Research and Design of an Intelligent IoT Monitoring System for Coal Mine Gas Based on the Fuzzy-PID Algorithm
—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 applicati...
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| Veröffentlicht in: | International journal of advanced network, monitoring, and controls Jg. 10; H. 1; S. 11 - 28 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Xi'an
Sciendo
01.01.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
| Schlagworte: | |
| ISSN: | 2470-8038, 2470-8038 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | —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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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2470-8038 2470-8038 |
| DOI: | 10.2478/ijanmc-2025-0002 |