. Note I: lumped kinetic model of methyl butanoate and small methyl esters. Energy 43(1), 124–139 (2012)

    Article  CAS  Google Scholar 

  5. A. Stagni, A. Cuoci, A. Frassoldati, T. Faravelli, E. Ranzi, Lumping and reduction of detailed kinetic schemes: an effective coupling. Ind. Eng. Chem. Res. 53(22), 9004–9016 (2013)

    Article  CAS  Google Scholar 

  6. C.K. Westbrook, W.J. Pitz, H.J. Curran, Chemical kinetic modeling study of the effects of oxygenated hydrocarbons on soot emissions from diesel engines. J. Phys. Chem. A 110, 6912–6922 (2006)

    Article  CAS  PubMed  Google Scholar 

  7. E.M. Fisher, W.J. Pitz, H.J. Curran, C.K. Westbrook, Detailed chemical kinetic mechanisms for combustion of oxygenated fuels. Proc. Combust. Inst. 28(2), 1579–1586 (2000)

    Article  CAS  Google Scholar 

  8. M.S. Graboski, R.L. McCormick, Combustion of fat and vegetable oil derived fuels in diesel engines. Prog. Energy Combust. Sci. 24(2), 125–164 (1998)

    Article  CAS  Google Scholar 

  9. C.K. Westbrook, C.V. Naik, O. Herbinet, W.J. Pitz, M. Mehl, S.M. Sarathy, H.J. Curran, Detailed chemical kinetic reaction mechanisms for soy and rapeseed biodiesel fuels. Combust. Flame 158(4), 742–755 (2011)

    Article  CAS  Google Scholar 

  10. J. Coniglio, H. Bennadji, P.A. Glaude, O. Herbinet, F. Billaud, Combustion chemical kinetics of biodiesel and related compounds (methyl and ethyl esters): experiments and modeling—advances and future refinements. Prog. Energy Combust. Sci. 39(4), 340–382 (2013)

    Article  Google Scholar 

  11. J.Y.W. Lai, K.C. Lin, A. Violi, Biodiesel combustion: advances in the chemical kinetic modeling. Prog. Energy Combust. Sci. 37, 1–14 (2011)

    Article  CAS  Google Scholar 

  12. J. Yang, V.I. Golovitchev, P.R. Lurbe, J.J.L. Sánchez, Chemical kinetic study of nitrogen oxides formation trends in biodiesel combustion. Int. J. Chem. Eng. ID 898742, 22 pages (2012)

  13. S.B. Hosseini, M. Ahmadvand, R.H. Khoshkhoo, H. Khosravi, The experimental and simulations effect of air swirler on pollutants from biodiesel combustion. Res. J. Appl. Sci. Eng. Tech. 5(18), 4556–4562 (2013)

    Article  CAS  Google Scholar 

  14. Y. Ra, R.D. Reitz, A combustion model for IC engine combustion simulations with multi-component fuels. Combust. Flame 158(1), 69–90 (2011)

    Article  CAS  Google Scholar 

  15. P. Diévart, S.H. Won, J. Gong, S. Dooley, Y. Ju, A comparative study of the chemical kinetic characteristics of small methyl esters in diffusion flame extinction. Proc. Combust. Inst. 34, 821–829 (2013)

    Article  CAS  Google Scholar 

  16. J.L. Brakora, Y. Ra, R. Reitz, J. McFarlane, C.S. Daw, Development and validation of a reduced reaction mechanism for biodiesel fueled engine simulations. SAE Int. J. Fuels Lubr. 1(1), 675–702 (2009)

    Article  CAS  Google Scholar 

  17. H.K. Ng, S. Gan, J.H. Ng, K.M. Pang, Development and validation of a reduced combined biodiesel–diesel reaction mechanism. Fuel 104, 620–634 (2013)

    Article  CAS  Google Scholar 

  18. C. Saggese, A. Frassoldati, A. Cuoci, T. Favarelli, E. Ranzi, A lumped approach to the kinetic modeling of pyrolysis and combustion of biodiesel fuels. Proc. Combust. Inst. 34, 427–434 (2013)

    Article  CAS  Google Scholar 

  19. Z. Luo, M. Plomer, T. Lu, S. Som, D.E. Longman, A reduced mechanism for biodiesel surrogates with low temperature chemistry for compression ignition engine applications. Combust. Theory Model. 16(2), 369–385 (2012)

    Article  CAS  Google Scholar 

  20. P. Diévart, S.H. Won, S. Dooley, F.L. Dryer, Y. Ju, A kinetic model for methyl decanoate combustion. Combust. Flame 159(5), 1793–1805 (2012)

    Article  CAS  Google Scholar 

  21. S. Turns, An Introduction to Combustion: Concepts and Applications, 2nd edn. (McGraw-Hill, New York, 2000)

    Google Scholar 

  22. K.K. Kuo, Principles of Combustion, 2nd edn. (Wiley, Hoboken, 2005)

    Google Scholar 

  23. A.L. De Bortoli, G.S.L. Andreis, F.N. Pereira, Modeling and Simulation of Reactive Flows (Elsevier, Amsterdam, 2015)

    Google Scholar 

  24. T. Turányi, Sensitivity analysis of complex kinetic systems: tools and applications. J. Math. Chem. 5(3), 203–248 (1990)

    Article  Google Scholar 

  25. N. Peters, Turbulent Combustion (Cambridge University Press, Cambridge, 2000)

    Book  Google Scholar 

  26. Y.Y. Wu, C.K. Chan, L.X. Zhou, Large eddy simulation of an ethylene–air turbulent premixed V-flame. J. Comput. Appl. Math. 235, 3768–3774 (2011)

    Article  Google Scholar 

  27. H. Watanabe, R. Kurose, S.M. Hwang, F. Akamatsu, Characterisitcs of flamelets in spray flames formed in a laminar counterflow. Combust. Flame 148, 234–248 (2007)

    Article  CAS  Google Scholar 

  28. D.S.S. Shieh, Y. Chang, G. Carmichael, The evaluation of numerical techniques for solution of stiff ordinary differential equations arising from chemical kinetic problems. Environ. Soft. 3(1), 28–38 (1998)

    Article  Google Scholar 

  29. R.C. Aiken, Stiff Computation (Oxford University Press, Oxford, 1985)

    Google Scholar 

  30. A. Sandu, J.G. Verwer, J.G. Blom, E.J. Spee, G.R. Carmichael, F.A. Potra, Benchmarking stiff ODE solver for atmospheric chemistry problems II: Rosenbrock solvers. Atmos. Environ. 31(20), 3459–3472 (1997)

    Article  CAS  Google Scholar 

  31. T.D. Bui, T.R. Bui, Numerical methods for extremely stiff systems of ordinary differential equations. Appl. Math. Model. 3, 355–358 (1979)

    Article  Google Scholar 

  32. T.D. Bui, A note on the Rosenbrock procedure. Math. Comput. 33, 971–975 (1979)

    Article  Google Scholar 

  33. J.H. Mathews, K.D. Fink, Numerical Methods Using MATLAB (Pearson, Prentice Hall, 2004)

    Google Scholar 

  34. B. Kovács, J. Tóth, Estimating reaction rate constants with neural networks. Int. J. Appl. Math. Comput. Sci. 4(1), 7–11 (2007)

    Google Scholar 

  35. B.A. Sen, S. Menon, Artificial neural networks based chemistry-mixing subgrid model for LES. 47th AIAA conference (2009), pp. 1–17

  36. J.A. Blasco, N. Fueyo, J.C. Larroya, C. Dopazo, Y.J. Chen, A single-step time-integrator of a methane–air chemical system using artificial neural networks. Comput. Chem. Eng. 23, 1127–1133 (1999)

    Article  CAS  Google Scholar 

  37. B.A. Sen, S. Menon, Representation of chemical kinetics by artificial neural networks for large eddy simulations. In: 43rd AIAA/ASME/SAE/ASEE joint propulsion conference & exhibit, No. AIAA 2007-5635 (2007), pp. 1–15

  38. Z.J. Zhou, Y. Lu, Z.H. Wang, Y.W. Xu, J.H. Zhou, K.F. Cen, Systematic method of applying ANN for chemical kinetics reduction in turbulent premixed combustion modeling. Chin. Sci. Bull. 58, 486–492 (2013)

    Article  CAS  Google Scholar 

  39. K.C. Lin, H. Tao, F.H. Kao, C.T. Chiu, A minimized skeletal mechanism for methyl butanoate oxidation and its application to the prediction of C3–C4 products in non-premixed flames: A base model of biodiesel fuels. Energy Fuels 30(2), 1354–1363 (2016)

    CAS  Google Scholar 

  40. Z.M. Nikolaou, J.Y. Chen, N. Swaminathan, A 5-step reduced mechanism for combustion of \(CO\)/\(H_2\)/\(H_2O\)/\(CH_4\)/\(CO_2\) mixtures with low hydrogen/methane and high \(H_2O\) content. Combust. Flame 160, 56–75 (2013)

    Article  CAS  Google Scholar 

  41. U. Niemann, R. Seiser, K. Seshadri, Ignition and extinction of low molecular weight esters in nonpremixed flows. Combust. Theory Model. 14(6), 875–891 (2010)

    Article  CAS  Google Scholar 

  42. Y. Chang, M. Jia, Y. Li, Y. Zhang, M. Xie, H. Wang, R.D. Reitz, Development of a skeletal oxidation mechanism for biodiesel surrogate. Proc. Combust. Inst. 35, 3037–3044 (2015)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research is being developed at UFRGS, Federal University of Rio Grande do Sul. Professor De Bortoli gratefully acknowledges the financial support from CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico, under Grant 303816/2015-5.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. L. De Bortoli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Bortoli, A.L., Pereira, F.N. Obtaining a reduced kinetic mechanism for Methyl Butanoate. J Math Chem 57, 812–833 (2019). https://doi.org/10.1007/s10910-018-0984-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10910-018-0984-4

Keywords

Mathematics Subject Classification

Navigation