Turn off MathJax
Article Contents
Abstract
References
Related Articles
MA Li, GAO Wenbo, TUO Longlong, ZHANG Pengyu, ZHENG Zhou, GUO Ruizhi. Research on Coal Spontaneous Combustion Characteristics and prediction Method of Deep Mining in XiMeng Mining Areas[J]. COAL GEOLOGY & EXPLORATION.
Citation: MA Li, GAO Wenbo, TUO Longlong, ZHANG Pengyu, ZHENG Zhou, GUO Ruizhi. Research on Coal Spontaneous Combustion Characteristics and prediction Method of Deep Mining in XiMeng Mining Areas[J]. COAL GEOLOGY & EXPLORATION.
shu

Research on Coal Spontaneous Combustion Characteristics and prediction Method of Deep Mining in XiMeng Mining Areas

  • 1. College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China;

  • 2. Key Laboratory of Coal Fire Disaster Prevention and Control, Xi'an University of Science and Technology, Xi'an 710054, China;

  • 3. Inner Mongolia Haosheng Coal Industry Co., Ltd., Ordos 017212, China

More Information
  • Received Date: October 27, 2024
  • Accepted Date: January 14, 2025
    Graphical Abstract
    • Abstract
  • The deep mining coal seams in the XiMeng mining area are affected by complex environmental conditions, such as high in-situ stress, large water inflow, and severe air leakage, which increase the risk of coal spontaneous combustion and make prediction more challenging. Coal samples from Yingpanhao and Shilawusu coal mines within the mining area were selected for temperature-programmed spontaneous combustion experiments to determine characteristic parameters of coal spontaneous combustion under different moisture and sulfur mass fraction conditions. Combined with coal quality parameters from industrial analysis, a prediction database was established, and the Crested Porcupine Optimization algorithm (CPO) was applied to optimize the hyperparameters of the random forest (RF) model, CPO-RF model was established to predict the degree of spontaneous combustion of coal. The results showed that the patterns of gas concentration and oxygen consumption rate during the oxidation heating process were similar in the Yingpanhao and Shilawusu coal samples. CO was identified as the main indicator gas, with an initial appearance temperature of about 30 ℃. The amount of gas produced increased with higher sulfur mass fraction, and initially decreased and then increased with higher moisture mass fraction. The critical temperature of coal spontaneous combustion was determined to be 67.5~70.5 ℃, and the dry cracking temperature was 113.5~115.4 ℃. Through CPO’s efficient global search capabilities, the RF model’s optimal tree depth and tree count were automatically identified, avoiding suboptimal solutions caused by improper settings, thus enhancing the model’s generalization and robustness. The constructed CPO-RF model significantly improved the accuracy of coal spontaneous combustion predictions, with high alignment between predicted and actual temperatures on the test set, a mean absolute error and root mean square deviation of 0.762 ℃ and 1.014, respectively, and a coefficient of determination reaching 0.9994. The predicted results of the CPO-RF model, when compared with the characteristic temperatures of coal spontaneous combustion, can enable efficient discrimination of the risk of coal spontaneous combustion. Based on this, targeted fire prevention and extinguishing methods can be adopted. The research findings provide a reference for the prevention of coal spontaneous combustion in deep mining in mining areas.
    • FullText(HTML)
    • References (33)
  • [1]
    蓝航，陈东科，毛德兵. 我国煤矿深部开采现状及灾害防治分析[J]. 煤炭科学技术，2016，44(1)：39–46.

    LAN Hang，CHEN Dongke，MAO Debing. Current status of deep mining and disaster prevention in China[J]. Coal Science and Technology，2016，44(1)：39–46.
    [2]
    张建民，李全生，张勇，等. 煤炭深部开采界定及采动响应分析[J]. 煤炭学报，2019，44(5)：1314–1325.

    ZHANG Jianmin，LI Quansheng，ZHANG Yong，et al. Definition of deep coal mining and response analysis[J]. Journal of China Coal Society，2019，44(5)：1314–1325.
    [3]
    王德明，邵振鲁，朱云飞. 煤矿热动力重大灾害中的几个科学问题[J]. 煤炭学报，2021，46(1)：57–64.

    WANG Deming，SHAO Zhenlu，ZHU Yunfei. Several scientific issues on major thermodynamic disasters in coal mines[J]. Journal of China Coal Society，2021，46(1)：57–64.
    [4]
    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.

    REN Wanxing，GUO Qing，SHI Jingtai，et al. Construction of early warning indicators for coal spontaneous combustion based on statistical characteristics of index gases[J]. Journal of China Coal Society，2021，46(6)：1747–1758.
    [5]
    马砺，任立峰，王乃国，等. 巨野矿区煤自燃特性及动力学研究[J]. 煤田地质与勘探，2016，44(6)：33–37.

    MA Li，REN Lifeng，WANG Naiguo，et al. Characteristics of coal spontaneous combustion and kinetics in Juye Mining Area[J]. Coal Geology & Exploration，2016，44(6)：33–37.
    [6]
    常绪华，王德明，贾海林. 基于热重实验的煤自燃临界氧体积分数分析[J]. 中国矿业大学学报，2012，41(4)：526–530.

    CHANG Xuhua，WANG Deming，JIA Hailin. Thermogravimetric determination of the critical oxygen volume fraction for spontaneous combustion of coal[J]. Journal of China University of Mining & Technology，2012，41(4)：526–530.
    [7]
    刘文永，文虎，闫旭斌. 煤自然发火过程温度、氧浓度的时空演化规律[J]. 西安科技大学学报，2017，37(5)：636–642.

    LIU Wenyong，WEN Hu，YAN Xubin. Spatial–temporal variation of temperature and oxygen concentration in coal spontaneous combustion process[J]. Journal of Xi’an University of Science and Technology，2017，37(5)：636–642.
    [8]
    刘垚，王福生，董轩萌，等. 基于程序升温试验的煤自燃特性及微观机理研究[J]. 煤炭科学技术，2024，52(增刊1)：94–106.

    LIU Yao，WANG Fusheng，DONG Xuanmeng，et al. Study on the characteristics and microscopic mechanism of coal spontaneous combustion based on programmed heating experiment[J]. Coal Science and Technology，2024，52(Sup. 1)：94–106.
    [9]
    周西华，姜延航，白刚，等. 氧气浓度对含硫无烟煤自燃特性的试验研究[J]. 煤炭科学技术，2023，51(5)：114–123.

    ZHOU Xihua，JIANG Yanhang，BAI Gang，et al. Experimental study on the influence of oxygen concentration on spontaneous combustion characteristics of sulfur–containing anthracite[J]. Coal Science and Technology，2023，51(5)：114–123.
    [10]
    徐永亮，刘泽健，步允川，等. 单轴应力下烟煤氧化–自燃灾变温度[J]. 工程科学学报，2021，43(10)：1312–1322.

    XU Yongliang，LIU Zejian，BU Yunchuan，et al. Catastrophic temperature of oxidation–spontaneous–combustion for bituminous coal under uniaxial stress[J]. Chinese Journal of Engineering，2021，43(10)：1312–1322.
    [11]
    李宗翔，张明乾，杨志斌，等. 断层构造对煤结构及氧化自燃特性的影响[J]. 煤炭学报，2023，48(3)：1246–1254.

    LI Zongxiang，ZHANG Mingqian，YANG Zhibin，et al. Effect of fault structure on the structure and oxidative spontaneous combustion characteristics of coal[J]. Journal of China Coal Society，2023，48(3)：1246–1254.
    [12]
    秦波涛，宋爽，戚绪尧，等. 浸水过程对长焰煤自燃特性的影响[J]. 煤炭学报，2018，43(5)：1350–1357.

    QIN Botao，SONG Shuang，QI Xuyao，et al. Effect of soaking process on spontaneous combustion characteristics of long–flame coal[J]. Journal of China Coal Society，2018，43(5)：1350–1357.
    [13]
    邓军，李鑫，王凯，等. 矿井火灾智能监测预警技术近20年研究进展及展望[J]. 煤炭科学技术，2024，52(1)：154–177.

    DENG Jun，LI Xin，WANG Kai，et al. Research progress and prospect of mine fire intelligent monitoring and early warning technology in recent 20 years[J]. Coal Science and Technology，2024，52(1)：154–177.
    [14]
    张玉涛，郭强，张园勃，等. 基于相关系数法的煤自燃危险性关联分析及预测[J]. 中国安全科学学报，2024，34(1)：125–132.

    ZHANG Yutao，GUO Qiang，ZHANG Yuanbo，et al. Correlation analysis and prediction of coal spontaneous combustion risk based on correlation coefficient method[J]. China Safety Science Journal，2024，34(1)：125–132.
    [15]
    雷昌奎，江莉娟，邓存宝，等. 采空区煤自燃极限参数灰色关联分析及预测[J]. 煤矿安全，2022，53(9)：113–121.

    LEI Changkui，JIANG Lijuan，DENG Cunbao，et al. Grey relational analysis and prediction on limit parameters of coal spontaneous combustion in goaf[J]. Safety in Coal Mines，2022，53(9)：113–121.
    [16]
    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.

    DENG Jun，LEI Changkui，CAO Kai，et al. Random forest method for predicting coal spontaneous combustion in gob[J]. Journal of China Coal Society，2018，43(10)：2800–2808.
    [17]
    王斌，贾澎涛，郭风景，等. 基于多特征融合的煤自燃温度深度预测模型[J]. 中国矿业，2024，33(2)：84–90.

    WANG Bin，JIA Pengtao，GUO Fengjing，et al. Deep prediction model of coal spontaneous combustion temperature based on multi–feature fusion[J]. China Mining Magazine，2024，33(2)：84–90.
    [18]
    邓军，雷昌奎，曹凯，等. 煤自燃预测的支持向量回归方法[J]. 西安科技大学学报，2018，37(2)：175–180.

    DENG Jun，LEI Changkui，CAO Kai，et al. Support vector regression approach for predicting coal spontaneous combustion[J]. Journal of Xi’an University of Science and Technology，2018，37(2)：175–180.
    [19]
    汪伟，梁然，祁云，等. 基于PSO–BPNN的煤自燃危险性预测模型[J]. 中国安全科学学报，2023，33(7)：127–132.

    WANG Wei，LIANG Ran，QI Yun，et al. Prediction model of coal spontaneous combustion risk based on PSO–BPNN[J]. China Safety Science Journal，2023，33(7)：127–132.
    [20]
    罗振敏，张利冬，宋泽阳. 基于全连接的长短期记忆网络实现采空区CO多步预测[J]. 清华大学学报(自然科学版)，2024，64(6)：940–952.

    LUO Zhenmin，ZHANG Lidong，SONG Zeyang. Multistep prediction of CO in the extraction zone based on a fully connected long short–term memory network[J]. Journal of Tsinghua University(Science and Technology)，2024，64(6)：940–952.
    [21]
    WANG Kai，LI Kangnan，DU Feng，et al. Research on prediction model of coal spontaneous combustion temperature based on SSA–CNN[J]. Energy，2024，290：130158.
    [22]
    孔彪，朱思想，胡相明，等. 基于改进鲸鱼算法优化BP神经网络的煤自燃预测研究[J]. 矿业安全与环保，2023，50(5)：30–36.

    KONG Biao，ZHU Sixiang，HU Xiangming，et al. Study on prediction of coal spontaneous combustion based on MSWOA–BP[J]. Mining Safety & Environmental Protection，2023，50(5)：30–36.
    [23]
    GUO Jun，CHEN Changming，WEN Hu，et al. Prediction model of goaf coal temperature based on PSO–GRU deep neural network[J]. Case Studies in Thermal Engineering，2024，53：103813.
    [24]
    LI Shuang，XU Kun，XUE Guangzhe，et al. Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression[J]. Fuel，2022，324：124670.
    [25]
    蔡海伦. 济宁矿区不同埋深煤低温氧化自燃特性研究[D]. 青岛：山东科技大学，2019. CAI Hailun. Study on spontaneous combustion characteristics of low temperature oxidation of coal under buried depth in Jining Mining Area[D]. Qingdao：Shandong University of Science and Technology，2019.
    [26]
    易欣，张敏，邓寅，等. 淮南矿区煤自燃指标气体及特征参数[J]. 西安科技大学学报，2023，43(3)：457–465.

    YI Xin，ZHANG Min，DENG Yin，et al. Spontaneous combustion indicator gases and characteristic parameters of coal in Huainan Mining Area[J]. Journal of Xi’an University of Science and Technology，2023，43(3)：457–465.
    [27]
    郑凯月. 淮北矿区浸水风干煤体微观结构及自燃特性研究[D]. 徐州：中国矿业大学，2021. ZHENG Kaiyue. Study on microstructure and spontaneous combustion characteristics of water–immersed air–dried coal in Huaibei Mining Area[D]. Xuzhou：China University of Mining and Technology，2021.
    [28]
    邬剑明，彭举，吴玉国. 平朔矿区煤自然发火指标气体选择的试验研究[J]. 煤炭科学技术，2012，40(2)：67–69.

    WU Jianming，PENG Ju，WU Yuguo. Experiment study on index gas selection of coal spontaneous combustion in Pingshuo Mining Area[J]. Coal Science and Technology，2012，40(2)：67–69.
    [29]
    朱自力. 神东矿区不粘煤二次氧化特性实验研究[D]. 阜新：辽宁工程技术大学，2021. ZHU Zili. Experimental study on secondary oxidation of noncaking coal in Shendong Mining Area[D]. Fuxin：Liaoning Technical University，2021.
    [30]
    徐精彩，文虎，葛岭梅，等. 松散煤体低温氧化放热强度的测定和计算[J]. 煤炭学报，2000，25(4)：387–390.

    XU Jingcai，WEN Hu，GE Lingmei，et al. Determination and calculation of oxidation heat liberation intensity of loose coal at low temperature stage[J]. Journal of China Coal Society，2000，25(4)：387–390.
    [31]
    ABDEL–BASSET M，MOHAMED R，ABOUHAWWASH M. Crested porcupine optimizer：A new nature–inspired metaheuristic[J]. Knowledge–Based Systems，2024，284：111257.
    [32]
    杨云浩，张国维，朱国庆，等. 基于机器学习的火源热释放速率预测方法[J]. 清华大学学报(自然科学版)，2024，64(5)：922–932.

    YANG Yunhao，ZHANG Guowei，ZHU Guoqing，et al. Machine learning based prediction method for the heat release rate of a fire source[J]. Journal of Tsinghua University(Science and Technology)，2024，64(5)：922–932.
    [33]
    GRINSZTAJN L，OYALLON E，VAROQUAUX G. Why do tree–based models still outperform deep learning on typical tabular data?[J]. Advances in Neural Information Processing Systems，2022，37：507–520.
    • Related Articles

  • Related Articles

    [1]MA Li, GAO Wenbo, TUO Longlong, ZHANG Pengyu, ZHENG Zhou, GUO Ruizhi. Characteristics and prediction methods of coal spontaneous combustion for deep coal mining in the Ximeng mining area[J]. COAL GEOLOGY & EXPLORATION, 2025, 53(2): 33-43. DOI: 10.12363/issn.1001-1986.24.10.0656
    [2]YIN Haiyang, CHEN Tongjun, SONG Xiong, XU Haicheng, LI Wan. Methods for predicting the thickness of coal seams based on seismic attribute optimization and machine learning[J]. COAL GEOLOGY & EXPLORATION, 2023, 51(5): 164-170. DOI: 10.12363/issn.1001-1986.22.10.0801
    [3]MA Li, CHEN Tongjun, WANG Xin, MA Guodong. Recent progress of quantitative prediction of tectonic coal thickness[J]. COAL GEOLOGY & EXPLORATION, 2018, 46(5): 66-72. DOI: 10.3969/j.issn.1001-1986.2018.05.011
    [4]LI Yanfang, CHENG Jianyuan, WANG Cheng. Seismic attribute optimization based on support vector machine and coalbed methane prediction[J]. COAL GEOLOGY & EXPLORATION, 2012, 40(6): 75-78. DOI: 10.3969/j.issn.1001-1986.2012.06.017
    [5]LIU Jin-hai, FENG Tao, XIE Dong-hai, LIU Hui. Prediction method of coal and gas outburst by distance discriminant analysis[J]. COAL GEOLOGY & EXPLORATION, 2009, 37(1): 26-28,32.
    [6]WANG Run-huai, LI Yong-shu, XIANG Zhong-lin, LIU Yong-he. Research and development of WebGIS for coal and gas outburst prediction based on ArcIMS[J]. COAL GEOLOGY & EXPLORATION, 2006, 34(6): 15-18.
    [7]TANG You-yi. The prediction of gas contentof coal beds in unmining areas on the basis of drilling data[J]. COAL GEOLOGY & EXPLORATION, 2001, 29(5): 23-25.
    [8]Mao Jiehua, Xu Huilong. CHINAS COAL RESOURCE DISTRIBUTION AND PERSPECTIVE PREDICTION[J]. COAL GEOLOGY & EXPLORATION, 1999, 27(3): 1-4.
    [9]Ye Daomin, Xiao Wenzhao, Luo Junwen, Huang Haizhi. STUDY ON THE COAL PETROLOGICAL BLENDING AND THE PREDICTION OF COKE STRENGTH[J]. COAL GEOLOGY & EXPLORATION, 1998, 26(S1): 5-9.
    [10]Shi Xianxin, Cai Shuanrong, Feng Hong, Li Hua. THE PREDICTION OF COAL AND GAS OUTBURST USING THE ACOUSTIC EMISSION TECHNIQUE[J]. COAL GEOLOGY & EXPLORATION, 1998, 26(3): 60-65.

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (29) PDF downloads (4) Cited by()
    Related

    Copyright of website design © Editorial Office of Coal Geology & Exploration 陕ICP备05001372号-6 陕公网安备 61019002002193号

    Address: Editorial Office of Coal Geology & Exploration, No.82 Jinye 1st Rd, High-Tech Zone, Xi'an City, Shaanxi, China

    Tel: 029-81778075 E-mail: ccrimtdzykt@vip.163.com

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

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return