A General Approach to Molecular Reconstruction Accuracy Estimation

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

A generalization to a few molecular reconstruction methods has been proposed along with a general approach for the accuracy estimation of molecular reconstruction methods. The proposed algorithm estimates the covariance matrix of model parameters based on the matrix of calculated property derivatives with respect to the model parameters and the covariance matrix of measurement error, which allows one to estimate the concentration range in which the solution will not violate collected experimental data. The proposed algorithm is especially easy to use when forward automatic differentiation is employed to find derivatives.

Full Text

Restricted Access

About the authors

N. A. Glazov

Boreskov Institute of Catalysis, SB RAS

Author for correspondence.
Email: glazov@catalysis.ru
Russian Federation, 5 Lavrentiev Ave., Novosibirsk, 630090

A. N. Zagoruiko

Boreskov Institute of Catalysis, SB RAS

Email: glazov@catalysis.ru
Russian Federation, 5 Lavrentiev Ave., Novosibirsk, 630090

References

  1. Зайнуллин Р.З., Коледина К.Ф., Губайдуллин И.М., Ахметов А.Ф., Коледин С.Н. // Кинетика и катализ. 2020. Т. 61. № 4. С. 550. (Zaynullin R.Z., Koledina K.F., Gubaydullin I.M., Ahmetov A.F., Koledin S.N. Kinetic Model of Catalytic Gasoline Reforming with Consideration for Changes in the Reaction Volume and Thermodynamic Parameters // Kinet. Catal. 2020. V. 61. P. 613. https://doi.org/10.1134/S002315842004014X)
  2. Заварухин С.Г., Яковлев В.А. // Кинетика и катализ. 2021. Т. 62. № 5 С. 647. (Zavarukhin S.G., Yakovlev V.A. Mathematical Modeling of the Nonisothermal Pyrolysis of Sorghum Biomass Based on a Three-Component Kinetic Model // Kinet. Catal. 2021. V. 62. P. 688. https://doi.org/10.1134/S0023158421050128)
  3. Zagoruiko A.N., Belyi A.S., Smolikov M.D, Noskov A.S. // Catal. Today. 2014. V. 220–222. P. 168.
  4. Temkin O.N., Zeigarnik A.V., Kuzmin A.E., Bruk L.G., Slivinskii E.V. // Russ. Chem. Bull. 2002. V. 51. № 1. Р. 1.
  5. Ren Y., Liao Z., Sun J., Jiang B., Wang J., Yang Y., Wu Q. // Chem. Eng. 2019. V. 257. P.761.
  6. Hudebine D., Verstraete J.J. // Oil Gas Sci. Technol. 2011. V. 66. № 3. P. 437.
  7. Neurock M., Libanati C., Nigam A., Klein M.T. // Chem. Eng. Sci. 1990. V. 45. № 8. P. 2083.
  8. Neurock M., Nigam A., Trauth D., Klein M.T. // Chem. Eng. Sci. 1994. V. 49. № 24. P. 4153.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Dependence of the calculated average molecular mass on the model parameter: a wide range of molecular masses corresponds to a narrow range of parameters (a) and a narrow range of molecular masses corresponds to a wide range of parameters (b).

Download (20KB)
Indexing metadata
3. Fig. 2. Dependence of the error of the found parameter on the parameter value.

Download (12KB)
Indexing metadata
4. Fig. 3. Dependence of the average absolute error in determining concentration on the model parameter.

Download (12KB)
Indexing metadata
5. Fig. 4. Dependence of the calculated molecular mass of the mixture on the value of the model parameter for exact and imprecise constraints.

Download (19KB)
Indexing metadata
6. Fig. 5. Average molecular weight (a) and mass fraction of carbon (b) of the mixture depending on the model parameters.

Download (29KB)
Indexing metadata
7. Fig. 6. Dependence of the logarithm of the standard deviation of the mean (a) and variance (b) on the true parameters of the model.

Download (36KB)
Indexing metadata
8. Fig. 7. Dependence of the logarithm of the standard deviation of the mean (a), variance (b) and correlation coefficient (c).

Download (28KB)
Indexing metadata