Identification of the Interval Constants of the Rates of the Chemical Reaction of Naphthalene Oxidation

 
Audio is AI-generated
107

Abstract

In this work, the previously developed approach of parametric identification of dynamic systems with interval parameters is applied to the problem of finding the rate constants of the chemical reaction of naphthalene oxidation. This reaction is of practical importance in the production of plastics and paints and varnishes. The essence of the considered approach lies in the compilation of the objective function in the space of the boundaries of the interval parameters and characterizing the deviation of the model solution from the experimental data. For the objective function, it is possible to construct a gradient and use first-order methods to optimize it. The approach is based on the adaptive interpolation algorithm, which makes it possible to obtain solutions for direct interval problems in the form of explicit parametric sets. The found interval estimates of the rate constants are consistent with the known ones, but at the same time they have a smaller width, which demonstrates the advantage of the approach used.

General Information

Keywords: interval parametric identification, adaptive interpolation algorithm, interval system of ordinary differential equations, optimization, gradient methods, chemical kinetics, rate constants, naphthalene oxidation

Journal rubric: Optimization Methods

OpenAlex citations: 0

OpenAlex trends: Numerical Methods and Algorithms, Probabilistic and Robust Engineering Design, Control Systems and Identification

Information about the work in OpenAlex

Number of citations: 0

Topics

Numerical Methods and Algorithms

This cluster of papers focuses on the theory, implementation, and optimization of floating-point arithmetic for scientific computation. It covers topics such as interval analysis, high-precision computation, hardware implementation on FPGAs, numerical verification methods, decimal floating-point arithmetic, accuracy optimization, Taylor models, and handling interval uncertainty.

Number of works: 42166  |  Total number of citations: 384366

Topic detailsв OpenAlex

Probabilistic and Robust Engineering Design

This cluster of papers focuses on uncertainty quantification and sensitivity analysis in complex mathematical and computational models. It explores methods such as polynomial chaos, Monte Carlo simulation, and sparse grids to assess and manage uncertainties in various engineering and scientific applications. The research also delves into topics like global sensitivity indices, reliability analysis, stochastic differential equations, and probabilistic design optimization.

Number of works: 75852  |  Total number of citations: 1208153

Topic detailsв OpenAlex

Control Systems and Identification

This cluster of papers covers advances in system identification techniques, including parameter estimation for nonlinear models, data-driven control, model-based control, feedback controllers, state estimation for multivariable systems, and the development of recursive algorithms. The papers also discuss model selection approaches and optimal experiment design for system identification.

Number of works: 58940  |  Total number of citations: 874485

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2023130305

Received 14.07.2023

Accepted

Published

For citation: Morozov, A.Y. (2023). Identification of the Interval Constants of the Rates of the Chemical Reaction of Naphthalene Oxidation. Modelling and Data Analysis, 13(3), 66–78. (In Russ.). https://doi.org/10.17759/mda.2023130305

© Morozov A.Y., 2023

License: CC BY-NC 4.0

References

  1. Moore R.E., Kearfott R.B., Cloud M.J. Introduction to Interval Analysis, SIAM, 2009.
  2. Dobronets B.S. Interval'naya matematika [Interval mathematics]. Krasnoyarsk: Publ. Krasnoyar. state un-t, 2007. (In Russ.)
  3. Sharyi S.P. Konechnomernyi interval'nyi analiz [Finite-dimensional interval analysis]. Novosibirsk: Publ. XYZ, 2017. (In Russ.)
  4. Morozov A.Yu., Reviznikov D.L. Modelirovanie dinamicheskikh sistem s interval'nymi parametrami. Obzor metodov i programmnykh sredstv [Modeling of dynamic systems with interval parameters. Overview of Methods and Software]. Modelirovanie i analiz dannykh = Modeling and data analysis, 2019, no. 4, pp. 5-31. DOI: 10.17759/mda.2019090401 (In Russ.)
  5. Diligenskaya A.N., Samokish A.V. Parametricheskaya identifikatsiya v obratnykh zadachakh teploprovodnosti v usloviyakh interval'noi neopredelennosti na osnove neironnykh setei [Parametric identification in inverse problems of heat conduction under conditions of interval uncertainty based on neural networks]. Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta = Bulletin of the Samara State Technical University, 2020, V. 28, no. 4 (68), pp. 6-18. (In Russ.)
  6. Petrikevich Ya. I. Strukturno-parametricheskaya identifikatsiya dinamicheskikh ob"ektov po interval'nym iskhodnym dannym: dis. kand. tekhn. nauk: 05.13.18 [Structural-parametric identification of dynamic objects by interval initial data: dis. … cand. tech. Sciences: 05.13.18.], Kemerovo: Kemer. state un-t, 2006. 225 p. (In Russ.)
  7. Milanese M., Norton J., Piet-Lahanier H., Walter E., eds. Bounding Approaches to System Identification. – New York: Plenum Press, 1996.
  8. Sharyi S.P. Vosstanovlenie funktsional'nykh zavisimostei po dannym s interval'noi neopredelennost'yu [Recovery of functional dependencies from data with interval uncertainty]. Informatika i sistemy upravleniya = Informatics and control systems, 2022, no. 3(73). pp. 130-143. DOI: 10.22250/18142400_2022_73_3_130 (In Russ.)
  9. Sharyi S.P. Zadacha vosstanovleniya zavisimostei po dannym s interval'noi neopredelennost'yu [The problem of recovering dependencies from data with interval uncertainty]. Zavodskaya laboratoriya. diagnostika materialov = Zavodskaya lab. material diagnostics, 2020, V. 86, no. 1. pp. 62-74. DOI: 10.26896/1028-6861-2020-86-1-62-74 (In Russ.)
  10. Morozov A. Yu., Reviznikov D. L. Interval approach to solving parametric identification problems for dynamical systems. Differential Equations, 2022, V. 58, no. 7, pp. 952–965. DOI: 10.1134/S0012266122070084
  11. Morozov A.Yu. Parallel'nyi algoritm parametricheskoi identifikatsii dinamicheskikh sistem s interval'nymi parametrami [Parallel Algorithm for Parametric Identification of Dynamic Systems with Interval Parameters]. Programmnaya inzheneriya=Software Engineering, 2022, V. 13, no. 10, pp. 497—507. DOI: 10.17587/prin.13.497-507 (In Russ.)
  12. Miftakhov E.N., Zigangirova D.R., Mustafina S.A., Morozkin N.D. Algoritm resheniya obratnoi zadachi khimicheskoi kinetiki v usloviyakh neopredelennosti iskhodnykh dannykh [Algorithm for solving the inverse problem of chemical kinetics under conditions of initial data uncertainty]. Vestnik tekhnologicheskogo universiteta = Vestnik tekhnologicheskogo universiteta, 2020, V. 23, no. 11, pp. 101–105. (In Russ.)
  13. Yablonskii G.S., Spivak S.I. Matematicheskie modeli khimicheskoi kinetiki [Mathematical models of chemical kinetics], Moscow: Publ. Knowledge, 1977. (In Russ.)
  14. Bykov V.I., Dobronets B.S. K interval'nomu analizu uravnenii khimicheskoi kinetiki [To the interval analysis of the equations of chemical kinetics]. Matematicheskie problemy khimicheskoi kinetiki = Mathematical problems of chemical kinetics. Novosibirsk: Nauka, 1989, pp. 226–232. (In Russ.)
  15. Gidaspov V.Yu., Kononov D.S. Chislennoe modelirovanie szhiganiya topliva v statsionarnoi detonatsionnoi volne v kanale peremennogo secheniya so sverkhzvukovym potokom na vkhode i vykhode [Elektronnyi resurs] [Numerical modeling of fuel combustion in a stationary detonation wave in a channel of variable cross section with a supersonic flow at the inlet and outlet]. Trudy MAI = Proceedings of the MAI, 2019, no. 109, URL: http://trudymai.ru/published.php?ID=111353. DOI: 10.34759/trd-2019-109-6 (In Russ.)
  16. Gill P. E., Murray W., Wright M. H. Practical Optimization, ACADEMIC PRESS, INC. San Diego, 1997.
  17. Panteleev A.V., Letova T.A. Metody optimizatsii v primerakh i zadachakh: Ucheb. posobie. 2-e izd [Optimization methods in examples and tasks: Proc. allowance. 2nd ed.] Moscow: Publ. Higher. school, 2005, 544 p. (In Russ.)
  18. Morozov A.Yu. Algoritm adaptivnoi interpolyatsii dlya resheniya zadach nebesnoi mekhaniki s interval'nymi neopredelennostyami [Elektronnyi resurs] [Adaptive interpolation algorithm for solving problems of celestial mechanics with interval uncertainties]. Trudy MAI = Proceedings of MAI, 2022, no. 123. URL: https://trudymai.ru/published.php?ID=165501. DOI: 10.34759/trd-2022-123-24 (In Russ.)
  19. Morozov A.Yu. Interpolyatsionnyi podkhod v zadachakh modelirovaniya dinamicheskikh sistem s ellipsoidnymi otsenkami parametrov [Elektronnyi resurs] [Interpolation approach in the problems of modeling dynamic systems with ellipsoid parameter estimates]. Trudy MAI = Proceedings of MAI, 2022, no. 124. URL: https://trudymai.ru/published.php?ID=167168. DOI: 10.34759/trd-2022-124-24 (In Russ.)
  20. Morozov A.Yu. Parallel'nyi algoritm adaptivnoi interpolyatsii na osnove razrezhennykh setok dlya modelirovaniya dinamicheskikh sistem s interval'nymi parametrami [Parallel adaptive interpolation algorithm based on sparse grids for modeling dynamic systems with interval parameters]. Programmnaya inzheneriya = Software Engineering, 2021, V. 12, no. 8, pp. 395–403. DOI: 10.17587/prin.12.395-403. (In Russ.)
  21. Berz M., Makino K. Verified integration of ODEs and flows with differential algebraic methods on Taylor models // Reliable Computing. Vol. 4. № 4. 1998. P. 361–369.
  22. Rogalev A.N. Garantirovannye metody resheniya sistem obyknovennykh differentsial'nykh uravnenii na osnove preobrazovaniya simvol'nykh formul [Guaranteed methods for solving systems of ordinary differential equations based on the transformation of symbolic formulas]. Vychislitel'nye tekhnologii = Computational technologies, 2003, V. 8, no. 5. pp. 102–116. (In Russ.)
  23. Fu C., Ren X., Yang Y.-F., Lu K., Qin W. Steady-state response analysis of cracked rotors with uncertain but bounded parameters using a polynomial surrogate method. Commun. Nonlinear Sci. Numer. Simul. 2019, 68, 240–256, doi:10.1016/j.cnsns.2018.08.004.
  24. Fu C., Xu Y., Yang Y., Lu K., Gu F., Ball A. Response analysis of an accelerating unbalanced rotating system with both random and interval variables. J. Sound Vib. 2020, 466, 115047, doi:10.1016/j.jsv.2019.115047.
  25. Smolyak S.A. Kvadraturnye i interpolyatsionnye formuly na tenzornykh proizvedeniyakh nekotorykh klassov funktsii [Quadrature and interpolation formulas on tensor products of some classes of functions]. Dokl. AN SSSR = Reports of the Academy of Sciences of the USSR, 1963, 148:5, pp. 1042–1045. (In Russ.)
  26. Bungatrz H-J., Griebel M. Sparse grids // Acta Numerica. 2004. Vol. 13, no. 1. pp. 147–269.
  27. Gerstner T., Griebel M. Sparse grids // Encyclopedia of Quantitative Finance / Ed. R. Cont. New York, 2010.
  28. Vaitiev V.A., Mustafina S.A. Postroenie dvustoronnikh otsenok resheniya pryamoi zadachi khimicheskoi kinetiki [Construction of two-sided estimates for the solution of the direct problem of chemical kinetics]. Zhurnal Srednevolzhskogo matematicheskogo obshchestva = Journal of the Middle Volga Mathematical Society, 2012, V. 14, no. 4, pp. 18–25. (In Russ.)

Information About the Authors

Alexander Y. Morozov, Candidate of Science (Physics and Matematics), Researcher, Department 27 "Mathematical Modeling of Heterogeneous Systems", Federal Research Center Computer Science and Control of the Russian Academy of Sciences, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-0364-8665, e-mail: morozov@infway.ru

Metrics

 Web Views

Whole time: 342
Previous month: 14
Current month: 12

 PDF Downloads

Whole time: 107
Previous month: 6
Current month: 7

 Total

Whole time: 449
Previous month: 20
Current month: 19