Науковий вісник НГУ

Research on stochastic properties of time series data on chemical analysis of cast iron

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №4 2024

Last Updated on 28 August 2024

Published on 30 November -0001

Hits: 2238

Authors:

V.V.Sidanchenko*, orcid.org/0000-0001-5581-9177, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Yu.Gusev, orcid.org/0000-0002-0548-728X, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (4): 135 - 140

https://doi.org/10.33271/nvngu/2024-4/135

Abstract:

Purpose. To provide a procedure for identifying chaotic processes in a dynamic system and to examine time series, describing the chemical composition of cast iron at the blast furnace output with the purpose of identifying the nonlinearity of the investigated system and detecting the presence of chaotic processes in it.

Methodology. The determination of the unique characteristics of the attractor of a dynamic chaotic system based on the time series of cast iron’s chemical composition values was carried out using methods of nonlinear dynamics and dynamic chaos theory, such as the autocorrelation function method, correlation and fractal dimensions.

Findings. The methods of nonlinear dynamics and dynamic chaos theory were used to study the behavior of time series data on the chemical composition of cast iron at the blast furnace output. The presence was identified of chaotic processes with a fractal structure in the studied dynamic system, leading to the inefficiency of traditional analysis methods based on the Gaussian properties of stochastic processes.

Originality. For the first time, the possibility and feasibility of applying chaos theory methods for the analysis and prediction of time series data on the chemical composition of cast iron at the blast furnace output were substantiated.

For the first time, the nonlinearity of the studied dynamic system was identified, and chaotic processes were discovered within it by determining the unique characteristics of the strange attractor of the system using the analyzed time series, such as embedding dimension, time delay, and the largest Lyapunov exponent.

Practical value. The obtained results open up the possibility for more effective and qualitative analysis of the behavior of the studied dynamic system by developing new tools for assessment and prediction that are adequate to the nature of the ongoing processes.

Keywords: nonlinear dynamics, dynamic chaos, strange attractor, fractal properties of time series, spectrogram

References.

1. Sirenko, K. A., & Mazur, V. L. (2021). Ideology of adjusting the chemical composition of synthetic cast iron in the process of casting. Metal ta lyttia Ukrainy, 29(4). https://doi.org/10.15407/scin15.04.005.

2. Sidanchenko, V. (2023). Examination of the data distribution nature on the chemical composition of cast iron at the output. Information Technology: Computer Science, Software Engineering and Cyber Security, (3), 65-69. https://doi.org/10.32782/IT/2023-3-8.

3. Gusev, O., & Sidanchenko, V. (2022). Fractal analysis of real data on the chemical compositionof cast iron at the output of a blast furnace. Information Technology: Computer Science, Software Engineering and Cyber Security, (2), 24-31. https://doi.org/10.32782/IT/2022-2-3.

4. Pechuk, V., Krasnopolska, T., & Pechuk, Ye. (2023). A universal algorithm for estimating the leading lyapunov exponent in a dissipative dynamic system. Prykladna heometriia ta inzhenerna hrafika, (105), 190-199. https://doi.org/10.32347/0131-579X.2023.105.190-199.

5. Derbentsev, V. D., Serdiuk, O. A., Soloviov, V. M., & Sharapov, O. D. (2010). Synergistic and econophysical methods of studying dynamic and structural characteristics of economic systems. Brama-Ukraina. https://doi.org/10.31812/0564/1045.

6. Danylov, V. Ya., Zinchenko, A. Yu., & Zhyrov, O. L. (2013). Detection of chaos in realizations of nonlinear dynamic systems and pseudo-phase reconstruction of their attractors. Naukovi pratsi Chornomorskoho derzhavnoho universytetu imeni Petra Mohyly. Ser.: Kompiuterni tekhnolohii, (201), 120-126.

7. Sidanchenko, V., & Nikolska, O. (2023). Methods of non-linear dynamics in the problem of forecasting the chemical composition of cast iron at the output. Information Technology: Computer Science, Software Engineering and Cyber Security, (2), 76-83. https://doi.org/10.32782/IT/2023-2-9.

8. Rusyn, V. B. (2014). Modeling and research of Chaotic Rossler system with LabView and Multisim software environments.  Visnyk Natsionalnoho Tekhnichnoho Universytetu Ukrainy Kyivskyi Politekhnichnyi Instytut. Seriia: Radiotekhnika. Radioaparatobuduvannia, (59).

9. Chikina, N. O., & Antonova, I. V. (2022). Prediction analysis of time series with long-term memory. Visnyk Natsionalnoho tekhnichnoho universytetu “KhPI”. Seriia: Matematychne modeliuvannia v tekhnitsi ta tekhnolohiiakh, (1), 130-136. https://doi.org/10.20998/2222-0631.2022.01.14.

10. Zaika, V. I., & Kyshenko, V. D. (2012). Forecasting the operation of the defecosaturation station using the theory of deterministic chaos. Visnyk Sumskoho derzhavnoho universytetu. Ser.: Tekhnichni nauky, (3), 72-79.

11. Budkova, L. V., & Korniyenko, V. I. (2013). Complex estimation of characteristics and traffic identification in information telecommunication networks. Information Processing Systems, (2), 109.

12. Koibichuk, V. V., Bozhenko, V. V., Yatsenko, V. V., Hrytsenko, K. H., Didenko, I. V., & Dotsenko, T. V. (2023). Development of financial asset price forecasting models using machine learning methods and statistical analysis. UKRNOIVI.

13. Zamula, O. A. (2019). Optimization of discrete complex signal synthesis methods in modern broadband multi-user communication systems. Radiotekhnika, (198), 182-191. https://doi.org/10.30837/rt.2019.3.198.13.

14. Chernetski, N., & Kishenko, V. (2014). Brewing unit time series analysis in the research of the complex system attractor properties. Eastern-European Journal of Enterprise Technologies, 6(2). https://doi.org/10.15587/1729-4061.2014.31094.

15. Unihovskyi, L. M., Oleshko, T. I., Horbacheva, O. M., Maru­sych, O. V., & Leshchynskyi, O. L. (2010). Quasi-cyclical pre-forecast analysis of world oil prices. Modeliuvannia ta informatsiini tekhnolohii.

16. Pechuk, V. D., & Krasnopolskaya, T. S. (2022). On the estimation of the senior Lyapunov exponent of the cross-wave model in a rectangular channel of finite dimensions. Matematychni metody ta fizyko-mekhanichni polia, 65(1-2), 209-215.

17. Voronov, H. H. (2020). Application of image recognition methods for classification of time series. Retrieved from https://openarchive.nure.ua/handle/document/21644.

18. Liushenko, L., Perehuda, Ya., & Sushchuk-Sliusarenko, V. (2023). A software solution for forecasting the dynamics of currency rates taking into account the influence of crisis factors. Nauka i tekhnika sohodni, (22). https://doi.org/10.52058/2786-6025-2023-8(22)-369-382.

19. Khomiak, A. (2022). Methods of time series analysis using recurrent neural networks. Materialy XI Mizhnarodnoi naukovo-praktychnoi konferentsii molodykh uchenykh ta studentiv “Aktualni zadachi suchasnykh tekhnolohii”, 128-128. Retrieved from https://elartu.tntu.edu.ua/bitstream/lib/39372/1/Zbirnyk_%D0%A1IIMT_2022.pdf#page=130.

20. Zeng, Z., Amin, M. G., & Shan, T. (2020). Arm motion classification using time-series analysis of the spectrogram frequency envelopes. Remote Sensing, 12(3), 454. https://doi.org/10.3390/rs12030454.

• < Prev
• Next >