Modern Methods for Reducing the Environmental Footprint of Automotive Paint Shops Through Process Parameter Control

The article examines modern methods for reducing the environmental footprint of automotive paint shops through process parameter control and digital optimization tools. The analysis is based on a comparative review of recent empirical and theoretical studies on paint shop energy efficiency, emission abatement systems, digital twins, and algorithmic control of the air environment. It is shown that isolated equipment modernization and the installation of individual purification units do not ensure a sustainable environmental effect and lead to increased capital expenditures without proportional emission reduction. It is substantiated that the key factor is parametric and predictive control of thermal and aerodynamic oven regimes, spraying processes, and ventilation circuits, which enables synchronization between energy consumption and pollutant removal efficiency. It is demonstrated that local optimization of airflow structures and temperature fields reduces the material intensity of repainting operations and the indirect carbon load of ventilation subsystems, while the integration of digital models and autonomous control systems forms a stable balance between energy and environmental indicators. Special attention is paid to the role of machine learning, IoT-based filter monitoring, and digital twins in maintaining VOC removal stability and predictability of thermal regimes. The results indicate that the effectiveness of environmental optimization is determined by the coherence of thermal, ventilation, and digital control loops rather than by the scale of technological modernization. The article may be useful for researchers in industrial ecology, energy-efficiency specialists, and engineers of automotive manufacturing enterprises.