VENTILATION CONTROL OF THE NEW SAFE CONFINEMENT OF THE CHORNOBYL NUCLEAR POWER PLANT BASED ON NEURO-FUZZY NETWORKS

KONTROLA WENTYLACJI NOWEJ BEZPIECZNEJ POWŁOKI CZARNOBYLSKIEJ ELEKTROWNI JĄDROWEJ OPARTA NA ROZMYTYCH SIECIACH NEURONOWYCH
Loboda P. Starovit I. Shushura O. Havrylko Y. Saveliev M. Sachaniuk-Kavets’ka N. Neprytskyi O. Oralbekova D. Mussayeva D.
2023 Politechnika Lubelska

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska
2023 #13 Issue 4 114 - 118 pp.

The accident at the Chornobyl Nuclear Power Plant (ChNPP) in Ukraine in 1986 became one of the largest technological disasters in human history. During the accident cleanup, a special protective structure called the Shelter Object was built to isolate the destroyed reactor from the environment. However, the planned operational lifespan of the Shelter Object was only 30 years. Therefore, with the assistance of the international community, a new protective structure called the New Safe Confinement (NSC) was constructed and put into operation in 2019. The NSC is a large and complex system that relies on a significant number of various tools and subsystems to function. Due to temperature fluctuations and the influence of wind, hydraulic processes occur within the NSC, which can lead to the release of radioactive aerosols into the environment. The personnel of the NSC prevents these leaks, including through ventilation management. Considering the long planned operational term of the NSC, the development and improvement of information technologies for its process automation is a relevant task. The purpose of this paper is to develop a method for managing the ventilation system of the NSC based on neuro-fuzzy networks. An investigation of the current state of ventilation control in the NSC has been conducted, and automation tools for the process have been proposed. Using an adaptive neuro-fuzzy inference system (ANFIS) and statistical data on the NSCs operation, neuro-fuzzy models have been formed, which allows to calculate the expenses of the ventilation system using the Takagi-Sugeno method. The verification of the proposed approaches on a test data sample demonstrated sufficiently high accuracy of the calculations, confirming the potential practical utility in decision-making regarding NSC’s ventilation management. The results of this paper can be useful in the development of digital twins of the NSC for process management and personnel training.

digital twin , fuzzy logic , information technology , neuro-fuzzy network , New Safe Confinement , ventilation management

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National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Department of Software Engineering in Energy, Kyiv, Ukraine
National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Department of Digital Technologies in Energy, Kyiv, Ukraine
Institute for Safety Problems of Nuclear Power Plants National Academy of Sciences of Ukraine, Chornobyl, Ukraine
Vinnytsia National Technical University, Vinnytsia, Ukraine
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Vinnytsia, Ukraine
Satbayev University, Almaty, Kazakhstan
Al Farabi Kazakh National University, Almaty, Kazakhstan

National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute
National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute
Institute for Safety Problems of Nuclear Power Plants National Academy of Sciences of Ukraine
Vinnytsia National Technical University
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University
Satbayev University
Al Farabi Kazakh National University

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