TY - JOUR

T1 - Comparative analysis of numerical methods for determining parameters of chemical reactions from experimental data

AU - Prikhodko, Aleksei

AU - Shishlenin, Maxim

AU - Stadnichenko, Olga

N1 - Funding Information: The work was supported by the budget of the Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch, Russian Academy of Sciences, project no. 0315-2019-0005. Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved.

PY - 2021/12/20

Y1 - 2021/12/20

N2 - The aim of this paper is to select an optimal numerical method for determining the parameters of chemical reactions. The importance of the topic is due to the modern needs of industry, such as the improvement of chemical reactors and oil or gas processing. The paper deals with the problem of determining reaction rate constants using gradient methods and stochastic optimization algorithms. To solve an forward problem, implicit methods for solving stiff ODE systems are used. A correlation method of practical identifiability of the required parameters is used. The genetic algorithm, particle swarm method, and fast annealing method are implemented to solve an inverse problem. The gradient method for the solution of the inverse problem is implemented, and a formula for gradient of the functional is given with the corresponding adjoint problem. We apply an identifiability analysis of the unknown coefficients and arrange the coefficients in the order of their identifiability. We show that the best approach is to apply global optimization methods to find the interval of global solution and after that we refine inverse problem solution using gradient approach.

AB - The aim of this paper is to select an optimal numerical method for determining the parameters of chemical reactions. The importance of the topic is due to the modern needs of industry, such as the improvement of chemical reactors and oil or gas processing. The paper deals with the problem of determining reaction rate constants using gradient methods and stochastic optimization algorithms. To solve an forward problem, implicit methods for solving stiff ODE systems are used. A correlation method of practical identifiability of the required parameters is used. The genetic algorithm, particle swarm method, and fast annealing method are implemented to solve an inverse problem. The gradient method for the solution of the inverse problem is implemented, and a formula for gradient of the functional is given with the corresponding adjoint problem. We apply an identifiability analysis of the unknown coefficients and arrange the coefficients in the order of their identifiability. We show that the best approach is to apply global optimization methods to find the interval of global solution and after that we refine inverse problem solution using gradient approach.

UR - http://www.scopus.com/inward/record.url?scp=85124015154&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2092/1/012011

DO - 10.1088/1742-6596/2092/1/012011

M3 - Conference article

AN - SCOPUS:85124015154

VL - 2092

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012011

T2 - 11th International Scientific Conference and Young Scientist School on Theory and Computational Methods for Inverse and Ill-posed Problems

Y2 - 26 August 2019 through 4 September 2019

ER -