Research on stochastic properties of time series data on chemical analysis of cast iron
- Details
- Category: Content №4 2024
- Last Updated on 28 August 2024
- Published on 30 November -0001
- Hits: 2042
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.
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.
Newer news items:
- The labor market as a component of the economic security system of Ukraine - 28/08/2024 03:24
- Ethical and social incentives for the transformation of the business model of enterprise management in conditions of sustainable development - 28/08/2024 03:24
- Innovative approaches to personnel security under the conditions of martial law - 28/08/2024 03:24
- The model of economic cooperation systems in the context of implementation of the “One Belt One Road” initiative - 28/08/2024 03:24
- Ukraine’s policy on brain drain in the wartime and post-war periods - 28/08/2024 03:24
- Intellectual potential assessing methodology of an innovation-oriented enterprise - 28/08/2024 03:24
Older news items:
- On the issue of load’s external ballistics under low-speed transportation - 28/08/2024 03:24
- Designing the predictive control of a drum dryer using multi-agent technology - 28/08/2024 03:24
- Cumulative triangle for visual analysis of empirical data - 28/08/2024 03:24
- The right to a safe environment: economic and legal guarantees of provision in Ukraine - 28/08/2024 03:23
- Floristic and ecological structure of the landfill vegetation in the Western Forest Steppe of Ukraine - 28/08/2024 03:23
- The effect of petroleum products pollution on environmental soil condition at airport adjacent territory - 28/08/2024 03:23
- Features of the assessment of occupational risks under hazardous working conditions - 28/08/2024 03:23
- Environmental toxicity assessment of mining waste from an abandoned Zn-Pb mine - 28/08/2024 03:23
- Application of modern mathematical apparatus for determining the dynamic properties of vehicles - 28/08/2024 03:23
- Strength analysis of the model 918 wagon under non-typical bulk loads - 28/08/2024 03:23