ADAPTIVE CONTROL OF AIR PURIFICATION PROCESSES IN INDUSTRIAL PAINTING WORKSHOPS: A REVIEW OF SCIENTIFIC LITERATURE

This article examines modern approaches to adaptive control of air purification processes in industrial facilities, particularly in paint shops for parts coating. The relevance of the study is driven by the need to enhance air purification efficiency, ensure worker safety, and comply with environmental regulations. The paper analyzes adaptive control methods, including the use of neural networks, machine learning, and adaptive control algorithms, which enable real-time optimization of purification systems. Special attention is given to the utilization of sensor data for predicting pollution levels and automatically adjusting purification parameters. The article also discusses the advantages and limitations of implementing adaptive technologies and proposes directions for future research.