Сибирский федеральный университет

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

Конференция: II International Scientific Conference on Advances in Material Science and Technology (CAMSTech-II 2021); Krasnoyarsk, Russia; Krasnoyarsk, Russia

Год издания: 2021

Идентификатор DOI: 10.1088/1757-899X/1181/1/012029

Аннотация: Every year on the planet there are many natural and man-made accidents that have a significant impact on the infrastructure and normal life of society. In 2020, on the territory of the Krasnoyarsk Territory, there were 2 major technogenic accidents associated with the bottling of oil products and causing significant damage to the environment, which indicates the high importance of the problem of monitoring and forecasting, as well as the need to supervise enterprises that store and refuel equipment with oil products. This issue also acquires great importance in connection with the development of the design resource, tank farm and technological equipment, which are still in operation. The solution to this problem lies in the mainstream of organizing continuous monitoring, increasing supervisory measures, organizing forecasting scenarios for the development of the situation in order to develop action plans for the implementation of one of the calculated scenarios, for a prompt response and reduce the consequences of an emergency. In this work, the problem of developing scenarios for the development of emergency situations at a tank farm and predicting possible consequences in order to reduce the likelihood of these events was solved. To solve this problem, 12 scenarios of the development of emergency situations were simulated and the situation with the ingress of oil products into the Yenisei River was simulated in order to assess the area of bottling of oil products. Modeling of emergency situations was carried out using the software product Toxy + risk and neural network forecasting of the bottling area until localization using the neural network simulator NeuroPro, developed at the Institute of Computational Modeling of the Federal Research Center of the KSC SB RAS. A comparison is made between the analytical method for determining the area of the oil spill in a reservoir and the neural network method.

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: Volume 1181

Номера страниц: 12029

Место издания: Krasnoyarsk, Russia

Издатель: IOP Publishing Ltd

• Grebnev Y.V. (Main Directorate of the Ministry of Emergency Situations of Russia for the Krasnoyarsk Territory)
• Moskalev A.K. (Siberian Federal University)

• РИНЦ (eLIBRARY.RU)