2024 Vol.7 Feb.N01

• Title: Automatic control method of underground gas extraction in coal mine based on simple recurrent neural network
• Name: Xinliang Wu
• Company: College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
• Abstract:

   The current conventional control method of underground gas extraction in coal mines mainly analyzes the spherical flow of gas in coal seams, so as to clarify the gas circulation situation, which is better than ignoring the constraints of extraction efficiency, resulting in poor control accuracy. In this regard, the automatic control method of underground gas extraction in coal mine based on simple recurrent neural network is proposed. Firstly, safety constraints as well as efficiency constraints are constructed by combining various gas content standards. Then the four major control tasks and theoretical control strategies of the gas extraction system are proposed, and a mathematical model for gas extraction optimization is established. Finally, the control quantity is predictively modeled, with the input being the current state of the controlled quantity of the gas extraction system, and the goal of dynamically approaching the reference curve, and the regulation strategy of the control quantity is output. In the experiments, the control accuracy of the proposed method is verified. The analysis of the experimental results shows that the power fluctuation range of the pumping pump is small when the proposed method is used to control the gas extraction, and it has a high control effect.

  
  

• Keyword: recurrent neural networks; gas extraction; control methods;
• DOI: 10.12250/jpciams2024090216
• Citation form: Xinliang Wu .Automatic control method of underground gas extraction in coal mine based on simple recurrent neural network [J]. Computer Informatization and Mechanical System,2024,Vol.7,pp.73-76

[1] Boustead K J , Grace J F , Buck R K ,et al. Comparative effects of three different ventilatory treatments on arterial blood gas values and oxygen extraction in healthy anaesthetized dogs[J].Veterinary anaesthesia and analgesia, 2022, 49(3):251-264.

[2] Jain R , Jain B , Kabir A ,et al.Fabric phase sorptive extraction-gas chromatography-mass spectrometry for the determination of favipiravir in biological and forensic samples[J].Advances in Sample Preparation, 2023, 6:100058 - 100058.

[3] Yu S , Su X , Song J ,et al.The Hole Sealing Technology of Solid-Liquid Materials with Three Pluggings and Two Injections for Gas Extraction Hole in the Coal Mine[J].ACS omega, 2022, 7(48):43847-43855.

[4] Vyunikov A A , Vorozhtsov S G , Khoyutanova N V ,et al.Underground operations in Internatsionalny mine rock mass susceptible to gas dynamic phenomena [ J].Gornyi Zhurnal, 2021:76-80.

[5] Gao K , Li S , Liu Y ,et al. Effect of flexible obstacles on gas explosion characteristic in underground coal mine[J].Process Safety and Environmental Protection, 2021, 149:362-369.

[6] Wang Z , Cui H , Wei G ,et al.Discrete Element Simulation of Fracture Evolution around a Borehole for Gas Extraction in Coal Seams[J].ACS omega, 2022, 7( 43):38426-38435.

[7] Gao Q , Zong C .Rotor position estimation method of PMSM based on recurrent neural network[J].Energy Reports, 2022, 8:883-889.

[8] Yang S B , Envelope Z L .Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control*[J].IFAC-PapersOnLine, 2022 , 55( 7):780-785.

[9] Geng X , He X , Xu L ,et al. Graph correlated attention recurrent neural network for multivariate time series forecasting[J].Inf. Sci. 2022, 606:126- 142.

[10] Onan A .Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification[J].Journal of King Saud University - Computer and Information Sciences, 2022, 34( 5):2098-2117.

[11]LI Da-peng, ZHAO Qi-hui, XING Tie-jun, ZHAO Da-zhe.Prison Term Prediction of Judicial Cases Based on Hierarchical Attentive Recurrent Neural Network[J].Journal of Northeastern University(Natural Science), 2022, 43(3):344-349.

[12] Noohi M , Faraji A , Sadrossadat S A ,et al. Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization[J].International Journal of Circuit Theory and Applications, 2022, 51:2351 - 2374.

[13] Otovi E , Njirjak M , Kalafatovic D ,et al. Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides[J].Journal of chemical information and modeling, 2022, 62(12):2961-2972.

[14] Pan S , Yan K , Yang H ,et al. A sparse spike deconvolution method based on Recurrent Neural Network like improved Iterative Shrinkage Thresholding Algorithm[J].Geophysical Prospecting for Petroleum, 2022, 58(4):533-540.

[15] Wang X , Yan B, Liu H , et al. Off-Design Model for the Condenser Based on Neural Network[J]. Computer Simulation, 2022(5)99-103.