Volume 50 Issue S1
May  2024
Turn off MathJax
Article Contents
References
Related Articles
DONG Kangning, YAN Shouqing, ZHANG Baolong. Research on quantitative evaluation of characteristic gas of coal seam spontaneous combustion based on AI self-learning[J]. Journal of Mine Automation, 2024, 50(S1): 104-109.
Citation: DONG Kangning, YAN Shouqing, ZHANG Baolong. Research on quantitative evaluation of characteristic gas of coal seam spontaneous combustion based on AI self-learning[J]. Journal of Mine Automation, 2024, 50(S1): 104-109.
shu

Research on quantitative evaluation of characteristic gas of coal seam spontaneous combustion based on AI self-learning

  • 1. The Suited(Beijing) Science and Technology Development Co., Ltd., Beijing 100083, China;

  • 2. JalAInur Coal Industry Liability Company, Hulunbuir 021410, China

More Information
  • Received Date: February 02, 2024
    Graphical Abstract
    • Abstract
    • FullText(HTML)
    • References (20)
  • [1]
    张天宇, 鲁义, 施式亮, 等.基于支持向量机分类算法的多煤种煤自燃危险性预测[J].湖南科技大学学报(自然科学版), 2019, 34(2):11-17.
    [2]
    邓军, 雷昌奎, 曹凯, 等.采空区煤自燃预测的随机森林方法[J].煤炭学报, 2018, 43(10):2800-2808.
    [3]
    刘宝, 穆坤, 叶飞, 等.基于相关向量机的煤自燃预测方法[J].工矿自动化, 2020, 46(9):104-108.
    [4]
    王坤.新疆弱粘煤自然发火标志性气体试验研究[J].煤炭技术, 2020, 39(9):87-89..
    [5]
    赵兴国, 戴广龙.氧化煤自燃特性实验研究[J].中国安全生产科学技术, 2020, 16(6):55-60.
    [6]
    郭军, 金彦, 文虎, 等.矿井煤层自燃程度精细划分与预警方法研究[J].煤炭科学技术, 2021, 49(10):115-121.
    [7]
    任万兴, 郭庆, 石晶泰, 等.基于标志气体统计学特征的煤自燃预警指标构建[J].煤炭学报, 2021, 46(6):1747-1758.
    [8]
    谭波, 邵壮壮, 郭岩, 等.基于指标气体关联分析的煤自燃分级预警研究[J].中国安全科学学报, 2021, 31(2):33-39.
    [9]
    邓军, 白祖锦, 肖旸, 等.煤自燃指标体系试验研究[J].安全与环境学报, 2018, 18(5):1756-1761.
    [10]
    蒋恒.煤自燃多参数气体主辅预测指标体系研究[J].中国矿业, 2023, 32(4):147-153.
    [11]
    闫磊.港煤矿不同含硫煤自燃标志性气体生成规律研究[J].煤, 2023, 32(7):59-62.
    [12]
    丁徐琴, 张雷林.氧气体积分数对煤自燃特性影响的实验研究[J].煤矿安全, 2023, 54(7):156-162.
    [13]
    许娜, 黄斌, 李强, 等.机器学习在煤的地球化学中的应用[J].煤炭学报, 2022, 47(5):1895-1907.
    [14]
    MA Li, ZOU Li, REN Lifeng, et al.Prediction indices and limiting parameters of coal spontaneous combustion in the HuAInan mining area in China[J].Fuel, 2020, 264:116883.DOI: 10.1016/j.fuel.2019.116883
    [15]
    ONIFADE M, GENC B.Spontaneous combustion liability of coal and coal-shale:a review of prediction methods[J].International Journal of Coal Science & Technology, 2019, 6(2):151-168.
    [16]
    LEI Changkui, DENG Jun, CAO KAI, et al.A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob[J].Fuel, 2019, 239(1):297-311.
    [17]
    QU Lina.A study on the prediction method of coal spontaneous combustion development period based on critical temperature[J].Environmental Science and Pollution Research, 2018, 25:35748-35760.
    [18]
    牛会永, 邓湘陵, 李石林, 等.Experiment study of optimization on prediction index gases of coal spontaneous combustion[J].Journal of Central South University, 2016, 23(9):2321-2328.
    [19]
    王丹.RVM在煤自燃预测中的应用研究[J].煤, 2022, 31(4):1-5.
    [20]
    谢振华.基于RBF神经网络的采空区煤自燃预测[J].矿山工程, 2020, 8(2):7.DOI: 10.12677/ME.2020.82011.
    • Related Articles

  • Related Articles

    [1]YAN Shouqing, DONG Kangning, ZHANG Lianrong. Research on fire early warning technology in complex coal seam working face advancing[J]. Journal of Mine Automation, 2024, 50(S1): 96-99.
    [2]JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin. Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning[J]. Journal of Mine Automation, 2024, 50(10): 97-104, 119. DOI: 10.13272/j.issn.1671-251x.2024080090
    [3]LI Baolin, FENG Jiaqi, WANG Enyuan, SUN Xinyu, WANG Shuowei. Intelligent identification of electromagnetic radiation signals induced by coal rock fractures using machine learning[J]. Journal of Mine Automation, 2024, 50(9): 144-152. DOI: 10.13272/j.issn.1671-251x.2024070019
    [4]JIA Pengtao, LIN Kaiyi, GUO Fengjing. A temperature prediction model for coal spontaneous combustion based on PSO-SRU deep artificial neural networks[J]. Journal of Mine Automation, 2022, 48(4): 105-113. DOI: 10.13272/j.issn.1671-251x.2021090047
    [5]ZHANG Lang, ZHANG Yinghui, ZHANG Yibin, LI Zuo. Research on fault diagnosis method of ventilation network based on machine learning[J]. Journal of Mine Automation, 2022, 48(3): 91-98. DOI: 10.13272/j.issn.1671-251x.2021120093
    [6]HAO Yu, YE Zhengliang. Research on index gas and activation energy of coal spontaneous combustion under different methane atmosphere[J]. Journal of Mine Automation, 2019, 45(11): 65-69. DOI: 10.13272/j.issn.1671-251x.2019040104
    [7]GU Zhoujie, LIU Zhentang, LIU Haoxiong, QIAN Jifa, LIN Song, LI Xiaoliang. Experimental research on characteristics of coal spontaneous combustion and its influence on lower limits of gas explosion[J]. Journal of Mine Automation, 2019, 45(11): 59-64. DOI: 10.13272/j.issn.1671-251x.2019070039
    [8]SUN Liutao, DUAN Yujian, HUANG Junze. Optimization of index gases and division method of spontaneous combustion "three zones" in 10 coal seam of Qingdong Coal Mine[J]. Journal of Mine Automation, 2018, 44(11): 56-61. DOI: 10.13272/j.issn.1671—251x.2018070050
    [9]WU Fusheng. Experimental study on composite gas indexes optimization for coal spontaneous combustion predictio[J]. Journal of Mine Automation, 2018, 44(7): 61-65. DOI: 10.13272/j.issn.1671-251x.17341
    [10]LEI Ming, HOU Yuan-bi. Monitoring and Prediction System of Spontaneous Combustion of Coal Seam Based on Virtual Instrument[J]. Journal of Mine Automation, 2005, 31(3): 19-21.

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (3) PDF downloads (0) Cited by()
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1172531    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return