Data from: An intelligent control algorithm for the gas precise drainage problem based on model predictive control

Intelligent extraction of coal seam gas constitutes a crucial development
direction for managing underground gas disasters. Building on an
established mathematical model, this study develops an
intelligent control model for gas extraction. In this model, controlled
variables include gas extraction concentration, gas extraction flow rate,
negative pressure, and extraction pump efficiency ratio, while control
variables are defined as the valve opening of extraction boreholes and the
power of extraction pumps. The ideal curve of the controlled
quantity with time is obtained by using the recurrent neural network
(SimpleRNN), and the controlled quantity is intelligently controlled by
the model predictive control (MPC) algorithm so that the actual value of
controlled quantity approaches the reference value at the corresponding
time of its ideal curve. Taking the simulated gas extraction data as an
example, an algorithm simulation experiment is performed. The experimental
results show that the ideal reference curve of the controlled quantity
obtained by the cyclic neural network has a good data fitting degree. The
dynamic control of the controlled quantity by the model predictive control
algorithm can overcome the interference of environmental and nonlinear
factors and achieve a better control effect, which provides a certain
reference for the intelligent control of gas drainage.