nav emailalert searchbtn searchbox tablepage yinyongbenwen piczone journalimg journalInfo journalinfonormal searchdiv searchzone qikanlogo popupnotification paper paperNew
2026, 06, v.47 137-142
基于TDLAS的便携式煤矿井下甲烷检测仪
基金项目(Foundation): 重庆市自然科学基金博士直通车项目(CSTB2024NSCQ-BSX006)
邮箱(Email):
DOI: 10.13436/j.mkjx.202606025
发布时间: 2026-05-27
出版时间: 2026-05-27
移动端阅读
摘要:

随着煤矿工业的不断发展,井下CH4浓度检测作为保障安全生产的关键环节,其检测精度和环境适应性面临严峻挑战。现有检测方法存在对复杂干扰信号识别能力弱、激光穿透性不足等问题,导致在复杂的煤矿井下环境中,CH4浓度检测偏差较大。为此,提出了一种融合由计算机断层扫描技术(CT)改进可调谐半导体激光吸收光谱(TDLAS)技术的甲烷检测仪。测试结果表明,该检测仪在对不同气体进行识别测试时,对每种气体的识别准确率均在99%以上,其中对CH4识别准确率为100%。同时,在激光技术穿透性测试中,其穿透性远高于对比检测仪。该检测仪解决了传统方法在复杂井下环境中抗干扰能力弱、动态跟踪滞后的问题,为煤矿井下CH4安全监测提供了新思路。

Abstract:

With the continuous development of the coal mining industry, the detection of CH4 concentration underground, as a key link to ensure safe production, is facing severe challenges in terms of detection accuracy and environmental adaptability. The existing detection methods have problems such as weak recognition ability for complex interference signals and insufficient laser penetration,which lead to large deviations in CH4 concentration detection in the complex underground coal mine environment. Therefore, proposed a methane detector that integrates Tunable Diode Laser Absorption Spectroscopy(TDLAS) technology improved by Computer Tomography(CT). The test results show that when this detector conducts recognition tests on different gases, the recognition accuracy rate for each gas is above 99%, among which the recognition accuracy rate for CH4 is 100%. At the same time, in the laser technology penetration test, its penetration is much higher than that of the comparison detector. This detector has solved the problems of weak anti-interference ability and lagging dynamic tracking of traditional methods in complex underground environments, providing a new idea for CH4 safety monitoring in underground coal mine.

参考文献

[1]李香格,胡迈,朱同宇,等.基于频率调制的煤矿高灵敏甲烷检测技术研究[J].光学精密工程,2024,32(24):3537-3544.

[2]向艳芳,梁龙,何巍.煤矿井下遥感式激光甲烷检测仪温度影响试验研究[J].矿业研究与开发,2023,43(2):189-193.

[3]孙长伟,刘亚辉,刘旭,等.基于全光路激光探头的甲烷气体检测方法研究[J].应用激光,2024,44(6):133-143.

[4]徐刚,史嘉慧,金洪伟,等.含气量对煤超声波传播速度的影响实验研究[J].中国安全生产科学技术,2025,21(1):64-71.

[5]王强,王浩,肖聪,等.基于石英音叉探测器的双光谱气体检测技术[J].光子学报,2023,52(3):228-236.

[6]权浩,孙建海,周天烨,等.基于TCN-Attention技术的混合气体高精度检测研究[J].仪表技术与传感器,2024(10):84-88.

[7]程绳,王身丽,董晓虎,等.基于中红外TDLAS的SF6,H2S背景下SO2浓度检测[J].分析仪器,2024(1):52-57.

[8]申晓良,胡澜夕,高炎旭,等.基于TDLAS技术的井下多组分气体浓度监测(特邀)[J].光子学报,2024,53(10):114-123.

[9]刘海芹,徐睿,王振翔,等.基于TDLAS的近红外甲烷高灵敏检测技术[J].光子学报,2024,53(3):250-257.

[10]徐嘉阳,蒙思宇,张志伟,等.基于负集加权迭代修正最小二乘拟合原理的快速自适应拉曼光谱基线校正算法[J].光谱学与光谱分析,2025,45(2):344-350.

[11]YAN X,CUI R,WANG G,et al. Ultra-compact multipass cell-based TDLAS sensor for simultaneous dualposition atmospheric methane detection[J]. Optics express,2025,33(5):9400-9411.

[12]马阳阳,李永建,孙鹤,等.基于深度置信网络算法的面向铁磁材料旋转磁滞损耗的矢量磁滞模型[J].电工技术学报,2023,38(15):4063-4075.

[13]汪强龙,高晓光,李新宇,等.基于深度置信网络效能拟合的火控系统精度全局敏感性分析[J].兵工学报,2024,45(10):3430-3444.

[14]张倩,李丽.深度置信网络大数据目标属性智能提取仿真[J].计算机仿真,2023,40(10):491-495.

基本信息:

DOI:10.13436/j.mkjx.202606025

中图分类号:TD712.55

引用信息:

[1]饶兴鑫.基于TDLAS的便携式煤矿井下甲烷检测仪[J].煤矿机械,2026,47(06):137-142.DOI:10.13436/j.mkjx.202606025.

基金信息:

重庆市自然科学基金博士直通车项目(CSTB2024NSCQ-BSX006)

发布时间:

2026-05-27

出版时间:

2026-05-27

检 索 高级检索

引用

GB/T 7714-2015 格式引文
MLA格式引文
APA格式引文