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复合采空区瓦斯治理优化:来自同心煤矿多源排放分析与数值模拟的见解

Optimizing Methane Control in Composite Goaf Areas: Insights from Multisource Emission Analysis and Numerical Modeling in the Tongxin Coal Mine.

作者信息

Liu Chungang

机构信息

China Coal Research Institute, Beijing 100013, China.

State Key Laboratory of Coal Mine Disaster Prevention and Control, Beijing 100013, China.

出版信息

ACS Omega. 2024 Sep 25;9(40):41171-41178. doi: 10.1021/acsomega.4c01389. eCollection 2024 Oct 8.

DOI:10.1021/acsomega.4c01389
PMID:39398187
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11465257/
Abstract

Coal mine methane poses a threat to both coal production and the well-being of workers. This study investigates the complex challenge of managing methane emissions from multiple sources in coal mines prone to gas. Coal mining creates multiple air leakage channels in the mine goaf, increasing the risk of gas explosions. Tracer SF measurements were employed to identify multiple air leakage channels. A comprehensive numerical model, incorporating multiple air leakage channels, was developed to simulate methane distribution in the goaf and upper corner. By adjusting the combination of gas drainage rate () and working face ventilation flux (), the concentration of methane at the upper corner is expected to be decrease, ensuring safe production at the Tongxin mine. A total of 48 simulation cases were conducted, determining that = 1080 m/min and = 2250 m/min are recommended for the 8201 working face in the Tongxin coal mine. This combination provides not only an effective solution but also a cost-efficient one for methane control. The findings offer a new insight into effective methane control providing a foundation for improving mine safety.

摘要

煤矿瓦斯对煤炭生产和工人的健康都构成威胁。本研究调查了在易瓦斯煤矿中管理多个来源甲烷排放的复杂挑战。煤炭开采在矿井采空区形成多个漏风通道,增加了瓦斯爆炸的风险。采用示踪剂SF测量来识别多个漏风通道。开发了一个包含多个漏风通道的综合数值模型,以模拟采空区和上角的甲烷分布。通过调整瓦斯抽采率()和工作面通风量()的组合,预计上角的甲烷浓度将降低,确保同心煤矿的安全生产。总共进行了48个模拟案例,确定同心煤矿8201工作面推荐=1080米/分钟和=2250米/分钟。这种组合不仅为甲烷控制提供了有效的解决方案,而且成本效益高。这些发现为有效控制甲烷提供了新的见解,为提高矿井安全性奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/6e2b93316b24/ao4c01389_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/0ebddea3818c/ao4c01389_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/061a55dd7dd4/ao4c01389_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/0cda66b30952/ao4c01389_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/6c00af20eae5/ao4c01389_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/c353ef191386/ao4c01389_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/e26fd92aa125/ao4c01389_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/6e2b93316b24/ao4c01389_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/0ebddea3818c/ao4c01389_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/061a55dd7dd4/ao4c01389_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/0cda66b30952/ao4c01389_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/6c00af20eae5/ao4c01389_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/c353ef191386/ao4c01389_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/e26fd92aa125/ao4c01389_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/11465257/6e2b93316b24/ao4c01389_0007.jpg

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本文引用的文献

1
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2
Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.利用先进的机器学习技术和受自然启发的方法改进页岩润湿性预测:对碳捕集利用和封存的影响。
Sci Total Environ. 2023 Jun 15;877:162944. doi: 10.1016/j.scitotenv.2023.162944. Epub 2023 Mar 20.