| Citation: |
YAN Li, WEN Hu, WANG Zhenping, et al. Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning[J]. Journal of Mine Automation，2025，51（3）：113-121. DOI: 10.13272/j.issn.1671-251x.2025020046
|
Current evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. Supervised learning models depend on labeled sample features, but manual labeling becomes costly when the sample size is large. Unsupervised learning models lack sample labeling, making qualitative evaluation infeasible. To address these issues, an evaluation method based on semi-supervised learning was proposed for the gas pre-extraction status evaluation of coal seam boreholes. A multi-dimensional evaluation system was established, incorporating eight indicators such as methane concentration, extraction negative pressure, and ambient temperature. The weighting method combining the analytic hierarchy process (AHP) and fuzzy evaluation method (FEM) was used to establish classification standards for extraction performance. Building on this, a semi-supervised learning model based on the Gaussian mixture model (GMM) and K-Means algorithm (SSGMM/SSK-Means) was developed. By integrating a small number of manually labeled samples and a large quantity of unlabeled data, the model enabled dynamic classification of single-borehole extraction status. The SSGMM demonstrated better clustering rate, while the SSK-Means achieved higher efficiency, developing a complementary "accuracy-efficiency" relationship. The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate.
| [1] |
王德明,张伟,王和堂,等. 煤矿热动力重大灾害的不确定性风险特性研究[J]. 采矿与安全工程学报,2023,40(4):826-837.
WANG Deming,ZHANG Wei,WANG Hetang,et al. Uncertain risk characteristics of major thermodynamic disasters in underground coal mines[J]. Journal of Mining & Safety Engineering,2023,40(4):826-837.
|
| [2] |
林志军,李敏,贺珊,等. 基于博弈论−贝叶斯网络的煤矿瓦斯爆炸风险评估[J]. 煤炭学报,2024,49(8):3484-3497.
LIN Zhijun,LI Min,HE Shan,et al. Risk assessment of gas explosion in coal mines based on game theory and Bayesian network[J]. Journal of China Coal Society,2024,49(8):3484-3497.
|
| [3] |
WEI Gaoming,WEN Hu,DENG Jun,et al. Liquid CO2 injection to enhance coalbed methane recovery:an experiment and in situ application test[J]. Fuel,2021,284. DOI: 10.1016/j.fuel.2020.119043.
|
| [4] |
MOHAMED T,MEHANA M. Coalbed methane characterization and modeling:review and outlook[J]. Energy Sources,Part A:Recovery,Utilization,and Environmental Effects,2025,47(1):2874-2896.
|
| [5] |
LI Tengteng,WU Bing,LEI Baiwei. Study on the optimization of a gas drainage borehole drainage horizon based on the evolution characteristics of mining fracture[J]. Energies,2019,12(23). DOI: 10.3390/en12234499.
|
| [6] |
TAO Shu,PAN Zhejun,TANG Shuling,et al. Current status and geological conditions for the applicability of CBM drilling technologies in China:a review[J]. International Journal of Coal Geology,2019,202:95-108. DOI: 10.1016/j.coal.2018.11.020
|
| [7] |
SONG Yawei,YANG Shengqiang,HU Xincheng,et al. Prediction of gas and coal spontaneous combustion coexisting disaster through the chaotic characteristic analysis of gas indexes in goaf gas extraction[J]. Process Safety and Environmental Protection,2019,129:8-16. DOI: 10.1016/j.psep.2019.06.013
|
| [8] |
ZHANG Yiwen,GUO Haishuai,LU Zhihui,et al. Distributed gas concentration prediction with intelligent edge devices in coal mine[J]. Engineering Applications of Artificial Intelligence,2020,92. DOI:10.1016/j.engappai. 2020.103643.
|
| [9] |
LIU Peng,FAN Long,LI Quangui,et al. Power ultrasound assisted coalbed methane enhancement recovery:field application and performance evaluation in underground coal mine[J]. Fuel,2022,324. DOI: 10.1016/j.fuel.2022.124575.
|
| [10] |
XIA Tongqiang,ZHOU Fubao,LIU Jishan,et al. Evaluation of the pre-drained coal seam gas quality[J]. Fuel,2014,130:296-305. DOI: 10.1016/j.fuel.2014.04.051
|
| [11] |
JIN Kan,CHENG Yuanping,WANG Wei,et al. Evaluation of the remote lower protective seam mining for coal mine gas control:a typical case study from the Zhuxianzhuang Coal Mine,Huaibei Coalfield,China[J]. Journal of Natural Gas Science and Engineering,2016,33:44-55. DOI: 10.1016/j.jngse.2016.05.004
|
| [12] |
XUE Yi,GAO Feng,GAO Yanan,et al. Quantitative evaluation of stress-relief and permeability-increasing effects of overlying coal seams for coal mine methane drainage in Wulan Coal Mine[J]. Journal of Natural Gas Science and Engineering,2016,32:122-137. DOI: 10.1016/j.jngse.2016.04.029
|
| [13] |
NI Xiaoming,TAN Xuebin,YANG Sen,et al. Evaluation of coal reservoir coalbed methane production potential in considering different coal structures in a coal seam section:a case study of the Shizhuang North Block in the Qinshui Basin[J]. Environmental Earth Sciences,2021,80. DOI: 10.1007/s12665-021-09979-8.
|
| [14] |
GUO Jianhong,ZHANG Zhansong,GUO Guangshan,et al. Evaluation of coalbed methane content by using kernel extreme learning machine and geophysical logging data[J]. Geofluids,2022(1). DOI: 10.1155/2022/3424367.
|
| [15] |
SONG Hongqing,DU Shuyi,YANG Jiaosheng,et al. Evaluation of hydraulic fracturing effect on coalbed methane reservoir based on deep learning method considering physical constraints[J]. Journal of Petroleum Science and Engineering,2022,212. DOI:10.1016/j.petrol. 2022.110360.
|
| [16] |
SOLEIMANI F,SI Guangyao,ROSHAN H,et al. Numerical modelling of gas outburst from coal:a review from control parameters to the initiation process[J]. International Journal of Coal Science & Technology,2023,10. DOI:10.1007/ s40789-023-00657-7.
|
| [17] |
LI Yunzhuo,SU Hetao,JI Huaijun,et al. Numerical simulation to determine the gas explosion risk in longwall goaf areas:a case study of Xutuan Colliery[J]. International Journal of Mining Science and Technology,2020,30(6):875-882. DOI: 10.1016/j.ijmst.2020.07.007
|
| [18] |
LIU Peng,FAN Jinyang,JIANG Deyi,et al. Evaluation of underground coal gas drainage performance:mine site measurements and parametric sensitivity analysis[J]. Process Safety and Environmental Protection,2021,148:711-723. DOI: 10.1016/j.psep.2021.01.054
|
| [19] |
LU Y Y,ZHANG H D,ZHOU Z,et al. Current status and effective suggestions for efficient exploitation of coalbed methane in China:a review[J]. Energy & Fuels,2021,35(11):9102-9123.
|
| [20] |
ZHOU Aitao,LI Jingwen,GONG Weili,et al. Theoretical and numerical study on the contribution of multi-hole arrangement to coalbed methane extraction[J]. Energy,2023,284. DOI: 10.1016/j.energy.2023.128598.
|
| [21] |
DU Feng,CUI Weilong,WANG Kai. Study on gas migration mechanism and multi-borehole spacing optimization in coal under negative pressure extraction[J]. Processes,2023,11(1). DOI: 10.3390/pr11010259.
|
| [22] |
LIU Jun,ZHANG Luwei,WANG Lin,et al. Negative pressure distribution of variable mass flow in coal mine drainage boreholes[J]. Energy Sources,Part A:Recovery,Utilization,and Environmental Effects,2025,47(1):1650-1667.
|
| [23] |
EL ISLEM KARABADJI N,AMARA KORBA A,ASSI A,et al. Accuracy and diversity-aware multi-objective approach for random forest construction[J]. Expert Systems with Applications,2023,225. DOI: 10.1016/j.eswa.2023.120138.
|
| [24] |
HUANG Qiuhao,WANG Chao,YE Ye,et al. Recognition of EEG based on improved black widow algorithm optimized SVM[J]. Biomedical Signal Processing and Control,2023,81. DOI: 10.1016/j.bspc.2022.104454.
|
| [25] |
NIE Feiping,CHEN Hong,XIANG Shiming,et al. On the equivalence of linear discriminant analysis and least squares regression[J]. IEEE Transactions on Neural Networks and Learning Systems,2024,35(4):5710-5720. DOI: 10.1109/TNNLS.2022.3208944
|
| [26] |
马莲净,王颂,赵宝峰,等. 基于PCA−SSA−BPNN模型的矿井突水水源识别方法 [J/OL]. 采矿与安全工程学报:1-10[2024-06-18]. http://kns.cnki.net/kcms/detail/32.1760.TD.20231221.1014.002.html.
MA Lianjing,WANG Song,ZHAO Baofeng,et al. A method for identifying the water inrush sources in mines based on PCA-SSA-BPNN model[J/OL]. Journal of Mining & Safety Engineering:1-10[2024-06-18]. http://kns.cnki.net/kcms/detail/32.1760.TD.20231221.1014.002.html.
|
| [27] |
AN Yiyao,ZHANG Ke,CHAI Yi,et al. Gaussian mixture variational-based transformer domain adaptation fault diagnosis method and its application in bearing fault diagnosis[J]. IEEE Transactions on Industrial Informatics,2024,20(1):615-625. DOI: 10.1109/TII.2023.3268750
|
| [28] |
NI Xiaoming,ZHAO Zheng,WANG Yanbin,et al. Optimisation and application of well types for ground development of coalbed methane from No. 3 coal seam in Shizhuang south block in Qinshui Basin,Shanxi Province,China[J]. Journal of Petroleum Science and Engineering,2020,193. DOI: 10.1016/j.petrol.2020.107453.
|
| [29] |
LOU Zhen,WANG Kai,KANG M,et al. Plugging methods for underground gas extraction boreholes in coal seams:a review of processes,challenges and strategies[J]. Gas Science and Engineering,2024,122. DOI:10.1016/j. jgsce.2024.205225.
|
| [30] |
YU Yang,ZENG Jun,ZHONG Lin,et al. Multi-views contrastive learning for dense text retrieval[J]. Knowledge-Based Systems,2023,274. DOI:10.1016/j.knosys.2023. 110624.
|
| 1. |
杨宁. 不连沟煤矿F6225工作面电阻率监测实践研究. 能源与环保. 2024(10): 119-123+134 .
|
Supported by: Beijing Renhe Information Technology Co.,
Ltd. Visits：1172262
DownLoad: