Smart and Automated Air Quality Monitoring System for Industrial Facilities Using Machine Learning and Pollution Prediction

Abstract

This project presents the design and development of a smart and autonomous system for air quality monitoring and prediction in industrial environments. It aims to address the environmental and health risks resulting from industrial emissions and poor ventilation conditions by integrating sensor-based data acquisition and smart analytical models. It gathers real-time environmental information and transmits this to a central processing unit wirelessly, where pollution trends are predicted and processed using machine learning algorithms. It operates air purification and ventilation automatically, according to predictions, to provide safe and sustainable working conditions. A dashboard offers simple monitoring, remote control, and instant alarms for more-than-safe air quality. The prototype created demonstrates the potential of combining smart sensing and data-driven prediction to aid in enhanced air quality control, energy conservation, and occupational safety in factory plants.