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An intelligent control algorithm for gas precise drainage problem based on Model Predictive Control.

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This study introduces an intelligent control model for coal seam gas extraction, enhancing safety. The model uses recurrent neural networks and model predictive control for effective gas drainage management.

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Control Systems

Background:

  • Underground gas disasters pose significant risks in coal mining.
  • Efficient coal seam gas extraction is vital for disaster prevention and resource management.

Purpose of the Study:

  • To develop an intelligent control model for optimizing coal seam gas extraction.
  • To enhance the safety and efficiency of underground gas management.

Main Methods:

  • Utilized a recurrent neural network (SimpleRNN) to determine ideal gas extraction curves.
  • Implemented a model predictive control (MPC) algorithm for dynamic control of extraction variables.
  • Controlled variables included gas concentration, flow rate, negative pressure, and pump efficiency.
  • Control variables comprised valve openings and pump power.

Main Results:

  • The SimpleRNN accurately fitted the ideal reference curve for controlled quantities.
  • The MPC algorithm demonstrated effective dynamic control, mitigating environmental and nonlinear interferences.
  • The intelligent control model achieved a superior control effect in simulated gas extraction data.

Conclusions:

  • The developed intelligent control model offers a robust approach for gas drainage.
  • This method provides a valuable reference for intelligent control strategies in underground gas management.
  • The integration of RNN and MPC enhances the safety and efficiency of coal seam gas extraction.