LINEAR AND POLYNOMIAL REGRESSION AS TOOLS FOR HISTORICAL DATA ANALYSIS IN OIL REFINING PROBLEMS
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.
Levina T.M., Almukhametova E.I., Chudakov N.M. Linear and Polynomial Regression as Tools for Historical Data Analysis in Oil Refining Problems // Research result. Informationtechnologies. – T.11, №2, 2026. – P. 26-35. DOI: 10.18413/2518-1092-2026-11-2-0-3
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