Prediction of the Composition of the Wide Light Hydrocarbon Fraction by Methods of Machine Learning in Pipeline Transportation

Prediction of the Composition of the Wide Light Hydrocarbon Fraction by Methods of Machine Learning in Pipeline Transportation

The approach of using machine learning methods for automated prediction of the component composition of the wide light hydrocarbon fraction in pipeline transportation is studied. Based on the CatBoost library network, a machine learning model allowing one to determine the component composition of th...

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Veröffentlicht in:Optoelectronics, instrumentation, and data processing instrumentation, and data processing, 2022-02, Vol.58 (1), p.85-90
Hauptverfasser: Tereshchenko, S. N., Osipov, A. L., Moiseeva, E. D.
Format: Artikel
Sprache:eng
Schlagworte:
Lasers
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
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Zusammenfassung:The approach of using machine learning methods for automated prediction of the component composition of the wide light hydrocarbon fraction in pipeline transportation is studied. Based on the CatBoost library network, a machine learning model allowing one to determine the component composition of the mixture with an error of 2.263 in the MAPE metric is developed.
ISSN:8756-6990
1934-7944
DOI:10.3103/S8756699022010125