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 mode...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems Ročník 14; číslo 3; s. 20
Hlavní autor: Kudriashov, Nikita S
Médium: Journal Article
Jazyk:angličtina
ruština
Vydáno: Saint Petersburg Peter the Great St. Petersburg State Polytechnical University 01.01.2021
Témata:
Algorithms
Artificial neural networks
Desalination
Energy consumption
Fractionation
Machine learning
Model accuracy
Modelling
Petroleum refining
Production planning
ISSN:2304-9766
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2304-9766
DOI:10.18721/JCSTCS.14302