Integrating AI in Air Quality Control Systems in Petrochemical and Chemical Manufacturing Facilities

Environmental degradation and health risks in and around petrochemical and chemical facilities are exacerbated by the continuous release of volatile organic compounds (VOCs), particulate matter (PM), and harmful gases such as nitrogen oxides (NOx) and sulfur oxides (SOx). Conventional air quality monitoring and control systems, which often rely on outdated technologies and manual interventions, lack the speed, adaptability, and predictive capabilities needed to effectively manage these issues. The integration of Artificial Intelligence (AI) offers a transformative approach, enabling more intelligent, responsive, and predictive environmental management solutions. This paper explores the application of AI technologies—such as machine learning (ML), deep learning, and IoTenabled sensors—in enhancing air quality monitoring and management within industrial settings. It reviews state-ofthe-art AI tools used for emissions forecasting and leak detection, demonstrating their role in optimizing operations and supporting regulatory compliance. Emerging research and innovations reveal that AI not only improves detection accuracy but also reduces pollution from industrial processes, contributing to more effective environmental stewardship. The paper also addresses key challenges in implementing AI solutions, including data quality management, model interpretability, infrastructure requirements, and regulatory alignment. Ultimately, incorporating AI into air quality monitoring and control systems can significantly boost operational efficiency, compliance, and sustainability, particularly when designing or upgrading petrochemical and chemical manufacturing plants.