14 / 2024-05-05 10:11:35
Frequency conversion ventilation monitoring system based on environmental parameters collaborative prediction of wind speed and its effect verification
Variable frequency ventilation,real-time detection,airflow prediction,PID control,ventilation verification
需要修改
杨向东 / 北京科技大学
In order to predict the wind speed of the excavation roadway, control the frequency conversion operation of the local fan in real-time, and realize the real-time monitoring, collaborative prediction and frequency conversion control of the ventilation state of the excavation face, the frequency conversion ventilation control system of the excavation face is designed. Based on the theory of frequency conversion control, the genetic-neural network wind speed prediction optimization model was established and the frequency conversion ventilation control system of the excavation face was designed by using S7-200 SMART PLC. The system test results show that the genetic-neural network optimization model can collaboratively predict wind speed according to the environmental parameters (dust concentration, methane concentration, temperature and humidity, etc.) of different working conditions. The frequency conversion ventilation control system realizes the real-time monitoring of the environmental parameters of the underground excavation surface, and also provides two control modes: automatic and manual. Compared with the traditional constant power frequency control fan air volume, the PID air volume closed-loop control technology can control the fan air volume by frequency conversion, so that the actual wind speed of the roadway continues to approach the predicted value stably. The variable frequency ventilation control system can be widely used in different types of mines to realize the adaptive control response of ventilation equipment.

 
重要日期
  • 会议日期

    10月18日

    2024

    10月21日

    2024

  • 09月10日 2024

    初稿截稿日期

  • 10月08日 2024

    报告提交截止日期

  • 10月21日 2024

    注册截止日期

主办单位
中国土木工程学会隧道及地下工程分会
中山大学
承办单位
中山大学土木工程学院
隧道工程灾变防控与智能建养全国重点实验室
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