PREDICTION OF THE COMPOSITION OF THE WIDE FRACTION OF LIGHT HYDROCARBONS IN PIPELINE TRANSPORTATION BY MACHINE LEARNING METHODS
S. N. Tereshchenko, A. L. Osipov, E. D. Moiseeva
Novosibirsk State University of Economics and Management, Novosibirsk, Russia
Keywords: wide fraction of light hydrocarbons, artificial intelligence, machine learning, gradient boosting, CatBoost, linear regression, pipeline
Abstract
The approach of applying machine learning methods for prediction of the component composition of the wide fraction of light hydrocarbons in pipeline transportation is investigated. The CatBoost library is used for building a machine learning model that allows the component composition of the mixture to be predicted with an error value of 2.263 by the MAPE metric.
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