Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022); Krasnoyarsk; Krasnoyarsk

Год издания: 2022

Идентификатор DOI: 10.15405/epct.23021.22

Ключевые слова: hydrocracking unit, machine learning, classifier, upsampling, downsampling

Аннотация: The paper is devoted to the study of the influence of class imbalance on the quality of hydrocracking unit failure prediction models. The use of machine learning methods finds an increasing response in various industries due to the increase in computing power and the reduction in the cost of creating advanced process control systems. The oil and gas industry are highly profitable and large in terms of its industrial capacity; thousands of pieces of technical equipment within one enterprise are involved in the production of petroleum products and their processing. Therefore, improving the operational reliability of oil refining process equipment is an urgent scientific task. In this paper, we consider a method for modeling a hydrocracking unit for the production of diesel fuels and creating models for predicting plant equipment failures. Particular attention is paid to the influence of class imbalance in data when solving the classification problem. The built-in weighting methods for classes of machine learning models are compared, as well as upsampling and downsampling methods.

Журнал: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS

Номера страниц: 178-185

Место издания: London, United Kingdom

Издатель: European Proceedings

• Nekrasov Ivan (Siberian Federal University)
• Bukhtoyarov Vladimir (Siberian Federal University)
• Eremeeva Svetlana (Siberian Federal University)

• РИНЦ (eLIBRARY.RU)