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

Abstract —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.

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Publication Details

Journal
International Journal of Advanced Network Monitoring and Controls
Published
2025-01-01
DOI
https://doi.org/10.2478/ijanmc-2025-0002
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Zhengxin Zhang, Jun Zhang, Shengquan Yang, Ruixin Ji
International Journal of Advanced Network Monitoring and Controls
Advanced Sensor and Control Systems
article

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

Zhengxin Zhang, Jun Zhang, Shengquan Yang, Ruixin Ji
article en

Abstract

Abstract —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.

International Journal of Advanced Network Monitoring and ControlsVol. 10(1)
Xi'an Technological University (CN)
Openalex Percentile: Top 85%
Advanced Sensor and Control Systems
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Research and Design of an Intelligent IoT Monitoring System for Coal Mine Gas Based on the Fuzzy-PID Algorithm — Zhengxin Zhang, Jun Zhang, et al. · International Journal of Advanced Network Monitoring and Controls (2025) | TGRS Research Map | TGRS