Integration of artificial intelligence into confinement ventilation control systems: development of specialized software based on the Mamdani algorithm

DOI: 10.31673/2412-9070.2025.025708

Abstract

The article proposes an innovative approach to controlling ventilation systems of the New Safe Confinement (NSC) of the Chernobyl NPP based on fuzzy logic using the Mamdani algorithm. The aim of the study is to optimize the operation of ventilation systems taking into account the inertia of air flows, dynamic external conditions (wind speed, pressure, humidity) and minimize radioactive emissions.
The developed system is based on a fuzzy inference model, where the input parameters are: pressure difference (∆P), wind speed (V) and air mass inertia (I).
Triangular membership functions were used to fuzzify the variables, and the fuzzy rule base was formed based on expert knowledge of physical processes in the NSC. The implementation of the Mamdani algorithm allowed adaptive control of fan power, ensuring pressure stability and energy efficiency.
Experiments conducted on real NSC data showed an 18% reduction in energy consumption compared to previous methods (genetic algorithms), a 25% reduction in pressure fluctuations, and a system response time of up to 5 minutes. The system is integrated with a SCADA platform for online monitoring and correction of ventilation operation.
These enhancements derive from the Mamdani algorithm’s capacity to process nonlinear rela-tionships and uncertainties without computationally intensive operations — a stark contrast to neural networks reliant on GPU clusters. The system’s modular architecture supports seamless integration of new sensors and rule updates, ensuring adaptability to evolving operational demands.
By demonstrating fuzzy logic’s robustness in extreme nuclear environments, this research marks a paradigm shift in containment facility management. Current efforts focus on hybrid neuro-fuzzy models to automate rule generation and refine pressure stability.
The results prove the effectiveness of the fuzzy approach for managing complex engineering facilities under uncertainty. Further research is aimed at implementing hybrid models (neural networks + fuzzy logic) for auto-formalization of rules.

Keywords: software engineering; artificial intelligence; neuro-fuzzy systems; fuzzy control; Mamdani algorithm; New Safe Confinement; air flow inertia; energy efficiency; radiation safety.