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Smart IoT-Driven Toxic Gas Monitoring and Alert System with Ventilation Performance Monitoring for Coal Miners’ Safety
* 1 , 2 , 1 , 3 , * 1
1  Department of Information Technology, KPR Institute of Engineering and Technology, Arasur, Coimbatore -641407, India
2  Department of Information Technology, Sri Krishna College of Technology, Coimbatore-641042, India
3  Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune - 412115, India
Academic Editor: Benoît PIRO

Abstract:

Miners face severe risks due to toxic gases such as methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S), which can cause suffocation, poisoning, and fatal accidents. This abstract proposes an IoT-based toxic gas sensor system designed for coal mining environments, providing real-time monitoring, early detection, and automated alerts to enhance miner safety. The system integrates Metal-Oxide Sensors (MOSs), Electrochemical Sensors, and Infrared Sensors (IR) for high-accuracy gas detection. MOSs detect H₂S and CH₄ by measuring electrical conductivity changes when gas molecules interact with a heated semiconductor. Electrochemical sensors measure gas concentration changes by generating an electric current proportional to CO levels, ensuring precise detection. IR sensors identify CO₂ by analyzing infrared absorption at specific wavelengths, enabling non-invasive and selective monitoring. This multi-sensor system achieves high analytical performance, detecting gases at parts per million (ppm) levels. The detection range is 0–500 ppm for CO and up to 5000 ppm for CH₄, with a limit of detection (LOD) of 1–5 ppm, ensuring early warning before dangerous concentrations are reached. The sensors offer high sensitivity, detecting concentration variations as low as 0.1 ppm, and strong selectivity, differentiating gases based on their chemical properties. With a response time of seconds and an error margin of less than ±2%, the system ensures accurate real-time data and rapid intervention during gas leaks. To further improve safety, Artificial Intelligence (AI) models, including ARIMA and Recurrent Neural Networks (RNNs), estimate occupancy levels and CO₂ accumulation, optimizing ventilation through the Key Performance Indicator for Ventilation (KPIv). This AI-driven airflow management system dynamically adjusts ventilation to reduce toxic gas buildup, improve air quality, and enhance mine-wide operational efficiency. By providing continuous monitoring, instant alerts, and intelligent ventilation control, this IoT-based gas detection system significantly mitigates exposure risks, ensuring a safer working environment for miners.

Keywords: Internet of Things; Coal mining; Toxic gas; Ventilation; Gas sensor; Safety engineering; Artificial Intelligence; Key Performance Indicator

 
 
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