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Coal Geology & Exploration

Abstract

As the weathered bedrock and burned rock aquifers seriously threaten the production safety of mines in the Jurassic coalfield of northern Shaanxi Province, accurate prediction of their waterrichness and water inflow at the working face is of great significance for water control in mines. Aiming at the weathered bedrock and burned rock aquifers with close hydraulic connection, the area where working face 15217 of Shaanxi Hongliulin Coal Mine is located was taken as the study area. Meanwhile, the aquifer thickness, lithological combination index, index of rock burning and weathering degree and core recovery was taken as the evaluation indexes. The prediction method for the water richness of aquifers based on the support vector machine using manta ray foraging optimization was put forward. Then, the working face was zoned according to different waterrichness levels through the accurate zoned prediction of waterrichness of the weathered bedrock and burned rock aquifers. On this basis, the hydrogeological conditions of the working face before mining were analysed after a long period of underground dewatering, and the water inflow of different water-rich zones in the working face was predicted using the dynamic and static storage method and the error of water influx prediction results was small compared with that of the water influx measured in mining activities, ranging from 0.30 to 6.98 m3/h, which indicates that this prediction method has high feasibility and accuracy. It provides new ideas and methods for the prediction of water influx in the working faces of Hongliulin Coal Mine and mines with similar conditions. provides new ideas and methods for the prediction of water influx in the working faces of Hongliulin Coal Mine and mines with similar conditions.

Keywords

weathered bedrock, burned rock, water richness, evaluation index, manta ray foraging optimization (MRFO), prediction of water inflow, Jurassic coalfield

DOI

10.12363/issn.1001-1986.23.01.0046

Reference

[1] 侯恩科,冯栋,谢晓深,等. 浅埋煤层沟道采动裂缝发育特征及治理方法[J]. 煤炭学报,2021,46(4):1297−1308.

HOU Enke,FENG Dong,XIE Xiaoshen,et al. Development characteristics and treatment methods of mining surface cracks in shallow–buried coal seam gully[J]. Journal of China Coal Society,2021,46(4):1297−1308.

[2] 陈天赐. 基于水文地质参数演化的矿井涌水量动态计算方法与应用[D]. 徐州:中国矿业大学,2022.

CHEN Tianci. Dynamic calculation method and application of mine water inflow based on hydrogeological parameter evolution[D]. Xuzhou:China University of Mining and Technology,2022.

[3] 姜小妮,张未,程东会,等. 非完整大井法和水平廊道法计算矿坑涌水量[J]. 水资源与水工程学报,2018,29(5):169−174.

JIANG Xiaoni,ZHANG Wei,CHENG Donghui,et al. Estimation of mine water inflow using the partially penetrated virtual large–diameter well method and partially horizontal collector gallery method[J]. Journal of Water Resources & Water Engineering,2018,29(5):169−174.

[4] 刘基. 复合含水层疏放水钻孔与工作面涌水量预测方法研究[D]. 北京:煤炭科学研究总院,2020.

LIU Ji. Study on the prediction method of water inflow of drainage boreholes and working faces in compound aquifers[D]. Beijing:China Coal Research Institute,2020.

[5] 阮泽宇. 干旱半干旱典型矿区富水性分区与工作面涌水量控制方法[D]. 徐州:中国矿业大学,2021.

RUAN Zeyu. Water–rich zone and control method of water inflow from working face in typical arid and semi−arid mining area[D]. Xuzhou:China University of Mining and Technology,2021.

[6] 罗奇斌,赵宝峰,毛旭阁,等. 基于数值模拟方法的煤矿开采涌水量预测分析[J]. 西北大学学报(自然科学版),2022,52(6):1100−1110.

LUO Qibin,ZHAO Baofeng,MAO Xuge,et al. Prediction and analysis of mine water inflow based on numerical simulation method[J]. Journal of Northwest University (Natural Science Edition),2022,52(6):1100−1110.

[7] 段俭君,徐会军,王子河. 相关分析法在矿井涌水量预测中的应用[J]. 煤炭科学技术,2013,41(6):114−116.

DUAN Jianjun,XU Huijun,WANG Zihe. Correlational analysis method applied to prediction of mine water inflow quantity[J]. Coal Science and Technology,2013,41(6):114−116.

[8] 李建林,李志强,王心义,等. 基于R/S分析的矿井涌水量灰色预测[J]. 安全与环境学报,2015,15(5):6−10.

LI Jianlin,LI Zhiqiang,WANG Xinyi,et al. Grey prediction of mine discharge based on the R/S analysis[J]. Journal of Safety and Environment,2015,15(5):6−10.

[9] 施龙青,王雅茹,邱梅,等. 时间序列模型在工作面涌水量预测中的应用[J]. 煤田地质与勘探,2020,48(3):108−115.

SHI Longqing,WANG Yaru,QIU Mei,et al. Application of time series model in water inflow prediction of working face[J]. Coal Geology & Exploration,2020,48(3):108−115.

[10] 陈思佳,骆祖江. “分段大井法”预测矿井工作面涌水量[J]. 中国煤炭地质,2016,28(1):41−43.

CHEN Sijia,LUO Zujiang. Mine working face water inflow prediction through“segmental virtual large–diameter well method”[J]. Coal Geology of China,2016,28(1):41−43.

[11] 虎维岳. 浅埋煤层回采中顶板含水层涌水量的时空动态预测技术[J]. 煤田地质与勘探,2016,44(5):91−96.

HU Weiyue. Water inflows prediction technique of water inflow from roof aquifer during extraction of shallow seam[J]. Coal Geology & Exploration,2016,44(5):91−96.

[12] 侯恩科,席慧琴,文强,等. 基于GMS的隐伏火烧区下煤层开采工作面涌水量预测[J]. 安全与环境学报,2022,22(5):2482−2492.

HOU Enke,XI Huiqin,WEN Qiang,et al. Prediction of water inflow volume in the coal mining workforce below the concealed fire area based on GMS[J]. Journal of Safety and Environment,2022,22(5):2482−2492.

[13] 王双明,黄庆享,范立民,等. 生态脆弱矿区含(隔)水层特征及保水开采分区研究[J]. 煤炭学报,2010,35(1):7−14.

WANG Shuangming,HUANG Qingxiang,FAN Limin,et al. Study on overburden aquclude and water protection mining regionazation in the ecological fragile mining area[J]. Journal of China Coal Society,2010,35(1):7−14.

[14] 武强,樊振丽,刘守强,等. 基于GIS的信息融合型含水层富水性评价方法:富水性指数法[J]. 煤炭学报,2011,36(7):1124−1128.

WU Qiang,FAN Zhenli,LIU Shouqiang,et al. Water–richness evaluation method of water–filled aquifer based on the principle of information fusion with GIS:Water–richness index method[J]. Journal of China Coal Society,2011,36(7):1124−1128.

[15] 侯恩科,闫鑫,郑永飞,等. Bayes判别模型在风化基岩富水性预测中的应用[J]. 西安科技大学学报,2019,39(6):942−949.

HOU Enke,YAN Xin,ZHENG Yongfei,et al. Application of Bayes discriminant model in prediction of water enrichment of weathered bedrock[J]. Journal of Xi’an University of Science and Technology,2019,39(6):942−949.

[16] 侯恩科,纪卓辰,车晓阳,等. 基于改进AHP和熵权法耦合的风化基岩富水性预测方法[J]. 煤炭学报,2019,44(10):3164−3173.

HOU Enke,JI Zhuochen,CHE Xiaoyang,et al. Water abundance prediction method of weathered bedrock based on improved AHP and the entropy weight method[J]. Journal of China Coal Society,2019,44(10):3164−3173.

[17] 张良良,石永奎,李俊勇. 基于混合核函数支持向量机的顶板砂岩富水性研究[J]. 矿业安全与环保,2018,45(2):72−76.

ZHANG Liangliang,SHI Yongkui,LI Junyong. Study on water–richness of roof sandstone based on hybrid kernel function support vector machine[J]. Mining Safety & Environmental Protection,2018,45(2):72−76.

[18] 郭宇航. 基于特征选择与集成算法的支持向量机短期电力负荷预测[D]. 徐州:中国矿业大学,2022.

GUO Yuhang. Short–term power load forecasting based on support vector machine of feature selection and ensemble algorithm[D]. Xuzhou:China University of Mining and Technology,2022.

[19] 刘佳,施龙青,韩进,等. 基于Grid–Search_PSO优化SVM回归预测矿井涌水量[J]. 煤炭技术,2015,34(8):184−186.

LIU Jia,SHI Longqing,HAN Jin,et al. Regression prediction of mine inflow based on SVM with Grid–Search_PSO optimization[J]. Coal Technology,2015,34(8):184−186.

[20] ZHAO Weiguo,ZHANG Zhenxing,WANG Liying. Manta ray foraging optimization:An effective bio−inspired optimizer for engineering applications[J]. Engineering Applications of Artificial Intelligence,2020,87:103300.

[21] 叶剑华,罗凤章,杨理. 基于改进蝠鲼觅食优化SVM的配电网拓扑辨识[J]. 电力系统及其自动化学报,2021,33(10):43−50.

YE Jianhua,LUO Fengzhang,YANG Li. Distribution network topology identification based on SVM optimized by improved manta ray foraging optimization algorithm[J]. Proceedings of the CSU–EPSA,2021,33(10):43−50.

[22] 黄鹤,李潇磊,杨澜,等. 引入改进蝠鲼觅食优化算法的水下无人航行器三维路径规划[J]. 西安交通大学学报,2022,56(7):9−18.

HUANG He,LI Xiaolei,YANG Lan,et al. Three dimensional path planning of unmanned underwater vehicle based on improved manta ray foraging optimization algorithm[J]. Journal of Xi’an Jiaotong University,2022,56(7):9−18.

[23] 李永涛,杨建. 基于顶板水预疏放的首采工作面涌水规律[J]. 煤田地质与勘探,2019,47(4):104−109.

LI Yongtao,YANG Jian. Water inflow law of the first working face based on water pre–draining from roof[J]. Coal Geology & Exploration,2019,47(4):104−109.

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