DOI: 10.5061/dryad.3r2280gwp
title: Data from: An intelligent control algorithm for the gas precise drainage problem based on model predictive control

description: 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.
description: # Data from: An intelligent control algorithm for the gas precise drainage
 problem based on model predictive control DOI:
 [10.5061/dryad.3r2280gwp](https://doi.org/10.5061/dryad.3r2280gwp) ####
 Data and File Structure The data serves as the basis for the algorithm
 simulation. The four control variables for the gas extraction system are:
 gas extraction concentration, gas extraction purity, gas extraction
 negative pressure, and gas extraction pump efficiency ratio. The raw data
 were cleaned, and missing and outlier values ​​were imputed using the
 nearest mean method. Due to the presence of values ​​with different
 dimensions in the dataset, the data was normalized and scaled to the range
 of 0-1. Files and Variables File: xiangmushuju.csv Description: Variables
 * Date/Time - 3356 - 3357 - 3358 - 3460 3356, 3357, 3358, 3460 correspond
 to Gas extraction concentration, Gas extraction purity, Gas extraction
 pump efficiency ratio, Gas extraction negative pressure * Gas extraction
 concentration: The overall trend of gas extraction concentration shows a
 decrease from 40% to 5% over time, with the actual value at the current
 moment being infinitely close to the optimal value of the ideal reference
 curve. （​Figure 6a in the manuscript） - Gas extraction purity: From an
 overall trend perspective, gas purity gradually decreases over time (with
 the original range specified as 9.0-5.0 m³/min), and the actual value at
 the current moment is infinitely close to the optimal value of the ideal
 reference curve.​（​Figure 6b in the manuscript） * Gas extraction pump
 efficiency ratio: This ratio should be maintained at approximately 1.5
 m³/(kW·h) to meet the requirements of economical gas extraction.​（​Figure
 6d in the manuscript） - Gas extraction negative pressure: The negative
 pressure during the extraction process must be maintained within the safe
 range of 10-30 kPa.​（​Figure 6c in the manuscript） ## Code/software
 Algorithm: Model epochs, batch size, and iteration count settings and
 conversion relationship.p Input: Dataset dataset 1.
 train_size=int(len(dataset)*0.70) 2. train,
 test=split_dataset(dataset,train_size,look_back) 3. Randomly divide 70% of
 the dataset into the training set data 4. batch_size=20 5. for in
 trange(i—iteration count) 6. history=model.fit(train_x,train_y, 7.
 batch_size=20—batch size, 8. nb_epoch—number of epochs 9. validation_data
 10. verbose, 11. Shuffle 12. Each epoch requires 7000 data samples for
 training 13. Each epoch requires 7000/20+1=35 batches 14. Each iteration
 uses 7000 data for training, with weights updated 351 times