Artificial Intelligence–Based Neural Network Framework for Monitoring and Control of Noise Emissions in Steel Plants

Managing environmental noise is a critical challenge for industries near residential areas. This study presents a data-driven framework for real-time monitoring and control of acoustic emissions in a largescale steelworks. A multi-stage approach for proactive acoustic governance is proposed, which integrates process and meteorological data. XGBoost classification models ensure regulatory compliance by effectively detecting overcoming of noise limit. Continuous noise estimation is addressed via Deep Learning: while a Standard Feedforward Network provides high predictive accuracy, an innovative Architecture-Aware Model demonstrates that embedding domain knowledge achieves comparable performance with a significant reduction in parameters, increasing robustness. Finally, Self-Organizing Maps offer an exploratory interface to identify quieter operational regimes through topology-preserving mapping. This integrated AI framework provides a powerful Decision Support System for industrial noise mitigation and operational optimization.