Research and Design of an Intelligent IoT Monitoring System for Coal Mine Gas Based on the Fuzzy-PID Algorithm

• Published in: International journal of advanced network, monitoring, and controls Vol. 10; no. 1; pp. 11 - 28
• Main Authors: Yang, Shengquan, Zhang, Jun, Zhang, Zhengxin, Ji, Ruixin
• Format: Journal Article
• Language: English
• Published: Xi'an Sciendo 01.01.2025; De Gruyter Poland
• Subjects: Coal Mine Gas; Coal mining; Communication; Control algorithms; Data analysis; Data encryption; Data transmission; Energy consumption; Fuzzy logic; Fuzzy PID; Gas monitoring; Humidity; Internet of Things; Mines; Mining accidents & safety; Mining industry; Monitoring System; Monitoring systems; Sensors; Ventilation
• ISSN: 2470-8038; 2470-8038
• DOI: 10.2478/ijanmc-2025-0002

—To enhance the safety and efficiency of coal mine gas monitoring, this study develops an intelligent Internet of Things (IoT) monitoring system incorporating a Fuzzy-PID control algorithm. The system is structured into four layers—sensing, network transmission, control service, and mobile application— ensuring real-time data acquisition, stable transmission, intelligent processing, and remote monitoring. The Fuzzy-PID algorithm dynamically adjusts control parameters to improve response time and accuracy under nonlinear and uncertain conditions. Simulation experiments validate the system's performance, comparing traditional PID, Fuzzy, and Fuzzy-PID control strategies. Results indicate that the traditional PID algorithm achieves a response time of 2.0 s but exhibits oscillations of ±0.1 concentration units. The Fuzzy control algorithm stabilizes gas concentration within 4.0 s with deviations below ±0.05 units. The proposed Fuzzy-PID algorithm achieves an optimal balance, stabilizing gas concentration within 2.5 s with deviations reduced to less than ±0.03 units. These improvements enhance mine safety by reducing gas concentration fluctuations and providing real-time risk alerts. Practical deployment in a coal mining enterprise confirms the system’s capability in reducing manual intervention by 30% and improving early warning accuracy by 25%, demonstrating its potential for intelligent mine development.