Dynamic energy consumption rationing based on machine learning algorithms for oil refining tasks

Energy consumption rationing is necessary for high-quality production planning, and allows optimizing their use. This paper provides an analysis of various approaches to building a model of energy consumption, describes their limitations and new approaches to dynamic rationing. As the object of modeling the ELOU-AVT-6 (CDU/VDU-6) unit has been taken. Such units are intended for desalination and primary fractionation of oil. Functional requirements for the algorithms have been formed, based on real production needs. As the solution, models based on machine learning algorithms have been analyzed. These algorithms include CatBoost Regressor, Gradient tree boosting, Random Forest, ElasticNet and artificial neural networks. The analysis of the modeling results and comparison of the accuracy of the models is carried out. The paper also demonstrates a scenario of using a dynamic rationing model to analyze the causes of deviations of the actual consumption values from the planned ones.