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

## TL;DR

This paper introduces an intelligent control algorithm using Model Predictive Control and neural networks to improve gas drainage in coal mines.

## Contribution

A novel intelligent control model for gas extraction using MPC and RNN to achieve precise drainage.

## Key findings

- The RNN-generated reference curve fits gas extraction data well.
- MPC algorithm effectively controls gas extraction despite environmental and nonlinear factors.

## Abstract

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.