Список литературы

2518-1092

Research result. Information technologies

2518-1092

10.18413/2518-1092-2026-11-2-0-3

4253

INFORMATION SYSTEM AND TECHNOLOGIES

<strong>LINEAR AND POLYNOMIAL REGRESSION AS TOOLS FOR HISTORICAL DATA ANALYSIS IN OIL REFINING PROBLEMS</strong>

Levina

Tatyana Mikhailovna

Levina

Tatyana Mikhailovna

tattin76@mail.ru

Almukhametova

Elina Ilnurovna

Almukhametova

Elina Ilnurovna

Chudakov

Nikita Mikhailovich

Chudakov

Nikita Mikhailovich

2026

11200

The article examines the application of regression methods (linear and polynomial regression) for predicting technological parameters in the oil and gas industry. The relevance of the study stems from the need for accurate forecasting tools that help optimize the control of oil refining units and reduce the risks of deviations from standard operating modes. The research methodology includes the analysis of historical data. The practical implementation was carried out using web technologies. The software architecture follows a client server approach, which ensures the system’s scalability and security. As a result, mathematical models were developed for three key parameters of oil refining, an interactive web platform with graphs was implemented, and the feasibility of integrating the solutions into an industrial environment based on a domestic operating system was confirmed. It is concluded that linear and polynomial regression are effective tools for analyzing historical data and predicting technological parameters. The research results provide clarity, cross platform compatibility, and reliability, making the solution promising for implementation in oil refining production processes.

linear regressionpolynomial regressiontechnological parameter forecastingoil and gas industrymathematical modelclient server architecturedata visualizationcharting library

Список литературы

1. Fires and Fire Safety in 2019: Statistical Digest / edited by D.M. Gordienko. Moscow: VNIIPO, 2020, –80 p.

2. Udartseva O.V. Analysis of the Conditions and Causes of Emergencies at Oil and Gas Enterprises // Problems of Risk Management in the Technosphere. 2020, No. 2(54), pp. 6-15. EDN FMPLMZ.

3. Leonova I.V. The Main Causes of Emergencies at Hazardous Industrial Facilities // Student Journal, 2022, No. 22-4 (192), pp. 29–36.

4. Federal Law of 08.08.2024 No. 318-FZ "On Amendments to Certain Legislative Acts of the Russian Federation and the Repeal of Certain Provisions of Legislative Acts of the Russian Federation" // Official Internet Portal of Legal Information. – URL: http://www.pravo.gov.ru (date of access: 15.10.2025).

5. Tanaka H., Uejima S., Asai K. Linear regression analysis with fuzzy model // IEEE Transactions on Systems, Man, and Cybernetics. – 1982. – Vol. 12, no. 6. – P. 903-907.

6. Yang Q., Liu Y., Chen T., Tong Y. Federated machine learning: Concept and applications // ACM Transactions on Intelligent Systems and Technology (TIST). – 2019. – Vol. 10, no. 2. – P. 1-19.

7. Taskin A.S. Linear regression with feature clustering on real-valued data / A.S. Taskin, E.M. Mirkes // Bulletin of the Siberian State Aerospace University named after Academician M.F. Reshetnev. – 2012. – No. 3(43). – P. 71-76. – EDN PCTYLN.

8. Geron A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. – O'Reilly Media, 2022. – 880 p.

9. Bazilevsky M.P., Karaulova A.V. Estimating the degree of nonlinearity of polynomial regression models // Information technologies and mathematical modeling in the management of complex systems. – 2022. – No. 3(15). – P. 1-6. – EDN QOAUOW.

10. Petrov AV First-level indicators in polynomial regression analysis / AV Petrov // Bulletin of Irkutsk State Technical University. – 2018. – Vol. 22, No. 5(136).

11. Montgomery D.C., Peck E.A., Vining G.G. Introduction to linear regression analysis. – John Wiley & Sons, 2021. – 608 p.

12. Kuvaev M.Yu., Antimonov O.V. Modern trends in the development of web development // StudNet. – 2020. – No. 9. – URL: https://cyberleninka.ru/article/n/sovremennye-tendentsii-razvitiya-veb-razrabotki

(date of access: 17.10.2025).

13. Gridin V.N., Anisimov V.I., Vasiliev S.A. Methods for improving the performance of modern web applications // Bulletin of SFedU. Technical sciences. – 2020. – No. 2 (212). – P. 193-200.

14. Introduction to npm // Node.js: [Electronic resource]. – URL: https://nodejs.org/en/learn/getting-started/an-introduction-to-the-npm-package-manager (date of access: 17.10.2025).

15. Shnaiderman B. User Interface Design. – Moscow: DMK Press, 2020. – 648 p.

16. Gainanova R.Sh., Shirokova O.A. Creating Client-Server Applications // Bulletin of the Technological University. – 2017. – Vol. 20, No. 9. – Pp. 79-84.

17. Poluektova N.R. Web Application Development. – Moscow: Yurait, 2024. – 205 p.

18. Official website of RED OS: Knowledge Base // RED Soft. – URL: https://redos.red-soft.ru/base (date of access: 18.10.2025).

19. Certificate of state registration of computer program No. 2025612664 Russian Federation. Automated workstation software for a bitumen plant operator: applied 10.01.2025: published 03.02.2025 / T.M. Levina, N.M. Chudakov, N.S. Klinkov; applicant Federal State Budgetary Educational Institution of Higher Education

“Ufa State Petroleum Technological University”. – EDN ODTXFK.

20. Certificate of state registration of computer program No. 2025612821 Russian Federation. Automated workstation software for a bitumen plant technologist: applied 10.01.2025: published 04.02.2025 / T.M. Levina, N.M. Chudakov, N.S. Klinkov; applicant Federal State Budgetary Educational Institution of Higher Education “Ufa State Petroleum Technological University”. – EDN ADHVFT.