Features of modelling in automation for the primary oil refining technological process

Aygun Safarova; Elchin Melikov; Tamella Magerramova

doi:10.1117/12.3035889

31 July 2024 Features of modelling in automation for the primary oil refining technological process

Aygun Safarova, Elchin Melikov, Tamella Magerramova

Proceedings Volume 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024); 132170M (2024) https://doi.org/10.1117/12.3035889
Event: Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 2024, Fergana and Bukhara, Uzbekistan

Abstract

In the presented article, problems associated with the modeling features in automation and control of processes occurring in a primary oil refining technological installation are studied and investigated. In the article, when solving the modeling problem, special attention is paid to the problem of increasing the completeness of the received information used for processing, and in this connection, based on an active experiment, the “noise” characteristics inherent to the main indicators of the technological process under study are determined. It also addresses issues related to the correlation regression analysis features and the basic principles of designing an optimal filter that ensures the necessary cleaning of the received information from harmful interference and noise inherent in information communication lines. The article proposes an adaptive filter with an adaptation algorithm, which, analyzing the current state of the process under study and taking into account the disturbing influences constantly acting on the technological process, promptly corrects the optimal tuning parameters of the filter, minimizing distortion of the useful signal for the automatic control system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Aygun Safarova, Elchin Melikov, and Tamella Magerramova "Features of modelling in automation for the primary oil refining technological process", Proc. SPIE 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 132170M (31 July 2024); https://doi.org/10.1117/12.3035889

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KEYWORDS

Tunable filters

Mathematical modeling

Digital filtering

Signal filtering

Signal processing

Modeling

Statistical analysis

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