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Response Characteristics of Gas Concentration Level in Mining Process and Intelligent Recognition Method Based on BI-LSTM

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Abstract

The change of gas emission or concentration level at the working face is one of the main precursor characteristics of coal and gas outburst. At present, coal and gas outburst monitoring and early warning are mainly based on whether it exceeds the limit and its change law. However, the gas concentration level is affected by factors such as coal seam gas content, permeability, and mining process, and the change law is complex to recognize manually. In this paper, the response characteristics of gas concentration level in the mining process are analyzed and revealed, and a bidirectional long short-term memory model is established. The change characteristics of the gas concentration level in the mining and non-mining processes are studied and recognized. The results show that the change law of gas concentration in the mining process has apparent periodicity and trapezoidal volatility. The proposed intelligent recognition method based on the bidirectional long short-term memory neural network can automatically recognize the underground mining and non-mining processes, and the recognition accuracy achieves \(97.7\mathrm{\%}\). The research can significantly help improve the level of coal mine safety management and the accuracy of early warning of coal and gas outburst.

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References

  1. Du F, Wang K, Zhang X, Xin C, Shu L, Wang G (2020) Experimental study of coal-gas outburst: insights from coal-rock structure, gas pressure and adsorptivity. J Nat Resour Res 29(4):2481–2493. https://doi.org/10.1007/s11053-020-09621-7

    Article  Google Scholar 

  2. Wang E, Zhang G, Zhang C, Li Z (2022) Research progress and prospect on theory and technology for coal and gas outburst control and protection in China. J China Coal Soc 47(01):297–322. https://doi.org/10.13225/j.cnki.jccs.yg21.1846

    Article  Google Scholar 

  3. National Mine Safety Administration (2022) National mine safety administration on notice on strengthening prevention of coal and gas outburst. https://www.chinamine-safety.gov.cn/ (Published 6 July 2022)

  4. Qiu L, Li Z, Wang E, Liu Z, Ou J, Li X et al (2018) Characteristics and precursor information of electromagnetic signals of mining-induced coal and gas outburst. J Loss Prev Process Ind 54:206–215. https://doi.org/10.1016/j.jlp.2018.04.004

    Article  Google Scholar 

  5. Wang Y, Liu L, Fu H, Xu Y (2018) Research on acoustic emission multi-parameter time series based prediction of gas outburst. J China Saf Sci J 28(05):129–34. https://doi.org/10.16265/j.cnki.issn1003-3033.2018.05.022

    Article  Google Scholar 

  6. Wang A, Song D, He X, Dou L, Li Z, Zu Z et al (2019) Investigation of coal and gas outburst risk by microseismic monitoring. J PLoS One 14(5):20. https://doi.org/10.1371/journal.pone.0216464

    Article  Google Scholar 

  7. Wang C, Wei L, Hu H, Wang J, Jiang M (2022) Early warning method for coal and gas outburst prediction based on indexes of deep learning model and statistical model. J Front Earth Sci 10:17. https://doi.org/10.3389/feart.2022.811978

    Article  Google Scholar 

  8. Li Z, Jiang Y, Zhu W (2012) Mine gas wireless monitoring and forecasting network based on support vector machine. C 2nd International Conference on Engineering Materials, Energy, Management and Control; Mar 17–18; Wuhan, PEOPLES R CHINA. STAFA-ZURICH: Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMR.424-425.232

  9. Dong D, Wang H, Jia P (2012) Mine gas concentration pre-warning based monitoring data relational analysis. C 2nd International Conference on Chemical, Material and Metallurgical Engineering (ICCMME 2012); Dec 15–16; Kunming, PEOPLES R CHINA. STAFA-ZURICH: Trans Tech Publications Ltd; 2013. https://doi.org/10.4028/www.scientific.net/AMR.634-638.3655

  10. Wu H, Shi S, Lu Y, Liu Y, Huang W (2020) Top corner gas concentration prediction using t-distributed Stochastic Neighbor Embedding and Support Vector Regression algorithms. J Concurr Comput-Pract Exp 32(14):10. https://doi.org/10.1002/cpe.5705

    Article  Google Scholar 

  11. Hou P, Xue Y, Gao F, Wang S, Jiao X, Zhu C (2022) Numerical evaluation on stress and permeability evolution of overlying coal seams for gas drainage and gas disaster elimination in protective layer mining. J Mining Metall Explor 39(3):1027–1043. https://doi.org/10.1007/s42461-022-00584-2

    Article  Google Scholar 

  12. Qiu L, Peng Y, Song D (2022) Risk prediction of coal and gas outburst based on abnormal gas concentrationin blasting driving face. J Geofluids 2022:1468–8115. https://doi.org/10.1155/2022/3917846

  13. Bassam A, Santoyo E, Andaverde J, Hernandez JA, Espinoza-Ojeda OM (2010) Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach. J Comput Geosci 36(9):1191–1199. https://doi.org/10.1016/j.cageo.2010.01.006

    Article  Google Scholar 

  14. Sun J, Niu Z, Innanen KA, Li JX, Trad DO (2020) A theory-guided deep-learning formulation and optimization of seismic waveform inversion. J Geophysics 85(2):R87–R99. https://doi.org/10.1190/geo2019-0138.1

    Article  Google Scholar 

  15. Thiele C, Araya-Polo M, Alpak FO, Riviere B, Frank F (2017) Inexact hierarchical scale separation: a two-scale approach for linear systems from discontinuous Galerkin discretizations. J Comput Math Appl 74(8):1769–1778. https://doi.org/10.1016/j.camwa.2017.06.025

    Article  MathSciNet  MATH  Google Scholar 

  16. Barros-Daza MJ, Luxbacher KD, Lattimer BY, Hodges JL (2022) Fire size and response time predictions in underground coal mines using neural networks. J Mining Metall Explor 39(3):1087–1098. https://doi.org/10.1007/s42461-022-00580-6

    Article  Google Scholar 

  17. Gu Q, Xue B, Song J, Li X, Wang Q (2022) A high-precision road network construction method based on deep learning for unmanned vehicle in open pit. J Mining Metall Explor 39(2):397–411. https://doi.org/10.1007/s42461-022-00548-6

    Article  Google Scholar 

  18. Bao W, Chu F, Shang C, Chen T, Wang F, Gao F et al (2021) A safe control scheme for the dense medium coal separation process based on Bayesian network and active learning. C 33rd Chinese Control and Decision Conference (CCDC); May 22–24; Kunming, PEOPLES R CHINA. NEW YORK: Ieee; 2021. https://doi.org/10.1109/CCDC52312.2021.9601924

  19. Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space Odyssey. J IEEE Trans Neural Netw Learn Syst 28(10):2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924

    Article  MathSciNet  Google Scholar 

  20. Le T, Vo MT, Vo B, Hwang E, Rho S, Baik SW (2019) Improving electric energy consumption prediction using CNN and Bi-LSTM. J Appl Sci Basel 9(20):12. https://doi.org/10.3390/app9204237

    Article  Google Scholar 

  21. Nelson DMQ, Pereira ACM, de Oliveira RA et al (2017) Stock market’s price movement prediction with LSTM neural networks. C International Joint Conference on Neural Networks (IJCNN); May 14–19; Anchorage, AK. NEW YORK: Ieee; 2017. https://doi.org/10.1109/IJCNN.2017.7966019

  22. Shahid F, Zameer A, Muneeb M (2020) Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. J Chaos Solitons Fractals 140:9. https://doi.org/10.1016/j.chaos.2020.110212

    Article  MathSciNet  Google Scholar 

  23. Cheng Q, Chen Y, Xiao Y, Yin H, Liu W (2022) A dual-stage attention-based Bi-LSTM network for multivariate time series prediction. J J Supercomput 78(14):16214–16235. https://doi.org/10.1007/s11227-022-04506-3

    Article  Google Scholar 

  24. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. J IEEE Trans Signal Process 45(11):2673–2681. https://doi.org/10.1109/78.650093

    Article  Google Scholar 

  25. Ramcharan A, Baranowski K, McCloskey et al (2017) Deep learning for image-based cassava disease detection. J Front Plant Sci 8:1852. https://doi.org/10.3389/fpls.2017.01852

    Article  Google Scholar 

  26. Jiang H et al (2018) Noise reduction analysis of rolling bearing vibration signal based on time domain synchronization. C 11th International Conference on Intelligent Computation Technology and Automation (ICICTA); Sep 22–23; Changsha, PEOPLES R CHINA. NEW YORK: Ieee; 2018. https://doi.org/10.1109/ICICTA.2018.00034

  27. Zhong Y, Fei F, Liu Y, Zhao B, Jiao H, Zhang L (2017) SatCNN: satellite image dataset classification using agile convolutional neural networks. J Remote Sens Lett 8(2):136–145. https://doi.org/10.1080/2150704X.2016.1235299

    Article  Google Scholar 

  28. Berrar D (2019) Cross-validation. J Ref Module Life Sci 2019. https://doi.org/10.1016/B978-0-12-809633-8.20349-X

  29. Jiang P, Chen J (2016) Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. J Neurocomputing 198:40–47. https://doi.org/10.1016/j.neucom.2015.08.118

    Article  Google Scholar 

  30. Rodriguez JD, Perez A, Lozano JA (2010) Sensitivity analysis of k-fold cross validation in prediction error estimation. J IEEE Trans Pattern Anal Mach Intell 32(3):569–575. https://doi.org/10.1109/TPAMI.2009.187

    Article  Google Scholar 

  31. Markoulidakis I, Rallis I, Georgoulas I et al (2021) Multiclass confusion matrix reduction method and its application on net promoter score classification problem. J Technol 9(4):81. https://doi.org/10.3390/technologies9040081

    Article  Google Scholar 

  32. Tharwat A (2020) Classification assessment methods. J Applied Computing and Informstics 2020. https://doi.org/10.1016/j.aci.2018.08.003

  33. Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. J Inf Sci 340:250–261. https://doi.org/10.1016/j.ins.2016.01.033

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (52174218, 51774280); and the Science and Technology Planning Project of Guizhou Province, China (No. [2022] General 078). The authors gratefully acknowledge the financial support of the above-mentioned agencies.

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Authors and Affiliations

  1. School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Zinan Du, Xiaofei Liu, Jinxin Wang, Guihang Jiang, Zifeng Meng, Huilin Jia, Hui Xie & Xin Zhou

  2. Key Laboratory of Coal Mine Gas and Fire Prevention, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Xiaofei Liu, Jinxin Wang & Huilin Jia

  3. Guizhou Anhe Yongzhu Techology Co., Ltd, Guiyang, 550081, Guizhou, China

    Jinxin Wang

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  1. Zinan Du
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Du, Z., Liu, X., Wang, J. et al. Response Characteristics of Gas Concentration Level in Mining Process and Intelligent Recognition Method Based on BI-LSTM. Mining, Metallurgy & Exploration 40, 807–818 (2023). https://doi.org/10.1007/s42461-023-00757-7

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