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An Interval-Simplex Approach to Determine Technological Parameters from Experimental Data

Author

Listed:
  • Ilia Beloglazov

    (Department of Automation of Technological Processes and Production, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Kirill Krylov

    (Department of Automation of Technological Processes and Production, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

Abstract

Statistical equations are widely used to describe the laws of various chemical technological processes. The values of constants and parameters included in these equations are determined by various methods. Methods that can determine the values of equation parameters using a limited amount of experimental data are of particular practical interest. In this manuscript, we propose a method to obtain simplex-interval equations. The proposed approach can be effectively used to control the values of technological process parameters. In this paper, we consider examples of chemical kinetics equation transformations and heterogeneous processes of solid particle dissolution. In addition, we describes mathematical model transformations, including equations for functions of the residence time distribution (RTD) of apparatus particles, the distribution of particles by size, etc. Finally, we apply the proposed approach to an example involving modeling of the calcination of coke in a tubular rotary kiln.

Suggested Citation

  • Ilia Beloglazov & Kirill Krylov, 2022. "An Interval-Simplex Approach to Determine Technological Parameters from Experimental Data," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2959-:d:889661
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    References listed on IDEAS

    as
    1. Lin, H. & Yamashita, K., 2002. "Hybrid simplex genetic algorithm for blind equalization using RBF networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(4), pages 293-304.
    2. Hsun-Heng Tsai & Chyun-Chau Fuh & Jeng-Rong Ho & Chih-Kuang Lin, 2021. "Design of Optimal Controllers for Unknown Dynamic Systems through the Nelder–Mead Simplex Method," Mathematics, MDPI, vol. 9(16), pages 1-14, August.
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