Regression analysis and classification of temperature modulated metal oxide semiconductor gas sensors responses on flue gas

Industrial emissions, particularly from flue gases, pose significant risks to environmental sustainability and public health. Conventional air quality monitoring systems often suffer from high costs, delayed reporting, and limited detection capabilities. This study presents a cost-effective, real-time air quality monitoring solution using an electronic nose (eNose) system integrated with Metal Oxide Semiconductor (MOS) gas sensors. These sensors target key pollutants, such as carbon monoxide (CO) and carbon dioxide (CO₂), which also serve as indicators of transformer faults in industrial settings. The eNose system leverages machine learning for both regression and classification tasks, enabling accurate quantification of pollutant levels and categorization of air quality into defined categories. Principal Component Analysis (PCA) is employed to optimize feature extraction, enhancing model precision and efficiency. Notably, the system integrates digitally controlled buck converters for automatic temperature regulation, reducing sampling time from 390 to 130 seconds. Additionally, a redesigned airtight sensor chamber and optimized airflow design, along with the use of Tedlar bags, improve sample integrity and minimize interference. Hardware development involved prototyping on breadboards using LM2575, LM2576, and LM2574 ICs, followed by the creation of a compact 10 cm × 10 cm PCB for efficient power management. Multimeter testing verified reliable electrical connections. Experimental validation showed the system achieved over 91% accuracy in distinguishing between "good" and "bad" air quality levels. Strong correlations between sensor output and pollutant concentrations confirm system reliability. This research demonstrates a scalable, efficient tool for real-time air quality monitoring and fault detection in industrial environments.